Remote monitoring and maintenance management system and method for traffic equipment

Abstract

The invention discloses a traffic equipment remote monitoring and maintenance management system and method, and the method comprises the steps: collecting the voltage, current, temperature, humidity and state data of traffic equipment through a sensor, and uploading the data to a remote platform in real time; setting a threshold value, triggering an alarm when abnormity occurs, and notifying operation and maintenance personnel; in combination with real-time and historical fault data, fault types are automatically identified, graded classification is performed, and priority processing is performed; a regular maintenance plan is generated according to the operation duration of the equipment, and maintenance personnel are intelligently scheduled; through historical data analysis, a prediction model is established, an alarm is given in advance, and preventive maintenance suggestions are provided; real-time data visualization is carried out, an operation report is generated, and the state, the fault rate and the maintenance record are analyzed; setting multi-level permissions to guarantee data security, supporting operation and maintenance feedback, and performing continuous optimization; according to the traffic equipment remote monitoring and maintenance management system and method, sensor data, historical records and real-time monitoring are combined, an intelligent algorithm is applied to develop a precise fault diagnosis system, and the recognition accuracy is improved.

Description

Technical Field

[0001] The present invention belongs to the field of remote monitoring and maintenance management of traffic equipment, and particularly relates to a traffic equipment remote monitoring and maintenance management system and method. Background Art

[0002] Traffic equipment remote monitoring and maintenance management is a system that uses modern information technology and communication networks to monitor, diagnose, maintain, and manage traffic facilities (such as traffic lights, traffic cameras, traffic monitoring equipment, road sensors, traffic light control systems, etc.) in real time. Its core goal is to achieve efficient management and timely maintenance of traffic equipment through remote monitoring and intelligent analysis, ensure the normal operation of traffic equipment, and improve the safety and efficiency of road traffic; transmit the data of traffic equipment (such as equipment status, operating parameters, alarm information, etc.) to the control center in real time through the network for remote monitoring, and the operating conditions and fault warnings of the equipment can be viewed to discover problems in a timely manner; through the monitoring system, automatically analyze the operating status of the equipment, identify possible faults or abnormalities, and send an alarm signal when a problem is found to prompt maintenance personnel to check and repair; for some simple faults, maintenance personnel can remotely connect to the equipment for debugging or repair to avoid on-site operations and improve efficiency; collect various data of equipment operation (such as traffic flow, vehicle speed, equipment operation duration, etc.), conduct data analysis, provide decision-making support for traffic management departments, and optimize the traffic system; automatically adjust the working parameters of the equipment according to real-time monitoring data, such as adjusting the cycle of traffic lights, scheduling traffic flow, etc., to adapt to different traffic conditions; conduct periodic inspections of the operating status of the equipment, arrange regular maintenance and upkeep to ensure that the equipment is in the best condition.

[0003] However, although the existing traffic equipment remote monitoring and maintenance management system can provide a certain degree of convenience and efficiency in practice, there are also some defects and deficiencies. The fault diagnosis of the existing remote monitoring system mostly relies on the status reported by the equipment itself or preset thresholds, and some minor and complex faults may not be accurately diagnosed in a timely manner, resulting in the need for manual intervention. Summary of the Invention

[0004] Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide a traffic equipment remote monitoring and maintenance management system and method, develop a more accurate fault diagnosis system, combine sensor data, historical operation and maintenance records, and real-time monitoring information, and use intelligent algorithms to improve the accuracy of fault identification.

[0005] The technical solution adopted by the present invention to solve its technical problems is:

[0006] A traffic equipment remote monitoring and maintenance management system includes:

[0007] The device remote monitoring module is used to collect the working status, running duration, voltage, temperature, and humidity data of the device through sensors, and upload the data to the monitoring platform in real time, and display the device status in real time through the visualization interface;

[0008] The data collection and transmission module is used to collect data from signal lights, monitoring cameras, and road surface temperature and humidity sensors, transmit the data to the central platform using MQTT, HTTP, and Modbus network protocols, and encrypt the data end-to-end;

[0009] The fault diagnosis and prediction module is used to automatically detect faults and abnormal conditions by monitoring the running status, outliers, and device logs of the device in real time, and analyze the historical running data of the device using machine learning algorithms to predict the fault points of the device;

[0010] The operation and maintenance management module is used to automatically generate maintenance plans based on the fault history and health status of the device, and intelligently dispatch maintenance personnel. According to the location, skills, and task priorities of the staff, it automatically dispatches the nearest technicians to carry out on-site repairs;

[0011] The intelligent optimization module is used to intelligently adjust the traffic signal light cycle and video monitoring parameters according to real-time traffic flow data, device operation conditions, and weather factors, predict future traffic trends based on historical data and real-time traffic flow data, and dynamically adjust the configuration of traffic devices;

[0012] The data analysis and reporting module is used to analyze the device operation data and fault history data using big data technology, predict the fault trend and maintenance cycle of the device by analyzing the historical data of the device, and generate operation reports regularly;

[0013] The security and permission management module is used to adopt multi-level user role management to access, operate, or modify system settings for authorized personnel, and perform access control on sensitive data;

[0014] The cloud platform and remote access module is used to centrally store and process data through the cloud platform, adopt cross-regional management and data sharing, and system administrators can remotely access, monitor, and control devices through the PC or mobile APP for scheduling and management.

[0015] A method for remote monitoring and maintenance management of traffic devices includes:

[0016] Collect the voltage, current, temperature, humidity, and working status data of traffic devices through sensors, upload the data to the remote platform, monitor the real-time status of traffic signal lights, cameras, and sensor devices, and set thresholds. When the device status deviates from the normal range, the system automatically triggers an alarm to notify the operation and maintenance personnel;

[0017] Based on real-time data and historical fault data, the system automatically identifies the fault type for preliminary diagnosis. According to the severity and impact scope of the fault, the faults are classified into different categories, and the faults are processed sequentially according to the processing priority.

[0018] According to the running duration and maintenance requirements of the equipment, a regular maintenance plan is automatically generated. The operation and maintenance personnel, based on the equipment location, fault priority, and personnel skills, intelligently dispatch maintenance personnel to the site for handling.

[0019] Through historical data analysis, the common fault types and occurrence rules of the equipment are identified, and a prediction model is established. When an abnormal equipment fault is predicted, the system issues an alarm in advance and generates preventive maintenance suggestions according to the equipment status.

[0020] Based on the real-time operation data of the equipment collected by sensors, the data is visually displayed, and an equipment operation report is generated to analyze the working status, failure rate, and maintenance records of the equipment.

[0021] Multi-level permissions are set, and only authorized personnel can access the equipment data and perform system configuration. The operation and maintenance personnel submit feedback through the system to report problems and improvement suggestions during equipment operation. According to the operation and maintenance feedback, equipment usage, and technological development, the system is updated and optimized regularly.

[0022] Preferably, the method of collecting the voltage, current, temperature, humidity, and working status data of traffic equipment through sensors, uploading the data to a remote platform, and performing real-time status monitoring on traffic signal lights, cameras, and sensor devices, and setting thresholds, and when the equipment status deviates from the normal range, the system automatically triggers an alarm to notify the operation and maintenance personnel is as follows:

[0023] The voltage, current, temperature, humidity, and working status data of traffic equipment are collected in real time through sensors, and the collected real-time data is transmitted through the network to the remote platform for centralized storage and processing.

[0024] Real-time status monitoring is performed on traffic equipment such as signal lights, cameras, and road surface sensors on the remote platform, and an operating range threshold is set for the monitored equipment. When one or more parameters of the equipment deviate from the predetermined normal range, the system identifies it as an abnormal equipment state.

[0025] Based on the equipment status deviating from the normal range, the system automatically triggers an alarm mechanism and notifies the operation and maintenance personnel by email and text message.

[0026] Preferably, the method of the system automatically identifying the fault type for preliminary diagnosis based on real-time data and historical fault data, classifying the faults into different categories according to the severity and impact scope of the faults, and processing the faults sequentially according to the processing priority is as follows:

[0027] Real-time collect the operation status data of the device through sensors, and compare and analyze it with historical fault data. Through the analysis of real-time data and historical data, adopt data mining and machine learning algorithms to automatically identify the fault types of the device;

[0028] Based on the identified fault types, the system conducts a preliminary diagnosis, evaluates the urgency of the fault according to the severity and impact scope of the fault. According to the severity and impact scope of the fault, the system classifies the faults into mild faults, severe faults and emergency faults, and sets priorities for each category;

[0029] According to the priorities of different fault categories, automatically generate the processing sequence of the faults.

[0030] Preferably, according to the operation duration and maintenance requirements of the device, automatically generate a regular maintenance plan. The method for intelligent dispatching of maintenance personnel to the site for handling according to the device location, fault priority and personnel skills is as follows:

[0031] Based on real-time tracking of the operation duration of each device, set the regular maintenance requirements according to the device's user manual or maintenance manual, and automatically generate a regular maintenance plan through the device's operation duration, maintenance requirements and maintenance cycle;

[0032] Optimize the dispatching of maintenance personnel by integrating the geographic information and fault history data of the device, combining the current location of the device with the fault urgency;

[0033] According to the skills, experience and qualification requirements of the maintenance personnel, assign the maintenance tasks to the maintenance personnel. According to the information such as the fault priority of the device, the device location and the personnel skills, automatically dispatch the nearest and skill-matched maintenance personnel to the site to handle the problem. After the maintenance personnel complete the maintenance task, the system records the maintenance results and feedback information, including the repaired fault types, the time used, and the component replacement situation.

[0034] Preferably, through historical data analysis, identify the common fault types and occurrence rules of the device, establish a prediction model. When predicting abnormal device faults, the method for the system to issue an alarm in advance and generate preventive maintenance suggestions according to the device status is as follows:

[0035] By collecting the voltage, current, temperature and humidity, and working status data during the operation of the device for a long time, and recording historical fault events. Based on the historical data, by statistically analyzing the time, frequency and environmental condition information of the fault occurrence, the system identifies the periodic or seasonal rules of the fault, or the high-incidence trend of the fault under specific operating conditions;

[0036] Using methods such as intelligent machine learning or time series analysis, the system establishes a fault prediction model. The model is based on the real-time operation data and historical fault data of the device to predict the faults that may occur to the device. When parameters such as current or temperature exceed the normal range, or when the device operation reaches the set threshold, the prediction model predicts the upcoming faults of the device.

[0037] When the fault prediction model determines that the device has an abnormality or a fault, the system issues an early warning or an alarm in advance, and the alarm notifies relevant personnel through SMS, email or App notification methods.

[0038] According to the current state of the device and the fault prediction, the system generates corresponding preventive maintenance suggestions. The maintenance suggestions include that if it is predicted that a certain device will have a fault next month, the system recommends that the operation and maintenance personnel conduct an early inspection, replace vulnerable parts or make environmental adaptability adjustments.

[0039] Preferably, based on the real-time operation data of the device collected by sensors, the data is visually displayed and a device operation report is generated. The method for analyzing the working state, failure rate and maintenance records of the device is as follows:

[0040] Sensors installed on the device are used to collect the voltage, current, temperature and humidity, vibration, operation duration, and working state operation data of the device in real time, and the real-time collected data is cleaned, de-noised and preprocessed.

[0041] A dashboard visualization tool is used to present the real-time data as charts and graphs. The display content includes but is not limited to the current voltage, current, temperature and humidity status information of the device, and the real-time status of each device is displayed as green, yellow, and red indicators.

[0042] Based on the real-time data collected, the system regularly generates a device operation report. The report content includes the basic operation state, working hours, energy consumption data, and environmental conditions of the device.

[0043] Based on the real-time data, the system automatically analyzes the working state of the device. Through the analysis of historical fault data, the system calculates the failure rate of each device, analyzes the causes of faults, and provides optimization suggestions.

[0044] Preferably, multi-level permissions are set so that only authorized personnel can access device data and perform system configuration. The operation and maintenance personnel submit feedback through the system to report problems and improvement suggestions during device operation. According to the operation and maintenance feedback, device usage and technological development, the method for regularly updating and optimizing the system is as follows:

[0045] According to the roles and responsibilities of different users, the permission levels of system administrators, operation and maintenance personnel, and ordinary users are set, and different access permissions are set according to the user's role.

[0046] Restrict different users from accessing corresponding data according to the type, region, or functional module of the device, and record each access to the device data or system configuration.

[0047] Set up a feedback platform where operation and maintenance personnel report problems during device operation, put forward improvement suggestions, or share maintenance experiences. The submitted feedback is sent to the system administrator or relevant person in charge for review and confirmation. For urgent fault reports, a priority processing process is automatically triggered.

[0048] The feedback from operation and maintenance personnel and the problems in device use are used as the basis for system optimization. Introduce artificial intelligence, big data analysis, and edge computing device management technologies, and regularly update the system version to add new functions and fix known vulnerabilities.

[0049] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements a traffic device remote monitoring and maintenance management system and method as described in any one of the above.

[0050] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium with a computer program stored thereon. When the program is executed by a processor, it implements a traffic device remote monitoring and maintenance management system and method.

[0051] The beneficial effects of the present invention are:

[0052] By using sensors to monitor key parameters such as voltage, current, temperature, and humidity of traffic equipment in real time, it is possible to automatically trigger an alarm when the equipment deviates from the normal operating range, quickly notify the operation and maintenance personnel for handling. This early warning mechanism can significantly reduce the potential risks brought by equipment failures, ensure the continuous and stable operation of traffic equipment, and improve the reliability of the equipment; by analyzing real-time data and historical fault data, the system can automatically identify the type of fault and conduct a preliminary diagnosis. This process can promptly detect equipment failures and give treatment suggestions, thus greatly shortening the fault response time; the system classifies faults according to the severity and scope of influence of the faults and processes them according to priorities. This intelligent fault management enables operation and maintenance personnel to prioritize the handling of critical faults, optimize resource allocation, avoid unnecessary manual judgments, and improve the fault repair efficiency; based on the operation duration and maintenance requirements of the equipment, the system automatically generates a regular maintenance plan. Operation and maintenance personnel can conduct intelligent scheduling according to the location of the equipment, fault priority, and skill requirements to ensure that maintenance tasks are handled promptly and professionally. This not only optimizes the operation and maintenance process but also improves work efficiency and reduces labor costs; by analyzing historical data and identifying the patterns of fault occurrences, the system can predict potential equipment failures in advance, issue early warnings, and provide preventive maintenance suggestions. This predictive maintenance helps to detect problems in advance, avoid emergency repairs or equipment replacements caused by equipment failures, and thus reduce the high costs of emergency repairs and downtime; through functions such as automated monitoring, fault diagnosis, alarm, and scheduling, the dependence on manual intervention is reduced. Operation and maintenance personnel can focus more on handling high-priority tasks, while avoiding human oversights or errors, and improving the overall operation and maintenance efficiency; the system visually displays the equipment data collected in real time and generates detailed equipment operation reports, including the working status of the equipment, failure rate, maintenance records, etc. These data can provide decision-making support for management, help analyze the long-term health status of the equipment, identify potential risks, and make optimization adjustments based on data-driven analysis; by deeply analyzing historical data, the system can identify common fault types and occurrence patterns of the equipment, guide future operation and maintenance strategies, and improve the scientific nature and accuracy of equipment management; the system ensures that only authorized personnel can access equipment data and perform system configuration by setting up multi-level permission management. This can prevent unauthorized access and abuse, ensure the security of equipment and data. In addition, permission management can also ensure that sensitive operations are performed by professional personnel, reducing the risk of system configuration errors; the strict management of user permissions by the system can ensure that sensitive information is not leaked, improve data security, especially when involving multiple departments or external service providers, it can clarify the scope of responsibilities and permissions; operation and maintenance personnel can submit feedback through the system to report problems and improvement suggestions during equipment operation. By collecting the feedback of operation and maintenance personnel, the system can continuously adjust and optimize, improve the comprehensive performance of the equipment and the system, and ensure the continuous improvement of equipment operation and maintenance management;According to the equipment usage and technological development, the system will be updated and optimized regularly. This not only ensures that the system keeps up with the latest technological development and improves management efficiency, but also ensures that the equipment management system can adapt to the ever-changing technological requirements. This solution presents key information such as equipment data, maintenance records, and fault handling in the form of reports or charts, enabling management or relevant decision-makers to grasp the equipment operation status and operation and maintenance situation in real time, providing a transparent management perspective. Through intelligent scheduling, automated fault diagnosis, and report generation, the workload of maintenance personnel is reduced, work efficiency is improved, thus enhancing the work experience and reducing the complexity of operations. Through intelligent fault warning and maintenance scheduling, over-maintenance of equipment or unnecessary resource waste is avoided, and preventive maintenance measures also help extend the service life of equipment and reduce resource consumption. The real-time collected data such as voltage and current can be used to optimize the energy consumption of equipment, further improve energy efficiency, and reduce energy consumption during equipment operation. Brief Description of the Drawings

[0053] Figure 1 It is a schematic flowchart of a remote monitoring and maintenance management system for transportation equipment of the present invention. Detailed Embodiments

[0054] The principles and features of the present invention will be described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention. The present invention will be described more specifically by way of example in the following paragraphs. The advantages and features of the present invention will be clearer according to the following description and claims.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0056] Embodiment

[0057] A remote monitoring and maintenance management system for transportation equipment includes:

[0058] An equipment remote monitoring module, which is used to collect the working status, operation duration, voltage, temperature, and humidity data of the equipment through sensors, upload them to the monitoring platform in real time, and display the equipment status in real time through a visualization interface;

[0059] A data collection and transmission module, which is used to collect data from signal lights, monitoring cameras, and road surface temperature and humidity sensors, transmit the data to the central platform using MQTT, HTTP, and Modbus network protocols, and encrypt the data end-to-end;

[0060] A fault diagnosis and prediction module, which is used to automatically detect faults and abnormal conditions by real-time monitoring the operating status, outliers, and device logs of the device, and analyze the historical operating data of the device using machine learning algorithms to predict the fault points of the device;

[0061] An operation and maintenance management module, which is used to automatically generate a maintenance plan based on the fault history and health status of the device, and intelligently dispatch maintenance personnel. According to the location, skills, and task priorities of the staff, it automatically dispatches the nearest technicians to carry out on-site maintenance;

[0062] An intelligent optimization module, which is used to intelligently adjust the traffic signal cycle and video monitoring parameters according to real-time traffic flow data, device operating conditions, and weather factors, predict future traffic trends based on historical data and real-time traffic flow data, and dynamically adjust the configuration of traffic devices;

[0063] A data analysis and reporting module, which is used to analyze the device operating data and fault history data using big data technology, predict the fault trend and maintenance cycle of the device by analyzing the historical data of the device, and regularly generate operation reports;

[0064] A security and permission management module, which is used to adopt multi-level user role management to access, operate, or modify system settings for authorized personnel, and perform access control on sensitive data;

[0065] A cloud platform and remote access module, which is used to centrally store and process data through the cloud platform, adopt cross-regional management and data sharing. System administrators can remotely access, monitor, and control devices through the PC side or mobile APP for scheduling and management.

[0066] Through automated fault diagnosis, prediction, and intelligent scheduling, the fault response time is reduced, the operation and maintenance efficiency is improved, and manual intervention is reduced; through preventive maintenance and fault prediction, the device wear and downtime are reduced, and the service life of the device is extended; the system automatically processes most of the monitoring and maintenance tasks, reducing labor costs. At the same time, through intelligent optimization, the traffic flow and energy consumption are reduced, and the resource utilization efficiency is improved; multi-level permission management and data encryption ensure the security of the system, preventing data leakage or tampering; big data analysis provides decision support for device management, and data-driven optimization helps to improve the long-term operation effect of the system; intelligent optimization of traffic signals and dynamic adjustment of device configuration effectively improve traffic fluency and passing efficiency, and optimize the user experience.

[0067] A method for remote monitoring and maintenance management of traffic devices, including:

[0068] Collect the voltage, current, temperature, humidity, and working status data of traffic equipment through sensors, and upload them to the remote platform. Real-time status monitoring is carried out on traffic lights, cameras, and sensor devices, and thresholds are set. When the device status deviates from the normal range, the system automatically triggers an alarm to notify the operation and maintenance personnel;

[0069] Based on the real-time data and historical fault data, the system automatically identifies the fault type for preliminary diagnosis. According to the severity and impact range of the fault, the faults are classified into different categories, and the faults are processed in sequence according to the processing priority;

[0070] According to the running duration and maintenance requirements of the equipment, a regular maintenance plan is automatically generated. The operation and maintenance personnel intelligently dispatch maintenance personnel to the site for handling according to the equipment location, fault priority, and personnel skills;

[0071] Through historical data analysis, identify the common fault types and occurrence rules of the equipment, establish a prediction model. When abnormal equipment faults are predicted, the system issues an alarm in advance and generates preventive maintenance suggestions according to the equipment status;

[0072] Based on the real-time data of equipment operation collected through sensors, the data is visually displayed, and an equipment operation report is generated to analyze the working status, failure rate, and maintenance records of the equipment;

[0073] Set multi-level permissions so that only authorized personnel can access the equipment data and perform system configuration. The operation and maintenance personnel submit feedback through the system to report problems and improvement suggestions during equipment operation. According to the operation and maintenance feedback, equipment usage, and technological development, the system is updated and optimized regularly.

[0074] Through real-time monitoring, fault prediction, and preventive maintenance, the system has greatly reduced the equipment failure rate and extended the service life of the equipment; intelligent scheduling and regular maintenance plans make the work of operation and maintenance personnel more targeted and efficient, avoiding ineffective or redundant maintenance activities; predictive maintenance and intelligent scheduling reduce unnecessary emergency repairs and downtime, reducing maintenance costs and losses caused by traffic interruptions; multi-level permission management ensures data security, preventing improper operations and data leakage; data visualization and equipment operation reports provide strong decision-making support for management, enabling effective optimization of equipment investment, maintenance plans, and resource allocation; the feedback mechanism and system optimization function ensure that the system can continuously adapt to new requirements and technological progress, improving long-term maintainability and scalability.

[0075] The method of collecting the voltage, current, temperature, humidity, and working status data of traffic equipment through sensors, and uploading them to the remote platform. Real-time status monitoring is carried out on traffic lights, cameras, and sensor devices, and thresholds are set. When the device status deviates from the normal range, the system automatically triggers an alarm to notify the operation and maintenance personnel is as follows:

[0076] The voltage, current, temperature, humidity and the working state data of traffic equipment are collected in real time through sensors, and the collected real-time data are transmitted to a remote platform through a network for centralized storage and processing;

[0077] On the remote platform, real-time status monitoring is carried out on traffic equipment such as signal lights, cameras and road surface sensors, and operation range thresholds are set for the monitored equipment. When one or more parameters of the equipment deviate from the predetermined normal range, the system identifies it as an abnormal state of the equipment;

[0078] Based on the fact that the state of the equipment deviates from the normal range, the system automatically triggers an alarm mechanism and notifies the operation and maintenance personnel by means of emails and text messages.

[0079] Through the real-time monitoring of the operation state of the equipment, the operation and maintenance personnel can always master the working state of the equipment, discover potential problems or faults in time, so as to avoid traffic interruption or safety hazards caused by equipment failures; Regular and real-time data collection can provide the health status of equipment operation, help to identify in advance whether the equipment is in an abnormal working state, and avoid failures caused by equipment aging or other factors; The system can automatically alarm when the equipment is abnormal and notify the operation and maintenance personnel in time, without manual monitoring, which greatly shortens the fault response time and reduces the impact of equipment failures on traffic operation; The alarm information includes the specific abnormal types and parameters of the equipment, which helps the operation and maintenance personnel to quickly diagnose problems and reduce the on-site investigation time and errors; Through the automated monitoring and alarm mechanism, the need for manual intervention and manual inspection is reduced, and the labor cost and the possibility of human errors are lowered; The operation and maintenance personnel can manage and monitor equipment in various places through the remote platform without geographical restrictions, improving the efficiency of cross-regional equipment maintenance; By setting thresholds, the system can warn in advance of abnormal changes in the equipment state, so as to carry out preventive maintenance and avoid major equipment failures. In the long run, this can greatly extend the service life of the equipment and reduce the maintenance cost; The real-time data and fault logs of the equipment provide a large amount of historical data for managers, which helps to analyze the common fault modes of the equipment, identify the trend of performance degradation, and thus formulate reasonable maintenance, update and replacement plans; Through real-time monitoring and rapid alarm, the system can effectively avoid problems such as traffic signal interruption and traffic camera failure caused by equipment failures, ensuring the stable operation of the traffic system; Especially the monitoring of traffic signal lights and camera equipment can ensure the normal operation of traffic facilities, reduce the occurrence of traffic accidents, and ensure public safety; The system can support the simultaneous monitoring of multiple traffic equipment, adapt to different types of equipment (such as signal lights, sensors, cameras, etc.), and can be easily expanded and new equipment can be connected; According to the characteristics of different equipment, the thresholds and monitoring parameters can be flexibly configured, and the system can be optimized and upgraded according to actual needs.

[0080] Based on real-time data and historical fault data, the system automatically identifies the fault type for preliminary diagnosis. According to the severity and impact scope of the fault, the faults are classified into different categories, and according to the processing priority, the method for processing the faults in sequence is as follows:

[0081] The system collects the operation status data of the device in real time through sensors, and compares and analyzes it with the historical fault data. Through the analysis of real-time data and historical data, data mining and machine learning algorithms are used to automatically identify the fault type of the device;

[0082] Based on the identification of the fault type, the system conducts a preliminary diagnosis, evaluates the urgency of the fault according to the severity and impact scope of the fault. According to the severity and impact scope of the fault, the system classifies the faults into mild faults, severe faults and emergency faults categories, and sets priorities for each category;

[0083] According to the priorities of different fault categories, the processing sequence of the faults is automatically generated.

[0084] By combining real-time data with historical data, the system can more accurately identify equipment anomalies and accurately diagnose the types of faults. Machine learning and data mining can improve the accuracy of identification based on historical fault patterns, reducing false positives and missed detections. The system can automatically classify faults into three categories: minor, severe, and urgent, according to the different types of faults, and set processing priorities for each category. This intelligent fault classification can help maintenance personnel efficiently handle equipment problems without manual intervention. By automatically identifying the type of fault and generating a processing sequence, the time for manual troubleshooting and handling is reduced, improving the work efficiency of the maintenance team. The automated priority ranking ensures that the most urgent faults can be processed quickly, preventing high-risk faults from being ignored or delayed. Based on the analysis of historical data, the system can identify potential fault trends. For example, certain equipment may be at risk of overload or overheating under specific conditions, and the system can issue early warnings for preventive maintenance to reduce the occurrence of sudden faults. The system can automatically determine the urgency of a fault based on real-time data and automatically generate a processing sequence, thus helping maintenance personnel make accurate decisions in a timely manner and improving the fault response speed and decision-making quality. The automatic detection, classification, and prioritization of faults reduce the need for manual intervention, thereby reducing the possibility of human errors. Especially in complex fault situations, the system's automatic diagnosis and sorting mechanism can help avoid overlooking important issues. The automated fault diagnosis and handling reduce the need for manual inspections and fault handling, lowering labor costs. In addition, through timely warnings and rapid responses, the system helps avoid major equipment failures and damages, thus reducing equipment repair and replacement costs. By analyzing the long-term operation data of equipment, the system can identify the aging trend and common fault patterns of equipment, providing data support for subsequent equipment maintenance, replacement, and upgrade, and optimizing the management of the equipment lifecycle. Through intelligent and automated fault identification and handling, the overall reliability and safety of the traffic management system are enhanced. Especially during the fault handling process of high-risk equipment (such as traffic lights, traffic monitoring cameras, etc.), the continuous and stable operation of the equipment is ensured, guaranteeing traffic safety.

[0085] According to the running time and maintenance requirements of the equipment, automatically generate a regular maintenance plan. The method for intelligent dispatching of maintenance personnel for on-site handling based on the equipment location, fault priority, and personnel skills is as follows:

[0086] Based on real-time tracking of the running time of each piece of equipment, set regular maintenance requirements according to the equipment's user manual or maintenance manual. Automatically generate a regular maintenance plan based on the equipment's running time, maintenance requirements, and maintenance cycle;

[0087] By integrating the geographical information and fault history data of the equipment, and combining the current location of the equipment with the fault urgency, optimize the dispatching of maintenance personnel;

[0088] According to the skills, experience and qualification requirements of maintenance personnel, maintenance tasks are assigned to them. Based on information such as the fault priority of the equipment, equipment location, and personnel skills, the system automatically schedules the nearest and skill-matched maintenance personnel to go to the site to handle problems. After the operation and maintenance personnel complete the maintenance tasks, the system records the maintenance results and feedback information, including the types of faults repaired, the time taken, and the replacement of components.

[0089] Automated maintenance plan generation ensures that equipment is maintained in a timely and regular manner, preventing equipment failures or premature damage caused by long-term neglect of maintenance. By tracking the running hours of equipment in real time, the planning of maintenance tasks can be made more accurate, thus extending the service life of the equipment and optimizing its performance. By combining the geographical information of the equipment, the urgency of the fault, and the skills of maintenance personnel, the system can intelligently schedule maintenance personnel, avoiding the inefficiency and errors of manual arrangements. Maintenance tasks are assigned to the most suitable and closest-on-site maintenance personnel, thus reducing the maintenance response time and transportation costs and improving the maintenance efficiency. The intelligent scheduling and priority ranking of fault maintenance tasks ensure that the most urgent faults are handled first, reducing equipment downtime, ensuring the normal operation of the equipment, and avoiding production losses or service interruptions caused by equipment downtime. The system makes intelligent matches based on the skills and experience of maintenance personnel to ensure that maintenance tasks are carried out by the most suitable personnel, which helps to improve the quality of fault repair and reduce secondary faults or system problems caused by improper maintenance. Automated maintenance planning and maintenance personnel scheduling reduce manual intervention and management costs. Through intelligent scheduling, the system can efficiently allocate maintenance resources, avoiding repetitive work or improper scheduling of maintenance personnel, thus optimizing the use of human resources. Maintenance personnel can obtain clear task guidance and work arrangements through the system, without spending a lot of time on task assignment and scheduling, improving work efficiency. Task assignment is clear and scientific, avoiding repetitive labor and inefficient work of maintenance personnel. The system records detailed maintenance logs, including information such as fault types, repair times, and component replacements, providing rich data support. These data can be used for fault trend analysis of equipment, helping to predict possible future faults and optimizing equipment management and operation and maintenance strategies. Through the analysis of historical maintenance data, enterprises can identify potential risks of equipment failures and take preventive maintenance measures in advance to avoid sudden failures affecting production and services. Automated maintenance and fault handling processes improve the reliability and safety of equipment. Timely maintenance and fault repair avoid safety accidents caused by equipment failures, ensuring the stable operation of the equipment and the safety of the production environment. All maintenance tasks and maintenance operations are recorded in detail, enabling tracing back to specific maintenance personnel and operation processes, facilitating later inspection and analysis. The transparent operation and maintenance process helps to improve the management's control over equipment management and reduce risks.

[0090] The method of identifying common fault types and occurrence rules of equipment through historical data analysis, establishing a prediction model, and when the equipment fault anomaly is predicted, the system issues an alarm in advance and generates preventive maintenance suggestions based on the equipment status is as follows:

[0091] By collecting long-term data on voltage, current, temperature, humidity, and working status during equipment operation and recording historical fault events, based on the historical data, by statistically analyzing the time, frequency, and environmental condition information of fault occurrence, the system identifies the periodic or seasonal rules of faults, or the high-incidence trend of faults under specific operating conditions;

[0092] Using methods such as intelligent machine learning or time series analysis, the system establishes a fault prediction model. The model is based on the real-time operation data and historical fault data of the equipment to predict the faults that occur in the equipment. If parameters such as current or temperature exceed the normal range, or when the equipment operation reaches the set threshold, the prediction model predicts the upcoming faults of the equipment;

[0093] When the fault prediction model determines that the equipment has an anomaly or a fault, the system issues a warning or an alarm in advance, and the alarm notifies relevant personnel through text messages, emails, or App notifications;

[0094] Based on the current status of the equipment and the fault prediction, the system generates corresponding preventive maintenance suggestions. The maintenance suggestions include that if it is predicted that a certain equipment will have a fault next month, the system recommends that the operation and maintenance personnel conduct an early inspection, replace vulnerable parts, or make environmental adaptability adjustments.

[0095] Through fault prediction and early warning, potential problems can be detected in advance, reducing the occurrence of sudden equipment failures. Timely preventive maintenance can effectively extend the service life of equipment and improve the overall reliability of equipment. Since the system can predict and alert potential equipment failures in advance, maintenance personnel can take proactive measures to repair or replace vulnerable parts, reducing equipment downtime. Compared with traditional "repair after the fact", this approach can significantly reduce downtime caused by sudden equipment failures and avoid production or service interruptions. Through intelligent prediction models and maintenance recommendations, equipment maintenance can shift from "emergency repair" to "preventive maintenance", avoiding unnecessary repairs and component replacements. At the same time, the workload of maintenance personnel is optimized, enabling targeted resource allocation and avoiding over-maintenance or omission of critical tasks. Predictive maintenance ensures that equipment fault problems are resolved in advance, allowing maintenance personnel to schedule maintenance tasks according to the maintenance plan recommended by the system, avoiding random or unplanned maintenance and improving work efficiency and maintenance quality. By reducing downtime caused by equipment failures, lowering emergency repair costs, and avoiding production losses caused by equipment failures, equipment operation and maintenance costs can be effectively reduced. In addition, preventive maintenance can reduce unnecessary component replacements, reducing material procurement and inventory management costs. The system can continuously optimize the prediction model and improve prediction accuracy based on long-term accumulated equipment operation data and fault events. The collection and analysis of equipment operation data provide detailed decision-making support for management, helping them make more scientific and effective equipment management decisions. The system can not only make predictions based on the normal working conditions of equipment but also flexibly adjust maintenance strategies according to specific environmental changes (such as temperature and humidity changes) and changes in workload. Such personalized maintenance recommendations are more in line with the actual needs of each piece of equipment, avoiding overly uniform or "blind" maintenance strategies. Through the fault history, maintenance records, and prediction results recorded by the system, management can comprehensively understand the operation status and health of equipment, improving the transparency and traceability of equipment management. This not only helps management make reasonable resource allocation but also strengthens fault management and quality control. The system optimizes and learns based on historical data and fault trends, continuously improving prediction accuracy. Over time, the prediction model for equipment failures will become more accurate, gradually forming a virtuous cycle. Long-term accumulated data and experience contribute to optimizing equipment selection, operating conditions, and maintenance strategies.

[0096] The method for analyzing the working status, failure rate, and maintenance records of equipment based on real-time collection of equipment operation data through sensors, visualizing the data, and generating an equipment operation report is as follows:

[0097] Through sensors installed on the equipment, real-time collection of equipment operation data such as voltage, current, temperature and humidity, vibration, operation duration, and working status is carried out, and the real-time collected data is cleaned, de-noised, and preprocessed;

[0098] Using a dashboard visualization tool, present the real-time data as charts and graphs. The displayed content includes, but is not limited to, the current voltage, current, temperature and humidity status information of the device, and display the real-time status of each device with green, yellow, and red indicators;

[0099] According to the collected real-time data, the system regularly generates device operation reports, and the report content includes the basic operation status, working hours, energy consumption data, and environmental conditions of the device;

[0100] Based on the real-time data, the system automatically analyzes the working status of the device. By analyzing the historical fault data, the system calculates the failure rate of each device, analyzes the causes of faults, and provides optimization suggestions.

[0101] Through real-time data collection and visual display, operation and maintenance personnel can always understand the working status of equipment. When the equipment status is abnormal, the system will issue an alarm in a timely manner to help staff take measures in time to prevent the spread of faults or affect production; automated equipment operation reports and failure rate analysis provide strong data support for management and operation and maintenance personnel. Through regularly generated reports, managers can quickly grasp the overall operation status, energy consumption situation, and potential risks of equipment, so as to make more reasonable decisions; calculating and analyzing the failure rate of equipment can help the company identify the weak links of equipment and optimize equipment procurement and maintenance strategies; based on failure rate analysis and historical data, the system can provide accurate maintenance suggestions to help the company identify possible problems that may occur in equipment in advance. Through this predictive maintenance, the occurrence of sudden failures is reduced, and emergency repair and downtime costs are lowered; the system can also optimize the allocation of maintenance resources, making maintenance work more targeted and time-effective, avoiding unnecessary inspections and repairs, thereby saving maintenance costs; by analyzing the operation data and failure history of equipment, the operation conditions and maintenance cycles of equipment can be optimized, the equipment failure rate can be reduced, and the service life of equipment can be extended; by monitoring the equipment status in real time and adjusting the operation parameters of equipment (such as temperature, current, load, etc.) in a timely manner, equipment failures caused by overload or environmental factors can be prevented; the system can continuously accumulate equipment operation data and continuously optimize according to these data. Through failure analysis and trend prediction, it helps enterprises discover long-term accumulated problems and make improvements. Over time, the system can more accurately predict equipment problems and optimization solutions, thereby achieving continuous improvement of equipment performance; real-time monitoring and data visualization can help operation and maintenance personnel discover potential equipment failures, perform preventive maintenance in advance, and avoid unexpected downtime caused by equipment failures, which can maximize production efficiency and reduce the risk of production interruption; the energy consumption data of equipment is crucial for energy management. By analyzing the energy consumption data of equipment, the system can identify high-energy-consuming equipment, put forward energy-saving optimization suggestions, help enterprises reduce energy costs, and promote green production; the operation reports and failure records of equipment provide a traceable basis for equipment management. Operation and maintenance personnel and management can view the historical operation status, failure conditions, and maintenance records of equipment at any time. This transparency improves the efficiency of equipment management and helps meet compliance and audit requirements; if this solution is applied to customer on-site equipment or outsourcing services, it can ensure that the equipment is always in the best working state, reduce the occurrence of customer equipment failures, and timely failure warnings and solutions can improve customer satisfaction and enhance the competitiveness of services.

[0102] The method of setting multi-level permissions so that only authorized personnel can access equipment data and perform system configuration, and operation and maintenance personnel submit feedback through the system to report problems and improvement suggestions in equipment operation, and regularly update and optimize the system according to operation and maintenance feedback, equipment usage, and technological development is as follows:

[0103] Set the permission levels for system administrators, operation and maintenance personnel, and ordinary users according to the roles and responsibilities of different users, and set different access permissions according to the roles of users;

[0104] Restrict different users from accessing corresponding data according to the type, region, or functional module of the device, and record each access to device data or system configuration;

[0105] Set up a feedback platform where operation and maintenance personnel report problems in device operation, put forward improvement suggestions or share maintenance experience. The feedback submitted is sent to the system administrator or relevant person in charge for review and confirmation. For urgent fault reports, a priority processing process is automatically triggered;

[0106] The feedback from operation and maintenance personnel and the problems in device use are used as the basis for system optimization. Introduce artificial intelligence, big data analysis, and edge computing device management technologies to regularly update the system version, add new functions, and fix known vulnerabilities.

[0107] Through multi-level permission management, it is ensured that users of different roles can only access the data and configurations relevant to their responsibilities, avoiding unauthorized access and modification. Every data access and configuration modification will be recorded, guaranteeing the transparency and traceability of the system, enhancing security and ensuring compliance; The system administrator has the highest authority and can flexibly manage the permission settings to safeguard the security of device data and system configurations, preventing misoperations or malicious behaviors; The operation and maintenance personnel can promptly report device failures and improvement suggestions through the feedback platform, which helps to discover potential problems in the system and make improvements. Through the review by the system administrator and the priority handling of urgent issues, the fault response time can be accelerated, the device downtime can be reduced, and the operation and maintenance efficiency can be improved; The feedback mechanism promotes the positive interaction between the operation and maintenance personnel and the system, making the device management more refined and intelligent; Introducing artificial intelligence, big data, and edge computing technologies can make the system more intelligent. For example, AI algorithms can deeply analyze the device operation data, identify potential faults, and provide predictive maintenance suggestions; Big data analysis can help optimize device configurations and operation strategies, improving the overall operation efficiency; Edge computing can process data locally, reducing data transmission latency and improving the response speed; The system automatically triggers the priority handling process, providing a quick response to emergency faults, reducing the manual intervention of the operation and maintenance personnel, and enhancing the processing speed and accuracy; The system is regularly updated and optimized based on the feedback from the operation and maintenance personnel and the problems in device use, continuously adding new functions or fixing vulnerabilities according to technological developments and market demands, enabling the system to keep pace with the times and adapt to the changing needs; Regular updates can not only fix known problems but also enhance the stability, scalability, and compatibility of the system, enabling it to better support various tasks of device management; Through timely feedback and quick response, the system can quickly repair and improve the faults occurring in the device. This fast feedback mechanism helps to reduce the frequency of device failures, thereby improving the overall reliability of the device; The continuous optimization of the system can reduce the maintenance cost of the device, extend the service life of the device, and increase the return on investment of the overall assets; The system improves the collaboration efficiency of the operation and maintenance team through permission management and the feedback mechanism. The feedback platform can centrally manage all the reports and suggestions of the operation and maintenance personnel and arrange the handling according to the priorities.This centralized management method reduces the time for communication and response, improves the work efficiency of the entire team. For urgent issues, the automated prioritization process can help initiate a response quickly, ensure that high-priority faults are resolved in a timely manner, and reduce equipment downtime. By introducing big data analysis, the system can analyze and mine equipment operation data, generate insightful reports and predictions. These data-driven analyses help management make more accurate decisions, including aspects such as equipment procurement, maintenance planning, and resource allocation. The system can analyze equipment operation trends, energy consumption data, and fault history, providing data support for management to formulate long-term equipment optimization strategies. If this solution is applied to equipment at the customer site or in outsourced services, it can ensure the healthy operation of the equipment, respond promptly to equipment faults and optimization suggestions, improve customer satisfaction. Through intelligent optimization and continuous updates, the equipment management system can continuously improve its performance, provide higher-quality services to customers, and thus enhance the enterprise's market competitiveness.

[0108] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements a traffic equipment remote monitoring and maintenance management system and method as described above.

[0109] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by the processor, it implements a traffic equipment remote monitoring and maintenance management system and method as described above.

[0110] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0111] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0112] The above embodiments of the present invention do not limit the protection scope of the present invention. The embodiments of the present invention are not limited thereto. All kinds of modifications, substitutions, or changes made to the above structure of the present invention in other various forms according to the above content of the present invention, in accordance with the common general knowledge and conventional means in the art, without departing from the above basic technical idea of the present invention, shall fall within the protection scope of the present invention.

Claims

1. A remote monitoring and maintenance management system for transportation equipment, characterized in that, It includes: The device remote monitoring module is used to collect the working status, running duration, voltage, temperature, and humidity data of the device through sensors, upload the data to the monitoring platform in real time, and display the device status through a visual interface in real time; The data collection and transmission module is used to collect data from signal lights, monitoring cameras, and road surface temperature and humidity sensors, transmit the data to the central platform using MQTT, HTTP, and Modbus network protocols, and encrypt the data end to end; The fault diagnosis and prediction module is used to automatically detect faults and abnormal conditions by monitoring the running status, abnormal values, and device logs of the device in real time, and analyze the historical running data of the device using machine learning algorithms to predict the fault points of the device; The operation and maintenance management module is used to automatically generate a maintenance plan based on the fault history and health status of the device, and intelligently dispatch maintenance personnel. According to the location, skills, and task priorities of the staff, it automatically dispatches the nearest technician to carry out on-site maintenance; The intelligent optimization module is used to intelligently adjust the traffic signal light cycle and video monitoring parameters according to real-time traffic flow data, device operation conditions, and weather factors, predict future traffic trends based on historical data and real-time traffic flow data, and dynamically adjust the configuration of traffic devices; The data analysis and reporting module is used to analyze the device operation data and fault history data using big data technology, predict the fault trend and maintenance cycle of the device by analyzing the historical data of the device, and generate operation reports regularly; The security and permission management module is used to adopt multi-level user role management to access, operate, or modify system settings for authorized personnel, and perform access control on sensitive data; The cloud platform and remote access module is used to centrally store and process data through the cloud platform, adopt cross-regional management and data sharing. System administrators can remotely access, monitor, and control devices through the PC or mobile APP to carry out scheduling and management.

2. A method for remote monitoring and maintenance management of transportation equipment, characterized in that, It includes: Collect the voltage, current, temperature, humidity, and working status data of traffic devices through sensors, upload the data to the remote platform, monitor the real-time status of traffic signal lights, cameras, and sensor devices, and set thresholds. When the device status deviates from the normal range, the system automatically triggers an alarm to notify the operation and maintenance personnel; Based on real-time data and historical fault data, the system automatically identifies the fault type for preliminary diagnosis, classifies the faults into different categories according to the severity and impact range of the faults, and processes the faults in sequence according to the processing priorities; Automatically generate a regular maintenance plan according to the running duration and maintenance requirements of the device. The operation and maintenance personnel intelligently dispatch maintenance personnel to carry out on-site processing according to the device location, fault priority, and personnel skills; Through historical data analysis, identify the common fault types and occurrence rules of the device, establish a prediction model. When the device fault anomaly is predicted, the system issues an alarm in advance and generates preventive maintenance suggestions according to the device status; Based on the device operation data collected in real time through sensors, visualize the data and generate a device operation report to analyze the working status, failure rate, and maintenance records of the device; Set multi-level permissions so that only authorized personnel can access device data and perform system configuration. Operation and maintenance personnel submit feedback through the system to report problems and improvement suggestions during device operation. The system is regularly updated and optimized based on operation and maintenance feedback, device usage, and technological development.

3. The traffic equipment remote monitoring and maintenance management method according to claim 2, characterized in that, The method of collecting voltage, current, temperature, humidity, and working status data of traffic devices through sensors and uploading them to a remote platform, and performing real-time status monitoring on traffic signal lights, cameras, and sensor devices, and setting thresholds. When the device status deviates from the normal range, the system automatically triggers an alarm to notify operation and maintenance personnel is as follows: Collect the voltage, current, temperature, humidity, and working status data of traffic devices in real time through sensors, and transmit the collected real-time data to a remote platform through the network for centralized storage and processing; Perform real-time status monitoring on traffic devices such as signal lights, cameras, and road surface sensors on the remote platform, and set an operating range threshold for the monitored devices. When one or more parameters of the device deviate from the predetermined normal range, the system identifies it as an abnormal device status; Based on the device's status deviating from the normal range, the system automatically triggers an alarm mechanism and notifies operation and maintenance personnel via email and text message.

4. The remote monitoring and maintenance management method for transportation equipment according to claim 3, wherein The method of automatically identifying the fault type for preliminary diagnosis based on real-time data and historical fault data, classifying faults into different categories according to the severity and impact range of the faults, and processing the faults in sequence according to the processing priority is as follows: Collect the device operation status data in real time through sensors and compare and analyze it with historical fault data. Through the analysis of real-time data and historical data, use data mining and machine learning algorithms to automatically identify the fault type of the device; Based on the identification of the fault type, the system conducts a preliminary diagnosis, evaluates the urgency of the fault according to the severity and impact range of the fault, and classifies the faults into minor faults, serious faults, and emergency faults according to the severity and impact range of the fault, and sets a priority for each category; Automatically generate the processing sequence of the faults according to the priorities of different fault categories.

5. The remote monitoring and maintenance management method for transportation equipment according to claim 4, characterized in that The method of automatically generating a regular maintenance plan based on the operation duration and maintenance requirements of the device, and operation and maintenance personnel intelligently dispatching maintenance personnel to the site for handling according to the device location, fault priority, and personnel skills is as follows: Based on real-time tracking of the operation duration of each device, set regular maintenance requirements according to the device's user manual or maintenance manual, and automatically generate a regular maintenance plan through the operation duration, maintenance requirements, and maintenance cycle of the device; Optimize the dispatching of maintenance personnel by integrating the geographical information and fault history data of the device, combining the current location of the device with the fault urgency; Assign maintenance tasks to maintenance personnel according to the skills, experience, and qualification requirements of the maintenance personnel. According to information such as the fault priority of the device, device location, and personnel skills, automatically dispatch the nearest and skill-matched maintenance personnel to the site to handle the problem. After the operation and maintenance personnel complete the maintenance task, the system records the maintenance results and feedback information, including the fault type repaired, the time used, and the component replacement situation.

6. The traffic equipment remote monitoring and maintenance management method according to claim 5, characterized in that, The method of identifying common fault types and occurrence patterns of equipment through historical data analysis, establishing a prediction model, and when predicting abnormal equipment faults, the system issues an alarm in advance and generates preventive maintenance suggestions based on the equipment status is as follows: By long-term collecting data such as voltage, current, temperature, humidity, and working status during equipment operation, and recording historical fault events, based on the historical data, by statistically analyzing the time, frequency, and environmental condition information of fault occurrence, the system identifies the periodic or seasonal patterns of faults, or the high-incidence trend of faults under specific operating conditions; Using methods such as intelligent machine learning or time series analysis, the system establishes a fault prediction model. The model is based on the real-time operation data and historical fault data of the equipment to predict the faults that occur in the equipment. If parameters such as current or temperature exceed the normal range, or when the equipment operation reaches the set threshold, the prediction model predicts the upcoming faults of the equipment; When the fault prediction model determines that the equipment has an abnormality or a fault, the system issues a warning or an alarm in advance, and the alarm notifies relevant personnel through SMS, email, or App notification; According to the current status of the equipment and the fault prediction, the system generates corresponding preventive maintenance suggestions. The maintenance suggestions include that if it is predicted that a certain equipment will have a fault next month, the system recommends that the operation and maintenance personnel conduct an advance inspection, replace vulnerable parts, or make environmental adaptability adjustments.

7. The traffic equipment remote monitoring and maintenance management method according to claim 6, wherein The method of visualizing the data collected in real time by sensors, generating a device operation report, and analyzing the working status, failure rate, and maintenance records of the device based on the real-time data is as follows: Through sensors installed on the equipment, real-time collect operation data such as voltage, current, temperature, humidity, vibration, operation duration, and working status of the equipment, and clean, denoise, and preprocess the real-time collected data; Using a dashboard visualization tool, present the real-time data as charts and graphs. The display content includes but is not limited to the current voltage, current, temperature, and humidity status information of the equipment, and display the real-time status of each equipment with green, yellow, and red indicators; According to the real-time data collected, the system regularly generates a device operation report. The report content includes the basic operation status, working hours, energy consumption data, and environmental conditions of the equipment; Based on the real-time data, the system automatically analyzes the working status of the equipment. By analyzing the historical fault data, the system calculates the failure rate of each equipment, analyzes the causes of faults, and provides optimization suggestions.

8. The remote monitoring and maintenance management method of transportation equipment according to claim 7, characterized in that The method of setting multi-level permissions so that only authorized personnel can access device data and perform system configuration, operation and maintenance personnel submit feedback through the system to report problems and improvement suggestions during equipment operation, and regularly update and optimize the system according to operation and maintenance feedback, equipment usage, and technological development is as follows: According to the roles and responsibilities of different users, set the permission levels of system administrators, operation and maintenance personnel, and ordinary users, and set different access permissions according to the roles of users; According to the type, region, or functional module of the equipment, restrict different users from accessing the corresponding data, and record each access to device data or system configuration; A feedback platform is set up, where operation and maintenance personnel report problems during equipment operation, put forward improvement suggestions or share maintenance experiences. The submitted feedback is sent to the system administrator or relevant person in charge for review and confirmation. For urgent fault reports, a priority processing process is automatically triggered; The feedback from operation and maintenance personnel and the problems in equipment use are used as the basis for system optimization. Artificial intelligence, big data analysis, and edge computing device management technologies are introduced to regularly update the system version, add new functions, and fix known vulnerabilities.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements a method for remote monitoring and maintenance management of a traffic device as described in any one of claims 2-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a method for remote monitoring and maintenance management of a traffic device as described in any one of claims 2-8.

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