Traffic light supplementing equipment operation and maintenance management method and platform based on LoRa transmission

Abstract

The invention discloses a traffic light supplement equipment operation and maintenance management method and platform based on LoRa transmission, and relates to the related technical field of data communication transmission, and the method comprises the steps: deploying a LoRa wireless communication module, and carrying out the real-time collection of target traffic light supplement lamp equipment; dynamic analysis is carried out based on the light supplement lamp operation data set, abnormity judgment is carried out according to a dynamic analysis result, and abnormity alarm information is generated; transmitting to an operation and maintenance management platform, and triggering a remote maintenance instruction; a control parameter adjusting instruction is issued to the target traffic light supplementing lamp device; and performing real-time operation and maintenance management optimization on the target traffic light supplementing lamp equipment in combination with the control parameter adjustment instruction. The technical problems that in the prior art, traffic light supplementing equipment is widely distributed, faults are difficult to find in time through manual inspection, the equipment is prone to being interfered by the environment, then abnormal warning information is inaccurate, and the operation and maintenance efficiency and reliability of the traffic light supplementing equipment are poor are solved, and the technical effect of improving the operation and maintenance efficiency and reliability of the equipment is achieved.

Description

Technical Field

[0001] This application relates to the technical field of data communication transmission, and specifically to a method and platform for the operation and maintenance management of traffic supplementary lighting devices based on LoRa transmission. Background Art

[0002] The stable operation of traffic supplementary lighting devices ensures that the traffic monitoring system can clearly capture key information such as vehicles and pedestrians under various lighting conditions, playing a crucial role in maintaining traffic order, preventing and handling accidents. However, the operation and maintenance management of traditional traffic supplementary lighting devices are extremely difficult. In terms of the distribution range, they are widely distributed in urban streets, highways, bridges, tunnels, etc. The manual inspection method is inefficient and cannot detect equipment failures in a timely manner, often resulting in malfunctioning equipment being unable to work properly for a long time, seriously affecting the effectiveness of traffic monitoring. In terms of the operating environment, traffic supplementary lighting devices are easily interfered by complex environmental factors such as bad weather, large changes in temperature and humidity, and electromagnetic interference, greatly increasing the probability of problems such as bulb damage, power supply failures, and abnormal control circuits in the equipment, further exacerbating the complexity of the operation and maintenance management of traffic supplementary lighting devices and affecting the operation management efficiency and reliability of traffic supplementary lighting devices.

[0003] Therefore, in the current related technologies, there are technical problems such as the wide distribution of traffic supplementary lighting devices, difficulty in manually inspecting and timely discovering faults, and the susceptibility of the devices to environmental interference, resulting in inaccurate abnormal alarm information, and poor operation and maintenance efficiency and reliability of traffic supplementary lighting devices. Summary of the Invention

[0004] This application provides a method and platform for the operation and maintenance management of traffic supplementary lighting devices based on LoRa transmission, solving the technical problems in the prior art, such as the wide distribution of traffic supplementary lighting devices, difficulty in manually inspecting and timely discovering faults, and the susceptibility of the devices to environmental interference, resulting in inaccurate abnormal alarm information, and poor operation and maintenance efficiency and reliability of traffic supplementary lighting devices, achieving the technical effect of improving the operation and maintenance efficiency and reliability of the devices.

[0005] This application provides a method for the operation and maintenance management of traffic supplementary lighting devices based on LoRa transmission. The method includes: deploying a LoRa wireless communication module, and performing real-time acquisition on target traffic supplementary lighting devices through the LoRa wireless communication module to obtain a supplementary lighting device operation data set; performing dynamic analysis based on the supplementary lighting device operation data set, making an abnormal determination according to the dynamic analysis result, and generating an abnormal alarm information; transmitting the abnormal alarm information to an operation and maintenance management platform to trigger a remote maintenance instruction; sending a control parameter adjustment instruction to the target traffic supplementary lighting device through the LoRa wireless communication module; and performing real-time operation and maintenance management optimization on the target traffic supplementary lighting device according to the remote maintenance instruction combined with the control parameter adjustment instruction.

[0006] In a possible implementation manner, the operation and maintenance management method for traffic supplementary lighting devices based on LoRa transmission further performs the following processing: Deploy a LoRa wireless communication module on the road traffic monitoring supplementary lighting device; collect real-time parameters of the target traffic supplementary lighting device at fixed intervals through the LoRa wireless communication module to determine a device cycle data group; classify and clean the device cycle data group according to data types to generate a standardized data set; encapsulate the standardized data set into a LoRa protocol frame through the LoRa wireless communication module to determine the operation data set of the supplementary lighting lamp.

[0007] In a possible implementation manner, the operation and maintenance management method for traffic supplementary lighting devices based on LoRa transmission further performs the following processing: Send the operation data set of the supplementary lighting lamp to the operation and maintenance management platform for data classification and storage through a dynamic channel allocation mechanism to determine multiple operation storage data information; retrieve historical operation data for training to construct a random forest model; divide the multiple operation storage data information through the random forest model to determine multiple fault status data; perform clustering analysis on the target traffic supplementary lighting device in different environmental scenarios to set an adaptive threshold; determine a composite decision rule by judging the multiple fault status data according to the adaptive threshold, and perform real-time judgment and update according to the composite decision rule to obtain the dynamic analysis result.

[0008] In a possible implementation manner, the operation and maintenance management method for traffic supplementary lighting devices based on LoRa transmission further performs the following processing: Monitor the channel of the LoRa wireless communication module to obtain a signal quality index; set a signal expectation threshold. When the signal quality index is lower than the signal expectation threshold, switch the channel of the LoRa wireless communication module to a standby channel to obtain a channel switching record; dynamically adjust the data sending interval of the operation data set of the supplementary lighting lamp according to the channel switching record to obtain a signal status log; activate the dynamic channel allocation mechanism according to the signal status log to synchronize the operation data set of the supplementary lighting lamp to the operation and maintenance management platform.

[0009] In a possible implementation manner, the operation and maintenance management method for traffic supplementary lighting devices based on LoRa transmission further performs the following processing: Retrieve the operation data of the target traffic supplementary lighting device under different meteorological conditions in a historical period to construct a multi-dimensional training data set; perform time series analysis on the multi-dimensional training data set, and extract periodic features according to the time series analysis results; cross-validate the multi-dimensional training data set with the actual device fault records according to the periodic features, and adjust the model hyperparameters according to the validation results to obtain the random forest model.

[0010] In a possible implementation, the operation and maintenance management method for traffic supplementary lighting devices based on LoRa transmission further performs the following processing: constructing a dynamic topology map according to the physical deployment location of the target traffic supplementary lighting device; traversing the dynamic topology map according to the dynamic analysis result for layout identification to determine multiple node identification results; performing abnormal determination extraction based on the multiple node identification results to determine multiple abnormal device nodes, and associating and analyzing the multiple abnormal device nodes with historical alarm records to generate the abnormal alarm information.

[0011] In a possible implementation, the operation and maintenance management method for traffic supplementary lighting devices based on LoRa transmission further performs the following processing: analyzing the historical alarm records and historical maintenance records, and extracting high-frequency fault modes and fault resolution strategies; verifying the effectiveness of the fault resolution strategies through simulation tests according to the high-frequency fault modes to generate strategy priority scores; integrating the fault resolution strategies with strategy priority scores higher than a preset score to construct a maintenance strategy library; parsing the abnormal alarm information to determine the abnormal type, matching the abnormal type with the maintenance strategy library, and determining the control parameter adjustment instruction according to the matching result.

[0012] In a possible implementation, the operation and maintenance management method for traffic supplementary lighting devices based on LoRa transmission further performs the following processing: integrating the remote maintenance instruction and the control parameter adjustment instruction according to the abnormal type, and executing the integrated instruction to determine the execution status data; judging whether the supplementary lighting lamp operation data set has returned to the normal threshold interval based on the execution status data. If the supplementary lighting lamp operation data set has not returned to the normal threshold interval, triggering an optimization signal; adaptively optimizing the integrated instruction through the optimization signal to generate a supplementary lighting device health report; performing operation and maintenance management on the target traffic supplementary lighting lamp device according to the supplementary lighting device health report.

[0013] In a possible implementation, the operation and maintenance management method for traffic supplementary lighting devices based on LoRa transmission further performs the following processing: dynamically adjusting the integrated instruction based on the optimization signal to determine the device operation and maintenance optimization log; performing operation and maintenance analysis according to the device operation and maintenance optimization log and the supplementary lighting lamp operation data set to generate a health score matrix; grading the target traffic supplementary lighting device based on the health score matrix to determine the supplementary lighting device health report.

[0014] The present application also provides an operation and maintenance management platform for traffic supplementary lighting devices based on LoRa transmission, including: an operation data set acquisition unit, configured to deploy a LoRa wireless communication module, and perform real-time acquisition on target traffic supplementary lighting devices through the LoRa wireless communication module to obtain an operation data set of the supplementary lighting devices; an abnormal alarm information generation unit, configured to perform dynamic analysis based on the operation data set of the supplementary lighting devices, make an abnormal determination according to the dynamic analysis result, and generate abnormal alarm information; a remote maintenance instruction triggering unit, configured to transmit the abnormal alarm information to the operation and maintenance management platform to trigger a remote maintenance instruction; a control parameter adjustment instruction issuing unit, configured to issue a control parameter adjustment instruction to the target traffic supplementary lighting devices through the LoRa wireless communication module; and an operation and maintenance management optimization unit, configured to perform real-time operation and maintenance management optimization on the target traffic supplementary lighting devices according to the remote maintenance instruction in combination with the control parameter adjustment instruction.

[0015] It is intended to deploy a LoRa wireless communication module through the operation and maintenance management method and platform for traffic supplementary lighting devices based on LoRa transmission proposed in the present application to perform real-time acquisition on target traffic supplementary lighting devices; perform dynamic analysis based on the operation data set of the supplementary lighting devices, make an abnormal determination according to the dynamic analysis result, and generate abnormal alarm information; transmit it to the operation and maintenance management platform to trigger a remote maintenance instruction; issue a control parameter adjustment instruction to the target traffic supplementary lighting devices; and perform real-time operation and maintenance management optimization on the target traffic supplementary lighting devices in combination with the control parameter adjustment instruction. This solves the technical problems in the prior art that traffic supplementary lighting devices are widely distributed, it is difficult for manual inspections to timely detect faults, and the devices are vulnerable to environmental interference, resulting in inaccurate abnormal alarm information and poor operation and maintenance efficiency and reliability of traffic supplementary lighting devices, and achieves the technical effect of improving the operation and maintenance efficiency and reliability of the devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in the present application to illustrate the operations performed by the platform according to the embodiments of the present application. It should be understood that the operations before or below do not necessarily need to be precisely executed in sequence. On the contrary, according to needs, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0017] Figure 1 It is a schematic flowchart of the operation and maintenance management method for traffic supplementary lighting devices based on LoRa transmission provided by an embodiment of the present application.

[0018] Figure 2 It is a schematic structural diagram of the operation and maintenance management platform for traffic supplementary lighting devices based on LoRa transmission provided by an embodiment of the present application.

[0019] Description of the attached drawing reference numerals: The operation dataset acquisition unit 10, the abnormal alarm information generation unit 20, the remote maintenance instruction trigger unit 30, the control parameter adjustment instruction issuing unit 40, and the operation and maintenance management optimization unit 50. Detailed implementation manners

[0020] The above description is only an overview of the technical solution of this application. In order to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of this application more obvious and understandable, the following specifically gives the detailed implementation manners of this application.

[0021] In order to make the purpose, technical solution, and advantages of this application clearer, the following will further describe this application in detail with reference to the attached drawings. The described embodiments should not be regarded as limitations of this application. All other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0022] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first / second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, platform, product, or server including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products, or devices. 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 this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application.

[0023] The embodiment of this application provides an operation and maintenance management method for traffic supplementary lighting devices based on LoRa transmission, as Figure 1 shown. The method includes: Step S100: Deploy a LoRa wireless communication module, and perform real-time acquisition on the target traffic supplementary lighting device through the LoRa wireless communication module to obtain a supplementary lighting operation dataset.

[0024] Preferably, a LoRa wireless communication module is deployed on the road traffic monitoring supplementary lighting device, and this module is used to collect data from the target traffic supplementary lighting device in real time to obtain a supplementary lighting device operation dataset. Among them, the traffic supplementary lighting device is an important auxiliary device in the traffic monitoring system, mainly used to provide additional lighting for the monitoring camera in case of insufficient light to ensure clear image and video acquisition effects. Traffic supplementary lighting devices generally use high-brightness LEDs (light-emitting diodes) as light sources. The supplementary lighting device drives the LED to emit light through a control circuit. According to different working modes (constant on, stroboscopic, or flash), the control circuit will supply current to the LED at corresponding frequencies and duty cycles to make it emit light of different intensities and frequencies; LoRa is an ultra-long-distance wireless transmission solution based on spread-spectrum technology, belonging to the low-power wide-area network (LPWAN) wireless communication technology, which is widely used in the Internet of Things field and has the characteristics of long distance, low power consumption, multiple nodes, and low cost.

[0025] Preferably, deploying a LoRa wireless communication module on the road traffic monitoring supplementary lighting device means integrating this module into the control system of the traffic supplementary lighting device. The traffic supplementary lighting device uses LoRa technology to communicate wirelessly with other devices (such as gateways, operation and maintenance management platforms) to achieve data upload and receive control instructions. Specifically, through the deployed LoRa wireless communication module, various operating parameters of the target traffic supplementary lighting device (including power, temperature, health status, and trigger times, etc.) are collected in real time to form a supplementary lighting device operation dataset. Among them, power is the electrical power consumed by the supplementary lighting device during operation. If the power suddenly increases, it may indicate problems such as a short circuit or component aging inside the supplementary lighting device, resulting in additional power consumption. If the power is too small, it may mean that the bulb is damaged or the power supply is insufficient, affecting the supplementary lighting effect. By monitoring the power data in real time, potential faults of the supplementary lighting device can be detected in time and maintenance can be carried out in advance; temperature is the internal temperature or surface temperature of the supplementary lighting device during operation; continuous high temperature may cause the filament of the bulb to age faster, the performance of electronic components to decline or even be damaged. By monitoring the temperature data, it can be monitored that if the temperature exceeds the normal range, it may be necessary to check whether the heat dissipation device is working properly, or whether the working environment of the supplementary lighting device is not conducive to heat dissipation, and take measures in time to prevent the device from being damaged due to overheating.

[0026] Preferably, the health status is used to comprehensively evaluate the overall operation status of the supplementary light, and the judgment may be based on multiple parameters (such as power, temperature, flicker frequency, etc.) and the historical operation data of the device, which can intuitively reflect whether the supplementary light is in a normal working state. For example, when the health status shows "abnormal", it indicates that there may be a fault or potential problem with the supplementary light, and it is necessary to further analyze the specific reasons and perform repairs; the trigger count refers to the number of times the traffic supplementary light device is triggered and lit within a certain period. In road traffic monitoring, the supplementary light is usually lit according to the trigger signal of the monitoring device (such as a camera) to provide sufficient light for shooting. The trigger count reflects the usage frequency and workload of the supplementary light. If the trigger count increases abnormally, it means that the traffic volume on this section has increased, or there is a problem with the trigger setting of the monitoring device. Too few trigger counts mean that there is a problem with the monitoring device or the trigger mechanism of the supplementary light. By analyzing the trigger count data, the usage strategy of the supplementary light can be optimized, the trigger parameters can be reasonably adjusted, and the service life of the device can be extended. By collecting the operation data of the supplementary light device in real time, potential problems can be discovered and solved in a timely manner, thereby improving the operation and maintenance management efficiency, reliability, and stability of the traffic supplementary light device.

[0027] Further, step S100 further includes step S110 of deploying a LoRa wireless communication module on the road traffic monitoring supplementary light device; step S120 of collecting the real-time parameters of the target traffic supplementary light device at fixed intervals through the LoRa wireless communication module to determine the device cycle data set; step S130 of classifying and cleaning the device cycle data set according to the data type to generate a standardized data set; step S140 of encapsulating the standardized data set into a LoRa protocol frame through the LoRa wireless communication module to determine the supplementary light operation data set.

[0028] Preferably, deploying a LoRa wireless communication module on the road traffic monitoring supplementary light device means integrating this module into the traffic supplementary light device to enable it to have wireless communication capabilities and be able to interact with other devices (such as gateways, servers, etc.); then using the LoRa wireless communication module to collect the real-time parameters of the target traffic supplementary light device at fixed intervals. Specifically, fixed-interval collection means setting a time interval, such as every 5 minutes, 10 minutes, etc. The LoRa wireless communication module collects the real-time parameters of the target traffic supplementary light device according to the set time interval. The real-time parameters include but are not limited to the power, temperature, health status (such as whether it is working properly, whether there is a fault, etc.), trigger count, etc. of the supplementary light, which reflect the operation status and performance of the supplementary light; combine all the real-time parameters within each collection cycle into a data set, that is, the device cycle data set. For example, collect once every 10 minutes, and each time the parameters such as power, temperature, and trigger count are collected, it is a device cycle data set.

[0029] Preferably, according to the nature and type of the collected data, the data in the device cycle data group are classified. For example, power data are classified into one category, temperature data into one category, trigger count data into one category, etc. Then, the classified data are cleaned to remove noise data, error data, duplicate data, etc. For example, due to sensor failures or interference, some obviously unreasonable temperature values may be collected, and these data are cleaned. After the data cleaning is completed, all types of data are sorted according to a unified format and standard to generate a standardized data set. LoRa communication has its specific protocol format. In order to accurately and reliably transmit the standardized data set in the LoRa network, it needs to be encapsulated according to the requirements of the LoRa protocol to form a LoRa protocol frame. The encapsulation process includes adding information such as protocol headers and check bits to ensure the integrity and accuracy of the data during transmission. The data contained in the encapsulated LoRa protocol frame is the running data set of the fill light, which can be sent to a specified receiving end (such as a gateway, server, etc.) through the LoRa wireless communication module for subsequent data analysis, anomaly monitoring, operation and maintenance management, etc. The whole process realizes the collection and processing of the operation parameters of the road traffic monitoring fill light device, thereby improving the accuracy and reliability of device operation and maintenance management.

[0030] Step S200, perform dynamic analysis based on the running data set of the fill light, make an anomaly determination according to the dynamic analysis result, and generate an anomaly warning message.

[0031] Preferably, perform dynamic analysis on the running data set of the fill light, that is, study the situation of the fill light running data changing with time. Specifically, observe the change trends of various parameters with time. For example, analyze whether the power of the fill light shows a gradually increasing or decreasing trend. If the power continues to rise, it may mean that there is an internal fault in the fill light, such as component aging resulting in a change in resistance, thereby increasing power consumption. Study the correlation between different parameters. For example, there is usually a certain correlation between temperature and power. When the power increases, the temperature usually also rises. By analyzing this correlation, the running state of the fill light can be understood more comprehensively. If it is found that the power increases but the temperature does not change correspondingly, it may indicate that there is a problem with the heat dissipation system of the fill light. Compare the current running data with historical data or preset standard data. For example, compare the current temperature of the fill light with the average temperature in the same period in the past. If the current temperature exceeds the normal range, it means there is a problem.

[0032] Preferably, after the dynamic analysis is completed, it is necessary to determine whether there is an abnormal situation of the supplementary light according to the analysis result. Specifically, a reasonable threshold range is set for each parameter. When the value of a certain parameter exceeds this threshold range, it is determined as abnormal. For example, the normal power range of the supplementary light is set to 50 - 100 watts. If the power monitored in real time is greater than 100 watts or less than 50 watts, the power parameter is considered abnormal; judgment is made according to the change pattern of the data. For example, the triggering times of the supplementary light are usually less during the day and more at night. If there are abnormally frequent triggers during the day, even if the number of triggers does not exceed the threshold, it can be determined as abnormal; if it is determined that there is an abnormality in the supplementary light, corresponding abnormal alarm information is generated to notify the operation and maintenance personnel for processing in a timely manner. Among them, the abnormal alarm information should include a detailed description of the abnormal situation, such as the time when the abnormality occurred, the name of the abnormal parameter (such as power, temperature, etc.), the specific value of the abnormality, and the difference from the normal range, etc., so as to timely detect the abnormal situation of the traffic supplementary light device and take corresponding measures for processing to ensure the normal operation of the traffic monitoring device.

[0033] Further, step S200 further includes step S210, sending the supplementary light operation data set to the operation and maintenance management platform for data classification storage through a dynamic channel allocation mechanism, and determining multiple operation storage data information; step S220, retrieving historical operation data for training to construct a random forest model; step S230, dividing the multiple operation storage data information through the random forest model to determine multiple fault status data; step S240, performing clustering analysis on the target traffic supplementary light device in different environmental scenarios and setting an adaptive threshold; step S250, judging the multiple fault status data according to the adaptive threshold to generate a composite judgment rule, and performing real-time judgment update according to the composite judgment rule to obtain the dynamic analysis result.

[0034] Preferably, in wireless communication, channel resources are limited. The dynamic channel allocation mechanism can dynamically select a suitable communication channel for the supplementary light device according to factors such as the current network condition, signal strength, and interference situation, avoiding channel congestion and improving the reliability and efficiency of data transmission. For example, when a certain channel has a large interference, the system will automatically switch to a channel with less interference for data transmission; after sending the supplementary light operation data set to the operation and maintenance management platform, the data is classified and stored according to dimensions such as data type, time, and device number. For example, power data, temperature data, trigger times data, etc. are stored in different database tables respectively, and sorted in chronological order. Through data classification storage, each data category forms an independent data set, thus constituting multiple operation storage data information. For example, the storage information of power data includes power value, acquisition time, corresponding supplementary light device number, etc.

[0035] Preferably, historical operation data of the supplementary light device is retrieved, including normal operation data and data when a fault occurs, to construct a random forest model. By training with the historical operation data, the relationship between the monitoring parameters and the operation status of the supplementary light device is learned, so as to predict the operation status of the supplementary light device according to the collected real-time operation data. Among them, the random forest includes multiple decision trees and synthesizes the prediction results; using the multiple decision trees of the random forest model, multiple operation storage data information is analyzed and judged. Specifically, the operation data of the supplementary light device is divided into different categories, including normal operation status and various fault statuses. For example, according to the combination of parameters such as power and temperature, it is judged whether there are fault statuses such as bulb damage and power supply failure in the supplementary light device, so as to obtain fault status data, which contains various parameter information when the fault occurs.

[0036] Preferably, clustering analysis is performed on the target traffic light device in different environmental scenarios (such as day, night, sunny day, rainy day, etc.). Specifically, the target traffic supplementary light device is divided into different categories according to the similarity of the data. For example, the supplementary light devices operating during the day are grouped into one category, and the supplementary light devices operating at night are grouped into one category; according to the clustering analysis results, different thresholds (such as power threshold, temperature threshold) are set for each category, and can be adaptively adjusted according to the change of the environmental scenario. For example, during the day, due to the strong environmental light, the power threshold of the supplementary light may be set lower, while at night, due to the weak environmental light, the power threshold may be set higher; finally, the fault status data is compared and judged with the adaptive threshold, and multiple parameters and conditions are comprehensively considered to generate a composite determination rule. Then, during the operation of the supplementary light device, the new operation data is continuously compared with the composite determination rule, and the determination result is updated in real time. If it is found that the new data meets the fault determination conditions, an alarm message is sent in time. If the data returns to normal, it is updated to the normal state; through real-time determination and update, the dynamic analysis result of the operation status of the supplementary light device is finally obtained, helping the operation and maintenance personnel to timely discover the faults and abnormal conditions of the device and take corresponding measures for processing.

[0037] Further, step S210 further includes step S211, monitoring the channel of the LoRa wireless communication module to obtain a signal quality index; step S212, setting a signal expectation threshold. When the signal quality index is lower than the signal expectation threshold, the channel of the LoRa wireless communication module is switched to a standby channel to obtain a channel switching record; step S213, dynamically adjusting the data sending interval of the supplementary light operation data set according to the channel switching record to obtain a signal status log; step S214, activating the dynamic channel allocation mechanism according to the signal status log to synchronize the supplementary light operation data set to the operation and maintenance management platform.

[0038] Preferably, a detection device (such as a spectrum analyzer) is used to continuously monitor the channels of the LoRa wireless communication module to obtain various interferences that the channels may be subject to in a complex communication environment, such as signal interference from other wireless devices, electromagnetic interference in the natural environment, etc. Through monitoring, multiple indicators reflecting the signal quality of the channels can be obtained, such as Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), etc. RSSI represents the strength of the received signal, and generally, the larger the value, the stronger the signal. SNR reflects the ratio of the signal strength to the noise strength. The higher the ratio, the clearer the signal and the less affected by noise interference. According to the actual communication requirements and historical experience, an expected threshold for signal quality is preset to ensure that the LoRa wireless communication module can stably and reliably transmit the operation dataset of the supplementary light. For example, the expected threshold for RSSI can be set to -100 dBm, and the expected threshold for SNR can be set to 5 dB. When the monitored signal quality indicators are lower than the set expected thresholds, it means that the communication quality of the current channel is poor, which may lead to data transmission errors or losses. Then, the channel of the LoRa wireless communication module is automatically switched to the backup channel. Among them, the backup channel is pre-planned, and its signal quality is relatively good, which can provide a more stable environment for data transmission. When performing channel switching, relevant information about the switching is recorded, including the switching time, the original channel number, the backup channel number switched to, etc., to form a channel switching record.

[0039] Preferably, the channel switching record reflects the stability and communication quality of the channels. According to the channel switching record, the data sending interval of the operation dataset of the supplementary light is dynamically adjusted. Specifically, if the channel switching is frequent, it indicates that the current communication environment is poor. To reduce the probability of data errors during transmission, the data sending interval can be appropriately increased to reduce the data transmission frequency. On the contrary, if the channel is relatively stable and the signal quality is good, the data sending interval can be appropriately reduced to improve the real-time performance of data transmission. And during the process of adjusting the data sending interval, relevant information about each adjustment is recorded, such as the adjustment time, the data sending intervals before and after the adjustment, the signal quality indicators at that time, etc., to form a signal status log to comprehensively reflect the signal status and data sending situation of the LoRa wireless communication module. Finally, the dynamic channel allocation mechanism selects the most suitable channel for the LoRa wireless communication module to transmit data according to the signal status log (including the current channel conditions and communication requirements), that is, comprehensively considering multiple factors such as the signal quality, idle status, and interference situation of the channels. According to the information recorded in the signal status log, when it is found that the current channel is not suitable for data transmission, the dynamic channel allocation mechanism is activated to re-allocate a better channel for the LoRa wireless communication module, and the operation dataset of the supplementary light is synchronized to the operation and maintenance management platform using this newly allocated channel to ensure that the operation and maintenance management platform can obtain the operation data of the supplementary light in a timely and accurate manner, thereby improving the overall operation and maintenance management efficiency of the traffic supplementary light equipment.

[0040] Further, step S220 further includes step S221 of retrieving the operation data of the target traffic supplementary lighting device under different meteorological conditions during a historical period and constructing a multi-dimensional training data set; step S222 of extracting periodic features according to the time series analysis result by performing time series analysis on the multi-dimensional training data set; and step S223 of cross-verifying the multi-dimensional training data set with the actual device failure records according to the periodic features and adjusting the model hyperparameters according to the verification result to obtain the random forest model.

[0041] Preferably, retrieve the operation data of the target traffic supplementary lighting device in the past period from the relevant database, which not only includes the operation parameters of the device itself, such as the brightness, current, voltage of the supplementary light, etc., but also covers different meteorological condition information, such as temperature, humidity, light intensity, whether it is raining, whether there is fog, etc. Organize the collected data and construct it into a multi-dimensional training data set. Specifically, each data record contains multiple dimensions of information such as device operation parameters and corresponding meteorological conditions. For example, a record can be represented in the vector form of [brightness, current, voltage, temperature, humidity, light intensity, weather condition (rainy / sunny, etc.)]. Multiple such records constitute the multi-dimensional training data set.

[0042] Preferably, perform time series analysis on the multi-dimensional training data set, that is, study the changes of various operation data of the target traffic supplementary lighting device under different meteorological conditions over time. For example, use time series analysis techniques such as moving average method, exponential smoothing method, autoregressive model (AR), autoregressive moving average model (ARMA), seasonal decomposition, etc. to deeply analyze the change patterns of each variable (such as operation parameters such as the power, temperature, switch state of the supplementary light, and meteorological condition parameters such as temperature, humidity, light intensity) in the data set over time, and extract periodic features according to the analysis result. Among them, the operation of the traffic supplementary lighting device may have multiple periodicities, such as daily (different operation states during day and night), weekly (usage differences between weekdays and weekends), seasonal (the impact of meteorological condition changes in different seasons on device operation), etc. For example, through analysis, it is found that the power of the supplementary lighting device will increase significantly at dusk (traffic peak period) every day and then gradually decrease at night, or the failure rate of the device is relatively high during the high-temperature period in summer, showing a seasonal periodic law.

[0043] Preferably, according to the extracted periodic features, the multi-dimensional training data set is reasonably divided. For example, the data set is divided into four subsets: spring, summer, autumn, and winter according to seasons, or into two subsets: day and night according to the time period of each day, etc. Then, the subsets of the divided data set are compared and verified with the actual fault records of the device, that is, to check the relationship between the operation data of the device and the actual faults occurring during this period. For example, in the data set subset of summer, count the operation parameters and meteorological conditions when the device fails to see if there are certain specific patterns or rules. Through cross-validation, evaluate the prediction ability of the model for device faults under different periodic conditions; according to the results of cross-validation, if it is found that the prediction results of the model under certain periodic conditions deviate greatly from the actual fault records, it is necessary to adjust the hyperparameters (such as the number of decision trees, the maximum depth of the tree, the minimum sample split number, etc.). For example, if the accuracy of the model in predicting device faults in summer is low, it may be tried to increase the number of decision trees or adjust the maximum depth of the tree to improve the fitting ability of the model to complex data patterns. By continuously adjusting the random forest hyperparameters and performing cross-validation, the model is gradually optimized until the model can achieve good prediction performance under different periodic conditions, and finally a random forest model is obtained. This model can use the operation data and periodic features of the device to more accurately predict the possible faults of the device. For example, Table 1 gives an example of adjusting the hyperparameters of the random forest model:

[0044] Further, step S200 further includes step S260 of constructing a dynamic topology map according to the physical deployment location of the target traffic supplementary lighting device; step S270 of traversing the dynamic topology map according to the dynamic analysis result for layout identification to determine a plurality of node identification results; step S280 of performing anomaly determination extraction based on the plurality of node identification results to determine a plurality of abnormal device nodes, and associating and analyzing the plurality of abnormal device nodes with historical alarm records to generate the abnormal alarm information.

[0045] Preferably, accurate physical deployment location information of the target traffic lighting equipment is obtained through technologies such as Geographic Information System (GIS), equipment installation records, or on-site surveys. For example, each traffic lighting equipment has its specific longitude and latitude coordinates, as well as the specific location on the road (such as intersections, middle of sections, etc.). Then, taking each traffic lighting equipment as a node and the communication connections or physical adjacency relationships between the equipment as edges, a dynamic topology graph reflecting the spatial relationships and connection states between the equipment is constructed. For instance, if the communication module of a certain equipment fails and causes the connection with other equipment to be interrupted, the dynamic topology graph will correspondingly update and display this change; traverse each node in the dynamic topology graph (i.e., traffic lighting equipment) one by one, and identify the layout of each equipment in the topology graph according to the corresponding dynamic analysis results. For example, if the dynamic analysis result of a certain equipment shows normal operation, its node may be marked as green; if it shows an anomaly, it is marked as red; for equipment with potential faults, it is marked as yellow, thereby visually seeing the operating states of each equipment on the topology graph and determining the identification results of multiple nodes.

[0046] Preferably, according to the node identification results, extract those equipment nodes marked as anomalies (such as red markings) from the dynamic topology graph (i.e., abnormal equipment nodes), and then associate the abnormal equipment nodes with historical alarm records (including detailed information when the equipment had faults or anomalies in the past, such as fault types, occurrence times, handling measures, etc.) to find out whether these abnormal equipment nodes have had similar anomalies in history or are related to the faults of other equipment. For example, if an equipment has the same type of anomaly multiple times, or multiple adjacent equipment have anomalies simultaneously, there may be some common causes, such as power supply problems or communication interference in a certain area; according to the results of the correlation analysis, generate detailed anomaly alarm information, which should not only include the basic information of the abnormal equipment nodes (such as equipment numbers, locations, etc.), but also explain the type of anomaly, possible causes (obtained through correlation analysis), and the association with historical alarm records, etc., so as to provide comprehensive and accurate information for maintenance personnel and improve the efficiency of operation and maintenance management.

[0047] Step S300, transmit the abnormal alarm information to the operation and maintenance management platform to trigger a remote maintenance instruction.

[0048] Preferably, the generated abnormal alarm information is sent using the LoRa wireless communication module, and it is ensured that the information can be reliably transmitted to the operation and maintenance management platform in different environments and distances. For example, for traffic supplementary lighting devices distributed in different areas of the city, the LoRa wireless communication module can transmit the alarm information to the operation and maintenance management platform located in the city monitoring center by virtue of its long-distance communication ability. Among them, the operation and maintenance management platform is used for centralized management and monitoring of traffic supplementary lighting devices and has the function of receiving and processing various device information. When the abnormal alarm information is transmitted, the platform will receive, analyze and store it. After receiving the abnormal alarm information, the operation and maintenance management platform determines whether to trigger a remote maintenance instruction according to the preset rules and strategies. The rules may include the severity of the abnormality, the importance of the device, etc. For example, if the abnormal alarm information indicates that the device has a serious fault, a remote maintenance instruction may be triggered immediately; for some minor abnormalities, further monitoring and analysis may be carried out first, and then it is decided whether to trigger the instruction; the remote maintenance instruction refers to an operation and maintenance operation instruction sent to the abnormal device through the network for remote troubleshooting, parameter adjustment or system repair of the device. For example, if there is a problem with the parameter setting of the device, the remote maintenance instruction may be to modify the relevant parameters of the device; if the communication module of the device fails, the instruction may be to try to restart the communication module or reconfigure the communication parameters; after receiving the remote maintenance instruction, the abnormal device performs corresponding operations according to the requirements of the instruction, and the internal control system of the device analyzes the content of the instruction and executes the corresponding actions to achieve remote maintenance of the device, so as to achieve rapid response and remote fault handling of traffic supplementary lighting devices and improve the maintenance efficiency and reliability of the devices.

[0049] Step S400, issue a control parameter adjustment instruction to the target traffic supplementary lighting device through the LoRa wireless communication module.

[0050] Step S400 further includes Step S410, analyzing the historical alarm records and historical maintenance records, and extracting high-frequency fault modes and fault resolution strategies; Step S420, verifying the effectiveness of the fault resolution strategies through simulation tests according to the high-frequency fault modes, and generating strategy priority scores; Step S430, integrating the fault resolution strategies with strategy priority scores higher than the preset score to construct a maintenance strategy library; Step S440, parsing based on the abnormal alarm information to determine the abnormal type, matching the abnormal type with the maintenance strategy library, and determining the control parameter adjustment instruction according to the matching result.

[0051] Preferably, historical alarm records and historical maintenance records generated by traffic supplementary light devices in the past are collected, which contain various fault information that occurred during the past operation of the devices, as well as the maintenance measures taken for these faults. A large number of collected historical records are deeply analyzed using data mining and statistical analysis, etc., to find out the frequently occurring fault modes. For example, it is found that faults such as frequent bulb damage and overheating of the power supply module in the supplementary lights are relatively common, which belong to high-frequency fault modes. At the same time, the solution strategies adopted for different faults are extracted from the historical maintenance records. For example, when encountering the fault of bulb damage, the strategy is to replace the bulb; for the problem of overheating of the power supply module, the strategy is to increase the heat dissipation device or adjust the power parameters.

[0052] Preferably, for the extracted high-frequency fault modes, a corresponding simulation environment is constructed to simulate the conditions and scenarios of fault occurrence, and then different fault solution strategies are applied respectively to observe the operation status of the device and the effect of fault solution. Specifically, through simulation tests, the effectiveness of each fault solution strategy in solving the corresponding fault is evaluated. For example, it is judged whether the strategy of replacing the bulb can really solve the problem of bulb damage, and whether the strategy of adjusting the power parameters can effectively reduce the temperature of the power supply module. According to the results of the simulation tests, a priority score is generated for each fault solution strategy based on the success rate of the strategy, the time required to solve the fault, the cost, etc. For example, if a certain strategy can solve the fault quickly, at low cost and efficiently, its priority score will be higher; then a preset score is set as the screening criterion, and the fault solution strategies with a priority score higher than the preset score are screened out and integrated into a maintenance strategy library, that is, a database that centrally stores and manages effective fault solution strategies.

[0053] Preferably, the currently received abnormal alarm information is analyzed in detail. According to the information such as the device operation parameters and fault characteristics contained therein, the specific type of the abnormality is determined. For example, by analyzing the data such as abnormal power and overheating temperature in the alarm information, it is judged whether it is a power supply fault or a bulb fault, and then the determined abnormality type is matched with the fault solution strategies in the maintenance strategy library to find the most effective solution strategy corresponding to this abnormality type in the maintenance strategy library; then according to the matched fault solution strategy, the control parameter adjustment instruction to be sent to the target traffic supplementary light device is determined, which may include adjusting the corresponding power value or switching the working mode. For example, if the matched strategy is to adjust the power parameters to solve the power supply fault, the control parameter adjustment instruction may be to adjust the output voltage or current of the power supply to a specified value; finally, the determined control parameter adjustment instruction is sent to the target traffic supplementary light device through the LoRa wireless communication module, and the device makes corresponding parameter adjustments after receiving the instruction to solve the abnormal problem of the device, thereby improving the overall operation and maintenance management efficiency of the traffic supplementary light device.

[0054] Step S500: Perform real-time operation and maintenance management optimization on the target traffic supplementary light device according to the remote maintenance instruction in combination with the control parameter adjustment instruction.

[0055] Preferably, when an abnormality occurs in the traffic supplementary light device, the remote maintenance instruction (device ID, abnormality type, and environmental parameters in the abnormality warning information) and the control parameter adjustment instruction (power adjustment value of the device, working mode switching instruction, and priority score of effectiveness) are comprehensively utilized to perform real-time operation and maintenance management optimization on the target traffic supplementary light device to restore the normal operation of the device and improve its performance. Specifically, according to the actual situation of the device and the characteristics of the abnormality problem, the execution order and method of the instructions are reasonably arranged. For example, first perform the device diagnosis operation in the remote maintenance instruction to determine the specific fault point of the device, and then execute the corresponding control parameter adjustment instruction according to the fault type. Through communication means such as the LoRa wireless communication module, the integrated instruction is sent to the target traffic supplementary light device in real time. After receiving the instruction, the device immediately operates according to the requirements of the instruction. By executing the remote maintenance instruction and the control parameter adjustment instruction, the fault of the device is repaired to restore its normal operation, realizing efficient and precise maintenance of the device. At the same time, the operation parameters of the device are optimized and adjusted to improve the performance and stability of the device, enhance the reliability and operation efficiency of the device, and ensure the normal operation of the road traffic monitoring system.

[0056] Further, step S500 further includes step S510: Integrate the remote maintenance instruction and the control parameter adjustment instruction according to the abnormality type, and execute the integrated instruction to determine the execution status data; step S520: Based on the execution status data, judge whether the supplementary light operation data set has returned to the normal threshold range. If the supplementary light operation data set has not returned to the normal threshold range, trigger an optimization signal; step S530: Perform adaptive optimization on the integrated instruction through the optimization signal to generate a supplementary light device health report; step S540: Perform operation and maintenance management on the target traffic supplementary light device according to the supplementary light device health report.

[0057] Preferably, when an abnormality occurs in the traffic supplementary light device, remote maintenance instructions (such as remote operation instructions for restarting the device, updating software, etc.) and control parameter adjustment instructions (for device operating parameters, such as instructions for adjusting brightness, power, etc.) are generated. According to the type of abnormality, these two types of instructions are integrated. For example, if the abnormality is the abnormal brightness of the supplementary light, the control parameter adjustment instruction related to brightness adjustment and the remote maintenance instruction (such as the instruction to restart the relevant control module) that may assist in solving the problem are integrated. The integrated instruction is sent to the device for execution. During the execution process, the device will feedback various information, such as whether the instruction is received, the start execution time, the status of key steps during the execution process, etc. These information form the execution status data. With the help of the execution status data, check whether the parameters in the supplementary light operation data set have returned to the normal threshold range. For example, under normal circumstances, the power of the supplementary light is between 50 - 80 watts. If the power parameter is still lower than 50 watts or higher than 80 watts after executing the integrated instruction, it means that the supplementary light operation data set has not returned to the normal threshold range. Among them, the normal threshold range is pre-set, which is a parameter range used to measure whether the supplementary light operates normally.

[0058] Preferably, if it is determined that the supplementary light operation data set has not returned to normal, an optimization signal is triggered to optimize the integrated instruction, that is, according to the execution status data, the abnormal situation of the supplementary light operation data set, etc., the execution order, parameter settings, etc. of the instruction are re-adjusted. For example, it may try to change the amplitude of the control parameter adjustment, or adjust the execution timing of some operations in the remote maintenance instruction. Then, a comprehensive review of the current operating conditions of the traffic supplementary light device is carried out, including the current operating parameters of the device, whether there are still potential risks, and which aspects have been improved after optimization. A health report of the supplementary light device is generated to present the health status of the device in detail. Finally, the operation and maintenance personnel take corresponding operation and maintenance management measures according to the content of the health report of the supplementary light device to ensure the efficiency of operation and maintenance management and the continuous and stable operation of the traffic supplementary light device.

[0059] Further, step S530 further includes step S531, dynamically adjusting the integrated instruction based on the optimization signal to determine the device operation and maintenance optimization log; step S532, performing operation and maintenance analysis according to the device operation and maintenance optimization log and the supplementary light operation data set to generate a health score matrix; step S533, grading the target traffic supplementary light device based on the health score matrix to determine the health report of the supplementary light device.

[0060] Preferably, when it is received that the optimization signal indicates that the integration instructions (including remote maintenance instructions and control parameter adjustment instructions) fail to restore the operation dataset of the supplementary light to the normal threshold range, the dynamic adjustment of the integration instructions is performed according to the historical fault data of the device, the specific manifestations of the current abnormality, the instruction execution effect feedback by the execution status data, etc. For example, if it is found that the control parameter adjustment instruction for adjusting the power of the supplementary light has poor effect, the adjustment range and direction of the power parameter may be recalculated and adjusted; if the restart operation in the remote maintenance instruction fails to solve the communication problem, other remote operation instructions related to communication restoration may be tried, such as reconfiguring the communication protocol parameters; then a large amount of device operation and maintenance related data during the dynamic adjustment and re-execution process is recorded to form a device operation and maintenance optimization log, which may include but is not limited to alarm count statistics (the number of times each traffic supplementary light device triggers an abnormal alarm within a specified time period), average response time calculation (the average value of the time interval from the device triggering an alarm to the start of executing the corresponding maintenance instruction), and maintenance success rate evaluation (the proportion of the number of times the device is successfully restored to the normal operation state to the total number of maintenance attempts).

[0061] Preferably, the data such as alarm count, average response time, and maintenance success rate in the device operation and maintenance optimization log are combined with the operation dataset of the supplementary light for comprehensive analysis, that is, by comparing the data of different devices in different time periods to deeply understand the relationship between the operation status of the device and the operation and maintenance effect, and based on the results of the operation and maintenance analysis, a health score matrix is generated for each traffic supplementary light device. The elements in the matrix may include scores for various key operation indicators and operation and maintenance indicators of the device. For example, for the power parameter, a corresponding score is given according to the degree of deviation from the normal threshold; for the alarm count, it is converted into a score according to certain rules, and the fewer the alarm count, the higher the score; by comprehensively considering the indicator scores in multiple dimensions, a matrix that comprehensively reflects the health status of the device is formed.

[0062] Preferably, according to the generated health score matrix, the target traffic light supplementing devices are classified. Specifically, different health levels can be set, such as high, medium, low, or A, B, C, etc. The basis for classification is the comprehensive score of the devices in the health score matrix. For example, devices with a score above 80 points (out of 100) are classified as high health level (Grade A), indicating that the devices are in good operating condition and rarely have abnormalities; devices with a score between 60 and 80 points are classified as medium health level (Grade B), indicating that there are certain potential problems with the devices, but they can still operate normally at present; devices with a score below 60 points are classified as low health level (Grade C), and such devices may have more faults or unstable operation and need to be focused on and maintained. The generated health report of the light supplementing devices includes the classification information of the devices and a detailed health condition analysis of each device, and is presented in a visual way, such as intuitively showing the quantity distribution of devices at different health levels, statistical data of various operating indicators, etc. through charts (bar charts, line charts, pie charts, etc.). At the same time, the report is pushed to the operation and maintenance terminal, facilitating the operation and maintenance personnel to view it at any time and quickly understand the health condition of the entire traffic light supplementing device system, taking targeted operation and maintenance measures for devices at different levels, and improving the overall operation and maintenance management efficiency.

[0063] In the above text, reference is made to Figure 1 A traffic light supplementing device operation and maintenance management method based on LoRa transmission according to an embodiment of the present invention is described in detail. Next, reference will be made to Figure 2 Describe a traffic light supplementing device operation and maintenance management platform based on LoRa transmission according to an embodiment of the present invention.

[0064] The traffic light supplementing device operation and maintenance management platform based on LoRa transmission according to an embodiment of the present invention is used to solve the technical problems existing in the prior art, such as the wide distribution of traffic light supplementing devices, difficult manual inspection to timely detect faults, and the devices are vulnerable to environmental interference, resulting in inaccurate abnormal alarm information, and poor operation and maintenance efficiency and reliability of traffic light supplementing devices, and achieves the technical effect of improving the operation and maintenance efficiency and reliability of the devices. As Figure 2 shown, the traffic light supplementing device operation and maintenance management platform based on LoRa transmission includes: an operation data set acquisition unit 10, an abnormal alarm information generation unit 20, a remote maintenance instruction trigger unit 30, a control parameter adjustment instruction issuing unit 40, and an operation and maintenance management optimization unit 50.

[0065] The operating dataset acquisition unit 10 is used to deploy a LoRa wireless communication module, and through the LoRa wireless communication module, it performs real-time acquisition on the target traffic light supplementary lighting device to obtain a supplementary lighting operation dataset; the abnormal alarm information generation unit 20 is used to perform dynamic analysis based on the supplementary lighting operation dataset, make an abnormal determination according to the dynamic analysis result, and generate abnormal alarm information; the remote maintenance instruction trigger unit 30 is used to transmit the abnormal alarm information to the operation and maintenance management platform to trigger a remote maintenance instruction; the control parameter adjustment instruction issuing unit 40 is used to issue a control parameter adjustment instruction to the target traffic light supplementary lighting device through the LoRa wireless communication module; the operation and maintenance management optimization unit 50 is used to perform real-time operation and maintenance management optimization on the target traffic light supplementary lighting device according to the remote maintenance instruction in combination with the control parameter adjustment instruction.

[0066] Next, the specific configuration of the operating dataset acquisition unit 10 will be described in detail. The operating dataset acquisition unit 10 further includes: deploying a LoRa wireless communication module on the road traffic monitoring supplementary lighting device; collecting the real-time parameters of the target traffic light supplementary lighting device at fixed intervals through the LoRa wireless communication module to determine the device cycle data group; classifying and cleaning the device cycle data group according to the data type to generate a standardized dataset; encapsulating the standardized dataset into a LoRa protocol frame through the LoRa wireless communication module to determine the supplementary lighting operation dataset.

[0067] Next, the specific configuration of the abnormal alarm information generation unit 20 will be described in detail. The abnormal alarm information generation unit 20 further includes: sending the supplementary lighting operation dataset to the operation and maintenance management platform for data classification and storage through a dynamic channel allocation mechanism to determine multiple operation storage data information; retrieving historical operation data for training to construct a random forest model; dividing the multiple operation storage data information through the random forest model to determine multiple fault status data; performing clustering analysis on the target traffic light supplementary lighting device in different environmental scenarios to set an adaptive threshold; determining the multiple fault status data according to the adaptive threshold to generate a composite determination rule, and performing real-time determination and update according to the composite determination rule to obtain the dynamic analysis result.

[0068] Next, the specific configuration of the abnormal alarm information generation unit 20 will be further described in detail. The abnormal alarm information generation unit 20 further includes: monitoring the channel of the LoRa wireless communication module to obtain a signal quality index; setting a signal expectation threshold, and when the signal quality index is lower than the signal expectation threshold, switching the channel of the LoRa wireless communication module to a standby channel to obtain a channel switching record; dynamically adjusting the data sending interval of the supplementary light operation data set according to the channel switching record to obtain a signal status log; activating a dynamic channel allocation mechanism according to the signal status log to synchronize the supplementary light operation data set to the operation and maintenance management platform.

[0069] Next, the specific configuration of the abnormal alarm information generation unit 20 will be further described in detail. The abnormal alarm information generation unit 20 further includes: retrieving the operation data of the target traffic supplementary light device under different meteorological conditions in a historical period to construct a multi-dimensional training data set; performing time series analysis on the multi-dimensional training data set, and extracting periodic features according to the time series analysis results; performing cross-validation on the multi-dimensional training data set and the actual device failure records according to the periodic features, and adjusting the model hyperparameters according to the verification results to obtain the random forest model.

[0070] Next, the specific configuration of the abnormal alarm information generation unit 20 will be further described in detail. The abnormal alarm information generation unit 20 further includes: constructing a dynamic topology map according to the physical deployment location of the target traffic supplementary light device; traversing the dynamic topology map according to the dynamic analysis results for layout identification to determine multiple node identification results; performing abnormal determination extraction based on the multiple node identification results to determine multiple abnormal device nodes, and associating the multiple abnormal device nodes with historical alarm records to generate the abnormal alarm information.

[0071] Next, the specific configuration of the control parameter adjustment instruction issuing unit 40 will be described in detail. The control parameter adjustment instruction issuing unit 40 further includes: analyzing the historical alarm records and historical maintenance records to extract high-frequency failure modes and failure solution strategies; verifying the effectiveness of the failure solution strategies through simulation tests according to the high-frequency failure modes to generate strategy priority scores; integrating the failure solution strategies with strategy priority scores higher than a preset score to construct a maintenance strategy library; parsing the abnormal alarm information to determine the abnormal type, matching the abnormal type with the maintenance strategy library, and determining the control parameter adjustment instruction according to the matching result.

[0072] Next, the specific configuration of the operation and maintenance management optimization unit 50 will be described in detail. The operation and maintenance management optimization unit 50 further includes: integrating the remote maintenance instruction and the control parameter adjustment instruction according to the exception type, and executing the integrated instruction to determine the execution status data; judging whether the operation data set of the supplementary light is restored to the normal threshold range based on the execution status data. If the operation data set of the supplementary light is not restored to the normal threshold range, an optimization signal is triggered; adaptively optimizing the integrated instruction through the optimization signal to generate a health report of the supplementary light device; performing operation and maintenance management on the target traffic supplementary light device according to the health report of the supplementary light device.

[0073] Next, the specific configuration of the operation and maintenance management optimization unit 50 will be further described in detail. The operation and maintenance management optimization unit 50 further includes: dynamically adjusting the integrated instruction based on the optimization signal to determine the operation and maintenance optimization log of the device; performing operation and maintenance analysis according to the operation and maintenance optimization log and the operation data set of the supplementary light to generate a health score matrix; grading the target traffic supplementary light device based on the health score matrix to determine the health report of the supplementary light device.

[0074] The operation and maintenance management platform for traffic supplementary light devices based on LoRa transmission provided by the embodiments of the present invention can execute the method for operation and maintenance management of traffic supplementary light devices based on LoRa transmission provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0075] Although various references are made to certain modules in the platform according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or the server. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0076] The above specific implementation manners do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for operation and maintenance management of traffic supplementary lighting equipment based on LoRa transmission, characterized in that The method includes: Deploy a LoRa wireless communication module, and perform real-time acquisition on the target traffic fill light device through the LoRa wireless communication module to obtain a fill light operation data set; Perform dynamic analysis based on the fill light operation data set, make an anomaly determination according to the dynamic analysis result, and generate an anomaly warning message; Transmit the anomaly warning message to the operation and maintenance management platform to trigger a remote maintenance instruction; Send a control parameter adjustment instruction to the target traffic fill light device through the LoRa wireless communication module; Perform real-time operation and maintenance management optimization on the target traffic fill light device according to the remote maintenance instruction combined with the control parameter adjustment instruction.

2. The operation and maintenance management method of the traffic supplementary lighting device based on LoRa transmission according to claim 1, characterized in that Deploy a LoRa wireless communication module, and perform real-time acquisition on the target traffic fill light device through the LoRa wireless communication module to obtain a fill light operation data set. The method includes: Deploy a LoRa wireless communication module on the road traffic monitoring fill light device; Collect the real-time parameters of the target traffic fill light device at fixed intervals through the LoRa wireless communication module to determine the device cycle data group; Classify and clean the device cycle data group according to the data type to generate a standardized data set; Encapsulate the standardized data set into a LoRa protocol frame through the LoRa wireless communication module to determine the fill light operation data set.

3. The operation and maintenance management method of traffic supplementary lighting equipment based on LoRa transmission according to claim 1, characterized in that, Perform dynamic analysis based on the fill light operation data set. The method includes: Send the fill light operation data set to the operation and maintenance management platform for data classification and storage through a dynamic channel allocation mechanism to determine multiple operation storage data information; Retrieve historical operation data for training to construct a random forest model; Divide the multiple operation storage data information through the random forest model to determine multiple fault status data; Perform clustering analysis on the target traffic fill light device under different environmental scenarios and set an adaptive threshold; Judge the multiple fault status data according to the adaptive threshold to generate a composite judgment rule, and perform real-time judgment update according to the composite judgment rule to obtain the dynamic analysis result.

4. The operation and maintenance management method of traffic supplementary lighting equipment based on LoRa transmission according to claim 3, wherein, Send the fill light operation data set to the operation and maintenance management platform through a dynamic channel allocation mechanism. The method includes: Monitor the channel of the LoRa wireless communication module to obtain a signal quality index; Set a signal expectation threshold. When the signal quality index is lower than the signal expectation threshold, switch the channel of the LoRa wireless communication module to a standby channel to obtain a channel switching record; Dynamically adjust the data sending interval of the fill light operation data set according to the channel switching record to obtain a signal status log; Activate the dynamic channel allocation mechanism according to the signal status log to synchronize the fill light operation data set to the operation and maintenance management platform.

5. The operation and maintenance management method of traffic supplementary lighting equipment based on LoRa transmission according to claim 3, characterized in that, Retrieve historical operation data for training to construct a random forest model. The method includes: Retrieve the operation data of the target traffic fill light device under different meteorological conditions in a historical period to construct a multi-dimensional training data set; Perform time series analysis on the multi-dimensional training data set, and extract periodic features according to the time series analysis result; Cross-validate the multi-dimensional training data set with the actual device failure records according to the periodic characteristics, and adjust the model hyperparameters according to the verification results to obtain the random forest model.

6. The operation and maintenance management method of traffic supplementary lighting equipment based on LoRa transmission according to claim 1, characterized in that Perform anomaly determination based on the dynamic analysis results and generate anomaly warning information. The method includes: Construct a dynamic topology map according to the physical deployment location of the target traffic supplementary lighting device; Traverse the dynamic topology map according to the dynamic analysis results for layout identification to determine multiple node identification results; Perform anomaly determination extraction based on the multiple node identification results to determine multiple abnormal device nodes, and perform correlation analysis on the multiple abnormal device nodes with the historical warning records to generate the anomaly warning information.

7. The operation and maintenance management method of traffic supplementary lighting equipment based on LoRa transmission according to claim 6, characterized in that Send a control parameter adjustment instruction to the target traffic supplementary lighting device through the LoRa wireless communication module. The method includes: Analyze the historical warning records and historical maintenance records, and extract high-frequency failure modes and failure solution strategies; Verify the effectiveness of the failure solution strategies through simulation tests according to the high-frequency failure modes, and generate strategy priority scores; Integrate the failure solution strategies with the strategy priority scores higher than the preset score to construct a maintenance strategy library; Parse the anomaly warning information to determine the anomaly type, match the anomaly type with the maintenance strategy library, and determine the control parameter adjustment instruction according to the matching result.

8. The operation and maintenance management method of the traffic supplementary lighting device based on LoRa transmission according to claim 7, characterized in that, Perform real-time operation and maintenance management optimization on the target traffic supplementary lighting device according to the remote maintenance instruction combined with the control parameter adjustment instruction. The method includes: Integrate the remote maintenance instruction and the control parameter adjustment instruction according to the anomaly type, and execute the integrated instruction to determine the execution status data; Judge whether the supplementary lighting operation data set has recovered to the normal threshold range based on the execution status data. If the supplementary lighting operation data set has not recovered to the normal threshold range, trigger an optimization signal; Perform adaptive optimization on the integrated instruction through the optimization signal to generate a health report of the supplementary lighting device; Perform operation and maintenance management on the target traffic supplementary lighting device according to the health report of the supplementary lighting device.

9. The operation and maintenance management method of traffic supplementary lighting equipment based on LoRa transmission according to claim 8, characterized in that Perform adaptive optimization on the integrated instruction through the optimization signal to generate a health report of the supplementary lighting device. The method includes: Perform dynamic adjustment on the integrated instruction based on the optimization signal to determine the device operation and maintenance optimization log; Perform operation and maintenance analysis on the device operation and maintenance optimization log and the supplementary lighting operation data set to generate a health score matrix; Classify the target traffic supplementary lighting device based on the health score matrix to determine the health report of the supplementary lighting device.

10. The operation and maintenance management platform for traffic supplementary lighting equipment based on LoRa transmission is characterized in that, The platform is used to implement the operation and maintenance management method of the traffic supplementary lighting device based on LoRa transmission according to any one of claims 1 to 9. The platform includes: An operation data set acquisition unit for deploying a LoRa wireless communication module, and performing real-time acquisition on the target traffic supplementary lighting device through the LoRa wireless communication module to obtain a supplementary lighting operation data set; An anomaly warning information generation unit for performing dynamic analysis based on the supplementary lighting operation data set, performing anomaly determination according to the dynamic analysis results, and generating anomaly warning information; A remote maintenance instruction triggering unit, which is used to transmit the abnormal alarm information to an operation and maintenance management platform to trigger a remote maintenance instruction; A control parameter adjustment instruction issuing unit, which is used to issue a control parameter adjustment instruction to a target traffic light device through a LoRa wireless communication module; An operation and maintenance management optimization unit, which is used to perform real-time operation and maintenance management optimization on the target traffic light device according to the remote maintenance instruction in combination with the control parameter adjustment instruction.

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