Outdoor power equipment temperature abnormity monitoring method, device and equipment

Abstract

The invention discloses an outdoor power equipment temperature abnormity monitoring method, device and equipment, and the method comprises the steps: carrying out the calibration processing of power equipment temperature data collected based on a dynamic collection frequency through employing a preset calibration algorithm, and obtaining the calibration temperature data; performing temperature anomaly analysis according to the calibration temperature data based on the temperature threshold, the trend threshold, the mutation mechanism and the baseline standard, and generating an anomaly analysis data packet; sending the abnormal analysis data packet to a relay gateway according to a spread spectrum technology and a check retransmission mechanism, so that the relay gateway performs data aggregation and protocol conversion processing based on an abnormal priority to obtain a transmission data packet; and triggering different temperature early warning mechanisms through the cloud platform according to the abnormality level in the received transmission data packet to realize temperature abnormality early warning. The technical problems of lack of timeliness, accuracy and remote control capability of monitoring due to lack of timeliness, weak data acquisition anti-interference capability and easy loss in the data transmission process in the prior art can be solved.

Description

Technical Field

[0001] This application relates to the field of power system monitoring, and in particular to a method, device and equipment for monitoring abnormal temperatures of outdoor power equipment. Background Technology

[0002] Currently, temperature monitoring of overhead power line electrical equipment mainly relies on the traditional manual inspection mode, where maintenance personnel regularly conduct on-site inspections of the line equipment by carrying temperature measuring instruments. Some existing monitoring devices operate in a single power consumption mode, transmit data through simple wireless communication, and have not been systematically optimized for complex outdoor environments, and lack a sound remote control and data protection mechanism.

[0003] In other words, current power system equipment temperature monitoring technology relies on manual inspections, which is inefficient and lacks timeliness, making it prone to causing major power grid failures. Moreover, the weak anti-interference capability of data acquisition during the monitoring process results in temperature monitoring not reflecting the actual temperature changes. In addition, data transmission is easily lost, leading to poor remote monitoring capabilities. Overall, it is difficult to meet current monitoring needs. Summary of the Invention

[0004] This application provides a method, device, and equipment for monitoring abnormal temperatures in outdoor power equipment, which addresses the technical problems of existing technologies that rely on manual labor, lack timeliness, have weak anti-interference capabilities in data acquisition, and are prone to data loss during transmission, resulting in a lack of timeliness, accuracy, and remote control capabilities in monitoring abnormal temperatures in power equipment.

[0005] In view of this, the first aspect of this application provides a method for monitoring abnormal temperatures in outdoor power equipment, comprising:

[0006] A preset calibration algorithm is used to calibrate the temperature data of power equipment collected based on the dynamic acquisition frequency to obtain calibrated temperature data. The preset calibration algorithm includes segmented calibration and real-time single-point calibration.

[0007] Based on temperature thresholds, trend thresholds, mutation mechanisms, and baseline standards, temperature anomaly analysis is performed on the calibrated temperature data, and anomaly analysis data package is generated, which includes anomaly levels.

[0008] Based on spread spectrum technology and verification retransmission mechanism, the anomaly analysis data packet is sent to the relay gateway, which performs data aggregation and protocol conversion based on anomaly priority to obtain the transmission data packet;

[0009] Temperature anomaly warnings are achieved by triggering different temperature warning mechanisms based on the anomaly level in the received data packets through the cloud platform.

[0010] Preferably, the step of calibrating the power equipment temperature data acquired based on the dynamic acquisition frequency using a preset calibration algorithm to obtain calibrated temperature data includes:

[0011] Calculate the rate of change of ambient temperature for a preset number of consecutive cycles based on ambient temperature.

[0012] The temperature acquisition frequency of the power equipment is dynamically set according to the ambient temperature change rate to obtain the dynamic acquisition frequency.

[0013] Temperature data of the power equipment is collected using the aforementioned dynamic acquisition frequency;

[0014] The temperature data is subjected to segmented interpolation calibration based on a preset temperature range to obtain segmented calibrated temperatures.

[0015] After a preset time period, the temperature data is calibrated in real time based on the reference temperature value to obtain the single-point calibration temperature.

[0016] The segmented calibration temperature and the single-point calibration temperature constitute the calibration temperature data.

[0017] Preferably, the step of calibrating the power equipment temperature data acquired based on the dynamic acquisition frequency using a preset calibration algorithm to obtain calibrated temperature data further includes:

[0018] The calibration temperature data is then processed using median filtering based on a dynamic odd-numbered window, or...

[0019] The calibration temperature data is subjected to adaptive moving average filtering based on an exponential weighting strategy.

[0020] The filtered calibration temperature data is quantized and compressed using a preset quantization bit depth.

[0021] Preferably, the step of performing temperature anomaly analysis based on the calibrated temperature data according to temperature threshold, trend threshold, mutation mechanism, and baseline standard, and generating anomaly analysis data package, includes:

[0022] If the calibrated temperature data is lower than the lower limit of the temperature threshold or higher than the upper limit of the temperature threshold, the power equipment is determined to be in a level one abnormality.

[0023] The temperature change slope is calculated based on the calibration temperature data. If the temperature change slope exceeds the trend threshold and continues for a preset window duration, the power equipment is determined to be a level two anomaly.

[0024] Based on the calibration temperature data, calculate the positive and negative cumulative temperature values ​​respectively. If the positive or negative cumulative temperature value is greater than the sudden change threshold, the power equipment is determined to be a level three abnormality.

[0025] The temperature deviation is calculated based on the calibration temperature data and historical temperature data. If the temperature deviation is greater than the reference deviation value, the power equipment is determined to be at level four abnormality.

[0026] An anomaly analysis data package is generated based on the first-level anomaly, the second-level anomaly, the third-level anomaly, and the fourth-level anomaly.

[0027] Preferably, the step of sending the anomaly analysis data packet to the relay gateway based on spread spectrum technology and a check retransmission mechanism, so that the gateway performs data aggregation and protocol conversion processing based on anomaly priority to obtain a transmission data packet, includes:

[0028] The optimal channel is selected using spread spectrum technology, and the anomaly analysis data packet is sent to the relay gateway through the optimal channel.

[0029] During the data packet transmission process, the abnormal analysis data packet is subjected to a first-level XOR check and a second-level CRC16 check in sequence. If the frame header check fails, the current frame is discarded.

[0030] If the data segment verification fails, the anomaly analysis data packet is resent to the relay gateway based on the signal-to-noise ratio.

[0031] The relay gateway determines the anomaly priority based on the anomaly level in the anomaly analysis data packet;

[0032] Data is aggregated and cached based on the exception priority and timestamp, and the data is converted into the platform protocol format to obtain the transmission data packet.

[0033] Preferably, if the data segment verification fails, retransmitting the anomaly analysis data packet to the relay gateway based on the signal-to-noise ratio includes:

[0034] If the data segment verification fails, the communication quality is assessed based on the signal-to-noise ratio of the wireless channel to obtain the communication level.

[0035] Different retransmission counts are set according to different communication levels;

[0036] The anomaly analysis data packet is resent to the relay gateway based on the number of retransmissions.

[0037] Preferably, the step of triggering different temperature warning mechanisms based on the anomaly level in the received transmission data packet via the cloud platform to achieve temperature anomaly warning further includes:

[0038] If the cloud platform receives status abnormality information sent by the relay gateway, it will trigger the status early warning mechanism to realize status abnormality warning. The status abnormality information includes communication abnormality information, power supply abnormality information, and equipment offline information.

[0039] A second aspect of this application provides an outdoor power equipment temperature anomaly monitoring device, comprising:

[0040] The data calibration unit is used to calibrate the temperature data of power equipment collected based on the dynamic acquisition frequency using a preset calibration algorithm to obtain calibrated temperature data. The preset calibration algorithm includes segmented calibration and real-time single-point calibration.

[0041] The temperature analysis unit is used to perform temperature anomaly analysis based on the calibrated temperature data according to the temperature threshold, trend threshold, mutation mechanism and baseline standard, and generate anomaly analysis data package, which includes anomaly level;

[0042] The data transmission unit is used to send the anomaly analysis data packet to the relay gateway according to spread spectrum technology and check retransmission mechanism, so that the gateway performs data aggregation and protocol conversion based on anomaly priority to obtain the transmission data packet.

[0043] The abnormality warning unit is used to trigger different temperature warning mechanisms based on the abnormality level in the received transmission data packet through the cloud platform to achieve temperature abnormality warning.

[0044] Preferably, the data calibration unit is specifically used for:

[0045] Calculate the rate of change of ambient temperature for a preset number of consecutive cycles based on ambient temperature.

[0046] The temperature acquisition frequency of the power equipment is dynamically set according to the ambient temperature change rate to obtain the dynamic acquisition frequency.

[0047] Temperature data of the power equipment is collected using the aforementioned dynamic acquisition frequency;

[0048] The temperature data is subjected to segmented interpolation calibration based on a preset temperature range to obtain segmented calibrated temperatures.

[0049] After a preset time period, the temperature data is calibrated in real time based on the reference temperature value to obtain the single-point calibration temperature.

[0050] The segmented calibration temperature and the single-point calibration temperature constitute the calibration temperature data.

[0051] A third aspect of this application provides an outdoor power equipment temperature anomaly monitoring device, the device including a processor and a memory;

[0052] The memory is used to store program code and transmit the program code to the processor;

[0053] The processor is used to execute the outdoor power equipment temperature anomaly monitoring method described in the first aspect according to the instructions in the program code.

[0054] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0055] This application provides a method for monitoring abnormal temperatures in outdoor power equipment, comprising: calibrating power equipment temperature data acquired based on a dynamic acquisition frequency using a preset calibration algorithm to obtain calibrated temperature data; the preset calibration algorithm includes segmented calibration and real-time single-point calibration; performing temperature anomaly analysis based on the calibrated temperature data according to temperature thresholds, trend thresholds, abrupt change mechanisms, and baseline standards, and generating anomaly analysis data packets, the anomaly analysis data packets including anomaly levels; sending the anomaly analysis data packets to a relay gateway based on spread spectrum technology and a verification retransmission mechanism, so that the gateway performs data aggregation and protocol conversion processing based on anomaly priority to obtain transmission data packets; and triggering different temperature warning mechanisms through a cloud platform according to the anomaly levels in the received transmission data packets to achieve temperature anomaly warning.

[0056] The outdoor power equipment temperature anomaly monitoring method provided in this application, in the data acquisition stage, not only adopts dynamic acquisition frequency to adapt to the influence of changing environments and ensure the reliability of acquired data, but also calibrates the acquired data through different preset calibration algorithms to further ensure data accuracy. In the anomaly analysis stage, it analyzes the temperature status of power equipment through various means to determine different anomaly levels, which is more in line with actual conditions. In the data transmission stage, it constructs a hierarchical architecture of local-relay gateway-cloud platform, and relies on spread spectrum technology and retransmission mechanism to ensure the speed and stability of data transmission. The gateway intelligently transmits data according to anomaly priority, ensuring the reliability of data transmission from multiple aspects. The three-level architecture also improves the remote control capability of power equipment temperature anomaly monitoring, providing an efficient and accurate monitoring solution for actual scenarios. Therefore, this application can solve the technical problems of existing technologies that rely on manual labor, lack timeliness, have weak anti-interference ability in data acquisition, and are prone to data loss during transmission, resulting in a lack of timeliness, accuracy, and remote control capability in power equipment temperature anomaly monitoring. Attached Figure Description

[0057] Figure 1 A flowchart illustrating a method for monitoring abnormal temperatures in outdoor power equipment, provided as an embodiment of this application;

[0058] Figure 2 This is a schematic diagram of the structure of an outdoor power equipment temperature anomaly monitoring device provided in an embodiment of this application;

[0059] Figure 3 Communication topology diagram of an outdoor power equipment temperature anomaly monitoring system provided for application examples in this application;

[0060] Figure 4A schematic diagram of the principle of an outdoor power equipment temperature anomaly monitoring system provided as an application example of this application. Detailed Implementation

[0061] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0062] For easier understanding, please refer to Figure 1 An embodiment of a method for monitoring abnormal temperatures in outdoor power equipment provided in this application includes:

[0063] Step 101: Use a preset calibration algorithm to calibrate the temperature data of the power equipment collected based on the dynamic acquisition frequency to obtain calibrated temperature data. The preset calibration algorithm includes segmented calibration and real-time single-point calibration.

[0064] Further, step 101 includes:

[0065] Calculate the rate of change of ambient temperature for a preset number of consecutive cycles based on ambient temperature.

[0066] The temperature acquisition frequency of the power equipment is dynamically set according to the rate of change of ambient temperature to obtain the dynamic acquisition frequency.

[0067] Temperature data of power equipment is collected through dynamic acquisition frequency;

[0068] The temperature data is interpolated and calibrated in segments according to a preset temperature range to obtain segmented calibrated temperatures.

[0069] After a preset time period, the temperature data is calibrated in real time based on the reference temperature value to obtain the single-point calibration temperature.

[0070] The calibration temperature data consists of segmented calibration temperatures and single-point calibration temperatures.

[0071] Furthermore, step 101, followed by:

[0072] Median filtering is applied to the calibration temperature data based on a dynamic odd-numbered window, or...

[0073] An adaptive moving average filtering process is applied to the calibration temperature data based on an exponential weighting strategy.

[0074] The filtered calibration temperature data is quantized and compressed using a preset quantization bit depth.

[0075] It should be noted that the data acquisition process in this implementation is a dynamically adjusted process, that is, the temperature of the power equipment is acquired based on a dynamic acquisition frequency. This method of data acquisition allows for "slow sampling for low fluctuations and fast sampling for high fluctuations," thus adapting to the data acquisition needs of various complex environments and ensuring the stability of the data acquisition. The dynamic acquisition frequency in this embodiment is determined based on the rate of change of ambient temperature, for example, defined as... ,in, The basic sampling period can be set to 10 seconds. Then, threshold analysis can be performed based on the rate of change of ambient temperature to set the dynamic sampling frequency for different stages.

[0076] like Then set the dynamic acquisition frequency to times / minute;

[0077] like Then set the dynamic acquisition frequency to times / minute;

[0078] like Then set the dynamic acquisition frequency to times / minute;

[0079] Based on the above settings, the method for determining the dynamic acquisition frequency can be obtained as follows:

[0080]

[0081] After each temperature data collection, the rate of change of ambient temperature can be calculated. If the rate of change of ambient temperature calculated for a preset number of consecutive periods meets a certain range, the sampling frequency within that range can be used as the dynamic acquisition frequency. This allows for flexible adjustment of the acquisition frequency to adapt to the influence of the scene, while avoiding instability caused by frequent switching of the acquisition frequency. The preset number can be set according to the actual situation, such as 2 or 3 periods; that is, 3 periods.

[0082] To ensure the accuracy of the collected temperature data, this embodiment employs different calibration methods for calibration. First, a calibration temperature range can be set according to the sensor's measurement range, i.e., a preset temperature range. The calibration temperature points are -40℃, -10℃, 25℃, 75℃, and 125℃. These five characteristic temperature points can be calibrated to obtain the correspondence between the standard temperature T0 and the sensor's original output V0. Then, the temperature range is configured, specifically including four intervals: [-40℃, -10℃), [-10℃, 25℃), [25℃, 75℃), and [75℃, 125℃].

[0083] Next, for any sensor output V, determine the two calibration points (V1, T1) and (V2, T2) corresponding to its interval, and then calculate the calibrated temperature T through linear interpolation:

[0084]

[0085] This allows for the calibration of temperature data in different segments, resulting in segmented calibration temperatures.

[0086] Real-time calibration can also be performed based on a specific calibration point after each preset time period. In this embodiment, the preset time period is 24 hours, meaning a single-point calibration is initiated every 24 hours. This embodiment performs single-point calibration based on a reference temperature of 25°C, which can correct drift errors.

[0087]

[0088] in, This refers to the single-point calibration temperature obtained after calibration. The current original temperature, The reference temperature value This is the sensor's raw output at the reference temperature.

[0089] To further improve data accuracy and data processing and transmission speed, this embodiment also includes filtering and quantization compression processes. In the median filtering process, an odd-numbered window N=2k+1 is used, where k is greater than or equal to 1, and the initial window size is N=5. Then, N consecutive calibration temperature data are sorted, and the median value is taken as the filtered output. :

[0090]

[0091] When the variance of the calibration temperature data When, dynamically expand the window to N=7; when When the window is dynamically reduced to N=3, the variance calculation process is as follows:

[0092]

[0093] in:

[0094]

[0095] The adaptive moving average filtering process can employ an exponential weighting strategy, with more recent data receiving higher weights, and the weighting coefficients need to satisfy the following:

[0096]

[0097] Where M is the length of the sliding window, and its value is 6.

[0098] The weights can be dynamically adjusted based on the least mean square algorithm to minimize the mean square error between the filtered output and the ideal signal. This error is defined as... ,in, For the desired signal, the smoothed value of the median filter output can be taken. This is the current filter output; therefore, the weight coefficients are updated as follows:

[0099]

[0100] in, The learning rate takes a value between Within the range, For the first The median filter output at time t. The final output of the average filter is expressed as:

[0101]

[0102] Compressing the processed data ensures the integrity and reliability of data transmission and also reduces transmission power consumption. In this embodiment, the preset quantization bit depth is 16, and the quantization compression process is as follows:

[0103]

[0104] in, , , This is a rounding function; 65535 represents the quantization coefficient. In this example, 65535 is the maximum value of a 16-bit unsigned binary integer. The temperature data can be extracted by reverse calculation during decompression.

[0105] Step 102: Based on temperature threshold, trend threshold, mutation mechanism and baseline standard, perform temperature anomaly analysis on the calibration temperature data and generate anomaly analysis data package, which includes anomaly level.

[0106] Further, step 102 includes:

[0107] If the calibration temperature data is lower than the lower limit of the temperature threshold or higher than the upper limit of the temperature threshold, the power equipment is judged to be a level one abnormality.

[0108] The slope of temperature change is calculated based on the calibration temperature data. If the slope of temperature change exceeds the trend threshold and continues for a preset window duration, the power equipment is determined to be a level two anomaly.

[0109] Calculate the positive and negative cumulative temperature values ​​based on the calibration temperature data. If either the positive or negative cumulative temperature value is greater than the sudden change threshold, the power equipment is determined to be at level three abnormality.

[0110] The temperature deviation is calculated based on the calibration temperature data and historical temperature data. If the temperature deviation is greater than the reference deviation value, the power equipment is judged to be a level four abnormality.

[0111] Anomaly analysis data packages are generated based on level 1, level 2, level 3, and level 4 anomalies.

[0112] This embodiment overcomes the limitations of single-threshold temperature analysis by integrating multiple anomaly analysis methods to detect temperature anomalies in power equipment. Temperature anomalies are categorized into different levels, triggering different early warning actions. While the first-level anomaly in this embodiment is determined based on a temperature threshold, this threshold is dynamic. It can be calculated based on historical temperature data to determine the normal operating temperature range of the equipment. For example, using seven consecutive days of anomaly-free historical temperature data, the mean and standard deviation can be calculated, and then the temperature threshold can be calculated based on the mean and standard deviation.

[0113]

[0114]

[0115] in, , These are the upper and lower limits of the temperature threshold. The mean, The standard deviation is denoted as .

[0116] The temperature range formed by the two temperature thresholds can cover 99.7% of normal data; therefore, if the temperature exceeds this range, and the calibrated temperature data is lower than the lower limit or higher than the upper limit of the temperature thresholds, the power equipment is judged to have a level one anomaly.

[0117] In addition to first-level anomaly analysis based on temperature thresholds, predictive analysis of the temperature development trend of power equipment can also be performed. In this embodiment, a sliding window L with a value of 10 minutes is used to calculate the slope of temperature change in the calibration temperature data. :

[0118]

[0119] You can set a slope threshold, i.e., a trend threshold. This value can be set according to the actual type of power equipment; this is just an example. If... If the duration reaches the preset window duration, the power equipment is determined to have experienced a level two abnormal situation. The preset window duration here refers to the duration of several consecutive windows, such as the duration of three consecutive windows.

[0120] This embodiment uses a cumulative sum control chart algorithm to detect sudden temperature changes, defining a positive cumulative value as:

[0121]

[0122] The negative cumulative value is:

[0123]

[0124] in, The minimum detectable offset is set to a value of Set the mutation threshold to ;when or At that time, the power equipment was determined to have experienced a level three anomaly.

[0125] Historical temperature baselines can be established based on historical temperature data for the same period in previous years. Based on this, the temperature deviation between the calibration temperature data and the historical temperature baseline can be calculated:

[0126]

[0127] when If so, the power equipment is determined to have a level four anomaly. This is the baseline deviation, which is also the standard deviation of historical data.

[0128] The anomaly analysis data packet is generated based on the above four levels of anomalies. Among them, level 1 and level 4 anomalies are both latent anomalies. Different measures can be taken for power system maintenance according to different levels of anomalies, such as early warning. Different early warning mechanisms can also be set for different levels of anomalies to provide prompts or alarms.

[0129] Step 103: Based on spread spectrum technology and verification retransmission mechanism, send the anomaly analysis data packet to the relay gateway, so that it can perform data aggregation and protocol conversion processing based on anomaly priority to obtain the transmission data packet.

[0130] Further, step 103 includes:

[0131] The optimal channel is selected using spread spectrum technology, and the anomaly analysis data packets are sent to the relay gateway through the optimal channel.

[0132] During the data packet transmission process, the abnormal analysis data packet is subjected to a first-level XOR check and a second-level CRC16 check in sequence. If the frame header check fails, the current frame is discarded.

[0133] If the data segment verification fails, the anomaly analysis data packet is resent to the relay gateway based on the signal-to-noise ratio.

[0134] The relay gateway determines the anomaly priority based on the anomaly level in the anomaly analysis data packet;

[0135] Data is aggregated and cached based on exception priority and timestamp, and the data is converted into the platform protocol format to obtain the transmission data packet.

[0136] Furthermore, if the data segment verification fails, the anomaly analysis data packet is retransmitted to the relay gateway based on the signal-to-noise ratio, including:

[0137] If the data segment verification fails, the communication quality is assessed based on the signal-to-noise ratio of the wireless channel to obtain the communication level.

[0138] Different retransmission counts are set according to different communication levels;

[0139] The anomaly analysis data packet is resent to the relay gateway based on the number of retransmissions.

[0140] It should be noted that this embodiment uses spread spectrum wireless communication technology to send data packets to the relay gateway. This process employs a direct sequence spread spectrum algorithm to modulate the data using a pseudo-random code. It supports automatic switching between eight communication channels, real-time detection of channel quality, and selection of the channel with the optimal signal-to-noise ratio for transmission. Furthermore, to ensure the integrity of data transmission, this embodiment sets up a first-level XOR check and a second-level CRC16 check mechanism to verify and analyze data packets. If the frame header check fails, the current frame is discarded; if the data segment check fails, the data packet can be retransmitted, ensuring the stability of data transmission.

[0141] Specifically, anomaly analysis data packets are sent to the gateway in the form of data frames. A data frame can be divided into a frame header, a data segment, and a checksum. The frame header contains 8 bytes, the data segment contains 16 bytes, including information such as temperature data, device status, and anomaly level, and the checksum contains 4 bytes. The first-level XOR check verifies the frame header bytes, calculating the checksum by XOR, and comparing it with the checksum bit attached to the frame header. If they do not match, the check fails. The second-level CRC16 checksum is expressed as a polynomial. Then, a checksum is generated and stored in the checksum section.

[0142] If the data segment verification fails in this embodiment, the anomaly analysis data packet is retransmitted to the relay gateway based on the signal-to-noise ratio (SNR). The SNR of the wireless channel can be collected in real time by the wireless module. Based on the SNR, the communication quality of the channel can be classified to obtain different communication levels. This indicates that the communication quality is excellent; If the value is 0, it indicates that the communication quality is medium. This indicates low communication quality. Based on this, different retransmission counts R can be set:

[0143]

[0144] In the event of a data segment verification failure, the anomaly analysis data packet can be resent to the relay gateway based on the number of retransmissions. If five consecutive retransmissions fail, a channel switching mechanism can be triggered to select the channel with the best SNR for retransmission, such as automatic switching on channel 8.

[0145] As an intermediary bridge between local temperature monitoring devices and the cloud, the relay gateway can aggregate and convert the received data packets. In this embodiment, the gateway flexibly aggregates and processes data based on anomaly priority and timestamps. For example, it prioritizes urgent anomaly data, reporting it to the cloud first and triggering an alert first. Furthermore, the gateway can also use the TCP protocol to directly report urgent anomaly data, setting its anomaly priority to the highest level.

[0146] If the data is normal, it can be aggregated and cached normally based on the timestamp, converted into the platform protocol format, and uploaded to the cloud platform as a data packet. Furthermore, cached data can be adjusted accordingly, such as storing abnormal data for 90 days and normal data for 30 days, to provide data support for historical temperature data.

[0147] Step 104: Trigger different temperature warning mechanisms based on the anomaly level in the received data packets through the cloud platform to achieve temperature anomaly warning.

[0148] Furthermore, step 104 also includes:

[0149] If the cloud platform receives status abnormality information sent by the relay gateway, it will trigger the status early warning mechanism to realize status abnormality warning. Status abnormality information includes communication abnormality information, power supply abnormality information, and equipment offline information.

[0150] When the cloud platform receives anomalies in the transmitted data packets, it can issue targeted warnings. Different anomaly levels correspond to different warning mechanisms. For example, a Level 1 anomaly plus a Level 2 anomaly requires an emergency warning because these anomalies necessitate immediate inspection. A Level 3 anomaly can trigger a special warning, indicating that the equipment may be about to malfunction. A Level 4 anomaly only requires a preliminary warning, as only trend changes need to be monitored. In this embodiment, the anomaly level is placed in the anomaly identifier bit of the data packet, using 4 binary bits to indicate the anomaly level in the data frame. For example, 0000 indicates no anomaly, 0001 represents Level 1, and 1000 represents Level 4, etc.

[0151] In addition to issuing warnings for abnormal temperatures, the cloud platform can also issue warnings for other anomalies in the entire system, such as communication anomalies, power supply anomalies, and equipment offline. If these anomalies are detected, they can also be reported to the cloud platform to trigger the corresponding warning mechanism. The specifics will not be elaborated further.

[0152] For ease of understanding, this application also provides a system architecture for a method of monitoring abnormal temperatures in outdoor power equipment. Please refer to [link / reference]. Figure 3 and Figure 4 This can be described as a three-layer collaborative architecture: "local temperature acquisition module → relay gateway → cloud management platform." The core hardware consists of the temperature acquisition module and the relay gateway, while the software runs through the entire process of data acquisition, transmission, aggregation, and reporting, achieving low-power operation, highly reliable communication, and remotely controllable data management. Each layer achieves deep integration through modular design. Specifically, the local temperature acquisition module is responsible for front-end temperature data acquisition and local processing, and uploads the data to the relay gateway via wireless communication. The relay gateway aggregates data from multiple acquisition modules, performs protocol conversion, and transmits it uplink, while also forwarding instructions from the cloud platform. The cloud management platform provides data storage, analysis, early warning, and remote control functions, forming a closed loop of "acquisition-transmission-control."

[0153] The main control chip of the local temperature acquisition module in this system structure is a domestic SOC chip, which serves as the core of the module software and supports functions such as hierarchical sleep and command parsing. The infrared temperature sensor supports the IIC communication protocol and can be directly controlled by software to achieve on-demand acquisition. The wireless communication unit integrates spread spectrum technology to ensure the software's anti-interference transmission requirements from the hardware level and adapt to the complex electromagnetic environment of overhead lines.

[0154] Furthermore, the local temperature acquisition module employs low-power management, utilizing a layered sleep strategy with three modes: deep sleep, shallow sleep, and sensor-only sleep, automatically switching according to a preset cycle. It supports three wake-up methods: timed wake-up, command wake-up, and abnormal temperature wake-up, optimizing power consumption through a logic of "system initialization → data acquisition and processing → wireless transmission → cycle determination → corresponding sleep." Moreover, the module integrates a standard IIC communication protocol stack, supporting sensor address configuration, data reading, and sleep command issuance. It integrates a multi-point calibration algorithm, correcting acquired data through preset calibration parameters to improve temperature measurement accuracy. It monitors IIC communication status in real time, automatically triggering a retry mechanism upon timeout or data anomaly, recording and reporting fault status upon failure. It uses a direct sequence spread spectrum algorithm, modulating data with a pseudo-random code; it supports automatic switching between eight communication channels, real-time detection of channel quality, and selection of the channel with the optimal signal-to-noise ratio for transmission; and it employs a custom frame format with CRC16 verification to ensure transmission integrity. The module employs a combined algorithm of moving average filtering and median filtering to eliminate spike interference; it performs 16-bit quantization compression on temperature data to reduce transmission power consumption; and it adds an anomaly flag to the data frame when the detected temperature exceeds a preset threshold. The module also supports platform-initiated data collection commands, parameter configuration commands, and firmware upgrade commands. After receiving a command, it parses the type, executes the corresponding operation, and returns response data upon completion.

[0155] The power supply unit in the relay gateway uses a solar power module to provide energy for the continuous operation of the gateway and support the software to realize power supply status monitoring and power consumption adaptation; the wireless receiving unit supports spread spectrum technology, is compatible with the communication protocol of the temperature acquisition module, and realizes multi-module data reception; the uplink communication unit is equipped with a CAT1 wireless module, providing a hardware foundation for the protocol interface between the software and the cloud platform; the storage unit has a local data caching function, supports the software's breakpoint resume function, and avoids data loss.

[0156] Furthermore, the relay gateway collects parameters such as solar panel voltage, lithium battery voltage, and charging / discharging current in real time to determine the power supply status; it implements overcharge, over-discharge, and overcurrent protection logic, triggering hardware protection commands and reporting to the platform when an anomaly is detected. It supports simultaneous access from up to 128 temperature acquisition modules, employing a Time Division Multiple Access (TDMA) mechanism to avoid data conflicts; it is compatible with the spread spectrum protocol of the acquisition modules, performing CRC verification after demodulating the received data; if successful, it enters the aggregation process; otherwise, it requests retransmission; it maintains a list of devices connected to the acquisition modules and supports automatic offline device identification. It parses the custom frame format uploaded by the acquisition modules, extracting information such as device ID, temperature data, and status identifiers; when multiple acquisition modules report data simultaneously, it sorts and processes data according to device ID priority and reporting timestamp; it caches data through a local storage module to support breakpoint resumption. It converts the aggregated local data into the MQTT / TCP protocol format supported by the platform for standardized reporting; it monitors the CAT1 network signal strength and connection status in real time and automatically handles reconnection logic; after network interruption recovery, it reads the cached unreported data and reports it in batches according to time order. It receives instructions from the cloud platform, parses the target device ID, and transmits them to the target acquisition module through the spread spectrum wireless module. After receiving the response data, it encapsulates it and reports it to the platform, forming a closed-loop instruction system.

[0157] The outdoor power equipment temperature anomaly monitoring method of this application, based on the above system framework, can drastically reduce power consumption and ensure long-term battery life; it also features highly reliable transmission, adaptability to complex environments, flexible remote control, and reduced operation and maintenance costs; moreover, high-precision temperature measurement ensures the authenticity and reliability of the data; and it can guarantee the timeliness, accuracy, and remote control capabilities of power equipment temperature anomaly monitoring.

[0158] The outdoor power equipment temperature anomaly monitoring method provided in this application, in the data acquisition stage, not only adopts dynamic acquisition frequency to adapt to the influence of changing environments and ensure the reliability of acquired data, but also calibrates the acquired data through different preset calibration algorithms to further ensure data accuracy. In the anomaly analysis stage, it analyzes the temperature status of power equipment through various means to determine different anomaly levels, which is more in line with the actual situation. In the data transmission stage, it constructs a hierarchical architecture of local-relay gateway-cloud platform, and relies on spread spectrum technology and retransmission mechanism to ensure the speed and stability of data transmission. The gateway will intelligently transmit data according to anomaly priority, ensuring the reliability of data transmission from multiple aspects. The three-level architecture also improves the remote control capability of power equipment temperature anomaly monitoring, and can provide an efficient and accurate monitoring solution for actual scenarios. Therefore, this application embodiment can solve the technical problems of existing technologies that rely on manual labor and lack timeliness, have weak anti-interference ability in data acquisition, and are prone to data loss during data transmission, resulting in a lack of timeliness, accuracy and remote control capability in power equipment temperature anomaly monitoring.

[0159] For easier understanding, please refer to Figure 2 This application provides an embodiment of an outdoor power equipment temperature anomaly monitoring device, comprising:

[0160] The data calibration unit 201 is used to calibrate the temperature data of the power equipment collected based on the dynamic acquisition frequency using a preset calibration algorithm to obtain calibrated temperature data. The preset calibration algorithm includes segmented calibration and real-time single-point calibration.

[0161] Temperature analysis unit 202 is used to perform temperature anomaly analysis based on calibrated temperature data according to temperature threshold, trend threshold, mutation mechanism and baseline standard, and generate anomaly analysis data package, which includes anomaly level;

[0162] Data transmission unit 203 is used to send anomaly analysis data packets to the relay gateway based on spread spectrum technology and verification retransmission mechanism, so that the gateway can perform data aggregation and protocol conversion based on anomaly priority to obtain transmission data packets;

[0163] The abnormal warning unit 204 is used to trigger different temperature warning mechanisms based on the abnormality level in the received transmission data packet through the cloud platform to realize temperature abnormality warning.

[0164] Furthermore, the data calibration unit 201 is specifically used for:

[0165] Calculate the rate of change of ambient temperature for a preset number of consecutive cycles based on ambient temperature.

[0166] The temperature acquisition frequency of the power equipment is dynamically set according to the rate of change of ambient temperature to obtain the dynamic acquisition frequency.

[0167] Temperature data of power equipment is collected through dynamic acquisition frequency;

[0168] The temperature data is interpolated and calibrated in segments according to a preset temperature range to obtain segmented calibrated temperatures.

[0169] After a preset time period, the temperature data is calibrated in real time based on the reference temperature value to obtain the single-point calibration temperature.

[0170] The calibration temperature data consists of segmented calibration temperatures and single-point calibration temperatures.

[0171] This application also provides an outdoor power equipment temperature anomaly monitoring device, the device including a processor and a memory;

[0172] The memory is used to store program code and transfer the program code to the processor;

[0173] The processor is used to execute the outdoor power equipment temperature anomaly monitoring method in the above method embodiments according to the instructions in the program code.

[0174] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0175] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0176] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0177] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of this application through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0178] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An outdoor power equipment temperature anomaly monitoring method, characterized by, The application relates to a temperature anomaly early warning method and device for power equipment. The application comprises the following steps: A preset calibration algorithm is used to calibrate temperature data of power equipment collected based on a dynamic collection frequency, so that calibrated temperature data are obtained, wherein the preset calibration algorithm comprises segmented calibration and real-time single-point calibration; Temperature anomaly analysis is performed based on a temperature threshold value, a trend threshold value, a mutation mechanism and a baseline standard according to the calibrated temperature data, and an anomaly analysis data packet is generated, wherein the anomaly analysis data packet comprises an anomaly level; The anomaly analysis data packet is sent to a relay gateway according to a spread spectrum technology and a check and retransmission mechanism, so that data convergence and protocol conversion processing are performed based on an anomaly priority, and a transmission data packet is obtained; 2. The outdoor power equipment temperature anomaly monitoring method of claim 1, wherein, Different temperature early warning mechanisms are triggered according to the anomaly level in the received transmission data packet through a cloud platform, so that temperature anomaly early warning is realized. The application comprises the following steps: An environmental temperature change rate of a continuous preset number of periods is calculated according to an environmental temperature; A temperature collection frequency of power equipment is dynamically set according to the environmental temperature change rate, so that a dynamic collection frequency is obtained; Temperature data of power equipment are collected through the dynamic collection frequency; The temperature data are segmented and interpolated for calibration processing according to a preset temperature range, so that segmented calibration temperature is obtained; After a preset time period, the temperature data are subjected to real-time single-point calibration processing according to a reference temperature value, so that single-point calibration temperature is obtained; 3. The outdoor power equipment temperature anomaly monitoring method of claim 1, wherein, The segmented calibration temperature and the single-point calibration temperature constitute the calibrated temperature data. The application further comprises the following steps: The calibrated temperature data are subjected to median filtering processing according to a dynamic odd window, or The calibrated temperature data are subjected to adaptive sliding average filtering processing based on an exponential weighting strategy; 4. The outdoor power equipment temperature anomaly monitoring method of claim 1, wherein, The calibrated temperature data after filtering are quantized and compressed by using a preset quantization bit number. The application comprises the following steps: If the calibrated temperature data are lower than a lower limit value in a temperature threshold value or higher than an upper limit value in the temperature threshold value, the power equipment is determined to be in a first anomaly; A temperature change slope is calculated according to the calibrated temperature data, if the temperature change slope exceeds a trend threshold value and lasts for a preset window time length, the power equipment is determined to be in a second anomaly; Temperature positive accumulation value and temperature negative accumulation value are respectively calculated according to the calibrated temperature data, if the temperature positive accumulation value or the temperature negative accumulation value is greater than a mutation threshold value, the power equipment is determined to be in a third anomaly; A temperature deviation is calculated according to the calibrated temperature data and historical temperature data, if the temperature deviation is greater than a reference deviation value, the power equipment is determined to be in a fourth anomaly; An anomaly analysis data packet is generated based on the first anomaly, the second anomaly, the third anomaly and the fourth anomaly.

5. The outdoor power equipment temperature anomaly monitoring method of claim 1, wherein, The abnormal analysis data packet is sent to the relay gateway according to the spread spectrum technology and the check retransmission mechanism, so that data aggregation and protocol conversion processing are performed based on an abnormal priority, and a transmission data packet is obtained, including: The optimal channel is selected through the spread spectrum technology, and the abnormal analysis data packet is sent to the relay gateway through the optimal channel; In the data packet sending process, the abnormal analysis data packet is sequentially subjected to a first-level XOR check and a second-level CRC16 check, and if the frame header check fails, the current frame is discarded; If the data segment check fails, the abnormal analysis data packet is re-sent to the relay gateway based on a signal-to-noise ratio; The relay gateway determines an abnormal priority according to the abnormal level in the abnormal analysis data packet; Data aggregation and caching are performed according to the abnormal priority and a time stamp, and data is converted into a platform protocol format, so that a transmission data packet is obtained.

6. The outdoor power equipment temperature anomaly monitoring method of claim 5, wherein, If the data segment check fails, the abnormal analysis data packet is re-sent to the relay gateway based on a signal-to-noise ratio, including: If the data segment check fails, the communication quality is evaluated according to the signal-to-noise ratio of the wireless channel, and a communication level is obtained; Different retransmission times are set according to different communication levels; The abnormal analysis data packet is re-sent to the relay gateway according to the retransmission times.

7. The outdoor power equipment temperature anomaly monitoring method of claim 1, wherein, The cloud platform triggers different temperature warning mechanisms according to the abnormal level in the received transmission data packet to realize temperature abnormality warning, and the method further includes: If the cloud platform receives state abnormality information sent by the relay gateway, a state warning mechanism is triggered to realize state abnormality warning, and the state abnormality information includes communication abnormality information, power supply abnormality information and device offline information.

8. An outdoor power equipment temperature anomaly monitoring device, comprising: The method includes: A data calibration unit is configured to calibrate temperature data of a power device collected based on a dynamic collection frequency by using a preset calibration algorithm to obtain calibrated temperature data, and the preset calibration algorithm includes segmented calibration and real-time single-point calibration; A temperature analysis unit is configured to perform temperature abnormality analysis on the calibrated temperature data based on a temperature threshold, a trend threshold, a mutation mechanism and a baseline standard, and generate an abnormal analysis data packet, and the abnormal analysis data packet includes an abnormal level; A data transmission unit is configured to send the abnormal analysis data packet to a relay gateway according to a spread spectrum technology and a check retransmission mechanism, so that data aggregation and protocol conversion processing are performed based on an abnormal priority, and a transmission data packet is obtained; An abnormality warning unit is configured to trigger different temperature warning mechanisms according to the abnormal level in the received transmission data packet through a cloud platform to realize temperature abnormality warning.

9. The outdoor power equipment temperature anomaly monitoring apparatus of claim 8, wherein, The data calibration unit is specifically configured to: Calculate an environmental temperature change rate of a continuous preset number of periods according to an environmental temperature; Dynamically set a temperature collection frequency of the power device according to the environmental temperature change rate to obtain a dynamic collection frequency; Collect temperature data of the power device through the dynamic collection frequency; Perform segmented interpolation calibration processing on the temperature data according to a preset temperature range to obtain segmented calibration temperature; After a preset time period, perform real-time single-point calibration processing on the temperature data according to a reference temperature value to obtain single-point calibration temperature; The segmented calibration temperature and the single-point calibration temperature constitute the calibrated temperature data.

10. An outdoor power equipment temperature anomaly monitoring device, comprising: The device comprises a processor and a memory; The memory is used for storing program codes and transmitting the program codes to the processor; The processor is used for executing the outdoor power equipment temperature anomaly monitoring method according to the instructions in the program codes.

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