Intelligent terminal supporting multi-parameter fusion perception

Abstract

The invention relates to the technical field of intelligent power grids, and provides an intelligent terminal supporting multi-parameter fusion perception, which comprises a data acquisition module for acquiring parameters of a transformer body and parameters of a far-end intelligent electric meter through multiple types of sensors; the data processing module is used for executing a dynamic window cleaning algorithm on the collected data, and the data fusion module is used for outputting power grid state characteristics by adopting a fixed weight and dynamic weight dual-mode fusion mechanism; a multi-mode wireless communication unit is arranged in the communication module, and the communication module uploads the fused data and the early warning information to a cloud master station platform; according to the method, the defects in the prior art are overcome, a sliding window self-adaptive adjustment mechanism is established, and the window length is automatically adjusted and calculated by analyzing data distribution characteristics in real time; when abnormal fluctuation is detected, the window is contracted to improve the sensitivity, and the window is expanded to enhance the immunity under a steady-state working condition.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a smart terminal supporting multi-parameter fusion perception. Background Art

[0002] With the rapid development of smart grids, the requirements for monitoring and management of power equipment are increasing. Traditional power monitoring terminals can only collect a single type of data, such as transformer data or smart meter data, and cannot meet the needs of comprehensive, real-time, and accurate monitoring of modern power grids. In addition, traditional terminals lack computing power and cannot generate effective conclusions in real time, which affects the efficiency and accuracy of power grid management.

[0003] The voltage / current signals collected by the sensor are susceptible to transient pulse interference (such as lightning inrush current). The existing fixed window filtering algorithm cannot dynamically adapt to changes in data distribution, resulting in missed detection of outliers.

[0004] The transformer and meter data are fused using fixed weights. When meter communication is interrupted or the oil temperature changes drastically, the fusion results deviate from the actual operating conditions.

[0005] After the early warning, the fault type is manually confirmed on site, and there is a lack of automatic positioning and continuous verification mechanism for the treatment effect, which leads to delayed troubleshooting.

[0006] Based on the above problems, we propose an intelligent terminal that supports multi-parameter fusion perception. Summary of the Invention

[0007] In response to the shortcomings of the existing technology, the present invention provides an intelligent terminal that supports multi-parameter fusion perception, which overcomes the shortcomings of the existing technology, has a reasonable design and a compact structure, and solves the existing problems of delayed warning, poor dynamic practicality and passive protection measures.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0009] An intelligent terminal supporting multi-parameter fusion perception, comprising:

[0010] Data acquisition module: collects transformer parameters and remote smart meter parameters through multiple types of sensors;

[0011] Data processing module: Execute dynamic window cleaning algorithm on collected data,

[0012] Data fusion module: adopts fixed weight and dynamic weight dual-mode fusion mechanism to output grid status characteristics;

[0013] Communication module: Built-in multi-mode wireless communication unit to upload integrated data and warning information to the cloud master platform.

[0014] Preferably, the multiple types of sensors include but are not limited to voltage sensors, current sensors and temperature sensors.

[0015] The voltage sensor is directly connected to the phase line of the low-voltage output end of the transformer, and the current sensor is sleeved on the conductor of the low-voltage output loop of the transformer. The output power frequency voltage / current is proportionally converted into a safe low voltage / current signal, which is transmitted to the smart terminal through a shielded signal line.

[0016] The temperature sensor probe is immersed in the oil at the top of the transformer tank, and then the physical temperature value is converted into a standard analog signal and transmitted to the intelligent terminal;

[0017] The smart terminal presets a communication key and device identity that matches the target smart meter, and obtains the corresponding data stored in the internal memory of the smart meter; the smart terminal locally caches the data of the smart meter and the data of the transformer.

[0018] Preferably, the data processing module reads the voltage, current and temperature cache data in the transformer and the smart meter from the local cache, and extracts k consecutive sampling points from the cached data;

[0019] Assume that the sampling points of the basic window are k=30, the number of effective points calculated is m=k / 3, and the standard deviation σ and kurtosis k0 of the first k points are calculated.

[0020] If k0>3 or σ>5% of the range, dynamic adjustment is triggered where δ max is the maximum deviation of the front window, ‖C val ‖ is the covariance matrix norm, a=0.5 is the attenuation coefficient; update the number of valid points m=K new / 2; window K new The value range is [15,60],

[0021] Set the current data point to be processed to x t , take the data point x t The first m valid points, calculate the mean μ, based on the current data point x to be processed t Calculate the deviation from the mean μ: δ=|x t -μ|, if the deviation δ> threshold θ, the data point x t is a transient pulse; replace the abnormal data point x with the mean μ t Output the cleaned data point x t ; If the deviation δ≤threshold θ, determine the data point x t Normal, then directly output the cleaned data point x t .

[0022] Preferably, the data fusion module obtains the data points x of the voltage, current and temperature of the cleaned transformer and the smart meter.t ; Calculate the voltage U measured within the set time after each cleaning meter / Current I meter and transformer voltage U transformer / Current I transformer The weighted average is calculated based on the fixed weights of 70% for the smart meter and 30% for the transformer, and the fused voltage value is obtained: U fused =0.7U meter +0.3U transformer And current value: I fused =0.7I meter +0.3I transformer ;

[0023] When the smart meter data missing rate calculated by fixed weights is greater than 10% or the transformer oil temperature gradient is greater than 10°C / min, dynamic weighting is introduced and weight allocation is based on the real-time data quality score: Among them, MR i is the missing rate, is the fluctuation coefficient, if the meter data MR i >10%, the weight is automatically reduced to 50%, and the weight is updated in real time; when the voltage value U after real-time output fusion is fused , current value I fused Send to smart terminal;

[0024] The line loss rate is calculated in a rolling manner within the set period, and the output fused voltage value U fused , current value I fused The product of gets the total transformer output and the total smart meter consumption at the current moment, and compares the difference between the total transformer output and the total smart meter consumption to dynamically refresh the line loss rate:

[0025] If the line loss rate of three consecutive rolling windows exceeds the set threshold, a fault warning is triggered and the fault warning information is sent to the smart terminal; if the line loss rate of three consecutive rolling windows does not exceed the set threshold, the smart terminal continues to monitor and record data.

[0026] Preferably, by calculating the temperature value T within the set time after each cleaning fused The product of the corresponding time weight and then the weight assigned by the temperature sensor is removed to obtain the temperature value T within the fusion time. fused ; When the fusion temperature value T within the set time fused When the temperature exceeds the set value, the intelligent terminal automatically triggers an early warning, pushes the early warning information and starts fault analysis; when the temperature does not exceed the set value, the intelligent terminal continuously monitors and records any continuous abnormal trends.

[0027] Preferably, the intelligent terminal sends a structured data packet to the cloud master platform through the communication unit, and the data packet includes:

[0028] Fusion parameter: voltage value U fused , current value I fused and the corresponding timestamp,

[0029] Derived data: electrical energy parameters and power parameters;

[0030] Early warning signs: line loss rate classification, temperature status;

[0031] Equipment identification: GPS coordinates and transformer number;

[0032] After receiving the data, the cloud master platform uses the topology association algorithm to locate the fault section, automatically matches the disposal plan, and sends the disposal plan to the operation and maintenance app;

[0033] Operations and maintenance personnel confirm the order by scanning the QR code through the APP and conduct on-site processing; the intelligent terminal continuously monitors the recovery parameters and reports verification data every 5 minutes; the cloud master station platform compares the data before and after the processing. If the line loss rate does not exceed the set threshold for 60 minutes, it is normal; if the temperature is within 85°C, it is within the normal range; if it does not meet the standards, it will automatically be upgraded to a level 2 work order, triggering the intervention of the on-duty engineer; until it is restored to the set requirements.

[0034] This invention provides an intelligent terminal that supports multi-parameter fusion perception. It has the following beneficial effects: establishing a sliding window adaptive adjustment mechanism that automatically adjusts the calculation window length by analyzing data distribution characteristics in real time; shrinking the window to increase sensitivity when abnormal fluctuations are detected; and expanding the window to enhance interference resistance under steady-state conditions.

[0035] By default, intelligent weighted fusion fuses voltage and current data in a fixed ratio, with the meter as the primary and the transformer as the auxiliary. When the meter loss rate exceeds the standard or the oil temperature fluctuates drastically, a dynamic weighting mechanism is activated: weights are redistributed based on a negative exponential function of the loss rate and fluctuation coefficient, ensuring that fusion errors are controlled under extreme operating conditions. Temperature fusion also incorporates the oil heating time characteristic to design attenuation weights, improving state perception accuracy.

[0036] The cloud master station platform automatically locates the short-circuit, leakage, and overload fault types through topology algorithms and pushes customized disposal plans to the operation and maintenance terminals. The intelligent terminals regularly report verification data, and the cloud master station platform continuously verifies whether the line loss rate and temperature have returned to a safe range. If they do not meet the standards, it automatically upgrades the processing flow, achieving full closed-loop management from fault discovery to resolution, significantly shortening fault response time.

[0037] By simultaneously collecting and fusing data from multiple sources, the limitations of traditional single data collection can be overcome, enabling real-time and accurate monitoring of the grid's operating status and equipment health. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A physical deployment topology diagram of the multi-source perception layer of the present invention;

[0039] Figure 2 This is the edge intelligent processing topology diagram of the present invention;

[0040] Figure 3 This is the cloud closed-loop processing topology diagram of the present invention; DETAILED DESCRIPTION

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0042] Refer to the attached Figure 1-Figure 3 , an intelligent terminal supporting multi-parameter fusion perception, comprising:

[0043] Data acquisition module: collects transformer body parameters and remote smart meter parameters through multiple types of sensors; the multiple types of sensors include but are not limited to voltage sensors, current sensors and temperature sensors,

[0044] The voltage sensor is directly connected to the phase line of the low-voltage output end of the transformer, and the current sensor is sleeved on the conductor of the low-voltage output loop of the transformer. The output power frequency voltage / current is proportionally converted into a safe low voltage / current signal, which is transmitted to the smart terminal through a shielded signal line.

[0045] The temperature sensor probe is immersed in the oil at the top of the transformer tank, and then the physical temperature value is converted into a standard analog signal and transmitted to the intelligent terminal;

[0046] The smart terminal presets a communication key and device identity that matches the target smart meter, and obtains the corresponding data stored in the internal memory of the smart meter; the smart terminal locally caches the data of the smart meter and the data of the transformer.

[0047] Data processing module: performs a dynamic window cleaning algorithm on the collected data to eliminate transient pulse interference; the data processing module reads the voltage, current, and temperature cache data in the transformer and smart meter from the local cache, and extracts k consecutive sampling points from the cached data;

[0048] Assume that the sampling points of the basic window are k=30, the number of effective points calculated is m=k / 3, and the standard deviation σ and kurtosis k0 of the first k points are calculated.

[0049] If k0>3 (heavy-tailed distribution) or σ>5% of the range, dynamic adjustment is triggered where δ max is the maximum deviation of the front window, ‖C val ‖ is the covariance matrix norm, a=0.5 is the attenuation coefficient; update the number of valid points m=K new / 2 (rounded to an integer); window K new The value range is [15,60] to avoid overfitting or underfitting.

[0050] Set the current data point to be processed to x t , take the data point x t The first m valid points, calculate the mean μ, based on the current data point x to be processed t Calculate the deviation from the mean μ: δ=|x t -μ|, if the deviation δ> threshold θ (θ = 3 times the standard deviation) judge the data point x t is a transient pulse; replace the abnormal data point x1 with the mean value μ and output the cleaned data point x t ; If the deviation δ≤threshold θ, determine the data point x t Normal, then directly output the cleaned data point x t .

[0051] Data fusion module: adopts fixed weight and dynamic weight dual-mode fusion mechanism to output grid status characteristics; the data fusion module obtains the voltage, current and temperature data points of the cleaned transformer and smart meter x t ; Calculate the voltage U measured within the set time after each cleaning meter / Current I meter and transformer voltage U transformer / Current I transformer The weighted average is calculated based on the fixed weights of 70% for the smart meter and 30% for the transformer, and the fused voltage value is obtained: U fused =0.7U meter +0.3U transformer And current value: I fused =0.7I meter +0.3I transformer ;

[0052] When the smart meter data missing rate calculated by fixed weights is greater than 10% or the transformer oil temperature gradient is greater than 10°C / min, dynamic weighting is introduced and weight allocation is based on the real-time data quality score: Among them, MR i is the missing rate, is the fluctuation coefficient, if the meter data MR i >10%, the weight is automatically reduced to 50%, and the weight is updated in real time; when the voltage value U after real-time output fusion is fused, current value I fused Send to smart terminal;

[0053] The line loss rate is calculated in a rolling manner within the set period (60 minutes), according to the output fusion voltage value U fused , current value I fused The product of gets the total transformer output and the total smart meter consumption at the current moment, and compares the difference between the total transformer output and the total smart meter consumption to dynamically refresh the line loss rate:

[0054] If the line loss rate of three consecutive rolling windows exceeds the set threshold, a fault warning is triggered and the fault warning information is sent to the smart terminal; if the line loss rate of three consecutive rolling windows does not exceed the set threshold, the smart terminal continues to monitor and record data.

[0055] By calculating the temperature value T within the set time after each cleaning fused Its corresponding aging weight (weight attenuation coefficient is based on the transformer oil thermal time constant T: T = 25min, warning threshold classification: oil-immersed transformer > 85℃, dry-type transformer > 60℃ trigger warning) product, and then remove the weight assigned by the temperature sensor to obtain the temperature value T within the fusion time fused ; When the fusion temperature value T within the set time fused When the temperature exceeds the set value (85°C), the intelligent terminal automatically triggers an early warning, pushes the early warning information and starts fault analysis; when the temperature does not exceed the set value (85°C), the intelligent terminal continuously monitors and records any continuous abnormal trends.

[0056] Communication module: Built-in multi-mode wireless communication unit (LoRa, ZigBee, Wi-Fi, NB-IoT), uploads integrated data and warning information to the cloud master platform; the smart terminal sends structured data packets to the cloud master platform through the communication unit. The data packets contain:

[0057] Fusion parameter: voltage value U fused , current value I fused and the corresponding timestamp,

[0058] Derived data: electrical energy parameters (kWh) and power parameters (kW);

[0059] Early warning signs: line loss rate classification, temperature status;

[0060] Equipment identification: GPS coordinates and transformer number;

[0061] After receiving the information, the cloud master platform locates the fault section (short circuit / leakage / overload) through the topology association algorithm, automatically matches the response plan, and sends the response plan to the operation and maintenance app;

[0062] Operations and maintenance personnel confirm the order by scanning the QR code through the APP and conduct on-site processing; the intelligent terminal continuously monitors the recovery parameters and reports verification data every 5 minutes; the cloud master station platform compares the data before and after the processing. If the line loss rate does not exceed the set threshold for 60 minutes, it is normal; if the temperature is within 85°C, it is within the normal range; if it does not meet the standards, it will automatically be upgraded to a level 2 work order, triggering the intervention of the on-duty engineer; until it is restored to the set requirements.

[0063] Example 1: Equipment Deployment: Voltage and current sensors are installed on phases A, B, and C on the low-voltage side of the transformer, and the oil temperature sensor probe is immersed in the top oil layer of the transformer tank. A smart terminal with a built-in NB-IoT communication module and pre-set target meter identification code and AES encryption key collects transformer parameters and remote smart meter parameters. The smart terminal then locally caches the smart meter and transformer data for subsequent data processing.

[0064] Dynamic data cleaning: The smart terminal reads 30 sampling points (sampling frequency 10Hz) from the local cache for data quality testing. The test found that the current data had a kurtosis k0 of 3.8 (>3) and a standard deviation exceeding the range by 6%, triggering a dynamic adjustment mechanism. The original number of valid points, m = 30 / 3 = 10, was reduced to 15 points (15∈[15,60]) after adjustment to avoid overfitting or underfitting.

[0065] The current data point to be processed x t =85A, take the first 15 valid points to calculate the mean μ = 72A, and calculate the deviation δ = |85-72| = 13A according to the formula. At the same time, the standard deviation is known to be 2.8A, and the threshold θ = 3×2.8A = 8.4A. Since δ>θ, it is determined that the data point x t is a transient pulse. Then the abnormal data point x is replaced by the mean value μ = 72A t , output the cleaned data points to ensure the accuracy of the data.

[0066] Intelligent data fusion: This system acquires voltage, current, and temperature data points from the cleaned transformer and smart meter. Under normal operating conditions, the voltages are weighted averaged, with fixed weights of 70% for the smart meter and 30% for the transformer. Given a smart meter voltage of 225V and a transformer voltage of 228V, the fused voltage value is 225 × 70% + 228 × 30% = 225.9V.

[0067] Abnormal intervention: Meter data missing rate MR is detected i =15% (>10% threshold), the dynamic weighting mechanism is activated. The meter weight is reduced to 50%, and the fusion voltage is recalculated as 225×50%+228×50%=226.5V.

[0068] Line loss monitoring: Calculate the line loss rate over a rolling 60-minute period using the formula: (total transformer output - total smart meter consumption) / total transformer output × 100%. Given a total transformer output of 1520 kWh and a total smart meter consumption of 1386 kWh, the calculated line loss rate is (1520 - 1386) / 1520 × 100% = 8.8%. If this line loss rate exceeds the 7% threshold for three consecutive cycles, an overload warning is triggered.

[0069] Temperature warning: Oil temperature data points were collected at 82°C, 83°C, and 85°C, and a decay weight of 25 minutes was applied (weight of 1.0 for the current data and 0.85 for the data from 5 minutes ago). The fusion temperature was calculated using the formula: (82 × 0.85 + 83 × 0.89 + 85 × 1.0) / (0.85 + 0.89 + 1.0) = 83.6°C. A temperature rise gradient of 12°C / min (greater than the 10°C / min threshold) was detected, triggering an 85°C overtemperature warning 15 minutes in advance.

[0070] Closed-loop handling: The intelligent terminal uploads the fused data and warning information to the cloud master platform in the form of a structured data packet. The data packet contains fused parameters (voltage value, current value and corresponding timestamp), derived data (electric energy parameter kWh and power parameter kW), warning signs (line loss rate classification, temperature status), and equipment identification (GPS coordinates and transformer number). After receiving the data, the cloud master platform locates the B-phase overload fault section through a topological association algorithm and automatically matches the corresponding handling plan. The cloud master platform sends the handling plan to the operation and maintenance app. The operation and maintenance personnel confirm the order by scanning the QR code on the app and remove the faulty load upon arrival.

[0071] The intelligent terminal continuously monitors recovery parameters and reports verification data every five minutes. The cloud master platform compares data before and after the treatment. Sixty minutes after the treatment, the line loss rate stabilized at 4.2%, and the oil temperature dropped to 65°C. Because the line loss rate remained within the set threshold for 60 minutes and the temperature remained within 85°C, the system determined that the fault had been eliminated, completing the entire treatment process.

[0072] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent terminal supporting multi-parameter fusion perception, characterized in that: include: Data acquisition module: collects transformer parameters and remote smart meter parameters through multiple types of sensors; Data processing module: Execute dynamic window cleaning algorithm on collected data, Data fusion module: adopts fixed weight and dynamic weight dual-mode fusion mechanism to output grid status characteristics; Communication module: Built-in multi-mode wireless communication unit to upload integrated data and warning information to the cloud master platform.

2. The intelligent terminal supporting multi-parameter fusion perception according to claim 1, characterized in that: The multiple types of sensors include but are not limited to voltage sensors, current sensors and temperature sensors. The voltage sensor is directly connected to the phase line of the low-voltage output end of the transformer, and the current sensor is sleeved on the conductor of the low-voltage output loop of the transformer. The output power frequency voltage / current is proportionally converted into a safe low voltage / current signal, which is transmitted to the smart terminal through a shielded signal line. The temperature sensor probe is immersed in the oil at the top of the transformer tank, and then the physical temperature value is converted into a standard analog signal and transmitted to the intelligent terminal; The smart terminal presets a communication key and device identity that matches the target smart meter, and obtains the corresponding data stored in the internal memory of the smart meter; the smart terminal locally caches the data of the smart meter and the data of the transformer.

3. The intelligent terminal supporting multi-parameter fusion perception according to claim 1, characterized in that: The data processing module reads the voltage, current and temperature cache data in the transformer and the smart meter from the local cache, and extracts k consecutive sampling points from the cached data; Assume that the sampling points of the basic window are k=30, the number of effective points calculated is m=k / 3, and the standard deviation σ and kurtosis k0 of the first k points are calculated. If k0>3 or σ>5% of the range, dynamic adjustment is triggered where δ max is the maximum deviation of the front window, ‖C val ‖ is the covariance matrix norm, a=0.5 is the attenuation coefficient; update the number of valid points m=K new / 2; window K new The value range is [15,60], Set the current data point to be processed to x t , take the data point x t The first m valid points, calculate the mean μ, based on the current data point x to be processed t Calculate the deviation from the mean μ: δ=|x t -μ|, if the deviation δ> threshold θ, the data point x t is a transient pulse; replace the abnormal data point x with the mean μ t Output the cleaned data point x t ; If the deviation δ≤threshold θ, the data point x t Normal, then directly output the cleaned data point x t .

4. The intelligent terminal supporting multi-parameter fusion perception according to claim 1, characterized in that: The data fusion module obtains the voltage, current and temperature data points x of the cleaned transformer and the smart meter t ; Calculate the voltage U measured within the set time after each cleaning meter / Current I meter and transformer voltage U transformer / Current I transformer The weighted average is calculated based on the fixed weights of 70% for the smart meter and 30% for the transformer, and the fused voltage value is obtained: U fused =0.7U meter +0.3U transformer And current value: I fused =0.7I meter +0.3I transformer ; When the smart meter data missing rate calculated by fixed weights is greater than 10% or the transformer oil temperature gradient is greater than 10°C / min, dynamic weighting is introduced and weight allocation is based on the real-time data quality score: Among them, MR i is the missing rate, is the fluctuation coefficient, if the meter data MR i If it is >10%, the weight will automatically drop to 50%, and the weight will be updated in real time; After the real-time output is integrated, the voltage value U fused , current value I fused Send to smart terminal; The line loss rate is calculated in a rolling manner within the set period, and the output fused voltage value U fused , current value I fused The product of gets the total transformer output and the total smart meter consumption at the current moment, and compares the difference between the total transformer output and the total smart meter consumption to dynamically refresh the line loss rate: If the line loss rate of three consecutive rolling windows exceeds the set threshold, a fault warning is triggered and the fault warning information is sent to the smart terminal; If the line loss rate of three consecutive rolling windows does not exceed the set threshold, the intelligent terminal will continue to monitor and record data.

5. The intelligent terminal supporting multi-parameter fusion perception according to claim 4, characterized in that: By calculating the temperature value T within the set time after each cleaning fused The product of the corresponding time weight and then the weight assigned by the temperature sensor is removed to obtain the temperature value T within the fusion time. fused ; When the fusion temperature value T within the set time fused When the temperature exceeds the set value, the intelligent terminal automatically triggers an early warning, pushes the early warning information and starts fault analysis; when the temperature does not exceed the set value, the intelligent terminal continuously monitors and records any continuous abnormal trends.

6. The intelligent terminal supporting multi-parameter fusion perception according to claim 1, characterized in that: The intelligent terminal sends a structured data packet to the cloud master platform through the communication unit. The data packet contains: Fusion parameter: voltage value U fused , current value I fused and the corresponding timestamp, Derived data: electrical energy parameters and power parameters; Early warning signs: line loss rate classification, temperature status; Equipment identification: GPS coordinates and transformer number; After receiving the data, the cloud master platform uses the topology association algorithm to locate the fault section, automatically matches the disposal plan, and sends the disposal plan to the operation and maintenance app; Operations and maintenance personnel confirm the order by scanning the QR code through the app and conduct on-site processing. The smart terminal continuously monitors the recovery parameters and reports verification data every 5 minutes. The cloud master station platform compares the data before and after the processing. If the line loss rate does not exceed the set threshold for 60 minutes, it is considered normal, and if the temperature is within 85°C, it is within the normal range. If the standards are not met, it will automatically be upgraded to a level 2 work order, triggering the intervention of the on-duty engineer. Until it is restored to the set requirements.

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