Multi-loop heat tracing point wireless cluster monitoring and intelligent early warning system

Abstract

The application discloses a multi-loop heat tracing point wireless cluster monitoring and intelligent early warning system and relates to the technical field of heat tracing point monitoring and early warning.The system comprises a multi-loop heat tracing point data calculation component and a hierarchical early warning response component.The loop heat tracing point data calculation component comprises a heat tracing point temperature unit, a heat tracing loop coupling unit and a dynamic early warning decision unit.The hierarchical early warning response component comprises a loop level risk early warning unit and a system level dynamic early warning unit.The heat tracing point temperature unit, the heat tracing loop coupling unit and the dynamic early warning decision unit are used to respectively output a heat tracing point temperature abnormal correction deviation, a heat tracing loop coupling risk level index and a system dynamic early warning decision index.By introducing multi-dimensional correction, coupling and system level factors, the problems of traditional monitoring misjudgment, difficulty in risk differentiation and blind early warning decision are solved, and accurate data, hierarchical risk basis and decision guidance are provided for multi-loop cluster monitoring.

Description

Technical Field

[0001] This invention relates to the field of hotspot monitoring and early warning technology, specifically a multi-loop hotspot wireless cluster monitoring and intelligent early warning system. Background Technology

[0002] In industries such as petrochemicals, power, and coal chemicals, heat tracing systems are core supporting facilities that ensure the normal flow of media in pipelines and equipment, prevent freezing, or maintain process temperatures. With the development of large-scale industrial plants, heat tracing systems are gradually upgrading from single-loop independent monitoring to multi-loop cluster monitoring. A set of industrial plants has a large number of heat tracing loops and a larger total number of heat tracing points. Therefore, a multi-loop heat tracing point wireless cluster monitoring and intelligent early warning system is needed.

[0003] Existing multi-loop heat tracing monitoring systems may mostly adopt a mode of single-point temperature acquisition combined with fixed threshold alarms, without correcting for multi-dimensional interference factors in the heat tracing temperature. In actual cluster monitoring scenarios, heat tracing temperature fluctuations are often caused by non-fault factors such as thermal inertia hysteresis, insulation layer dampness, ambient wind speed heat dissipation, and power supply voltage fluctuations. These fluctuations may be misjudged by the system as heat tracing faults, resulting in a large number of invalid alarms. Maintenance personnel need to spend a lot of time troubleshooting and verification, and real fault alarms may be buried in invalid information and cannot be dealt with in a timely manner. Existing monitoring systems may only focus on single-point temperature anomalies, without establishing a correlation analysis mechanism for multiple anomalies within the loop, and without considering the impact of coupling factors such as heat conduction in adjacent loops, overall load fluctuations in the loop, and equipment aging on the loop's operating status. When multiple single points in the loop exhibit minor anomalies but do not reach the threshold alarm conditions, the system may not be able to identify the cumulative effect of these anomalies. Furthermore, when heat tracing anomalies in adjacent loops interfere with the current loop through heat conduction, the system may not be able to distinguish whether it is due to its own fault or external coupling influence, which may ultimately lead to systemic loop failures and cause serious consequences such as pipeline freezing and media blockage. Existing monitoring systems may only trigger early warnings based on single-point alarms, without considering system-level factors such as the degree of process correlation of the loop, the accessibility of inspections, the unit's operating conditions, and external weather conditions. When multiple loops alarm simultaneously, the system cannot prioritize the alarms, and maintenance personnel can only handle them in the order of alarm time. This may result in untimely handling of faults in core process loops, while invalid alarms from remote and non-core loops consume a large amount of maintenance resources, reducing overall maintenance efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-loop wireless cluster monitoring and intelligent early warning system with hotspots, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a multi-loop hotspot wireless cluster monitoring and intelligent early warning system, including a multi-loop hotspot data calculation component and a hierarchical early warning response component; The multi-loop hotspot data calculation component: Used to acquire preprocessed temperature correlation data, loop coupling correlation data, and dynamic early warning related data of the hot spot; The multi-loop hotspot data calculation component includes: Heat tracing temperature unit, heat tracing circuit coupling unit, and dynamic early warning decision unit; The multi-loop heat tracing hotspot data calculation component calculates the real-time temperature value of the i-th heat tracing hotspot at monitoring time t, the reference temperature of the i-th heat tracing hotspot, the thermal inertia hysteresis correction coefficient of the i-th heat tracing hotspot, the humidity penetration correction coefficient of the insulation layer of the i-th heat tracing hotspot, the wind speed correction coefficient of the i-th heat tracing hotspot, and the power supply fluctuation correction coefficient of the i-th heat tracing circuit, and performs weighted processing to output the temperature anomaly correction deviation of the heat tracing hotspot. The multi-loop heat tracing hotspot data calculation component calculates the heat tracing loop coupling risk level index by weighting the thermal coupling correction coefficient, load fluctuation correction coefficient, equipment aging correction coefficient, and maintenance cycle correction coefficient of the j-th loop in the loop coupling correlation data, and combining them with the temperature anomaly correction deviation of the heat tracing hotspot. The multi-loop heat tracing data calculation component is based on the correlation influence coefficient of the j-th loop, the inspection accessibility correction coefficient of the j-th loop, the system operating condition adaptive correction coefficient, and the meteorological disaster linkage correction coefficient in the dynamic early warning related data, and performs weighted processing, and combines the heat tracing loop coupling risk level index to output the system dynamic early warning decision index. Tiered early warning response components: It is used to receive the deviation of abnormal temperature of the heat tracing hotspot, the risk level index of the coupling of the heat tracing circuit, and the dynamic early warning decision index of the system, so as to implement multi-dimensional hierarchical early warning response.

[0006] Optionally, the processing procedure of the heat tracing unit is as follows: S11. By introducing the real-time temperature value of the i-th heat source at monitoring time t, and combining it with the reference temperature of the i-th heat source, the temperature deviation is determined. S12. By analyzing the time required for the temperature to rise from the initial value to the stable value when the heat tracing system is started, the degree of delay effect of the thermal inertia of the heat tracing pipe and the medium on the temperature change is reflected, so as to calculate the thermal inertia hysteresis correction coefficient of the i-th heat tracing point. S13. By obtaining the real-time relative humidity of the pipeline insulation layer, the degree of interference of the insulation layer moisture on temperature monitoring is reflected, so as to calculate the insulation layer humidity penetration correction coefficient of the i-th heat source. S14. By acquiring wind speed data near the heat tracing hotspot, the influence of ambient wind speed on heat tracing heat dissipation is reflected, so as to output the wind speed correction coefficient of the i-th heat tracing hotspot. S15. Obtain the real-time power supply voltage by acquiring the voltage transformer in the heat tracing circuit distribution box, and combine it with the rated power supply voltage of the heat tracing circuit to reflect the degree of influence of power supply voltage fluctuation on heat tracing power, so as to output the power supply fluctuation correction coefficient of the i-th heat tracing circuit. S16. Dynamically weight the output values ​​of the above steps to finally output the deviation of the temperature anomaly correction of the hot spot.

[0007] Optionally, the processing procedure of the heat tracing circuit coupling unit is as follows: S21. The power supply fluctuation correction coefficient of the i-th heat tracing circuit is introduced into the coupling unit of this heat tracing circuit to convert the single-point temperature anomaly into a standardized deviation index, so as to facilitate the cumulative calculation of the risk of multiple heat tracing points in the circuit, and to combine it with the point weight of the i-th heat tracing point in the j-th circuit to achieve dynamic matching of weights. S22. By combining the average temperature anomaly deviation of adjacent circuits with the number of adjacent heat tracing circuits of the j-th circuit based on the summation function, the degree of heat conduction interference of adjacent heat tracing circuits on the current circuit is reflected, so as to output the thermal coupling correction coefficient of the j-th circuit. S23. By acquiring the real-time current in the heat tracing circuit distribution box and combining it with the rated current of the heat tracing cable, the degree of interference of the heat tracing cable current fluctuation on the circuit operation is reflected, so as to calculate the load fluctuation correction coefficient of the j-th circuit. S24. By obtaining the service life of the j-th circuit, analyze the impact of the aging degree of the heat tracing cable and sensor on the circuit operation, and output the equipment aging correction coefficient of the j-th circuit. S25. Based on the number of days the j-th circuit has been overdue for maintenance, analyze the impact of overdue maintenance on the operation of the circuit, and calculate the maintenance cycle correction coefficient of the j-th circuit. S26. The output values ​​of the above steps are dynamically weighted to finally output the heat tracing circuit coupling risk level index.

[0008] Optionally, the processing procedure of the dynamic early warning decision unit is as follows: S31. By introducing a heat tracing circuit coupling risk level index, the abnormality of a single point in the circuit, the coupling effect, the electrical risk and the aging risk are integrated into a standardized circuit risk index, and its correlation influence coefficient with the j-th circuit is combined through a summation function. S32. By analyzing the shortest inspection time from the main control room to the circuit, the impact of inspection difficulty on circuit fault handling is quantified, so as to analyze the inspection accessibility correction coefficient of the j-th circuit. S33. By obtaining the real-time load rate, analyze the impact of the unit's operating load on the heat tracing demand, and output the system operating condition adaptive correction coefficient. S34. By matching with the warning level of external meteorology, the impact of external extreme weather on the heat tracing system is reflected, so as to output the meteorological disaster linkage correction coefficient. S35. Dynamically weight the output values ​​of the above steps to finally output the system dynamic early warning decision index.

[0009] Optionally, the tiered early warning response component includes a loop-level risk early warning unit and a system-level dynamic early warning unit.

[0010] Optionally, the loop-level risk warning unit specifically comprises: If the coupling risk level index of the heat tracing circuit is ≥0.7, it indicates a high risk. At this time, an alarm will be immediately pushed to the mobile terminal of the operation and maintenance personnel, marking the circuit-level emergency risk and requiring them to arrive at the site within 1 hour. The system will automatically lock the three heat tracing points with the highest deviation in the circuit, highlight the location of the points on the electronic map, and simultaneously push the electrical parameters of the circuit and the operating status of adjacent circuits to help quickly locate the cause of the fault. If 0.4 ≤ heat tracing circuit coupling risk level index < 0.7, it indicates medium risk. At this time, an alarm is pushed to the plant inspection system, and the circuit is given special attention. It is clearly required that the two heat tracing points with the highest deviation in the circuit be checked first during the inspection. The system automatically retrieves the temperature and current data of the circuit in the past 24 hours to generate a circuit risk report, which is attached to the alarm information for the inspection personnel to refer to. If the risk level index of the heat tracing circuit coupling is less than 0.4, it indicates low risk. At this time, the system background automatically records the circuit risk data, maintains the regular inspection frequency of the circuit, does not need to actively push alarm information, and automatically generates a risk trend report of the circuit every month for updating the operation and maintenance ledger.

[0011] Optionally, the system-level dynamic early warning unit includes a threshold division subunit and a measure corresponding subunit.

[0012] Optionally, the threshold partitioning subunit specifically comprises: If the system dynamic early warning decision index is less than 0.5, it indicates low system-level risk; If 0.5 ≤ System Dynamic Early Warning Decision Index < 0.9, it indicates medium-level system risk; If the system dynamic early warning decision index is ≥0.9, it indicates a high system-level risk; The specific sub-units corresponding to the measures are: When the system is at a low system-level risk, the system backend automatically aggregates the risk data of all loops, generates a daily system operation report, pushes it to the operation and maintenance management department for archiving, and maintains the system's regular monitoring frequency without the need to adjust the heat tracing system's operating parameters. When the system is at medium-level risk, an alarm is pushed to the person in charge of operation and maintenance management, and the system is marked as a risk of concern. It is clearly required that high-risk loops be dealt with first. The system will automatically start adaptive adjustment of operating conditions to fine-tune the heat tracing power of medium-risk loops, optimize the thermal environment of the loops, and reduce the accumulation of risks. When a high system-level risk is detected, a cross-departmental alarm is triggered and pushed to the heads of the operation and maintenance, process and equipment management departments. The alarm is marked as a system-level emergency warning, and the system automatically starts the emergency monitoring mode, increasing the monitoring frequency of high-risk loops from once per minute to once every 10 seconds, updating risk data in real time. The system can also remotely control the activation of the backup heat tracing branch of the high-risk loop to temporarily increase the heat tracing power and mitigate the risk.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: I. This invention outputs the temperature anomaly correction deviation of the tracing hotspot through the tracing hotspot temperature unit. By introducing multi-dimensional correction coefficients such as thermal inertia hysteresis, insulation layer humidity penetration, ambient wind speed heat dissipation, and power supply voltage fluctuations, it eliminates interference in the original temperature data of the tracing hotspot across all scenarios. It upgrades the comparison of single-point temperature anomalies from a fixed threshold to a quantified deviation after multi-factor correction, solving the problem of non-fault temperature fluctuations being misjudged as real faults in traditional monitoring. It provides accurate and standardized single-point basic data for multi-loop cluster monitoring, enabling tracing hotspot data from different process locations and different reference temperatures that were previously impossible to compare horizontally to have a unified anomaly assessment standard, becoming a key bridge connecting single-point monitoring and loop cluster analysis.

[0014] Second, this invention outputs a heat tracing circuit coupling risk level index through a heat tracing circuit coupling unit. By establishing a correlation analysis mechanism for multiple anomalies within the circuit, it integrates multiple coupling factors such as point process weights, thermal coupling effects of adjacent circuits, heat tracing cable load fluctuations, equipment aging status, and maintenance cycles. This aggregates scattered single-point anomaly data into an overall circuit risk level, enabling a quantitative assessment of the circuit's systemic risk. This solves the problem of traditional monitoring failing to distinguish between single-point local anomalies and systemic circuit risks, providing hierarchical circuit risk basis for system-level early warning decisions. This allows maintenance personnel to directly locate high-risk circuits without having to check all heat tracing points within the circuit one by one.

[0015] Third, this invention outputs a dynamic early warning decision index through a dynamic early warning decision unit. By introducing system-level factors such as the impact of loop process correlation, inspection accessibility, unit operating condition self-adaptation, and meteorological disaster linkage, the risk levels of multiple loops are prioritized. This upgrades the system's early warning decision from passive single-point alarms to proactive system-level risk handling guidance, solving the problems of blind early warning decisions and unreasonable allocation of operation and maintenance resources in traditional monitoring. It can accurately focus on core loops that affect unit safety, remote loops that are difficult to inspect, and high-risk loops affected by extreme weather, providing the final decision output basis for multi-loop hotspot cluster monitoring and intelligent early warning systems. Attached Figure Description

[0016] Figure 1 This is a block diagram of the system of the present invention; Figure 2 This is a system flowchart of the multi-loop hotspot data calculation component and the hierarchical early warning response component of the present invention; Figure 3 This is a schematic diagram of the operation process of the multi-loop hotspot data calculation component of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figures 1 to 3 This implementation provides a multi-loop wireless cluster monitoring and intelligent early warning system with accompanying hotspots. The multi-loop wireless cluster monitoring and intelligent early warning system with accompanying hotspots includes: Multi-source data monitoring and acquisition components: Used to acquire temperature correlation data, loop coupling correlation data, and dynamic early warning related data of the heat source; Multi-source data preprocessing components: It is used to receive temperature correlation data, loop coupling correlation data and dynamic early warning related data of the hot spot, and preprocess the acquired data to input it into the multi-loop hot spot data calculation component; The multi-loop hotspot data calculation component includes: Heat tracing temperature unit, heat tracing circuit coupling unit, and dynamic early warning decision unit; Further: First, the temperature unit of the heat tracing hotspot is based on the real-time temperature value of the i-th heat tracing hotspot at monitoring time t, the reference temperature of the i-th heat tracing hotspot, the thermal inertia hysteresis correction coefficient of the i-th heat tracing hotspot, the humidity penetration correction coefficient of the insulation layer of the i-th heat tracing hotspot, the wind speed correction coefficient of the i-th heat tracing hotspot, and the power supply fluctuation correction coefficient of the i-th heat tracing circuit, and performs weighted processing to output the temperature anomaly correction deviation of the heat tracing hotspot. Second, the heat tracing loop coupling unit performs weighted processing on the heat coupling correction coefficient, load fluctuation correction coefficient, equipment aging correction coefficient, and maintenance cycle correction coefficient of the j-th loop in the loop coupling correlation data, and combines them with the temperature anomaly correction deviation of the heat tracing point to output the heat tracing loop coupling risk level index. Third, the dynamic early warning decision unit performs weighted processing on the correlation influence coefficient of the j-th loop, the inspection accessibility correction coefficient of the j-th loop, the system operating condition adaptive correction coefficient, and the meteorological disaster linkage correction coefficient in the dynamic early warning related data, and combines them with the heat tracing loop coupling risk level index to output the system dynamic early warning decision index. Tiered early warning response components: It is used to receive the deviation of the temperature anomaly correction of the heat tracing hot spot, the risk level index of the coupling of the heat tracing circuit and the dynamic early warning decision index of the system, so as to implement multi-dimensional hierarchical early warning response; The tiered early warning response component includes: Circuit-level risk warning unit and system-level dynamic warning unit; Further: The loop-level risk warning unit is as follows: If the coupling risk level index of the heat tracing circuit is ≥0.7, it indicates a high risk. At this time, an alarm will be immediately pushed to the mobile terminal of the operation and maintenance personnel, marking the circuit-level emergency risk and requiring them to arrive at the site within 1 hour. The system will automatically lock the three heat tracing points with the highest deviation in the circuit, highlight the location of the points on the electronic map, and simultaneously push the electrical parameters (current and voltage) of the circuit and the operating status of adjacent circuits to help quickly locate the cause of the fault. If 0.4 ≤ heat tracing circuit coupling risk level index < 0.7, it indicates medium risk. At this time, an alarm is pushed to the plant inspection system, and the circuit is given special attention. It is clearly required that during the inspection, the two heat tracing points with the highest deviation in the circuit (such as elbows and valves) should be checked first. The system will automatically retrieve the temperature and current data of the circuit in the past 24 hours to generate a circuit risk report, which will be attached to the alarm information for the inspection personnel to refer to. If the risk level index of the heat tracing circuit coupling is less than 0.4, it indicates low risk. At this time, the system background automatically records the circuit risk data, maintains the regular inspection frequency of the circuit (once every 7 days), does not need to actively push alarm information, and automatically generates a risk trend report of the circuit every month for updating the operation and maintenance ledger. The system-level dynamic early warning unit includes a threshold division subunit and a corresponding measure subunit; Threshold-based sub-unit division, specifically: If the system dynamic early warning decision index is less than 0.5, it indicates low system-level risk; If 0.5 ≤ System Dynamic Early Warning Decision Index < 0.9, it indicates medium-level system risk; If the system dynamic early warning decision index is ≥0.9, it indicates a high system-level risk; The corresponding sub-units for the measures are as follows: When the system is at a low system-level risk, the system backend automatically aggregates the risk data of all loops, generates a daily system operation report, pushes it to the operation and maintenance management department for archiving, and maintains the system's regular monitoring frequency without the need to adjust the heat tracing system's operating parameters. When a system-level risk is detected, an alarm should be pushed to the head of the operations and maintenance department, marking it as a system-level risk of concern, and explicitly requiring priority handling of high-risk circuits (heat tracing circuit coupling risk level index RB). j ≥0.7), and the system automatically starts adaptive adjustment of operating conditions, fine-tuning the heat tracing power of medium-risk circuits (±5%), optimizing the circuit thermal environment, and reducing risk accumulation; When a high system-level risk is detected, a cross-departmental alarm is triggered and pushed to the heads of the operation and maintenance, process and equipment management departments. The alarm is marked as a system-level emergency warning and the system automatically starts the emergency monitoring mode, increasing the monitoring frequency of high-risk loops from once per minute to once every 10 seconds, updating risk data in real time, and remotely controlling the system to start the backup heat tracing branch of the high-risk loop and temporarily increase the heat tracing power to mitigate the risk. Based on the above, the system's heat tracing hotspot temperature unit, heat tracing loop coupling unit, and dynamic early warning decision unit work together to form a complete logical chain of single-point correction, loop aggregation, and system decision-making. This provides a core algorithmic support system for the multi-loop heat tracing hotspot wireless cluster monitoring and intelligent early warning system. Single-point data correction by the heat tracing hotspot temperature unit ensures the accuracy of monitoring data. Loop risk aggregation by the heat tracing loop coupling unit enables hierarchical monitoring of multiple loops. System early warning decision-making by the dynamic early warning decision unit provides targeted risk management guidance. Combined, the system can comprehensively grasp the operating status of all heat tracing loops at the cluster level, accurately identify real risks, avoid invalid alarms, and realize the core function of intelligent early warning. This provides a systematic guarantee for the safe and stable operation of the multi-loop heat tracing system, shifting maintenance work from passive fault diagnosis to proactive risk prediction and handling.

[0019] Please refer to Figure 1 , Figure 2 as well as Figure 3 The processing flow of the hotspot temperature unit is as follows: S11. By introducing the real-time temperature value of the i-th heat source at monitoring time t, and combining it with the reference temperature of the i-th heat source, the temperature deviation is determined. S12. By analyzing the time required for the temperature to rise from the initial value to the stable value when the heat tracing system is started, the degree of delay effect of the thermal inertia of the heat tracing pipe and the medium on the temperature change is reflected, so as to calculate the thermal inertia hysteresis correction coefficient of the i-th heat tracing point. S13. By obtaining the real-time relative humidity of the pipeline insulation layer, the degree of interference of the insulation layer moisture on temperature monitoring is reflected, so as to calculate the insulation layer humidity penetration correction coefficient of the i-th heat source. S14. By acquiring wind speed data near the heat tracing hotspot, the influence of ambient wind speed on heat tracing heat dissipation is reflected, so as to output the wind speed correction coefficient of the i-th heat tracing hotspot. S15. Obtain the real-time power supply voltage by acquiring the voltage transformer in the heat tracing circuit distribution box, and combine it with the rated power supply voltage of the heat tracing circuit to reflect the degree of influence of power supply voltage fluctuation on heat tracing power, so as to output the power supply fluctuation correction coefficient of the i-th heat tracing circuit. S16. Dynamically weight the output values ​​of the above steps to finally output the deviation of the temperature anomaly correction of the tracing hotspot. The calculation formula for the temperature unit of the tracing hotspot is as follows: ; in: DA i Correction of deviation for abnormal temperature of accompanying hotspots; DAA iThe real-time temperature value of the i-th heat tracing point at monitoring time t is the core basic data for temperature monitoring of the heat tracing system. It can be directly obtained by a temperature sensor installed on the outer wall of the heat tracing pipe (close to the contact position between the heat tracing cable and the pipe). The sensor is equipped with a wireless transmission module to send the real-time temperature data to the monitoring host. The real-time temperature value of the i-th heat source at monitoring time t (DAA) i The introduction of this feature reflects the current actual temperature status of the heat source, providing the original monitoring basis for judging whether the temperature deviates from the reference value, and is the core input item for deviation calculation; DAB i The reference temperature for the i-th heat tracing point is its stable operating temperature under normal conditions. It serves as the standard for judging temperature anomalies and is calculated using the following formula: ; In the above formula, M refers to the number of effective temperature samples of the heat source under normal operating conditions for 30 consecutive days. In the above formula, DABA i，k Refers to the temperature value at the k-th sample time; For newly commissioned circuits, the heat tracing target temperature designed in the process can be used as the initial benchmark, and then updated using historical data after 15 days of operation. The reference temperature DAB of the i-th associated hot spot i The introduction of this feature is used to distinguish between normal temperature fluctuations and abnormal deviations, ensuring the accuracy of deviation calculations and avoiding misjudging temperature changes under normal operating conditions as abnormal. DAC i The thermal inertia hysteresis correction factor for the i-th heat tracing point is used to reflect the degree of delay in temperature change caused by the thermal inertia of the heat tracing pipe and the medium. It can be calculated by fitting the temperature change rate, and the calculation formula is as follows: ; In the above formula, DACA i The thermal time constant of the heat tracing point refers to the time required for the temperature of the heat tracing system to rise from the initial value to the stable value when it is started. The unit is seconds, and it can be obtained by fitting the continuous monitoring data of the temperature sensor. In the above formula, 3600 refers to the unit conversion factor, which converts the unit of the thermal time constant from seconds to hours. After conversion to hours, the coefficient changes more smoothly and is consistent with the magnitude of other correction factors, avoiding excessive amplification of the deviation calculation results by a single factor. Thermal inertia hysteresis correction coefficient DAC for the i-th associated hot spot i The introduction of this technology corrects the temperature monitoring lag caused by pipeline thermal inertia, avoids misjudging normal temperature delay changes as abnormalities, and improves the accuracy of single-point temperature anomaly identification. Traditional monitoring only focuses on real-time temperature, ignoring the lag in temperature changes caused by pipeline thermal inertia (e.g., the temperature rise of a pipeline with thick insulation layer may be 30% slower than that of a pipeline with thin insulation layer). This coefficient corrects for misjudgments of temperature deviation caused by thermal inertia, ensuring the accuracy of anomaly identification. DAD i The relative humidity DADA is the humidity penetration correction coefficient for the i-th heat source, used to reflect the degree of interference of moisture in the insulation layer on temperature monitoring. It can be obtained by a capacitive humidity sensor installed inside the insulation layer (20-30mm from the outer wall of the pipe) to acquire real-time relative humidity. i Then calculate using the following formula: DAD i =0.05×(DADA) i -40); In the above formula, the coefficient 0.05 refers to the dimension conversion coefficient, with the unit being ℃ / %; In the above formula, the coefficient 40 refers to the normal relative humidity reference value of the insulation layer; And when DADA i When ≤40, DAD i =0; The humidity permeability correction factor (DAD) for the insulation layer of the i-th heat source i The introduction of this technology is intended to correct temperature monitoring distortion caused by moisture in the insulation layer, avoid misjudging temperature drops caused by moisture as insufficient heating or freezing risk, and improve the accuracy of temperature anomaly identification in humid environments. The insulation layer of remote / high-altitude heat tracing circuits is prone to moisture absorption by rain and snow, which leads to a decrease in heat transfer efficiency. However, existing technologies rarely use humidity data to correct temperature monitoring values. This coefficient uses data from a miniature humidity sensor embedded in the insulation layer to accurately correct the false low temperature caused by moisture in the insulation layer, avoiding misjudgment as insufficient heat tracing. DAE i This is the wind speed correction coefficient for the i-th heat tracing point, used to reflect the influence of ambient wind speed on heat tracing. It can be obtained by acquiring real-time wind speed DAEA from a wind speed sensor installed near the heat tracing point (distance ≤ 1m, height level with the heat tracing point). i (Unit: m / s), then calculate using the following formula: DAE i =1 + 0.03 × DAEA i ; In the above formula, the coefficient 0.03 means that for every 1 m / s increase in ambient wind speed, the wind speed correction coefficient increases by 0.03, based on the fact that for every 1 m / s increase in wind speed, the heat dissipation rate of the pipe increases by approximately 3%. Furthermore, the value of the heat source in the indoor area without wind speed influence is 1; Wind speed correction factor DAE for the i-th associated hot spot iThe introduction of this technology is used to correct the accelerated heat dissipation caused by ambient wind speed, avoid misjudging the temperature drop caused by wind speed as an anomaly, and improve the accuracy of identifying abnormal temperature of tracing points in open areas. DAF i This is the power supply fluctuation correction factor for the i-th heat tracing circuit, used to reflect the degree of influence of power supply voltage fluctuations on the heat tracing power. The real-time power supply voltage (DAFA) can be obtained from the voltage transformer installed in the heat tracing circuit distribution box. i The unit is V, and it is then calculated using the following formula: ; In the above formula, DAFB refers to the rated power supply voltage of the heat tracing circuit, which is usually 220V or 380V. Power supply fluctuation correction factor DAF for the i-th heat tracing circuit i The introduction of this technology is used to correct changes in heat tracing power caused by power supply voltage fluctuations, avoid misjudging temperature changes caused by power supply fluctuations as abnormalities, and improve the accuracy of identifying abnormal circuit temperatures in areas with unstable power supply. By incorporating power supply stability into temperature anomaly detection, when voltage fluctuations exceed ±5%, the heat tracing power will change accordingly. At this time, the temperature deviation may be a power supply problem rather than a freezing risk. This parameter corrects the misjudgment caused by power supply interference and fills the gap in the correlation between electric heat tracing power supply and temperature monitoring. In the above formula, 0.02 means that when the power supply voltage of the heat tracing circuit deviates from the rated value by 1%, the power supply fluctuation correction coefficient increases by 0.02. A1 i This is the thermal inertia correction weight for the i-th heat tracing point, used to differentiate the influence of the thermal inertia correction coefficient on the reference temperature. It can be set according to the pipe diameter and insulation layer thickness. When the pipe diameter is ≥ DN100 or the insulation layer thickness is ≥ 50mm, the value is 1.2; when the pipe diameter is < DN100 and the insulation layer thickness is < 50mm, the value is 0.7. It can be manually adjusted through the system configuration interface or automatically matched based on pipe parameters. The introduction of this weight is used to adjust the correction intensity for heat tracing points with different thermal inertia characteristics, so that the thermal inertia correction is more in line with the actual working conditions and avoids over-correction of loops with small thermal inertia. A2 i This is the humidity penetration correction weight for the i-th heat tracing point, used to differentiate the influence of the humidity correction coefficient on the reference temperature. It can be set according to the installation environment of the heat tracing point. The value is 1.3 for heat tracing points in open-air or high-humidity areas (annual average relative humidity ≥60%), and 0.7 for indoor or dry areas. It can be manually adjusted through the system configuration interface or automatically matched based on environmental monitoring data. The introduction of this weight can adjust the humidity correction intensity for heat tracing points in different environments, making the correction more in line with the actual degree of moisture impact and avoiding over-correction of the circuit in dry areas. A3i This is the wind speed correction weight for the i-th heat tracing point, used to differentiate the influence of the wind speed correction coefficient on the deviation. It can be set according to the installation location of the heat tracing point. The value is 1.2 for heat tracing points located at high altitudes (height ≥ 10m) or in open, unobstructed areas, and 0.9 for indoor or obstructed areas. It can also be manually adjusted through the system configuration interface. The introduction of this weight is used to adjust the wind speed correction intensity for heat tracing points with different installation locations, so that the correction is more in line with the actual wind speed influence and avoids over-correction of loops in low wind speed areas. A4 i The power supply fluctuation correction weight for the i-th heat tracing circuit is used to differentiate the influence of the power supply fluctuation correction coefficient on the deviation. It can be set according to the power supply distance of the heat tracing circuit. For long-distance circuits with a power supply distance ≥ 500m, the value is 1.2-1.4, and for circuits with a power supply distance < 500m, the value is 0.7-1.0. It can be manually adjusted through the system configuration interface or automatically matched based on the circuit power supply parameters. Based on the above, this heat tracing unit systematically corrects single-point temperature data to address common interference factors in multi-loop heat tracing monitoring, such as thermal inertia hysteresis, insulation layer humidity penetration, ambient wind speed heat dissipation, and power supply voltage fluctuations. This eliminates non-fault-related temperature deviations, ensuring that the anomaly identification of each heat tracing point only targets real heat tracing faults or temperature anomalies. It provides accurate single-point basic data for the system's cluster monitoring, avoiding deviations in subsequent loop and system-level analysis due to single-point data distortion. Deviation of temperature anomaly correction DA i Transforming the raw temperature data of different tracing hotspots into a dimensionless deviation index unifies the anomaly assessment standards for tracing hotspots at different process locations and with different reference temperatures, making single-point temperature data that could not be directly compared horizontally comparable, and providing a standardized input unit for multi-point risk accumulation calculation at the loop level. It is a key bridge for realizing data aggregation from point to loop in cluster monitoring. When DA i The larger the value, the more significant the temperature deviation of the heat tracing point from the reference value. This means that there may be problems such as insufficient heat tracing power, heat tracing cable failure, insulation layer damage, or environmental interference exceeding the normal range at this point, and the operating status of this point needs to be closely monitored. When DA i The smaller the temperature, the closer the temperature of the tracing point is to the reference operating temperature, indicating that the point is operating stably with no obvious abnormal risks and no need for additional intervention. Deviation of temperature anomaly correction DA iAs the core input parameter of the heat tracing loop coupling unit, it provides the real abnormality data of each heat tracing hot spot for loop coupling risk assessment. It is the starting point of the entire system data chain. If the correction of the heat tracing hot spot temperature unit is not provided, the risk calculation at the loop level will be interfered with by the non-fault deviation of a single point, resulting in the distortion of the loop risk assessment and thus affecting the accuracy of the system's early warning decision.

[0020] Please refer to Figure 1 , Figure 2 as well as Figure 3 The processing flow of the heat tracing circuit coupling unit is as follows: S21. The power supply fluctuation correction coefficient of the i-th heat tracing circuit is introduced into the coupling unit of this heat tracing circuit to convert the single-point temperature anomaly into a standardized deviation index, so as to facilitate the cumulative calculation of the risk of multiple heat tracing points in the circuit, and to combine it with the point weight of the i-th heat tracing point in the j-th circuit to achieve dynamic matching of weights. S22. By combining the average temperature anomaly deviation of adjacent circuits with the number of adjacent heat tracing circuits of the j-th circuit based on the summation function, the degree of heat conduction interference of adjacent heat tracing circuits on the current circuit is reflected, so as to output the thermal coupling correction coefficient of the j-th circuit. S23. By acquiring the real-time current in the heat tracing circuit distribution box and combining it with the rated current of the heat tracing cable, the degree of interference of the heat tracing cable current fluctuation on the circuit operation is reflected, so as to calculate the load fluctuation correction coefficient of the j-th circuit. S24. By obtaining the service life of the j-th circuit, analyze the impact of the aging degree of the heat tracing cable and sensor on the circuit operation, and output the equipment aging correction coefficient of the j-th circuit. S25. Based on the number of days the j-th circuit has been overdue for maintenance, analyze the impact of overdue maintenance on the operation of the circuit, and calculate the maintenance cycle correction coefficient of the j-th circuit. S26. The output values ​​of the above steps are dynamically weighted to finally output the heat tracing circuit coupling risk level index. The calculation formula for the heat tracing loop coupling unit is as follows: ; in: RB j This is the risk level index for heat tracing circuit coupling; Deviation of temperature anomaly correction DA i The introduction of this feature transforms single-point temperature anomalies into standardized deviation indicators, facilitating the cumulative risk calculation of multiple hot spots within the loop and serving as a fundamental input for loop risk assessment. RBA i，jThis is the point weight of the i-th hot spot in the j-th loop, used to reflect the process importance of the i-th hot spot in the loop. It can be set according to the process location of the hot spot. The value is 1.2 for hot spots in easily frozen areas such as pipe elbows, valves and flanges, and 0.8 for straight pipe sections. It can be manually adjusted through the system configuration interface or automatically identified and weighted based on process drawings. The introduction of this weight highlights the abnormal contribution of key parts in the loop, making the loop risk assessment more focused on easily frozen areas and avoiding the risk of key parts being covered by normal fluctuations in straight pipe sections. RBB j is the thermal coupling correction coefficient for the j-th loop, used to reflect the degree of thermal conduction interference of adjacent heat tracing loops on the current loop. It can be calculated from the temperature anomaly deviation of adjacent loops, and the calculation formula is as follows: ; In the above formula, Q refers to the number of heat tracing circuits adjacent to the j-th circuit; In the above formula, RBBA refers to the average temperature anomaly deviation of the qth adjacent loop (take the average value of the deviation of all the tracing points in the loop). Traditional monitoring views each loop in isolation, ignoring the heat conduction effect of adjacent loops (e.g., for heat tracing loops less than 0.5 meters apart, an abnormal temperature in one loop can cause a temperature fluctuation of more than 2°C in another loop). This coefficient incorporates the risk of adjacent loops into the assessment of the current loop, enabling collaborative early warning through cluster monitoring. RBC j RBCA is the load fluctuation correction factor for the j-th circuit, used to reflect the degree of interference of the heat tracing cable current fluctuation on the circuit operation. It can be obtained in real time by the current transformer installed in the heat tracing circuit distribution box. j The unit is A, and it is then calculated using the following formula: ; In the above formula, RBCB refers to the rated current of the heat tracing cable, in amperes (A). The load fluctuation correction factor RBC for the j-th loop j The introduction of this technology corrects the changes in heat tracing power caused by fluctuations in cable current, identifies fault risks such as hidden overload or poor contact in cables, and enhances the monitoring capability of circuit electrical safety. By indirectly reflecting the aging or hidden faults of the heat tracing cable through load fluctuations (such as abnormal current fluctuations caused by local insulation degradation of the cable), the risk of heat tracing capacity attenuation can be identified in advance, avoiding the risk of freezing and condensation when the temperature is not abnormal but the heat tracing has failed. RBD j The equipment aging correction factor for the j-th circuit reflects the impact of the aging degree of the heat tracing cable and sensor on the circuit operation. It can be calculated based on the service life, and the calculation formula is as follows: RBD j =1 + 0.05 × RBDA j ; In the above formula, RBDA j This refers to the service life of the j-th circuit, in years, and, if combined with cable insulation resistance monitoring, when the insulation resistance is <10MΩ, RBD j An additional 0.2 can be added; RBE j Here is the maintenance cycle correction factor for the j-th circuit, used to reflect the impact of overdue maintenance on circuit operation. It can be calculated from maintenance records, and the calculation formula is as follows: ; In the above formula, RBEA j This refers to the number of days that the j-th loop has not been maintained. In the above formula, RBEB refers to the standard maintenance cycle of the circuit, which is usually 90 days. Maintenance cycle correction factor RBE for the j-th circuit j The introduction of this technology corrects the increased risk of failure caused by lack of operation and maintenance, improves the accuracy of risk assessment for overdue maintenance loops, and strengthens the early warning constraints of operation and maintenance management. Incorporating operation and maintenance management data (maintenance cycle) into the risk assessment model significantly increases the probability of failure for loops that have not been maintained within the specified period. This parameter enables cross-domain linkage between monitoring data and operation and maintenance management. Existing technologies often view monitoring and operation and maintenance in isolation, without systematically integrating the relationship between the two. Where i∈j represents all heat tracing hotspots i contained in the j-th heat tracing loop, and the summation symbol in the preceding term means summing the risk contribution values ​​of all heat tracing hotspots in the j-th loop, where the risk contribution of each heat tracing hotspot is... That is, the deviation of a single point × the weight of the point, and after summing them, we get the cumulative abnormal value of all the hot spots in the loop. Then, we multiply it by the loop-level correction coefficient to finally get the risk level index of the entire loop. B1 j This is the thermal coupling correction weight for the j-th loop, used to differentiate the impact of the thermal coupling coefficient on loop risk. It can be set according to the loop layout density. The value is 1.3 for densely arranged areas with loop spacing ≤ 0.5m and 0.8 for dispersed areas with loop spacing > 0.5m. It can be manually adjusted through the system configuration interface or automatically matched based on the loop layout drawing. The introduction of this weight can adjust the thermal coupling correction strength for loops with different layout densities, making the loop risk assessment more in line with the actual degree of heat conduction impact and avoiding over-correction of dispersed loops. B2 jThis is the load fluctuation correction weight for the j-th circuit, used to differentiate the impact of the load fluctuation coefficient on the circuit risk. It can be set according to the heating power of the circuit. The value is 1.4 for high-power circuits with heating power ≥ 5kW and 1.0 for low-power circuits with heating power < 5kW. It can be manually adjusted through the system configuration interface or automatically matched based on the circuit's electrical parameters. The introduction of this weight allows for adjustment of the load fluctuation correction for circuits with different power, making the circuit risk assessment more in line with the actual cable load capacity and avoiding over-correction of low-power circuits. B3 j The equipment aging correction weight for the j-th loop is used to differentiate the impact of the aging coefficient on the loop risk. It can be set according to the service life of the loop. The value is 1.5 for old loops with a service life of ≥8 years and 0.5 for new loops with a service life of <8 years. It can be manually adjusted through the system configuration interface or automatically matched based on equipment ledger data. The introduction of this weight highlights the contribution of aging risk of old loops, making the loop risk assessment more focused on the impact of equipment performance degradation and providing early warning of aging failures. B4 j The maintenance cycle adjustment weight for the j-th loop is used to differentiate the impact of the maintenance cycle coefficient on loop risk. It can be set according to the loop's maintenance records. The value is 1.4 for loops that have not been maintained for ≥30 days and 0.7 for loops that are maintained periodically. The maintenance data can be automatically obtained or manually entered through the system's connection with the operation and maintenance management system. The introduction of this weight highlights the risk contribution of loops that have not been maintained for a period of time, making the loop risk assessment more focused on the impact of operation and maintenance deficiencies, and promoting the closed-loop execution of operation and maintenance management. Based on the above, this heat tracing loop coupling unit has evolved from single-point monitoring to cluster risk assessment at the loop level. It integrates multiple factors such as the degree of anomaly of multiple heat tracing points within the loop, the process weight of the point, the thermal coupling effect of adjacent loops, the load fluctuation of the heat tracing cable, the aging status of equipment, and the maintenance cycle. It aggregates scattered single-point anomalies into the overall risk of the loop, solving the problem that single heat tracing point monitoring cannot reflect the overall operating status of the loop, and realizing hierarchical cluster monitoring of multiple loops by the system. Heat tracing circuit coupling risk level index RB j By integrating scattered anomaly information within a loop into a unified risk index, the risk levels of different heat-trapping loops are clearly distinguished, providing maintenance personnel with a basis for loop-level risk location. High-risk loops can be quickly identified without having to check all heat-trapping points within a loop one by one, improving the efficiency of multi-loop cluster monitoring and shifting the system's monitoring focus from single points to the entire loop. When RB jThe larger the value, the higher the overall risk of the heat tracing circuit. There may be a variety of problems such as multiple abnormal heat tracing points in the circuit, severe thermal interference from adjacent circuits, abnormal load on the heat tracing cable, severe equipment aging, or lack of maintenance. The circuit should be listed as a key maintenance target. When RB j The smaller the value, the more stable the operation of each heat tracing point in the heat tracing circuit is, with no obvious coupling risk or the impact of a single abnormal point not reaching the circuit-level risk threshold, and the overall operation of the circuit is good. The heat tracing loop coupling risk level index RBj, as the core input parameter of the dynamic early warning decision unit, transmits the risk at the loop level to the system level. It is a key link in realizing the aggregation of risks from loop to system. Through the calculation of the heat tracing loop coupling unit, the system can quantify the risks of multiple loops in a hierarchical manner, providing a risk basis at the loop dimension for the system's dynamic early warning decision, and ensuring that the system's early warning can cover the cluster risks of multiple loops.

[0021] Please refer to Figure 1 , Figure 2 as well as Figure 3 The processing flow of the dynamic early warning decision unit is as follows: S31. By introducing a heat tracing circuit coupling risk level index, the abnormality of a single point in the circuit, the coupling effect, the electrical risk and the aging risk are integrated into a standardized circuit risk index, and its correlation influence coefficient with the j-th circuit is combined through a summation function. S32. By analyzing the shortest inspection time from the main control room to the circuit, the impact of inspection difficulty on circuit fault handling is quantified, so as to analyze the inspection accessibility correction coefficient of the j-th circuit. S33. By obtaining the real-time load rate, analyze the impact of the unit's operating load on the heat tracing demand, and output the system operating condition adaptive correction coefficient. S34. By matching with the warning level of external meteorology, the impact of external extreme weather on the heat tracing system is reflected, so as to output the meteorological disaster linkage correction coefficient. S35. Dynamically weight the output values ​​of the above steps to finally output the system dynamic early warning decision index; The calculation formula for the dynamic early warning decision unit is as follows: ; in: WS is the system dynamic early warning decision index; Heat tracing circuit coupling risk level index RB j The introduction of this index integrates single-point anomalies, coupling effects, electrical risks, and aging risks within a loop into a standardized loop risk index, which serves as the basic input for multi-loop risk accumulation calculation at the system level. PALj The correlation influence coefficient of the j-th loop reflects the degree of process impact of the loop failure on the overall operation of the unit. It can be set according to the process importance of the loop. The value is 1.3 for heat tracing loops involving the core medium of the unit (such as fuel oil, lubricating oil and cooling water) and 0.9 for auxiliary medium loops. It can be manually adjusted through the system configuration interface or automatically identified and assigned coefficients based on the process flow diagram. The introduction of this weight highlights the contribution of the core loop to the system early warning, making the system early warning more focused on the key loops that affect the safety of the unit, and avoiding the risk of the core loop being covered by the abnormality of the auxiliary loop. PBL j The accessibility correction coefficient for the j-th loop reflects the impact of inspection difficulty on loop fault handling. It can be set according to the time of the inspection route, and the calculation formula is as follows: ; In the above formula, PBLA j This refers to the shortest inspection time (in minutes) from the main control room to the circuit, which can be obtained directly from the inspection management system or manually entered. In the above formula, 0.1 refers to the proportional coefficient of inspection time to risk weight, that is, when the inspection time of the loop exceeds the baseline time by 10 minutes, the inspection accessibility correction coefficient increases by 0.1; In the above formula, 10 refers to the baseline inspection time, which is the average inspection time of the heat tracing circuit in the plant area (the time from the main control room to the circuit and to complete the preliminary inspection). The inspection reachability correction coefficient PBL for the j-th loop. j The introduction of this technology corrects the delay in fault handling caused by the difficulty of inspection, improves the early warning sensitivity of circuits in blind spots of inspection, and ensures that anomalies in remote circuits can be noticed and handled in a timely manner. For loops that are not properly inspected, increase their warning weight to achieve differentiated management with higher warning sensitivity for higher maintenance difficulty, and avoid freezing accidents caused by blind spots in inspection. PCL is the system operating condition adaptive correction coefficient, used to reflect the degree of impact of unit operating load on heat tracing demand. It can be obtained from the real-time load rate PCLA through the unit's DCS system, and then calculated using the following formula: PCL=max(0.3,1-0.005×PCLA); Furthermore, when the unit load rate is ≥100%, the unit's own heat dissipation capacity is significantly improved, and the heat tracing requirement is reduced. Therefore, the PCL value is 0.5. The introduction of the system operating condition adaptive correction coefficient PCL is used to correct for changes in heat tracing demand caused by changes in unit load. The higher the unit load, the stronger its own heat dissipation and the lower the heat tracing demand. This appropriately reduces the warning sensitivity and avoids unnecessary warning interference. PDL is the meteorological disaster linkage correction coefficient, which reflects the degree of impact of external extreme weather on the heat tracing system. It can be directly matched according to the external meteorological warning level. The value is 1 when there is no warning, 1.2 when there is a blue warning, 1.5 when there is an orange warning, and 1.8 when there is a red warning. The warning level can be automatically obtained and the coefficient matched through the system's connection with the public meteorological service platform. The introduction of the meteorological disaster linkage correction coefficient (PDL) is used to correct for the increased demand for heat tracing caused by extreme external weather, improve the sensitivity of early warnings during extreme low temperatures, strong winds and other weather conditions, activate the heat tracing system in advance or send out early warnings, and prevent the risk of freezing. Because existing technologies mostly monitor internal temperature in isolation without taking into account the risks of external extreme weather, this coefficient automatically improves the early warning sensitivity before meteorological disasters arrive (because the warning threshold may be lowered during cold wave warnings), and prevents freezing risks caused by extreme low temperatures and wind speeds in advance, especially for remote inspection blind spot circuits, which greatly improves the early warning response time. In this context, j=1 to n represent all n heat tracing loops in the system, and the system contribution of each loop is... The summation is calculated by multiplying the loop risk index by the correlation influence coefficient and then by the system-level correction coefficient to obtain the dynamic early warning decision index of the entire heat tracing system. The summation of this unit is the point aggregation within the loop and the loop aggregation at the system level. The heat tracing loop coupling unit and the dynamic early warning decision unit form a hierarchical relationship of single-point anomaly, loop risk and system early warning, realizing the risk transmission and integration from the local to the overall. C1 is the accessibility correction weight for the j-th loop, used to differentiate the impact of the accessibility coefficient on the system's early warning. It can be set according to the difficulty of loop inspection. The value is 1.6 for loops in remote areas and at high altitudes (height ≥ 10m) or without dedicated inspection channels, and 1 for indoor or easily inspected areas. It can be manually adjusted through the system configuration interface or automatically matched based on the plant inspection route map. The introduction of this weight highlights the early warning weight of loops in the blind spot of inspection, making the system early warning more focused on the risks of loops that are difficult to deal with quickly, and improving the early warning priority of remote loops. C2 is the system operating condition adaptive correction weight, used to adjust the degree of influence of the operating condition adaptive coefficient on the system warning. It can be set according to the unit's operating load. The value is 1.4 for low load conditions when the unit load is <50%, and 0.9 for normal operating conditions when the unit load is ≥50%. Load data can be automatically obtained through the system's connection with the unit's DCS system or manually entered. The introduction of this weight is used to adjust the warning weight for different unit load conditions, so that the system warning is more in line with the actual heat dissipation capacity of the unit, improves the warning sensitivity at low load, and avoids the risk of freezing due to insufficient heat dissipation of the unit. C3 is the meteorological disaster linkage correction weight, used to differentiate the impact of meteorological disaster coefficients on system warnings. It can be set according to the external meteorological warning level, with a value of 1.8 for red or orange meteorological warnings, 1.1 for yellow or blue warnings, and 0.5 for no warning. Warning information can be automatically obtained through the system's connection with the public meteorological service platform or manually entered. The introduction of this weight is used to adjust the warning weight for different meteorological risk levels, making the system warnings more in line with external environmental risks, improving warning sensitivity during extreme weather, and preventing freezing risks in advance. Based on the above, this dynamic early warning decision unit elevates intelligent early warning decision-making from the loop level to the system level. It integrates multiple factors such as the risk level of multiple loops, the impact of loop process associations, inspection accessibility, unit operating condition self-adaptation, and meteorological disaster linkage, and aggregates the scattered loop risks into the overall system risk. This solves the problem that single loop early warning cannot reflect the overall risk priority of the system and realizes intelligent early warning decision-making under multi-loop cluster monitoring. The System Dynamic Early Warning Decision Index (WS) integrates the risks of all heat tracing circuits within the system into a unified early warning decision index, clarifies the overall risk level of the system, and triggers the corresponding early warning response mechanism based on the risk level. It provides system-level risk handling priority guidance for operation and maintenance personnel, enabling the system's early warning to shift from passive single-point alarms to proactive system-level risk decision-making. It is the core output for realizing intelligent early warning functions. When WS is larger, it indicates that the overall risk of the system is higher, which means that there are multiple high-risk loops in the system. These loops may involve core process links, are difficult to inspect, or are affected by extreme weather. The highest level of early warning response needs to be activated immediately, and the high-priority loop risks should be dealt with first. The smaller the WS value, the more stable the operation of each heat tracing circuit in the system is, with no major risks or hidden dangers. The system is in normal operation and only requires routine monitoring. The System Dynamic Early Warning Decision Index (WS) serves as the final output basis for the system's early warning function. It integrates the single-point correction of the heat tracing hotspot temperature unit and the loop aggregation of the heat tracing loop coupling unit into a complete monitoring and early warning closed loop. This achieves full-process coverage from single-point data acquisition and correction to loop risk assessment and system early warning decision-making. Through the combination of the three sets of units, the system can accurately identify the real risks of multi-loop heat tracing hotspots and output targeted early warning decisions, providing systematic support for the safe operation of multi-loop heat tracing systems and ensuring the effective implementation of cluster monitoring and intelligent early warning functions.

[0022] It is worth noting that this embodiment presents an iterative loop form based on the heat tracing loop coupling risk level index RB. j Thermal inertia correction weight A1 for the i-th hot spot in the hot spot temperature element i To exert a reverse influence, the deviation of the temperature anomaly correction DA of the tracing hotspot is increased.i Substituting the values ​​into the heat tracing loop coupling unit for calculations generates a positive influence relationship, which in turn forms a cyclical reverse influence relationship between the heat tracing loop coupling unit and the heat tracing point temperature, making the overall system more intelligent. The specific cyclical process is as follows: Firstly, if RB j The fact that the circuit is in a high-risk range indicates that the overall thermal environment fluctuations exceed initial expectations, and the impact of thermal inertia on single-point temperature has been underestimated. Therefore, the A1 value of all hot spots within the circuit should be adjusted. i The original design was improved by 20% to enhance the correction of single-point thermal inertia and match the overall thermal characteristics of the loop. Secondly, if RB j The risk level is in the medium-risk range; the original A1 level remains unchanged. i The settings remain unchanged, with only a slight adjustment of ±5% to the top 20% of the hot spots with the highest deviation within the circuit, to adapt to differences in local thermal environments. Thirdly, if RB j The fact that it is in the low-risk range indicates that the thermal environment of the loop is stable and the initial thermal inertia correction is relatively strong. Therefore, the A1 of all hot spots within the loop should be... i Reduce by 10% from the original level to minimize unnecessary correction interference; Furthermore, the iteration is considered convergent when the results of two consecutive iterations simultaneously satisfy the following two conditions: Condition 1: |RB j，n+1 -RB j，n | ≤0.01; Condition 2: |DA i，n+1 -DA i，n | ≤0.005; The maximum number of iterations shall not exceed 5. If convergence is not achieved after 5 iterations, the current result shall be used and a parameter calibration alarm shall be triggered. In this iterative system, the corrected deviation of the temperature anomaly correction of the heat tracing point can accurately distinguish between temperature fluctuations caused by single-point faults and changes in the overall thermal environment of the circuit. This avoids misjudging circuit-level thermal interference (such as heat conduction in adjacent circuits and abnormal heat dissipation of the circuit as single-point heat tracing faults) and allows single-point anomaly identification to focus only on single-point problems such as damaged heat tracing cables and damaged insulation layers, thus eliminating non-fault-related temperature fluctuation interference. Based on the corrected deviation of the temperature anomaly correction of the tracing hotspot DA i The calculated loop coupling risk level index can truly reflect the actual fault risk of the loop, eliminate the artificially high risk caused by single-point parameter setting errors or loop thermal environment interference, and make the loop risk assessment only quantify real loop-level problems such as multiple self-faults within the loop, abnormal cable load, and equipment aging, thus avoiding meaningless risk amplification. The role of this iterative framework in this system is as follows: To avoid misjudgments of single-point anomalies caused by loop thermal environment interference, reduce the number of invalid alarms pushed by the system, and enable operation and maintenance resources to focus on the actual fault points, the iterative results can clearly delineate the boundary between single-point faults and loop-level coupling risks. Operation and maintenance personnel can directly use the DA (Data Analysis) to... i Locating a single point of failure, based on RB j The overall risk of the positioning loop is eliminated without the need for repeated investigation and verification. As the loop's operating status changes (such as seasonal changes and adjustments to the operating conditions of adjacent loops), the iterative mechanism can automatically correct individual parameters, enabling the system's monitoring capabilities to adapt to dynamic operating conditions without the need for frequent manual adjustments to parameter settings. The bidirectional iteration between the heat tracing temperature unit and the heat tracing loop coupling unit provides accurate basic data for the system dynamic early warning decision index of the dynamic early warning decision unit: the corrected heat tracing loop coupling risk level index RB. j It can accurately reflect the actual risks of the loops. After being incorporated into the dynamic early warning decision unit, the system's early warning decisions will be more in line with the actual operating status of the multi-loop cluster. This avoids over-warning due to inflated loop risks or under-warning due to single-point misjudgments, making the intelligent early warning function more accurate and providing reliable support for the safe operation of the multi-loop heat tracing system. At the same time, the iterative mechanism allows the three sets of units to form a closed-loop logic: single-point data corrects loop risks, loop risk feedback optimizes single-point monitoring, and finally provides a reliable basis for system early warning, realizing the upgrade of cluster monitoring from passive alarm to active adaptation.

[0023] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-loop hotspot wireless cluster monitoring and intelligent early warning system, characterized in that, Includes a multi-loop hotspot data calculation component and a tiered early warning response component; The multi-loop hotspot data calculation component: Used to acquire preprocessed temperature correlation data, loop coupling correlation data, and dynamic early warning related data of the hot spot; The multi-loop hotspot data calculation component includes: Heat tracing temperature unit, heat tracing circuit coupling unit, and dynamic early warning decision unit; The multi-loop heat tracing hotspot data calculation component calculates the real-time temperature value of the i-th heat tracing hotspot at monitoring time t, the reference temperature of the i-th heat tracing hotspot, the thermal inertia hysteresis correction coefficient of the i-th heat tracing hotspot, the humidity penetration correction coefficient of the insulation layer of the i-th heat tracing hotspot, the wind speed correction coefficient of the i-th heat tracing hotspot, and the power supply fluctuation correction coefficient of the i-th heat tracing circuit, and performs weighted processing to output the temperature anomaly correction deviation of the heat tracing hotspot. The multi-loop heat tracing hotspot data calculation component calculates the heat tracing loop coupling risk level index by weighting the thermal coupling correction coefficient, load fluctuation correction coefficient, equipment aging correction coefficient, and maintenance cycle correction coefficient of the j-th loop in the loop coupling correlation data, and combining them with the temperature anomaly correction deviation of the heat tracing hotspot. The multi-loop heat tracing data calculation component is based on the correlation influence coefficient of the j-th loop, the inspection accessibility correction coefficient of the j-th loop, the system operating condition adaptive correction coefficient, and the meteorological disaster linkage correction coefficient in the dynamic early warning related data, and performs weighted processing, and combines the heat tracing loop coupling risk level index to output the system dynamic early warning decision index. Tiered early warning response components: It is used to receive the deviation of abnormal temperature of the heat tracing hotspot, the risk level index of the coupling of the heat tracing circuit, and the dynamic early warning decision index of the system, so as to implement multi-dimensional hierarchical early warning response.

2. The multi-loop hotspot wireless cluster monitoring and intelligent early warning system according to claim 1, characterized in that: The processing procedure of the temperature tracing unit is as follows: S11. By introducing the real-time temperature value of the i-th heat source at monitoring time t, and combining it with the reference temperature of the i-th heat source, the temperature deviation is determined. S12. By analyzing the time required for the temperature to rise from the initial value to the stable value when the heat tracing system is started, the degree of delay effect of the thermal inertia of the heat tracing pipe and the medium on the temperature change is reflected, so as to calculate the thermal inertia hysteresis correction coefficient of the i-th heat tracing point. S13. By obtaining the real-time relative humidity of the pipeline insulation layer, the degree of interference of the insulation layer moisture on temperature monitoring is reflected, so as to calculate the insulation layer humidity penetration correction coefficient of the i-th heat source. S14. By acquiring wind speed data near the heat tracing hotspot, the influence of ambient wind speed on heat tracing heat dissipation is reflected, so as to output the wind speed correction coefficient of the i-th heat tracing hotspot. S15. Obtain the real-time power supply voltage by acquiring the voltage transformer in the heat tracing circuit distribution box, and combine it with the rated power supply voltage of the heat tracing circuit to reflect the degree of influence of power supply voltage fluctuation on heat tracing power, so as to output the power supply fluctuation correction coefficient of the i-th heat tracing circuit. S16. Dynamically weight the output values ​​of the above steps to finally output the deviation of the temperature anomaly correction of the hot spot.

3. The multi-loop hotspot wireless cluster monitoring and intelligent early warning system according to claim 2, characterized in that: The processing procedure of the heat tracing circuit coupling unit is as follows: S21. The power supply fluctuation correction coefficient of the i-th heat tracing circuit is introduced into the coupling unit of this heat tracing circuit to convert the single-point temperature anomaly into a standardized deviation index, so as to facilitate the cumulative calculation of the risk of multiple heat tracing points in the circuit, and to combine it with the point weight of the i-th heat tracing point in the j-th circuit to achieve dynamic matching of weights. S22. By combining the average temperature anomaly deviation of adjacent circuits with the number of adjacent heat tracing circuits of the j-th circuit based on the summation function, the degree of heat conduction interference of adjacent heat tracing circuits on the current circuit is reflected, so as to output the thermal coupling correction coefficient of the j-th circuit. S23. By acquiring the real-time current in the heat tracing circuit distribution box and combining it with the rated current of the heat tracing cable, the degree of interference of the heat tracing cable current fluctuation on the circuit operation is reflected, so as to calculate the load fluctuation correction coefficient of the j-th circuit. S24. By obtaining the service life of the j-th circuit, analyze the impact of the aging degree of the heat tracing cable and sensor on the circuit operation, and output the equipment aging correction coefficient of the j-th circuit. S25. Based on the number of days the j-th circuit has been overdue for maintenance, analyze the impact of overdue maintenance on the operation of the circuit, and calculate the maintenance cycle correction coefficient of the j-th circuit. S26. The output values ​​of the above steps are dynamically weighted to finally output the heat tracing circuit coupling risk level index.

4. The multi-loop hotspot-following wireless cluster monitoring and intelligent early warning system according to claim 3, characterized in that: The processing procedure of the dynamic early warning decision unit is as follows: S31. By introducing a heat tracing circuit coupling risk level index, the abnormality of a single point in the circuit, the coupling effect, the electrical risk and the aging risk are integrated into a standardized circuit risk index, and its correlation influence coefficient with the j-th circuit is combined through a summation function. S32. By analyzing the shortest inspection time from the main control room to the circuit, the impact of inspection difficulty on circuit fault handling is quantified, so as to analyze the inspection accessibility correction coefficient of the j-th circuit. S33. By obtaining the real-time load rate, analyze the impact of the unit's operating load on the heat tracing demand, and output the system operating condition adaptive correction coefficient. S34. By matching with the warning level of external meteorology, the impact of external extreme weather on the heat tracing system is reflected, so as to output the meteorological disaster linkage correction coefficient. S35. Dynamically weight the output values ​​of the above steps to finally output the system dynamic early warning decision index.

5. The multi-loop hotspot wireless cluster monitoring and intelligent early warning system according to claim 1, characterized in that: The hierarchical early warning response component includes a loop-level risk early warning unit and a system-level dynamic early warning unit.

6. The multi-loop hotspot wireless cluster monitoring and intelligent early warning system according to claim 5, characterized in that: The loop-level risk early warning unit is specifically: If the coupling risk level index of the heat tracing circuit is ≥0.7, it indicates a high risk. At this time, an alarm will be immediately pushed to the mobile terminal of the operation and maintenance personnel, marking the circuit-level emergency risk and requiring them to arrive at the site within 1 hour. The system will automatically lock the three heat tracing points with the highest deviation in the circuit, highlight the location of the points on the electronic map, and simultaneously push the electrical parameters of the circuit and the operating status of adjacent circuits to help quickly locate the cause of the fault. If 0.4 ≤ heat tracing circuit coupling risk level index < 0.7, it indicates medium risk. At this time, an alarm is pushed to the plant inspection system, and the circuit is given special attention. It is clearly required that the two heat tracing points with the highest deviation in the circuit be checked first during the inspection. The system automatically retrieves the temperature and current data of the circuit in the past 24 hours to generate a circuit risk report, which is attached to the alarm information for the inspection personnel to refer to. If the risk level index of the heat tracing circuit coupling is less than 0.4, it indicates low risk. At this time, the system background automatically records the circuit risk data, maintains the regular inspection frequency of the circuit, does not need to actively push alarm information, and automatically generates a risk trend report of the circuit every month for updating the operation and maintenance ledger.

7. The multi-loop hotspot wireless cluster monitoring and intelligent early warning system according to claim 5, characterized in that: The system-level dynamic early warning unit includes a threshold division subunit and a measure corresponding subunit.

8. The multi-loop hotspot wireless cluster monitoring and intelligent early warning system according to claim 7, characterized in that: The threshold division subunit is specifically: If the system dynamic early warning decision index is less than 0.5, it indicates low system-level risk; If 0.5 ≤ System Dynamic Early Warning Decision Index < 0.9, it indicates medium-level system risk; If the system dynamic early warning decision index is ≥0.9, it indicates a high system-level risk; The specific sub-units corresponding to the measures are: When the system is at a low system-level risk, the system backend automatically aggregates the risk data of all loops, generates a daily system operation report, pushes it to the operation and maintenance management department for archiving, and maintains the system's regular monitoring frequency without the need to adjust the heat tracing system's operating parameters. When the system is at medium-level risk, an alarm is pushed to the person in charge of operation and maintenance management, and the system is marked as a risk of concern. It is clearly required that high-risk loops be dealt with first. The system will automatically start adaptive adjustment of operating conditions to fine-tune the heat tracing power of medium-risk loops, optimize the thermal environment of the loops, and reduce the accumulation of risks. When a high system-level risk is detected, a cross-departmental alarm is triggered and pushed to the heads of the operation and maintenance, process and equipment management departments. The alarm is marked as a system-level emergency warning, and the system automatically starts the emergency monitoring mode, increasing the monitoring frequency of high-risk loops from once per minute to once every 10 seconds, updating risk data in real time. The system can also remotely control the activation of the backup heat tracing branch of the high-risk loop to temporarily increase the heat tracing power and mitigate the risk.

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