A distributed optical fiber temperature online monitoring and early warning method, device, equipment and storage medium

Abstract

The application discloses a kind of distributed optical fiber temperature on-line monitoring early warning method, device, equipment and storage medium, by the region division of distributed optical fiber temperature measurement area, specific temperature threshold and alarm condition can be set for each region, to help accurately identify the area of temperature anomaly, reduce the probability of false alarm and miss report, and then realize the accuracy of early warning is improved. The influence of various environmental data on temperature anomaly index threshold is considered comprehensively, effectively solves the problem of miss report or false alarm in the prior art, improves the pertinence and accuracy of early warning signal. A hierarchical early warning strategy is used, different early warning levels are set according to the degree of temperature anomaly and the possible range of influence, and fine early warning is carried out.

Description

Technical Field

[0001] This invention relates to temperature detection technology, and more particularly to a distributed optical fiber online temperature monitoring and early warning method, device, equipment, and storage medium. Background Technology

[0002] Distributed fiber optic temperature sensing and early warning is based on the principle of single-mode fiber optic sensing and temperature measurement technology in communication optical cables. It utilizes the temperature effects of optical time-domain reflection and Raman scattering in optical fibers to detect the temperature at different locations within the fiber in real time, achieving distributed temperature measurement. Distributed fiber optic temperature measurement is widely used in fire prevention and disaster early warning systems for many important facilities or locations, such as urban underground power cables and pipelines, IDC data centers, oil pipelines, large power plants, integrated utility tunnels, tunnels, mines, buildings, and highway surfaces.

[0003] Currently, there are still some shortcomings in fiber optic temperature measurement. Specifically, most current distributed fiber optic temperature measurement systems improve measurement accuracy through methods such as fiber quality optimization, signal processing algorithm optimization, and splitter design optimization. These methods primarily focus on data acquisition and processing. However, in terms of generating and transmitting warning signals, existing distributed fiber optic temperature measurement systems mostly use a single threshold setting method. That is, when the temperature exceeds a certain preset threshold, the system issues a warning signal. While this method is simple and direct, it often lacks specificity and accuracy, easily leading to false alarms or missed alarms. Summary of the Invention

[0004] This invention provides a distributed optical fiber temperature online monitoring and early warning method, device, equipment, and storage medium to reduce the probability of false alarms and missed alarms, and improve the relevance and accuracy of early warning signals.

[0005] In a first aspect, the present invention provides a distributed optical fiber temperature online monitoring and early warning method, comprising:

[0006] The distributed optical fiber temperature measurement area is divided into multiple target monitoring areas;

[0007] Collect environmental data of the target monitoring area;

[0008] Find the interference level influencing factor corresponding to the environmental data from the fiber optic measurement database;

[0009] Based on the environmental data and the interference level influencing factors, calculate the temperature anomaly index interference level assessment index;

[0010] The threshold for temperature anomaly indicators is determined based on the interference level assessment index of the temperature anomaly indicators.

[0011] Collect the temperature of the target monitoring area, and determine the temperature anomaly index of the target monitoring area based on the temperature;

[0012] A temperature anomaly assessment index is calculated based on the temperature anomaly index threshold and the temperature anomaly index.

[0013] Early warnings are issued based on the temperature anomaly assessment index.

[0014] Optionally, the environmental data includes air convection coefficient, ambient temperature, and equipment density. The temperature anomaly index interference assessment index includes a temperature change rate interference assessment index, the calculation formula of which is as follows:

[0015]

[0016] In the formula, A represents the interference assessment index of the rate of temperature change of the i-th target monitoring area at the j-th time point. ij Q represents the air convection coefficient of the i-th target monitoring area at the j-th time point. ij ρ represents the ambient temperature of the i-th target monitoring area at the j-th time point. ij Let denot i represent the equipment density of the i-th target monitoring area at the j-th time monitoring point, A0 represent the critical air convection coefficient, Q0 represent the reference suitable ambient temperature, ρ0 represent the critical equipment density, ΓV1 represent the influence factor of air convection coefficient interference degree corresponding to the temperature change rate, ΓV2 represent the influence factor of ambient temperature interference degree corresponding to the temperature change rate, and ΓV3 represent the influence factor of equipment density interference degree corresponding to the temperature change rate. Let i represent the number of each target monitoring area, i = 1, 2, 3, ..., n, where n represents the total number of target monitoring areas, and j represent the number of each time monitoring point, j = 1, 2, 3, ..., m, where m represents the total number of time monitoring points.

[0017] Optionally, the temperature anomaly index interference assessment index also includes a temperature difference interference assessment index, the calculation formula of which is as follows:

[0018]

[0019] In the formula, ΓD1 represents the temperature difference interference level assessment index of the i-th target monitoring area at the j-th time monitoring point, ΓD2 represents the ambient temperature interference level influence factor corresponding to the temperature difference, and ΓD3 represents the equipment density interference level influence factor corresponding to the temperature difference.

[0020] Optionally, the temperature anomaly index interference level assessment index also includes an actual temperature interference level assessment index, the calculation formula of which is as follows:

[0021]

[0022] In the formula, ΓC1 represents the actual temperature interference level assessment index of the i-th target monitoring area at the j-th time monitoring point, ΓC2 represents the environmental temperature interference level influence factor corresponding to the actual temperature, and ΓC3 represents the equipment density interference level influence factor corresponding to the actual temperature.

[0023] Optionally, a temperature anomaly severity assessment index is calculated based on the temperature anomaly index threshold and the temperature anomaly index, including:

[0024] Extract the preset temperature anomaly assessment influence weighting factors corresponding to each temperature anomaly index from the optical fiber measurement database.

[0025] The quotient of the temperature anomaly index and the temperature anomaly index threshold is calculated to obtain the temperature anomaly quantification index.

[0026] By combining the temperature anomaly indicators corresponding to the various temperature anomaly indicators, a temperature anomaly degree assessment index is obtained.

[0027] Optionally, the formula for calculating the temperature anomaly assessment index is as follows:

[0028]

[0029] In the formula, δ i βV represents the temperature anomaly assessment index of the i-th target monitoring area at the current time point. i βD represents the rate of temperature change of the i-th target monitoring area at the current time point. i Let βC represent the temperature difference of the i-th target monitoring area at the current time point. i This represents the actual temperature of the i-th target monitoring area at the current time. This represents the threshold value for the rate of temperature change of the i-th target monitoring area at the current time point. This represents the temperature difference threshold of the i-th target monitoring area at the current time point. μ1 represents the actual temperature threshold of the i-th target monitoring area at the current time point, μ2 represents the temperature anomaly influence factor corresponding to the set temperature change rate, μ3 represents the temperature anomaly influence factor corresponding to the set temperature difference, and i represents the temperature anomaly influence factor corresponding to the set actual temperature. i represents the number of each target monitoring area, i = 1, 2, 3, ..., n, and n represents the total number of target monitoring areas.

[0030] Optionally, an early warning may be issued based on the temperature anomaly assessment index, including:

[0031] The anomaly level of the target monitoring area is determined based on the temperature anomaly assessment index.

[0032] The anomaly type in the target monitoring area is determined based on the aforementioned temperature anomaly indicators;

[0033] The system sends warning information, including the type of anomaly, to the user according to the warning strategy corresponding to the anomaly level.

[0034] Secondly, the present invention also provides a distributed optical fiber temperature online monitoring and early warning device, comprising:

[0035] The region division module is used to divide the distributed fiber optic temperature measurement area into multiple target monitoring areas;

[0036] The data acquisition module is used to collect environmental data of the target monitoring area;

[0037] The factor lookup module is used to find the interference level influence factor corresponding to the environmental data from the fiber optic measurement database.

[0038] The index calculation module is used to calculate the temperature anomaly index interference degree assessment index based on the environmental data and the interference degree influence factor.

[0039] The threshold calculation module is used to determine the threshold of the temperature anomaly index based on the interference degree evaluation index of the temperature anomaly index.

[0040] The indicator determination module is used to collect the temperature of the target monitoring area and determine the temperature anomaly indicator of the target monitoring area based on the temperature.

[0041] The assessment index calculation module is used to calculate the temperature anomaly degree assessment index based on the temperature anomaly index threshold and the temperature anomaly index.

[0042] The early warning module is used to issue early warnings based on the temperature anomaly assessment index.

[0043] Thirdly, the present invention also provides an electronic device, comprising:

[0044] One or more processors;

[0045] Storage device for storing one or more programs;

[0046] When the one or more programs are executed by the one or more processors, the one or more processors implement the distributed optical fiber temperature online monitoring and early warning method provided in the first aspect of the present invention.

[0047] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the distributed optical fiber temperature online monitoring and early warning method provided in the first aspect of the present invention.

[0048] This invention provides a distributed optical fiber temperature online monitoring and early warning method. The method divides the distributed optical fiber temperature measurement area into multiple target monitoring areas, collects environmental data for each target monitoring area, searches an optical fiber measurement database for interference level influencing factors corresponding to the environmental data, calculates a temperature anomaly index based on the environmental data and interference level influencing factors, determines a temperature anomaly index threshold based on the temperature anomaly index, collects the temperature of the target monitoring area, determines the temperature anomaly index for the target monitoring area based on the temperature, calculates a temperature anomaly index based on the temperature anomaly index threshold and the temperature anomaly index, and issues an early warning based on the temperature anomaly index. By dividing the distributed optical fiber temperature measurement area into regions, specific temperature thresholds and alarm conditions can be set for each region, thereby helping to accurately identify areas with temperature anomalies, reducing the probability of false alarms and missed alarms, and thus improving the accuracy of early warnings. By comprehensively considering the influence of various environmental data on the temperature anomaly index threshold, the method effectively solves the problem of missed or false alarms in existing technologies, improving the targeting and accuracy of early warning signals. A hierarchical early warning strategy is adopted, setting different early warning levels according to the degree of temperature anomaly and its potential impact range, thus providing refined early warning.

[0049] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart of a distributed optical fiber temperature online monitoring and early warning method provided in an embodiment of the present invention;

[0052] Figure 2 A graph showing the functional relationship between the temperature anomaly assessment index and the rate of temperature change;

[0053] Figure 3This is a schematic diagram of the structure of a distributed optical fiber temperature online monitoring and early warning device provided in an embodiment of the present invention;

[0054] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0055] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0056] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0057] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0058] Figure 1 This is a flowchart illustrating a distributed optical fiber temperature online monitoring and early warning method provided in this embodiment of the invention. This embodiment is applicable to situations where distributed optical fibers are used for temperature detection and early warning. The method can be executed by the distributed optical fiber temperature online monitoring and early warning device provided in this embodiment of the invention. This device can be implemented in software and / or hardware, and is typically configured in electronic devices, such as… Figure 1 As shown, the distributed optical fiber temperature online monitoring and early warning method specifically includes the following steps:

[0059] S101. Divide the distributed optical fiber temperature measurement area into multiple target monitoring areas.

[0060] In this embodiment of the invention, the distributed optical fiber temperature measurement area is divided into multiple target monitoring areas. The division of these areas requires consideration of the environmental conditions of each target monitoring area, such as temperature fluctuation range and the presence of heat or cold sources, to determine the rationality of the division. In one specific embodiment, historical data of the distributed optical fiber temperature measurement area is acquired, several location points are deployed, the temperature change rate of each location point is matched with the corresponding area radius, and the area is divided based on the matched area radius.

[0061] For example, the area of ​​each target monitoring zone is determined by the rate of temperature change in that zone. The greater the rate of temperature change, the more precise the temperature control required, and the smaller the area. Furthermore, areas with potential safety hazards, such as flammable and explosive sites, also require more precise delineation and monitoring. Zone delineation should consider the efficiency and accuracy of data processing and transmission to avoid data loss or confusion.

[0062] This embodiment divides the distributed fiber optic temperature measurement area into zones, allowing for the setting of specific temperature thresholds and alarm conditions for each zone. This helps to accurately identify areas with abnormal temperatures, reducing the probability of false alarms and missed alarms, thereby improving the accuracy of early warnings. Zone division clarifies the temperature monitoring needs and importance of each zone. For areas requiring precise temperature control and areas containing critical equipment, higher-precision fiber optic temperature measurement equipment and denser monitoring points can be configured to ensure accurate and timely temperature monitoring in these areas. Simultaneously, zone division helps to quickly locate fault sources, shorten fault handling time, and reduce the impact of faults on the system.

[0063] S102. Collect environmental data of the target monitoring area.

[0064] In this embodiment of the invention, environmental data for each target monitoring area is collected. For example, the environmental data may include air convection coefficient, ambient temperature, and equipment density.

[0065] For example, in this embodiment, the air convection coefficient of each target monitoring area can be monitored using an air monitoring station. The air monitoring station consists of an air sampler, sensors, and a data processor. The air sampler collects samples from the air through a sampling probe and converts the collected pollutant particles into monitorable electrical signals. At the same time, the sensors measure various physical and chemical parameters in the atmosphere, including temperature, humidity, and wind speed. The data processor monitors and analyzes the data sampled by the air sampler and sensors in real time to obtain the air convection coefficient of each target monitoring area.

[0066] The air convection coefficient directly affects the temperature distribution along an optical fiber. By monitoring the air convection coefficient, the temperature condition of the fiber at different locations can be more accurately assessed, providing more precise data support for the early warning system. Changes in the air convection coefficient affect the rate and range of temperature change in the optical fiber. In the early warning system, the early warning algorithm can be dynamically adjusted based on the real-time monitored air convection coefficient, improving the accuracy and timeliness of early warnings.

[0067] For example, in this embodiment, ambient temperature can be monitored using a temperature sensor or a distributed fiber optic temperature sensing system. Analysis of the ambient temperature monitoring data can reveal trends and patterns in temperature changes, allowing for more accurate setting of temperature anomaly thresholds. For instance, in industrial production, based on equipment operating conditions and ambient temperature changes, more reasonable temperature thresholds can be set to ensure safe equipment operation. Appropriate temperature anomaly threshold settings ensure the accuracy of the early warning system. If the threshold is set too high or too low, the early warning system may fail to respond promptly or experience frequent false alarms. By monitoring ambient temperature and adjusting the threshold, the accuracy of the early warning system can be improved, reducing false alarms and missed alarms.

[0068] For example, in this embodiment, the device density of each target monitoring area is analyzed. If the device density is too high, heat dissipation may be difficult. The thresholds of the constant temperature alarm, the area temperature difference alarm and the rapid temperature rise alarm are adjusted according to the device density to adapt to temperature changes under different heat dissipation conditions.

[0069] S103. Find the interference level influencing factor corresponding to the environmental data from the fiber optic measurement database.

[0070] In this embodiment of the invention, interference level influence factors for various environmental data are retrieved from the fiber optic measurement database, including air convection coefficient interference level influence factor, ambient temperature interference level influence factor, and equipment density interference level influence factor.

[0071] The fiber optic measurement database stores data related to fiber optic temperature measurement and early warning systems. This includes environmental interference factors for each temperature anomaly indicator, critical air convection coefficient, suitable ambient temperature, critical equipment density, threshold compensation parameters for each temperature anomaly indicator's interference assessment index range, reference temperature change rate threshold, reference temperature difference threshold, reference actual temperature threshold, detection cycle, temperature anomaly assessment weighting factors for each temperature anomaly indicator, temperature anomaly assessment index threshold, and temperature anomaly impact factors corresponding to temperature change rate, temperature difference, and actual temperature. Data in the fiber optic measurement database can be obtained by integrating external data sources, such as exchanging and sharing data with other sensors, monitoring equipment, or weather stations. Integrating external data sources requires data cleaning and verification to remove outliers, duplicates, or errors, and format conversion and standardization to ensure consistency with the fiber optic measurement data format.

[0072] S104. Calculate the interference degree assessment index of temperature anomaly index based on environmental data and interference degree influence factors.

[0073] In this embodiment of the invention, a temperature anomaly index interference level assessment index is calculated based on environmental data and interference level influencing factors. For example, in this embodiment, the temperature anomaly index includes the temperature change rate, temperature difference, and actual temperature.

[0074] Actual temperature refers to the temperature of each target monitoring area collected by the distributed fiber optic temperature sensing system. It is a parameter directly of interest to the system and is distinct from ambient temperature. Actual temperature is typically dynamic and influenced by various factors, such as ambient temperature, heat sources, and heat dissipation conditions. Ambient temperature, on the other hand, refers to the temperature of the air or other media surrounding each target monitoring area, representing the temperature conditions of the external environment in which the target monitoring area is located. Ambient temperature is relatively stable, but it may vary at different times and locations, influenced by factors such as weather, season, and geographical location.

[0075] For example, the temperature anomaly index interference assessment index includes the temperature change rate interference assessment index, the temperature difference interference assessment index, and the actual temperature interference assessment index.

[0076] For example, in one embodiment of the present invention, the formula for calculating the temperature change rate disturbance assessment index is as follows:

[0077]

[0078] In the formula, A represents the interference assessment index of the rate of temperature change of the i-th target monitoring area at the j-th time point. ijQ represents the air convection coefficient of the i-th target monitoring area at the j-th time point. ij ρ represents the ambient temperature of the i-th target monitoring area at the j-th time point. ij Let denot i represent the equipment density of the i-th target monitoring area at the j-th time monitoring point, A0 represent the critical air convection coefficient, Q0 represent the reference suitable ambient temperature, ρ0 represent the critical equipment density, ΓV1 represent the influence factor of air convection coefficient interference degree corresponding to the temperature change rate, ΓV2 represent the influence factor of ambient temperature interference degree corresponding to the temperature change rate, and ΓV3 represent the influence factor of equipment density interference degree corresponding to the temperature change rate. Let i represent the number of each target monitoring area, i = 1, 2, 3, ..., n, where n represents the total number of target monitoring areas, and j represent the number of each time monitoring point, j = 1, 2, 3, ..., m, where m represents the total number of time monitoring points.

[0079] Table 1 shows a data example of the temperature change rate interference assessment index. As shown in Table 1, in a specific embodiment, A0 = 1, Q0 = 25℃, and ρ0 = 50 units / km. 2 Let ΓV1 = 0.3, ΓV2 = 0.4, and ΓV3 = 0.3. To simplify the calculation, let... This indicates that the average temperature of each target monitoring area over the time series is 24℃. The temperature change rate interference assessment index is jointly determined by the air convection coefficient, ambient temperature, and equipment density. The smaller the air convection coefficient, the greater the deviation of the ambient temperature from the reference value; the greater the equipment density, the easier it is for the regional ambient temperature to rise, and the greater the corresponding temperature change rate interference assessment index.

[0080] Table 1

[0081]

[0082] For example, in one embodiment of the present invention, the formula for calculating the temperature difference interference degree assessment index is as follows:

[0083]

[0084] In the formula, ΓD1 represents the temperature difference interference level assessment index of the i-th target monitoring area at the j-th time monitoring point, ΓD2 represents the ambient temperature interference level influence factor corresponding to the temperature difference, and ΓD3 represents the equipment density interference level influence factor corresponding to the temperature difference.

[0085] For example, in one embodiment of the present invention, the formula for calculating the actual temperature interference level assessment index is as follows:

[0086]

[0087] In the formula, ΓC1 represents the actual temperature interference level assessment index of the i-th target monitoring area at the j-th time monitoring point, ΓC2 represents the environmental temperature interference level influence factor corresponding to the actual temperature, and ΓC3 represents the equipment density interference level influence factor corresponding to the actual temperature.

[0088] It should be understood that in this embodiment, the temperature change rate interference assessment index, the temperature difference interference assessment index, and the actual temperature interference assessment index are all quantitative indicators used to quantitatively assess the degree of influence of environmental factors on the temperature change rate, temperature difference, and actual temperature, respectively.

[0089] A higher temperature change rate interference assessment index indicates a greater influence of environmental factors on the rate of temperature change; a higher temperature difference interference assessment index indicates a greater influence of environmental factors on the temperature difference; and a higher actual temperature interference assessment index indicates a greater influence of environmental factors on the monitored actual temperature. The interference assessment indices for each temperature anomaly indicator are jointly determined by the air convection coefficient, ambient temperature, and equipment density. A smaller air convection coefficient and a greater deviation of the ambient temperature from the reference value, along with higher equipment density, result in higher interference assessment indices for each temperature anomaly indicator, indicating a greater need to adjust the warning thresholds for each temperature anomaly indicator based on environmental factors.

[0090] In a specific embodiment, the value range of the environmental data interference degree influence factor corresponding to each temperature anomaly index is between 0 and 1. The value of the influence factor in the database can be used to construct a comprehensive evaluation index system based on historical temperature anomaly index data. The relative importance of air convection coefficient, ambient temperature and equipment density to each temperature anomaly index is determined by the analytic hierarchy process. The corresponding influence factor is obtained based on the comprehensive evaluation index system.

[0091] In this embodiment, by quantitatively analyzing the impact of environmental factors on temperature monitoring, the trend of temperature change can be predicted more accurately, thereby establishing an accurate temperature prediction model and improving the accuracy of temperature prediction. Quantitative analysis helps to determine the key thresholds of temperature change, thereby enhancing the capabilities of the early warning system.

[0092] S105. Determine the threshold of temperature anomaly index based on the evaluation index of the degree of interference of temperature anomaly index.

[0093] For example, the interference degree evaluation index of each temperature anomaly index at each time monitoring point in each target monitoring area is matched with the temperature anomaly index threshold compensation parameter corresponding to the interference degree evaluation index interval of each temperature anomaly index stored in the optical fiber measurement database to obtain the temperature anomaly index threshold compensation parameter of each temperature anomaly index at each time monitoring point in each target monitoring area.

[0094] For example, the temperature anomaly index threshold compensation parameters include a reference temperature change rate threshold compensation parameter, a reference temperature difference threshold compensation parameter, and a reference actual temperature threshold compensation parameter. The temperature anomaly index threshold compensation parameters are used to correct the preset reference standard thresholds for each temperature anomaly index, thereby improving the accuracy of threshold warnings.

[0095] The reference standard thresholds for each temperature anomaly index are extracted from the fiber optic measurement database, including the reference temperature change rate threshold, the reference temperature difference threshold, and the reference actual temperature threshold. The compensation parameters for each temperature anomaly index threshold at each time point of each target monitoring area are summed with the reference standard thresholds for each temperature anomaly index to obtain the thresholds for each temperature anomaly index at each time point of each target monitoring area.

[0096] In one specific embodiment, by adjusting the threshold values ​​of various temperature anomalies in real time, the temperature warning threshold is dynamically adjusted according to the real-time changes in current environmental factors. Compared to a fixed warning threshold, this method more accurately reflects the true state of the current environment, thereby improving the accuracy and timeliness of warnings. Real-time adjustment of the temperature warning threshold can optimize resource allocation according to the needs of the current environment, helping to promptly identify and respond to potential risks brought about by temperature changes. By adjusting the warning threshold in a timely manner, the flexibility and adaptability of the warning system can be improved.

[0097] S106. Collect the temperature of the target monitoring area and determine the temperature anomaly index of the target monitoring area based on the temperature.

[0098] In this embodiment of the invention, the temperature of each target monitoring area is monitored and collected, and processed to obtain various temperature anomaly indicators for each target monitoring area at the current time point. These temperature anomaly indicators include the rate of temperature change, temperature difference, and actual temperature.

[0099] For example, the temperatures of each target monitoring area are acquired sequentially by a distributed fiber optic temperature sensing system. A temperature time series function for each target monitoring area is obtained through curve fitting, and the actual temperature of each target monitoring area at the current time point is extracted. For example, the optimal fit is determined by calculating the sum of squared residuals of the temperature time series function. The sum of squared residuals is the sum of the squares of the differences between all observed values ​​and the fitted values, where the observed values ​​refer to the actual temperatures in the time series, and the fitted values ​​refer to the temperatures obtained from the temperature time series function. Curve fitting is a mathematical method used to find a curve or mathematical function that best represents a given set of data points. It typically involves selecting a function form and then adjusting the parameters of the function to make the graph of the function as close as possible to or through the set of data points. In this embodiment, a polynomial regression fitting method is used to obtain the temperature time series function. Alternatively, plotting software (such as Excel, Python's matplotlib library, or other professional plotting software) can be used to plot the temperature time series curve.

[0100] The system retrieves a preset detection cycle from the fiber optic measurement database and, based on the current time point, extracts the temperature corresponding to the time point of the previous detection cycle. This is then processed to obtain the temperature change rate and temperature difference of each target monitoring area at the current time point. For example, this is based on the temperature time series function Q of each target monitoring area. i (t), processed to obtain the temperature change rate and temperature difference of each target monitoring area at the current time point, where Q i (t) represents the actual temperature of the i-th target monitoring area at time t, where t represents the time variable, t∈[t1,t2], t1 represents the start time of the monitoring cycle, and t2 represents the start time of the monitoring cycle. The numerical expression for the temperature difference is βD. i =|Q i (t1)-Q i The numerical expression for the rate of temperature change is (t2)|. In the formula, βD i βV represents the temperature difference of the i-th target monitoring area at the current time point. i The rate of temperature change in the i-th target monitoring area at the current time point is represented by T, and the length of the detection period is represented by T.

[0101] It should be understood that the detection cycle in this embodiment needs to be determined based on the specific needs of the system, the characteristics of temperature fluctuations, and real-time requirements, specifying the required time window for averaging the rate of temperature change and temperature difference. Understanding the characteristics of temperature fluctuations in the target monitoring area, such as fluctuation frequency and amplitude, is crucial for selecting an appropriate detection cycle to capture these changes. The length of the detection cycle is then determined based on real-time requirements.

[0102] In a specific embodiment of the present invention, by averaging the temperature over the detection period, short-term temperature fluctuations can be smoothed, noise interference reduced, and the accuracy of temperature change rate and temperature difference calculation improved. Averaging can reduce the impact of anomalies in individual data points on the overall results, improving the reliability of temperature monitoring. By selecting an appropriate detection period, the characteristics of temperature fluctuations in different target monitoring areas can be adapted, achieving more accurate temperature monitoring.

[0103] S107. Calculate the temperature anomaly assessment index based on the temperature anomaly index threshold and temperature anomaly index.

[0104] In this embodiment of the invention, a comprehensive assessment index of the degree of temperature anomaly in each target monitoring area at the current time point is calculated based on the temperature anomaly index threshold and the temperature anomaly index. This assessment index can reflect the changing trend of temperature anomalies, providing early warning before significant anomalies appear in the temperature anomaly index, thus improving the sensitivity of temperature anomaly warnings.

[0105] The temperature anomaly assessment index for each target monitoring area at the current time point is a quantitative index obtained through comprehensive analysis of the threshold values ​​and various temperature anomaly indicators. It is used to quantitatively assess the degree of temperature anomaly in each target monitoring area. For example, in one embodiment of the present invention, preset temperature anomaly assessment influence weighting factors corresponding to each temperature anomaly indicator are extracted from the fiber optic measurement database. The quotient of the temperature anomaly indicator and its threshold value is calculated to obtain the temperature anomaly quantitative index. Finally, the temperature anomaly degree assessment index is obtained by combining the temperature anomaly quantitative indices corresponding to each temperature anomaly indicator.

[0106] In one embodiment of the present invention, the formula for calculating the temperature anomaly assessment index is as follows:

[0107]

[0108] In the formula, δ i βV represents the temperature anomaly assessment index of the i-th target monitoring area at the current time point. i βD represents the rate of temperature change of the i-th target monitoring area at the current time point. i Let βC represent the temperature difference of the i-th target monitoring area at the current time point. i This represents the actual temperature of the i-th target monitoring area at the current time. This represents the threshold value for the rate of temperature change of the i-th target monitoring area at the current time point. This represents the temperature difference threshold of the i-th target monitoring area at the current time point. μ1 represents the actual temperature threshold of the i-th target monitoring area at the current time point, μ2 represents the temperature anomaly influence factor corresponding to the set temperature change rate, μ3 represents the temperature anomaly influence factor corresponding to the set temperature difference, and i represents the temperature anomaly influence factor corresponding to the set actual temperature. i represents the number of each target monitoring area, i = 1, 2, 3, ..., n, and n represents the total number of target monitoring areas.

[0109] This invention extracts preset temperature anomaly influencing factors corresponding to the rate of temperature change, temperature difference, and actual temperature from an optical fiber measurement database. The values ​​of these factors range from 0 to 1. By collecting historical temperature anomaly data, the invention uses the rate of temperature change, temperature difference, and actual temperature as independent variables, and a temperature anomaly severity assessment index as the dependent variable. Multiple regression analysis is then used to calculate the specific influencing factors of the rate of temperature change, temperature difference, and actual temperature on the assessment of the degree of temperature anomaly. In this embodiment, the multiple regression analysis assumes that the relationship between the independent and dependent variables can be approximated as linear through polynomial transformation.

[0110] Figure 2 A graph showing the functional relationship between the temperature anomaly assessment index and the rate of temperature change, such as... Figure 2 As shown, in one embodiment, the temperature difference, the actual temperature, and their warning thresholds are fixed values, meaning their values ​​do not change over time. When the rate of temperature change is constant, the functional relationship between the temperature anomaly assessment index and the temperature anomaly severity is shown by curve a; when When, the functional relationship between the rate of temperature change and the temperature anomaly assessment index is shown as curve b; when The functional relationship between the rate of temperature change and the temperature anomaly assessment index is shown by curve c. It should be understood that in this embodiment, the temperature anomaly assessment index is positively correlated with each temperature anomaly indicator; the larger the value of each temperature anomaly indicator, the larger the corresponding temperature anomaly assessment index, indicating a more abnormal temperature in the region. Simultaneously, the threshold values ​​of each temperature anomaly indicator also affect the temperature anomaly assessment index. When the values ​​of each temperature anomaly indicator are fixed, the larger the threshold values ​​of each temperature anomaly indicator, the closer each temperature anomaly indicator is to its normal range, and the smaller the corresponding temperature anomaly assessment index.

[0111] The formula for calculating the temperature anomaly assessment index comprehensively considers three factors: the rate of temperature change, the temperature difference, and the actual temperature, enabling a more comprehensive and accurate assessment of the degree of temperature anomaly. By setting different temperature anomaly influencing factors, the weights of the rate of temperature change, temperature difference, and actual temperature in the assessment can be adjusted according to actual needs, meeting different application scenarios and requirements, thus improving the flexibility and adaptability of the formula. Using the hyperbolic tangent function to map the temperature anomaly assessment index to the [0,1] interval makes the assessment results more intuitive, easier to understand, and easier to apply. Based on mathematical operations and function transformations, this formula provides rigorous mathematical reasoning and computational support for the assessment of temperature anomaly severity, contributing to improved accuracy and reliability of the assessment results.

[0112] S108. Early warning based on the temperature anomaly assessment index.

[0113] For example, in some embodiments of the present invention, the temperature anomaly assessment index of each target monitoring area at the current time point is compared with the temperature anomaly assessment index threshold stored in the optical fiber measurement database. If the temperature anomaly assessment index of a target monitoring area at the current time point is greater than or equal to the temperature anomaly assessment index threshold, then the target monitoring area is marked as a temperature anomaly area.

[0114] In other embodiments of the present invention, the number of temperature anomaly regions at the current time point within the distributed optical fiber temperature measurement area is counted and fed back, and the anomaly level and type of each temperature anomaly region are analyzed. An early warning method is selected based on the anomaly level and type of each temperature anomaly region. For example, for each temperature anomaly region, its anomaly level is determined based on a temperature anomaly severity assessment index. Several anomaly level ranges are set, such as slight anomaly, moderate anomaly, and severe anomaly. The temperature anomaly severity assessment index of each temperature anomaly region is compared with each anomaly level range to determine the anomaly level of each temperature anomaly region.

[0115] Anomaly types are typically determined based on the characteristics of temperature changes, including the rate of temperature change, the temperature difference, and whether the actual temperature exceeds a threshold. For example, a rapid rise or fall in temperature within a short period may indicate a potential safety hazard, such as a fire or leak; a large temperature difference between a certain area and adjacent areas may indicate an anomaly in that area, such as a pipe rupture or equipment failure. These characteristics are used to determine the anomaly type for each temperature anomaly area, such as a fire warning, leak warning, or equipment failure warning.

[0116] The system sends warning messages to users, including the type of anomaly, according to the warning strategy corresponding to the anomaly level. For example, based on the anomaly level and type of each temperature anomaly area, the system can select an appropriate warning method. For minor anomalies and equipment malfunctions, warning messages can be sent to relevant personnel via email, SMS, etc., for timely handling. For moderate and higher-level anomalies, as well as anomalies involving safety risks (such as fire or leakage), in addition to sending warning messages, the system can also trigger audible and visual alarms, initiate emergency response procedures, etc., to ensure timely response and handling. This invention, through area division and anomaly area statistics, can more efficiently conduct temperature warnings, helping to quickly understand the overall status of the current system. If there are a large number of anomaly areas, it may indicate a significant safety hazard in the system, requiring immediate emergency handling.

[0117] The distributed optical fiber temperature online monitoring and early warning method provided in this invention divides the distributed optical fiber temperature measurement area into multiple target monitoring areas, collects environmental data of the target monitoring areas, searches for interference level influencing factors corresponding to the environmental data from the optical fiber measurement database, calculates a temperature anomaly index interference level assessment index based on the environmental data and interference level influencing factors, determines a temperature anomaly index threshold based on the temperature anomaly index interference level assessment index, collects the temperature of the target monitoring areas, determines the temperature anomaly index of the target monitoring areas based on the temperature, calculates a temperature anomaly level assessment index based on the temperature anomaly index threshold and temperature anomaly index, and issues an early warning based on the temperature anomaly level assessment index. By dividing the distributed optical fiber temperature measurement area into regions, specific temperature thresholds and alarm conditions can be set for each region, thereby helping to accurately identify areas with temperature anomalies, reducing the probability of false alarms and missed alarms, and thus improving the accuracy of early warning. By comprehensively considering the influence of various environmental data on the temperature anomaly index threshold, the problem of missed alarms or false alarms in the prior art is effectively solved, improving the relevance and accuracy of the early warning signal. A hierarchical early warning strategy is adopted, setting different early warning levels according to the degree of temperature anomaly and the possible scope of impact, thereby providing refined early warning.

[0118] Figure 3 This is a schematic diagram of the structure of a distributed optical fiber temperature online monitoring and early warning device provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the distributed fiber optic temperature online monitoring and early warning device includes:

[0119] The region division module 201 is used to divide the distributed optical fiber temperature measurement area into multiple target monitoring areas;

[0120] Data acquisition module 202 is used to collect environmental data of the target monitoring area;

[0121] The factor lookup module 203 is used to look up the interference degree influence factor corresponding to the environmental data from the optical fiber measurement database.

[0122] Index calculation module 204 is used to calculate the temperature anomaly index interference degree assessment index based on the environmental data and the interference degree influence factor;

[0123] Threshold calculation module 205 is used to determine the threshold of the temperature anomaly index based on the interference degree evaluation index of the temperature anomaly index.

[0124] The indicator determination module 206 is used to collect the temperature of the target monitoring area and determine the temperature anomaly indicator of the target monitoring area based on the temperature.

[0125] The evaluation index calculation module 207 is used to calculate the temperature anomaly degree evaluation index based on the temperature anomaly index threshold and the temperature anomaly index.

[0126] The early warning module 208 is used to issue an early warning based on the temperature anomaly assessment index.

[0127] In some embodiments of the present invention, the environmental data includes air convection coefficient, ambient temperature, and equipment density, and the temperature anomaly index interference level assessment index includes a temperature change rate interference level assessment index, the calculation formula of which is as follows:

[0128]

[0129] In the formula, A represents the interference assessment index of the rate of temperature change of the i-th target monitoring area at the j-th time point. ij Q represents the air convection coefficient of the i-th target monitoring area at the j-th time point. ij ρ represents the ambient temperature of the i-th target monitoring area at the j-th time point. ij Let denot i represent the equipment density of the i-th target monitoring area at the j-th time monitoring point, A0 represent the critical air convection coefficient, Q0 represent the reference suitable ambient temperature, ρ0 represent the critical equipment density, ΓV1 represent the influence factor of air convection coefficient interference degree corresponding to the temperature change rate, ΓV2 represent the influence factor of ambient temperature interference degree corresponding to the temperature change rate, and ΓV3 represent the influence factor of equipment density interference degree corresponding to the temperature change rate. Let i represent the number of each target monitoring area, i = 1, 2, 3, ..., n, where n represents the total number of target monitoring areas, and j represent the number of each time monitoring point, j = 1, 2, 3, ..., m, where m represents the total number of time monitoring points.

[0130] In some embodiments of the present invention, the temperature anomaly index interference degree assessment index further includes a temperature difference interference degree assessment index, the calculation formula of which is as follows:

[0131]

[0132] In the formula, ΓD1 represents the temperature difference interference level assessment index of the i-th target monitoring area at the j-th time monitoring point, ΓD2 represents the ambient temperature interference level influence factor corresponding to the temperature difference, and ΓD3 represents the equipment density interference level influence factor corresponding to the temperature difference.

[0133] In some embodiments of the present invention, the temperature anomaly index interference degree assessment index further includes an actual temperature interference degree assessment index, the calculation formula of which is as follows:

[0134]

[0135] In the formula, ΓC1 represents the actual temperature interference level assessment index of the i-th target monitoring area at the j-th time monitoring point, ΓC2 represents the environmental temperature interference level influence factor corresponding to the actual temperature, and ΓC3 represents the equipment density interference level influence factor corresponding to the actual temperature.

[0136] In some embodiments of the present invention, the evaluation index calculation module 207 includes:

[0137] The weighting factor extraction submodule is used to extract the preset temperature anomaly assessment influence weighting factors corresponding to each temperature anomaly index from the optical fiber measurement database.

[0138] The calculation submodule is used to calculate the quotient of the temperature anomaly index and the temperature anomaly index threshold to obtain the temperature anomaly quantification index.

[0139] The comprehensive calculation submodule is used to integrate the temperature anomaly quantification indicators corresponding to each of the aforementioned temperature anomaly indicators to obtain a temperature anomaly degree assessment index.

[0140] In some embodiments of the present invention, the formula for calculating the temperature anomaly assessment index is as follows:

[0141]

[0142] In the formula, δ i βV represents the temperature anomaly assessment index of the i-th target monitoring area at the current time point. iβD represents the rate of temperature change of the i-th target monitoring area at the current time point. i Let βC represent the temperature difference of the i-th target monitoring area at the current time point. i This represents the actual temperature of the i-th target monitoring area at the current time. This represents the threshold value for the rate of temperature change of the i-th target monitoring area at the current time point. This represents the temperature difference threshold of the i-th target monitoring area at the current time point. μ1 represents the actual temperature threshold of the i-th target monitoring area at the current time point, μ2 represents the temperature anomaly influence factor corresponding to the set temperature change rate, μ3 represents the temperature anomaly influence factor corresponding to the set temperature difference, and i represents the temperature anomaly influence factor corresponding to the set actual temperature. i represents the number of each target monitoring area, i = 1, 2, 3, ..., n, and n represents the total number of target monitoring areas.

[0143] In some embodiments of the present invention, the early warning module 208 includes:

[0144] An anomaly level determination submodule is used to determine the anomaly level of the target monitoring area based on the temperature anomaly degree assessment index;

[0145] An anomaly type determination submodule is used to determine the anomaly type of the target monitoring area based on the temperature anomaly index;

[0146] The early warning submodule is used to send early warning information, including the type of abnormality, to the user according to the early warning strategy corresponding to the abnormality level.

[0147] The aforementioned distributed optical fiber temperature online monitoring and early warning device can execute the distributed optical fiber temperature online monitoring and early warning method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects of executing the distributed optical fiber temperature online monitoring and early warning method.

[0148] Figure 4 This is a schematic diagram of an electronic device provided for an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0149] like Figure 4As shown, the electronic device includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0150] Multiple components in the electronic device are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0151] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the distributed fiber optic temperature online monitoring and early warning method.

[0152] In some embodiments, the distributed fiber optic temperature online monitoring and early warning method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on an electronic device via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the distributed fiber optic temperature online monitoring and early warning method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the distributed fiber optic temperature online monitoring and early warning method by any other suitable means (e.g., by means of firmware).

[0153] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0154] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0155] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0156] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0157] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0158] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0159] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the distributed optical fiber temperature online monitoring and early warning method provided in any embodiment of this application.

[0160] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0161] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0162] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A distributed optical fiber temperature online monitoring and early warning method, characterized in that, include: The distributed optical fiber temperature measurement area is divided into multiple target monitoring areas; Collect environmental data of the target monitoring area; Find the interference level influencing factor corresponding to the environmental data from the fiber optic measurement database; Based on the environmental data and the interference degree influencing factors, the interference degree assessment index of the temperature anomaly index is calculated. The interference degree assessment index of the temperature anomaly index includes the interference degree assessment index of the temperature change rate, the interference degree assessment index of the temperature difference, and the interference degree assessment index of the actual temperature. The threshold for temperature anomaly indicators is determined based on the interference level assessment index of the temperature anomaly indicators. Collect the temperature of the target monitoring area, and determine the temperature anomaly index of the target monitoring area based on the temperature; A temperature anomaly assessment index is calculated based on the temperature anomaly index threshold and the temperature anomaly index. Early warnings are issued based on the temperature anomaly assessment index.

2. The distributed optical fiber temperature on-line monitoring and early warning method according to claim 1, characterized in that, The environmental data includes air convection coefficient, ambient temperature, and equipment density. The formula for calculating the temperature change rate interference assessment index is as follows: ； In the formula, represents the temperature change rate interference degree evaluation index of the ith target monitoring area at the jth time monitoring point, represents the air convection coefficient of the ith target monitoring area at the jth time monitoring point, represents the ambient temperature of the ith target monitoring area at the jth time monitoring point, represents the device density of the ith target monitoring area at the jth time monitoring point, represents the critical air convection coefficient, represents the reference suitable ambient temperature, represents the critical device density, represents the air convection coefficient interference degree influence factor corresponding to the temperature change rate, represents the ambient temperature interference degree influence factor corresponding to the temperature change rate, represents the device density interference degree influence factor corresponding to the temperature change rate, i represents the number of each target monitoring area, i = 1, 2, 3, …, n, n represents the total number of target monitoring areas, j represents the number of each time monitoring point, j = 1, 2, 3, …, m, and m represents the total number of time monitoring points. 3.The distributed optical fiber temperature on-line monitoring and early warning method according to claim 2, characterized in that, The formula for calculating the temperature difference interference assessment index is as follows: ； In the formula, represents the temperature difference interference degree evaluation index of the ith target monitoring area at the jth time monitoring point, represents the air convection coefficient interference degree influence factor corresponding to the temperature difference, represents the environmental temperature interference degree influence factor corresponding to the temperature difference, represents the equipment density interference degree influence factor corresponding to the temperature difference.

4. The distributed optical fiber temperature on-line monitoring and early warning method according to claim 2, characterized in that, The formula for calculating the actual temperature interference level assessment index is as follows: ； In the formula, represents the actual temperature interference degree evaluation index of the ith target monitoring area at the jth time monitoring point, represents the air convection coefficient interference degree influence factor corresponding to the actual temperature, represents the environmental temperature interference degree influence factor corresponding to the actual temperature, represents the equipment density interference degree influence factor corresponding to the actual temperature.

5. The distributed optical fiber temperature on-line monitoring and early warning method according to any one of claims 1-4, characterized in that, Based on the temperature anomaly index threshold and the temperature anomaly index, a temperature anomaly severity assessment index is calculated, including: Extract the preset temperature anomaly assessment influence weighting factors corresponding to each temperature anomaly index from the optical fiber measurement database. The quotient of the temperature anomaly index and the temperature anomaly index threshold is calculated to obtain the temperature anomaly quantification index. By combining the temperature anomaly indicators corresponding to the various temperature anomaly indicators, a temperature anomaly degree assessment index is obtained.

6. The distributed optical fiber temperature on-line monitoring and early warning method according to claim 5, characterized in that, The formula for calculating the temperature anomaly assessment index is as follows: ； In the formula, represents the temperature anomaly degree evaluation index of the ith target monitoring area at the current time point, represents the temperature change rate of the ith target monitoring area at the current time point, represents the temperature difference of the ith target monitoring area at the current time point, represents the actual temperature of the ith target monitoring area at the current time point, represents the temperature change rate threshold of the ith target monitoring area at the current time point, represents the temperature difference threshold of the ith target monitoring area at the current time point, represents the actual temperature threshold of the ith target monitoring area at the current time point, represents the temperature anomaly influence factor corresponding to the set temperature change rate, represents the temperature anomaly influence factor corresponding to the set temperature difference, represents the temperature anomaly influence factor corresponding to the set actual temperature, i represents the number of each target monitoring area, i = 1, 2, 3, …, n, and n represents the total number of target monitoring areas.

7. The distributed optical fiber temperature online monitoring and early warning method according to any one of claims 1-4, characterized in that, Early warnings are issued based on the aforementioned temperature anomaly assessment index, including: The anomaly level of the target monitoring area is determined based on the temperature anomaly assessment index. The anomaly type in the target monitoring area is determined based on the aforementioned temperature anomaly indicators; The system sends warning information, including the type of anomaly, to the user according to the warning strategy corresponding to the anomaly level.

8. A distributed optical fiber temperature online monitoring and early warning device, characterized in that, include: The region division module is used to divide the distributed fiber optic temperature measurement area into multiple target monitoring areas; The data acquisition module is used to collect environmental data of the target monitoring area; The factor lookup module is used to find the interference level influence factor corresponding to the environmental data from the fiber optic measurement database. The index calculation module is used to calculate the temperature anomaly index interference assessment index based on the environmental data and the interference degree influence factor. The temperature anomaly index interference assessment index includes the temperature change rate interference assessment index, the temperature difference interference assessment index, and the actual temperature interference assessment index. The threshold calculation module is used to determine the threshold of the temperature anomaly index based on the interference degree evaluation index of the temperature anomaly index. The indicator determination module is used to collect the temperature of the target monitoring area and determine the temperature anomaly indicator of the target monitoring area based on the temperature. The assessment index calculation module is used to calculate the temperature anomaly degree assessment index based on the temperature anomaly index threshold and the temperature anomaly index. The early warning module is used to issue early warnings based on the temperature anomaly assessment index.

9. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the distributed optical fiber temperature online monitoring and early warning method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the distributed optical fiber temperature online monitoring and early warning method as described in any one of claims 1-7.

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