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

Abstract

The invention discloses a distributed optical fiber temperature on-line monitoring and early warning method, device and equipment and a storage medium, a distributed optical fiber temperature measurement area is divided, and a specific temperature threshold value and an alarm condition can be set for each area, so that an area with abnormal temperature can be accurately identified, and the accuracy of temperature measurement is improved. And the probability of false alarm and missing alarm is reduced, so that the accuracy of early warning is improved. The influence of various environmental data on the temperature anomaly index threshold is comprehensively considered, the problem of missing report or false report in the prior art is effectively solved, and the pertinence and accuracy of the early warning signal are improved. A grading early warning strategy is adopted, different early warning levels are set according to the temperature abnormity degree and the possible influence range, and fine early warning is carried out.

Description

Technical Field

[0001] The present invention relates to temperature detection technology, and in particular to a distributed optical fiber temperature online monitoring and early warning method, device, equipment and storage medium. Background Art

[0002] Distributed optical fiber temperature sensing warning is based on the principle of single-mode optical fiber sensing monitoring and temperature measurement technology of communication optical cables. It uses the temperature effect of optical time domain reflection and optical fiber Raman scattering to detect the temperature of different positions of the optical fiber in real time and realize distributed temperature measurement. Distributed optical fiber temperature measurement is widely used in fire prevention and disaster warning protection of many important facilities or places such as urban power underground cables and pipelines, IDC data centers, oil pipelines, large power stations, integrated pipeline trenches, tunnels, mines, buildings, highway pavements, etc.

[0003] At present, there are still some deficiencies in the fiber optic temperature measurement. Specifically, most of the current distributed fiber optic temperature measurement systems use methods such as fiber quality optimization, signal processing algorithm optimization, and splitter design optimization to improve the measurement accuracy. These methods to improve measurement accuracy mainly focus on data collection and processing. In terms of the generation and transmission of warning signals, most of the existing distributed fiber optic temperature measurement systems use a single threshold setting method, that is, when the temperature exceeds a certain preset threshold, the system will issue a warning signal. Although this method is simple and direct, it often lacks pertinence and accuracy, and is prone to false alarms or missed alarms. Summary of the invention

[0004] The present invention provides a distributed optical fiber temperature online monitoring and early warning method, device, equipment and storage medium to reduce the probability of false alarm and missed alarm and improve the pertinence 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] Divide the distributed optical fiber temperature measurement area into multiple target monitoring areas;

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

[0008] Searching for interference degree influencing factors corresponding to the environmental data from an optical fiber measurement database;

[0009] Calculate the temperature anomaly index interference degree evaluation index based on the environmental data and the interference degree influencing factor;

[0010] Determining a temperature anomaly index threshold based on the temperature anomaly index interference degree evaluation index;

[0011] Collecting the temperature of the target monitoring area, and determining a temperature anomaly index of the target monitoring area based on the temperature;

[0012] Calculating a temperature anomaly degree evaluation index based on the temperature anomaly index threshold and the temperature anomaly index;

[0013] An early warning is issued based on the temperature anomaly degree assessment index.

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

[0015]

[0016] In the formula, A represents the interference degree evaluation index of the temperature change rate of the i-th target monitoring area at the j-th time monitoring point, ij represents the air convection coefficient of the i-th target monitoring area at the j-th time monitoring point, Q ij represents the ambient temperature of the i-th target monitoring area at the j-th monitoring point, ρ ij represents the equipment density of the i-th target monitoring area at the j-th time monitoring point, A0 represents the critical air convection coefficient, Q0 represents the reference suitable ambient temperature, ρ0 represents the critical equipment density, ΓV1 represents the air convection coefficient interference degree influence factor corresponding to the temperature change rate, ΓV2 represents the ambient temperature interference degree influence factor corresponding to the temperature change rate, ΓV3 represents the equipment 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, m represents the total number of time monitoring points.

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

[0018]

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

[0020] Optionally, the temperature anomaly indicator interference degree evaluation index also includes an actual temperature interference degree evaluation index, and the calculation formula of the actual temperature interference degree evaluation index is as follows:

[0021]

[0022] In the formula, It represents the actual temperature interference degree evaluation index of the i-th target monitoring area at the j-th time monitoring point, ΓC1 represents the air convection coefficient interference degree influence factor corresponding to the actual temperature, ΓC2 represents the ambient temperature interference degree influence factor corresponding to the actual temperature, and ΓC3 represents the equipment density interference degree influence factor corresponding to the actual temperature.

[0023] Optionally, calculating a temperature anomaly degree evaluation index based on the temperature anomaly index threshold and the temperature anomaly index includes:

[0024] Extracting temperature anomaly assessment impact weight factors corresponding to preset temperature anomaly indicators from the optical fiber measurement database;

[0025] Calculating the quotient of the temperature anomaly index and the temperature anomaly index threshold to obtain a temperature anomaly quantitative index;

[0026] The temperature anomaly degree assessment index is obtained by combining the temperature anomaly quantification indicators corresponding to the temperature anomaly indicators.

[0027] Optionally, the calculation formula of the temperature anomaly evaluation index is as follows:

[0028]

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

[0030] Optionally, issuing an early warning based on the temperature anomaly evaluation index includes:

[0031] Determining the abnormality level of the target monitoring area based on the temperature abnormality degree assessment index;

[0032] Determine the abnormality type of the target monitoring area based on the temperature abnormality index;

[0033] The warning information including the abnormality type is sent to the user through the warning strategy corresponding to the abnormality level.

[0034] In a second aspect, the present invention further provides a distributed optical fiber temperature online monitoring and early warning device, comprising:

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

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

[0037] A factor search module, used to search for interference degree influencing factors corresponding to the environmental data from an optical fiber measurement database;

[0038] An index calculation module, used to calculate a temperature anomaly index interference degree evaluation index based on the environmental data and the interference degree influencing factor;

[0039] A threshold calculation module, used to determine a temperature anomaly index threshold based on the temperature anomaly index interference degree evaluation index;

[0040] An index determination module, used for collecting the temperature of the target monitoring area and determining a temperature anomaly index of the target monitoring area based on the temperature;

[0041] An evaluation index calculation module, used to calculate a temperature anomaly degree evaluation index based on the temperature anomaly index threshold and the temperature anomaly index;

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

[0043] In a third aspect, the present invention further provides an electronic device, comprising:

[0044] one or more processors;

[0045] A 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] In a fourth aspect, the present invention further 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] The distributed optical fiber temperature online monitoring and early warning method provided by the present invention divides the distributed optical fiber temperature measurement area into multiple target monitoring areas, collects environmental data of the target monitoring area, searches for the interference degree influencing factor corresponding to the environmental data from the optical fiber measurement database, calculates the temperature anomaly index interference degree evaluation index based on the environmental data and the interference degree influencing factor, determines the temperature anomaly index threshold based on the temperature anomaly index interference degree evaluation index, collects the temperature of the target monitoring area, determines the temperature anomaly index of the target monitoring area based on the temperature, calculates the temperature anomaly degree evaluation index based on the temperature anomaly index threshold and the temperature anomaly index, and issues an early warning based on the temperature anomaly degree evaluation index. By dividing the distributed optical fiber temperature measurement area into regions, a specific temperature threshold and alarm condition can be set for each region, thereby helping to accurately identify the region with temperature anomaly, reducing the probability of false alarm and missed alarm, and thus achieving improved accuracy of early warning. Comprehensively considering the influence of multiple environmental data on the temperature anomaly index threshold, effectively solves the problem of missed alarm or false alarm in the prior art, and improves the pertinence and accuracy of the early warning signal. A hierarchical early warning strategy is adopted, and different early warning levels are set according to the degree of temperature anomaly and the possible impact range, and early warning is refined.

[0049] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

[0052] Figure 2 It is a functional relationship diagram of the temperature anomaly assessment index and the temperature change rate;

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

[0054] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0055] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0056] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work 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 and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0058] Figure 1 A flowchart of a distributed optical fiber temperature online monitoring and early warning method provided by an embodiment of the present invention. This embodiment is applicable to the case where distributed optical fibers are used for temperature detection and temperature early warning. The method can be executed by a distributed optical fiber temperature online monitoring and early warning device provided by an embodiment of the present invention. The device can be implemented by software and / or hardware, and is usually configured in an electronic device, 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 a distributed optical fiber temperature measurement area into multiple target monitoring areas.

[0060] In an embodiment of the present invention, the distributed optical fiber temperature measurement area is divided into multiple target monitoring areas. The regional division needs to consider the environmental conditions of each target monitoring area, such as the temperature fluctuation range, whether there is a heat source or a cold source, etc., to determine the rationality of the regional division. In a specific embodiment, the historical data of the distributed optical fiber temperature measurement area is obtained, a number of position points are deployed, the temperature change rate of each position point is matched with the regional radius corresponding to each temperature change rate, and the regional division is performed according to the regional radius obtained by matching.

[0061] For example, the area of ​​each target monitoring area is determined by the temperature change rate of the target monitoring area. The greater the temperature change rate, the more precise the temperature control is required and the smaller the area. At the same time, for areas with potential safety hazards, such as flammable and explosive places, more precise division and monitoring are also required. The division of areas should take into account the efficiency and accuracy of data processing and transmission to avoid data loss or confusion.

[0062] In this embodiment, the distributed optical fiber temperature measurement area is divided into regions, and specific temperature thresholds and alarm conditions can be set for each region, which helps to accurately identify areas with abnormal temperatures and reduce the probability of false alarms and missed alarms, thereby improving the accuracy of early warnings. Through regional division, the temperature monitoring needs and importance of each area can be clarified. For areas where precise temperature control is required and areas where key equipment is located, higher-precision optical fiber temperature measurement equipment and more dense monitoring points can be configured to ensure that the temperature of these areas is accurately and timely monitored. At the same time, regional division can help quickly locate the source of the fault, shorten the fault handling time, and reduce the impact of the fault on the system.

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

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

[0065] Exemplarily, in this embodiment, the air convection coefficient of each target monitoring area can be monitored through an air detection station. The air detection station is composed of an air collector, a sensor and a data processor. The air collector collects samples from the air through a sampling probe and converts the collected pollutant particles into a monitorable electrical signal. At the same time, the sensor is used to measure various physical and chemical parameters in the atmosphere, including temperature, humidity, wind speed, etc. The data processor monitors and analyzes the data sampled by the air collector and the sensor 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 the optical fiber. By monitoring the air convection coefficient, the temperature condition of the optical fiber at different locations can be more accurately evaluated, providing more accurate data support for the early warning system. The change of the air convection coefficient will affect the speed and range of the change of the optical fiber temperature. In the early warning system, according to the real-time monitoring of the air convection coefficient, the early warning algorithm can be dynamically adjusted to improve the accuracy and timeliness of the early warning.

[0067] Exemplarily, in this embodiment, the ambient temperature can be monitored by a temperature sensor or a distributed optical fiber temperature sensing system. By analyzing the ambient temperature monitoring data, the trend and law of temperature change can be understood, so as to more accurately set the temperature anomaly index threshold. For example, in industrial production, a more reasonable temperature threshold can be set according to the operation of the equipment and the change of the ambient temperature to ensure the safe operation of the equipment. The appropriate setting of the temperature anomaly index threshold can 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 in time or frequently report false alarms. By monitoring the ambient temperature and adjusting the threshold, the accuracy of the early warning system can be improved and false alarms and missed alarms can be reduced.

[0068] Illustratively, in this embodiment, the equipment density of each target monitoring area is analyzed. Too high equipment density may cause heat dissipation difficulties. The thresholds of fixed temperature alarm, regional temperature difference alarm and rapid temperature rise alarm are adjusted according to the needs of equipment density to adapt to temperature changes under different heat dissipation conditions.

[0069] S103, searching for interference degree influencing factors corresponding to the environmental data from the optical fiber measurement database.

[0070] In the embodiment of the present invention, the interference degree influencing factors of various environmental data are searched from the optical fiber measurement database, including the air convection coefficient interference degree influencing factor, the ambient temperature interference degree influencing factor and the equipment density interference degree influencing factor.

[0071] The optical fiber measurement database is used to store optical fiber temperature measurement warning related data, including the interference degree influencing factors of each environmental data corresponding to each temperature anomaly index, the critical air convection coefficient, the reference suitable ambient temperature, the critical equipment density, the temperature anomaly index threshold compensation parameters corresponding to the interference degree evaluation index interval of each temperature anomaly index, the reference temperature change rate threshold, the reference temperature difference threshold, the reference actual temperature threshold, the detection cycle, the temperature anomaly evaluation influence weight factor corresponding to each temperature anomaly index, the temperature anomaly degree evaluation index threshold, and the temperature anomaly influence factors corresponding to the temperature change rate, temperature difference and actual temperature. The data in the optical fiber measurement database can be obtained by integrating external data sources, such as exchanging and sharing data with other sensors, monitoring equipment or meteorological stations. The integration of external data sources requires cleaning and verification of data, removal of abnormal values, duplicate values ​​or erroneous data, and format conversion and standardization of external data to keep it consistent with the optical fiber measurement data format.

[0072] S104. Calculate the temperature anomaly indicator interference degree evaluation index based on the environmental data and the interference degree influencing factor.

[0073] In an embodiment of the present invention, the temperature anomaly index interference degree evaluation index is calculated based on the environmental data and the interference degree influencing factor. Exemplarily, in an embodiment of the present invention, the temperature anomaly index includes the temperature change rate, the temperature difference and the actual temperature.

[0074] The actual temperature refers to the temperature of each target monitoring area collected by the distributed fiber optic temperature sensing system. It is a parameter of direct concern to the system, which is different from the ambient temperature. The actual temperature usually changes dynamically and is affected by many factors, such as ambient temperature, heat source, and heat dissipation conditions. The ambient temperature refers to the temperature of the air or other medium surrounding each target monitoring area, representing the temperature condition of the external environment where the target monitoring area is located. The ambient temperature is relatively stable, but it may vary at different times and locations, and is affected by many factors such as weather, season, and geographical location.

[0075] Exemplarily, the temperature anomaly indicator interference degree assessment index includes a temperature change rate interference degree assessment index, a temperature difference interference degree assessment index, and an actual temperature interference degree assessment index.

[0076] Exemplarily, in one embodiment of the present invention, the calculation formula of the temperature change rate interference degree evaluation index is as follows:

[0077]

[0078] In the formula, A represents the interference degree evaluation index of the temperature change rate of the i-th target monitoring area at the j-th time monitoring point, ijrepresents the air convection coefficient of the i-th target monitoring area at the j-th time monitoring point, Q ij represents the ambient temperature of the i-th target monitoring area at the j-th monitoring point, ρ ij represents the equipment density of the i-th target monitoring area at the j-th time monitoring point, A0 represents the critical air convection coefficient, Q0 represents the reference suitable ambient temperature, ρ0 represents the critical equipment density, ΓV1 represents the air convection coefficient interference degree influence factor corresponding to the temperature change rate, ΓV2 represents the ambient temperature interference degree influence factor corresponding to the temperature change rate, ΓV3 represents the equipment 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, m represents the total number of time monitoring points.

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

[0080] Table 1

[0081]

[0082] Exemplarily, in one embodiment of the present invention, the calculation formula of the temperature difference interference degree evaluation index is as follows:

[0083]

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

[0085] Exemplarily, in one embodiment of the present invention, the calculation formula of the actual temperature interference degree evaluation index is as follows:

[0086]

[0087] In the formula, It represents the actual temperature interference degree evaluation index of the i-th target monitoring area at the j-th time monitoring point, ΓC1 represents the air convection coefficient interference degree influence factor corresponding to the actual temperature, ΓC2 represents the ambient temperature interference degree influence factor corresponding to the actual temperature, and ΓC3 represents the equipment density interference degree influence factor corresponding to the actual temperature.

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

[0089] The larger the temperature change rate interference degree assessment index is, the greater the impact of environmental factors on the temperature change rate is. The larger the temperature difference interference degree assessment index is, the greater the impact of environmental factors on the temperature difference is. The larger the actual temperature interference degree assessment index is, the greater the impact of environmental factors on the actual monitored temperature is. The interference degree assessment index of each temperature anomaly indicator is jointly determined by the air convection coefficient, ambient temperature and equipment density. The smaller the air convection coefficient is, the more the ambient temperature deviates from the reference value, and the greater the equipment density is, the greater the corresponding interference degree assessment index of each temperature anomaly indicator is, indicating that the warning threshold of each temperature anomaly indicator needs to be adjusted according to environmental factors.

[0090] In a specific embodiment, the value range of the interference degree influencing factor of each environmental data corresponding to each temperature anomaly indicator is between 0 and 1. The value of the influencing factor in the database can construct a comprehensive evaluation index system based on the historical temperature anomaly indicator data, and determine the relative importance of the air convection coefficient, ambient temperature and equipment density to each temperature anomaly indicator through the hierarchical analysis method, and obtain the corresponding influencing factor according to the comprehensive evaluation index system.

[0091] In this embodiment, by quantitatively analyzing the impact of environmental factors on temperature monitoring, the temperature change trend 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 threshold of temperature change, thereby enhancing the capabilities of the early warning system.

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

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

[0094] Exemplarily, the temperature anomaly index threshold compensation parameters include reference temperature change rate threshold compensation parameters, reference temperature difference threshold compensation parameters and reference actual temperature threshold compensation parameters. The temperature anomaly index threshold compensation parameters are used to correct the preset standard thresholds of each temperature anomaly index to improve the accuracy of threshold warnings.

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

[0096] In a specific embodiment, by adjusting the thresholds of each temperature anomaly indicator in real time, the temperature warning threshold is dynamically adjusted according to the real-time changes of the current environmental factors, which can more accurately reflect the real situation of the current environment compared to the fixed warning threshold, thereby improving the accuracy and timeliness of the warning. Real-time adjustment of the temperature warning threshold can optimize resource allocation according to the needs of the current environment, help to timely discover and respond to potential risks brought about by temperature changes, and by timely adjusting the warning threshold, 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 the embodiment of the present invention, the temperature of each target monitoring area is monitored and collected, and each temperature anomaly index of each target monitoring area at the current time point is obtained by processing, wherein the temperature anomaly index includes the temperature change rate, the temperature difference and the actual temperature.

[0099] Exemplarily, the temperature of each target monitoring area collected by the distributed optical fiber temperature sensing system is obtained in chronological order, and the temperature time series function of each target monitoring area is obtained by curve fitting, and the actual temperature of each target monitoring area at the current time point is extracted. Exemplarily, the best fit is determined by calculating the residual sum of squares of the temperature time series function, and the residual sum of squares is the sum of squares of the difference between all observed values ​​and fitted values, where the observed value refers to the actual temperature on the time series, and the fitted value refers to the temperature obtained according to the temperature time series function. Curve fitting is a mathematical method for finding a curve or mathematical function that best represents a given set of data points, usually involving the selection of a function form, and then adjusting the parameters of the function so that the function graph passes through or approaches the data point set as much as possible. In this embodiment, the temperature time series function is obtained by using the fitting method of polynomial regression, and a drawing software (such as Excel, Python's matplotlib library or other professional drawing software) can also be used to draw the temperature time series curve.

[0100] The preset detection cycle is obtained from the optical fiber measurement database, and based on the current time point, the temperature corresponding to the time point of the previous detection cycle length is extracted, and the temperature change rate and temperature difference of each target monitoring area at the current time point are obtained after processing. Exemplarily, according to the temperature time series function Q of each target monitoring area i (t), and the temperature change rate and temperature difference of each target monitoring area at the current time point are obtained by processing, where Q i (t) represents the actual temperature of the i-th target monitoring area at time t, t represents the time variable, t∈[t1,t2], t1 represents the starting time point of the detection cycle, and t2 represents the starting time point of the detection cycle. The numerical expression of the temperature difference is βD i =|Q i (t1)-Q i (t2)|, the numerical expression of the temperature change rate is Where, βD i represents the temperature difference of the i-th target monitoring area at the current time point, βV i It represents the temperature change rate of the i-th target monitoring area at the current time point, and T represents the length of the detection cycle.

[0101] It should be understood that the detection cycle in this embodiment needs to determine how long the time window is needed to average the temperature change rate and temperature difference according to the specific needs of the system, the temperature fluctuation characteristics and the real-time requirements. Understand the characteristics of the temperature fluctuation in the target monitoring area, such as the fluctuation frequency and amplitude, so as to select a suitable detection cycle to capture these changes, and determine the length of the detection cycle according to the real-time requirements.

[0102] In a specific embodiment of the present invention, by averaging the temperature within the detection period, short-term temperature fluctuations can be smoothed, noise interference can be reduced, and the accuracy of temperature change rate and temperature difference calculation can be improved. Averaging can reduce the impact of a single data point anomaly on the overall result and improve the reliability of temperature monitoring. By selecting a suitable detection period, the characteristics of temperature fluctuations in different target monitoring areas can be adapted to achieve more accurate temperature monitoring.

[0103] S107. Calculate a temperature anomaly degree evaluation index based on the temperature anomaly index threshold and the temperature anomaly index.

[0104] In the embodiment of the present invention, based on the temperature anomaly index threshold and the temperature anomaly index, the temperature anomaly degree evaluation index of each target monitoring area at the current time point is comprehensively calculated. The temperature anomaly degree evaluation index of each target monitoring area at the current time point can feedback the change trend of temperature anomaly, which is used for early warning before the temperature anomaly index appears obviously abnormal, and improves the sensitivity of temperature anomaly warning.

[0105] The temperature anomaly evaluation index of each target monitoring area at the current time point is a quantitative index obtained by comprehensively analyzing each temperature anomaly index threshold and each temperature anomaly index, and is used to quantitatively evaluate the temperature anomaly degree of each target monitoring area. Exemplarily, in one embodiment of the present invention, the temperature anomaly evaluation influence weight factor corresponding to each preset temperature anomaly index is extracted 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 quantitative index, and the temperature anomaly quantitative index corresponding to each temperature anomaly index is combined to obtain the temperature anomaly degree evaluation index.

[0106] In one embodiment of the present invention, the calculation formula of the temperature anomaly evaluation index is as follows:

[0107]

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

[0109] The present invention extracts the temperature anomaly influence factors corresponding to the preset temperature change rate, temperature difference and actual temperature from the optical fiber measurement database, the value range of the temperature anomaly influence factors corresponding to the temperature change rate, temperature difference and actual temperature is between 0 and 1, and by collecting temperature anomaly historical data, the temperature change rate, temperature difference and actual temperature are used as independent variables, and the temperature anomaly degree evaluation index is used as the dependent variable, and the specific influence factors of the temperature change rate, temperature difference and actual temperature on the temperature anomaly degree evaluation are calculated by multiple regression analysis. In this embodiment, the multiple regression analysis assumes that the relationship between the independent variable and the dependent variable can be close to a linear relationship through polynomial transformation.

[0110] Figure 2 It is a functional relationship diagram of the temperature anomaly assessment index and the temperature change rate, such as Figure 2 As shown, in one embodiment, the temperature difference and the actual temperature and their warning thresholds are fixed values, that is, the values ​​do not change with time. When , the functional relationship between the temperature change rate and the temperature anomaly evaluation index is shown in curve a; when When , the functional relationship between the temperature change rate and the temperature anomaly evaluation index is shown in curve b; when When , the functional relationship between the temperature change rate and the temperature anomaly degree assessment index is shown in curve c. It should be understood that in this embodiment, the temperature anomaly degree assessment index is positively correlated with each temperature anomaly index. The larger the value of each temperature anomaly index, the larger the corresponding temperature anomaly degree assessment index, indicating that the regional temperature is more abnormal. At the same time, the threshold value of each temperature anomaly index will also affect the temperature anomaly degree assessment index. When the value of each temperature anomaly index is fixed, the larger the threshold value of each temperature anomaly index, the closer each temperature anomaly index is to the normal value range, and the smaller the corresponding temperature anomaly degree assessment index.

[0111] The calculation formula of the temperature anomaly assessment index comprehensively considers three factors: temperature change rate, temperature difference and actual temperature, and can more comprehensively and accurately assess the degree of temperature anomaly. By setting different temperature anomaly influencing factors, the weights of the temperature change rate, temperature difference and actual temperature in the assessment can be adjusted according to actual needs to meet different application scenarios and needs, thereby improving the flexibility and adaptability of the formula. The hyperbolic tangent function is used to map the temperature anomaly assessment index to the [0,1] interval, making the assessment results more intuitive, easy to understand and apply. Based on mathematical operations and function transformations, this formula provides rigorous mathematical reasoning and calculation support for the assessment of the degree of temperature anomaly, which helps to improve the accuracy and reliability of the assessment results.

[0112] S108. Issue an early warning based on the temperature anomaly evaluation index.

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

[0114] In other embodiments of the present invention, the number of temperature anomaly areas at the current time point in the distributed optical fiber temperature measurement area is counted for feedback, and the anomaly level and anomaly type of each temperature anomaly area are analyzed to obtain an early warning method according to the anomaly level and anomaly type of each temperature anomaly area. Exemplarily, for each temperature anomaly area, its anomaly level is determined according to the temperature anomaly degree assessment index. Several anomaly level intervals are set, such as slight anomaly, moderate anomaly, severe anomaly, etc., and the temperature anomaly degree assessment index of each temperature anomaly area is compared with each anomaly level interval to determine the anomaly level of each temperature anomaly area.

[0115] The type of anomaly is usually determined based on the characteristics of temperature changes, including the temperature change rate, temperature difference, and whether the actual temperature exceeds the threshold. For example, if the temperature rises or falls rapidly in a short period of time, this may indicate a potential safety hazard, such as fire or leakage; if there is a large temperature difference between an area and an adjacent area, this may indicate an anomaly in that area, such as a pipe rupture or equipment failure. Based on these characteristics, the anomaly type of each temperature abnormal area is determined, such as fire warning, leakage warning, equipment failure warning, etc.

[0116] The warning information including the abnormal type is sent to the user through the early warning strategy corresponding to the abnormal level. Exemplarily, according to the abnormal level and abnormal type of each temperature abnormal area, the system can select a suitable early warning method. For minor abnormalities and equipment failure types, early warning information can be sent to relevant personnel by email, text message, etc. for timely processing. For moderate abnormalities and higher-level abnormalities, as well as abnormalities involving safety risks (such as fire, leakage), in addition to sending early warning information, the system can also trigger sound and light alarms, start emergency response processes, etc., to ensure timely response and disposal. The present invention can perform temperature early warning more efficiently through regional division and abnormal area statistics, which helps to quickly understand the overall status of the current system. If the number of abnormal areas is large, it may mean that there are major safety hazards in the system and emergency processing is required immediately.

[0117] The distributed optical fiber temperature online monitoring and early warning method provided by the embodiment of the present invention divides the distributed optical fiber temperature measurement area into multiple target monitoring areas, collects environmental data of the target monitoring area, searches for the interference degree influencing factor corresponding to the environmental data from the optical fiber measurement database, calculates the temperature anomaly index interference degree evaluation index based on the environmental data and the interference degree influencing factor, determines the temperature anomaly index threshold based on the temperature anomaly index interference degree evaluation index, collects the temperature of the target monitoring area, determines the temperature anomaly index of the target monitoring area based on the temperature, calculates the temperature anomaly degree evaluation index based on the temperature anomaly index threshold and the temperature anomaly index, and issues an early warning based on the temperature anomaly degree evaluation index. By dividing the distributed optical fiber temperature measurement area into regions, a specific temperature threshold and alarm condition can be set for each region, thereby facilitating accurate identification of the temperature anomaly area, reducing the probability of false alarms and missed alarms, and thus achieving improved accuracy of early warning. Comprehensively considering the impact of multiple environmental data on the temperature anomaly index threshold, effectively solves the problem of missed alarms or false alarms in the prior art, and improves the pertinence and accuracy of early warning signals. A hierarchical early warning strategy is adopted, and different early warning levels are set according to the degree of temperature anomaly and the possible impact range, and early warning is refined.

[0118] Figure 3 A schematic diagram of the structure of a distributed optical fiber temperature online monitoring and early warning device provided by an embodiment of the present invention, such as Figure 3 As shown, the distributed optical fiber temperature online monitoring and early warning device includes:

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

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

[0121] A factor search module 203 is used to search the interference degree influencing factor corresponding to the environmental data from the optical fiber measurement database;

[0122] An index calculation module 204 is used to calculate a temperature anomaly index interference degree evaluation index based on the environmental data and the interference degree influencing factor;

[0123] A threshold calculation module 205, configured to determine a temperature anomaly index threshold based on the temperature anomaly index interference degree evaluation index;

[0124] An index determination module 206 is used to collect the temperature of the target monitoring area and determine a temperature anomaly index of the target monitoring area based on the temperature;

[0125] An evaluation index calculation module 207, configured to calculate a 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 degree assessment index.

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

[0128]

[0129] In the formula, A represents the interference degree evaluation index of the temperature change rate of the i-th target monitoring area at the j-th time monitoring point, ij represents the air convection coefficient of the i-th target monitoring area at the j-th time monitoring point, Q ij represents the ambient temperature of the i-th target monitoring area at the j-th monitoring point, ρ ij represents the equipment density of the i-th target monitoring area at the j-th time monitoring point, A0 represents the critical air convection coefficient, Q0 represents the reference suitable ambient temperature, ρ0 represents the critical equipment density, ΓV1 represents the air convection coefficient interference degree influence factor corresponding to the temperature change rate, ΓV2 represents the ambient temperature interference degree influence factor corresponding to the temperature change rate, ΓV3 represents the equipment 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, m represents the total number of time monitoring points.

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

[0131]

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

[0133] In some embodiments of the present invention, the temperature anomaly indicator interference degree evaluation index also includes an actual temperature interference degree evaluation index, and the calculation formula of the actual temperature interference degree evaluation index is as follows:

[0134]

[0135] In the formula, It represents the actual temperature interference degree evaluation index of the i-th target monitoring area at the j-th time monitoring point, ΓC1 represents the air convection coefficient interference degree influence factor corresponding to the actual temperature, ΓC2 represents the ambient temperature interference degree influence factor corresponding to the actual temperature, and ΓC3 represents the equipment density interference degree influence factor corresponding to the actual temperature.

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

[0137] A weight factor extraction submodule, used to extract the temperature anomaly assessment impact weight factors corresponding to each preset temperature anomaly index from the optical fiber measurement database;

[0138] A calculation submodule, used for calculating the quotient of the temperature anomaly index and the temperature anomaly index threshold to obtain a temperature anomaly quantitative index;

[0139] The comprehensive calculation submodule is used to comprehensively calculate the temperature anomaly quantitative indicators corresponding to each of the temperature anomaly indicators to obtain a temperature anomaly degree evaluation index.

[0140] In some embodiments of the present invention, the calculation formula of the temperature anomaly evaluation index is as follows:

[0141]

[0142] In the formula, δ i represents the temperature anomaly evaluation index of the i-th target monitoring area at the current time point, βV irepresents the temperature change rate of the i-th target monitoring area at the current time point, βD i represents the temperature difference of the i-th target monitoring area at the current time point, βC i represents the actual temperature of the i-th target monitoring area at the current time point, represents the temperature change rate threshold of the i-th target monitoring area at the current time point, represents the temperature difference threshold of the i-th target monitoring area at the current time point, represents the actual temperature threshold of the i-th target monitoring area at the current time point, μ1 represents the temperature anomaly impact factor corresponding to the set temperature change rate, μ2 represents the temperature anomaly impact factor corresponding to the set temperature difference, μ3 represents the temperature anomaly impact factor corresponding to the set actual temperature, i represents the number of each target monitoring area, i=1,2,3,...,n, 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 abnormality level determination submodule, used to determine the abnormality level of the target monitoring area based on the temperature abnormality degree assessment index;

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

[0146] The early warning submodule is used to send early warning information including the abnormal type to the user through the early warning strategy corresponding to the abnormal level.

[0147] The above-mentioned distributed optical fiber temperature online monitoring and early warning device can execute the distributed optical fiber temperature online monitoring and early warning method provided by the embodiment 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 A schematic diagram of the structure 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 may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, 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 examples and are not intended to limit the implementation of the present invention described and / or required herein.

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

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

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

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

[0153] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0154] Computer programs for implementing 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

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

[0156] To provide interaction with a user, the systems and techniques described herein may 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0157] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0158] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

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

[0160] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0161] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0162] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A distributed optical fiber temperature online monitoring and early warning method, It is characterized in that include: Divide the distributed optical fiber temperature measurement area into multiple target monitoring areas; Collecting environmental data of the target monitoring area; Searching for interference degree influencing factors corresponding to the environmental data from an optical fiber measurement database; Calculate the temperature anomaly index interference degree evaluation index based on the environmental data and the interference degree influencing factor; Determining a temperature anomaly index threshold based on the temperature anomaly index interference degree evaluation index; Collecting the temperature of the target monitoring area, and determining a temperature anomaly index of the target monitoring area based on the temperature; Calculating a temperature anomaly degree evaluation index based on the temperature anomaly index threshold and the temperature anomaly index; An early warning is issued based on the temperature anomaly degree assessment index.

2. According to claim 1, the distributed optical fiber temperature online monitoring and early warning method, It is characterized in that The environmental data includes air convection coefficient, ambient temperature and equipment density. The temperature anomaly index interference degree evaluation index includes temperature change rate interference degree evaluation index. The calculation formula of temperature change rate interference degree evaluation index is as follows: In the formula, A represents the interference degree evaluation index of the temperature change rate of the i-th target monitoring area at the j-th time monitoring point, ij represents the air convection coefficient of the i-th target monitoring area at the j-th time monitoring point, Q ij represents the ambient temperature of the i-th target monitoring area at the j-th monitoring point, ρ ij A represents the device density of the i-th target monitoring area at the j-th time monitoring point, 0 represents the critical air convection coefficient, Q 0 Indicates the reference suitable ambient temperature, ρ 0 represents the critical equipment density, ΓV 1 Indicates the influence factor of the air convection coefficient interference degree corresponding to the temperature change rate, ΓV 2 Indicates the influence factor of ambient temperature interference degree corresponding to the temperature change rate, ΓV 3 It represents the influence factor of the equipment density interference degree 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, m represents the total number of time monitoring points.

3. The distributed optical fiber temperature online monitoring and early warning method according to claim 2, It is characterized in that The temperature anomaly index interference degree evaluation index also includes a temperature difference interference degree evaluation index, and the calculation formula of the temperature difference interference degree evaluation index is as follows: In the formula, It represents the temperature difference interference evaluation index of the i-th target monitoring area at the j-th time monitoring point, ΓD 1 Indicates the influence factor of the degree of interference of air convection coefficient corresponding to the temperature difference, ΓD 2 Indicates the influence factor of ambient temperature interference degree corresponding to the temperature difference, ΓD 3 Indicates the influence factor of the equipment density interference degree corresponding to the temperature difference.

4. The distributed optical fiber temperature online monitoring and early warning method according to claim 2, It is characterized in that The temperature anomaly index interference degree evaluation index also includes an actual temperature interference degree evaluation index, and the calculation formula of the actual temperature interference degree evaluation index is as follows: In the formula, represents the actual temperature interference evaluation index of the i-th target monitoring area at the j-th time monitoring point, ΓC 1 Indicates the influence factor of the air convection coefficient interference degree corresponding to the actual temperature, ΓC 2 Indicates the influence factor of the ambient temperature interference degree corresponding to the actual temperature, ΓC 3 Indicates the device density interference factor corresponding to the actual temperature.

5. The distributed optical fiber temperature online monitoring and early warning method according to any one of claims 1 to 4, It is characterized in that Calculating a temperature anomaly degree evaluation index based on the temperature anomaly index threshold and the temperature anomaly index includes: Extracting temperature anomaly assessment impact weight factors corresponding to preset temperature anomaly indicators from the optical fiber measurement database; Calculating the quotient of the temperature anomaly index and the temperature anomaly index threshold to obtain a temperature anomaly quantitative index; The temperature anomaly degree assessment index is obtained by combining the temperature anomaly quantification indicators corresponding to the temperature anomaly indicators.

6. The distributed optical fiber temperature online monitoring and early warning method according to claim 5, It is characterized in that The calculation formula of the temperature anomaly assessment index is as follows: In the formula, δ i represents the temperature anomaly evaluation index of the i-th target monitoring area at the current time point, βV i represents the temperature change rate of the i-th target monitoring area at the current time point, βD i represents the temperature difference of the i-th target monitoring area at the current time point, βC i represents the actual temperature of the i-th target monitoring area at the current time point, represents the temperature change rate threshold of the i-th target monitoring area at the current time point, represents the temperature difference threshold of the i-th target monitoring area at the current time point, represents the actual temperature threshold of the i-th target monitoring area at the current time point, μ 1 Indicates the temperature anomaly impact factor corresponding to the set temperature change rate, μ 2 Indicates the temperature anomaly impact factor corresponding to the set temperature difference, μ 3 It represents the temperature anomaly impact 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 to 4, It is characterized in that Providing an early warning based on the temperature anomaly assessment index includes: Determining the abnormality level of the target monitoring area based on the temperature abnormality degree assessment index; Determine the abnormality type of the target monitoring area based on the temperature abnormality index; The warning information including the abnormality type is sent to the user through the warning strategy corresponding to the abnormality level.

8. A distributed optical fiber temperature online monitoring and early warning device, It is characterized in that include: A region division module is used to divide the distributed optical fiber temperature measurement area into multiple target monitoring areas; A data acquisition module, used to collect environmental data of the target monitoring area; A factor search module, used to search for interference degree influencing factors corresponding to the environmental data from an optical fiber measurement database; An index calculation module, used to calculate a temperature anomaly index interference degree evaluation index based on the environmental data and the interference degree influencing factor; A threshold calculation module, used to determine a temperature anomaly index threshold based on the temperature anomaly index interference degree evaluation index; An index determination module, used for collecting the temperature of the target monitoring area and determining a temperature anomaly index of the target monitoring area based on the temperature; An evaluation index calculation module, used to calculate a temperature anomaly degree evaluation index based on the temperature anomaly index threshold and the temperature anomaly index; The early warning module is used to issue an early warning based on the temperature anomaly degree assessment index.

9. An electronic device, It is characterized in that include: one or more processors; A 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, It is characterized in that When the program is executed by a processor, the distributed optical fiber temperature online monitoring and early warning method as described in any one of claims 1 to 7 is implemented.

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