Optical cable early warning management and control method and early warning system based on DAS

Abstract

The invention relates to a DAS-based optical cable early warning management and control method and early warning system, and the method comprises the steps: collecting the monitoring data of an optical cable in real time through a DAS monitoring terminal, and transmitting the monitoring data to a pre-trained optical cable early warning model; the optical cable early warning model extracts feature information from the monitoring data in real time, performs feature recognition on the feature information, judges whether the optical cable has a safety risk, and obtains time information of the current monitoring data and calculates positioning information when the judgment result is that the risk exists; pre-stored environment data and real-time environment data of the position where the positioning information is located are obtained and sent to the optical cable early warning model; and the optical cable early warning model calls a security event judgment rule, generates security event information based on the pre-stored environment data, the real-time environment data and the feature information, and sends the security event information to the operation and maintenance terminal. The method and the device have the effect of improving the efficiency and the accuracy of safety event risk identification and positioning in optical cable monitoring.

Description

Technical Field

[0001] The present application relates to the technical field of optical cables, and in particular to an optical cable early warning control method and early warning system based on DAS. Background Art

[0002] DAS technology realizes real-time and continuous monitoring of acoustic vibration by analyzing the phase change of backward Rayleigh scattered light in optical fiber. Using optical fiber as a sensor array can cover long-distance optical cable lines and has high sensitivity and multi-dimensional signal characteristics.

[0003] The application of DAS technology in communication optical cable security early warning also includes the identification and positioning of optical cable security events. It monitors the vibration and sound signals along the optical fiber to determine whether the optical cable has security risks such as external force damage and human interference, and locates the risk position through the propagation time and characteristics of the signal in the optical fiber, so as to achieve the purpose of early warning and control of the optical cable.

[0004] At present, the early warning identification of optical cable safety incidents and the positioning identification of risk locations are generally carried out separately through different functional systems, and even the data is acquired and integrated by staff from different functional units. The overall operating efficiency is low and deviations are prone to occur during the data integration process, resulting in reduced accuracy, resulting in poor early warning control of optical cable safety, so it needs to be improved. Summary of the invention

[0005] In order to improve the efficiency and accuracy of risk identification and positioning of security incidents in optical cable monitoring, and to enhance the effect of early warning and control of optical cables, the present application provides an optical cable early warning and control method and early warning system based on DAS.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A DAS-based optical cable early warning control method comprises the following steps: The DAS monitoring terminal collects the monitoring data of the optical cable in real time and sends the monitoring data to the pre-trained optical cable early warning model. The monitoring data includes vibration and sound signals along the optical fiber in the optical cable. The optical cable early warning model extracts feature information from the monitoring data in real time, performs feature recognition on the feature information, determines whether there is a safety risk in the optical cable, and outputs the judgment result, which includes whether there is a risk or not; When the judgment result is that there is a risk, the optical cable early warning model obtains the time information of the current monitoring data and calculates the positioning information; Obtaining pre-stored environmental data and real-time environmental data at the location of the positioning information and sending them to the optical cable early warning model; The optical cable early warning model retrieves the security event judgment rules and generates security event information based on pre-stored environmental data, real-time environmental data and feature information. The security event information includes the event name and location information. Send security event information to the operation and maintenance terminal.

[0007] By adopting the above technical solution, the pre-trained optical cable early warning model is used to receive the monitoring data collected in real time by the DAS monitoring terminal. First, it is determined whether there is a risk through the detection data, and when there is a risk, the location calculation of the risk is completed at the same time. Furthermore, the optical cable early warning model also recognizes the pre-stored environmental conditions and real-time environmental conditions of the risk location through positioning information, and comprehensively judges the specific security event information occurring at the optical cable risk location by combining the environmental conditions and the optical fiber vibration and sound signals, and pushes the security event information to the operation and maintenance terminal. The judgment of the security event information is completed by independent analysis of a single model, and a comprehensive judgment is made by using multi-source data such as sound, vibration, pre-stored environmental data, real-time environmental data, and machine learning. The output security event information is more accurate, which improves the efficiency and accuracy of risk identification and positioning of security events in optical cable monitoring, so as to achieve the purpose of optimizing the performance of optical cable early warning management and control.

[0008] Optionally, the optical cable early warning model extracts feature information from the monitoring data in real time, performs feature recognition on the feature information, determines whether there is a safety risk in the optical cable, and outputs a judgment result, which includes whether there is a risk or not, including: The optical cable early warning model extracts characteristic information from the monitoring data in real time, including amplitude information, frequency information and waveform information of vibration and sound signals; Obtain the distribution of amplitude information, frequency information and waveform information and perform segment cutting to obtain several feature segments; Compare each feature segment with a pre-stored distribution feature image representing the risk; When the similarity between the feature segment and the pre-stored distribution feature is greater than or equal to the similarity threshold, the feature segment is judged to have a security risk. When the similarity between the feature segment and the stored distribution feature is less than the similarity threshold, the feature segment is judged to have no risk.

[0009] By adopting the above technical solution, after obtaining the distribution of the amplitude, frequency and waveform of the vibration and sound signals, image feature comparison is performed through machine learning, and the characteristics of the amplitude, frequency and waveform that pose safety risks can be identified. The optical cable early warning model continuously receives monitoring data, segments the feature information according to the pulsed light signal emitted by the DAS, and uses segmented comparison to accurately identify the feature segment that poses a risk, thereby accurately locating the feature segment.

[0010] Optionally: When the judgment result is that there is a risk, the optical cable early warning model obtains the time information of the current monitoring data and calculates the positioning information, including: When the judgment result of the characteristic segment is that there is a risk, the optical cable early warning model obtains the collection time and reception time of the current characteristic segment to obtain time difference information; The positioning information of the current feature segment is calculated based on the time difference information.

[0011] By adopting the above technical solution, by obtaining the collection time and reception time of the characteristic segment judged to have risks, the time when the DAS monitoring terminal sends the pulse light signal of this segment and the reception time when the pulse light reflected in the risk characteristic segment reaches the DAS monitoring terminal are known. Since the speed of light is known, the distance between the current risk characteristic segment and the DAS monitoring terminal can be calculated, thereby obtaining the positioning information of the risk characteristic segment.

[0012] Optionally, the step of acquiring the pre-stored environmental data and the real-time environmental data of the location where the positioning information is located and sending the data to the optical cable early warning model includes: Based on the positioning information, the pre-stored environmental data of the location of the positioning information is obtained from a preset database, the pre-stored environmental data including topographic information, optical cable laying information, etc.; Send a query request to the cloud server to obtain real-time environmental data at the location of the positioning information. The real-time environmental data includes real-time climate information, real-time environmental change information, and monitoring information.

[0013] By adopting the above technical solution, the burial environment and depth of the optical cable at the location of the risky characteristic segment, such as the soil quality and surrounding buildings and landforms, can be obtained through pre-stored information, providing background information for the judgment of security event information, and then the climate temperature and humidity at the location of the characteristic segment, environmental change information such as natural disasters, and monitoring information such as the picture taken at the location of the optical cable can be obtained through real-time environmental data, providing real-time judgment factors for the judgment of security event information, thereby improving the accuracy of security event information judgment.

[0014] Optionally, the optical cable early warning model retrieves security event judgment rules and generates security event information based on pre-stored environmental data, real-time environmental data and feature information, the security event information including event name and location information, including: The optical cable early warning model retrieves the security event judgment rules, obtains several distribution features with the highest similarity to the feature segment, and identifies and selects the event information pre-bound to the distribution features; From the selected event information, select the event information with the highest matching degree with the pre-stored environment data and the real-time environment data as the event name in the security event information; Generate security event information by combining event name and location information.

[0015] By adopting the above technical scheme, since the distribution of the feature segment may be similar to several distribution features at the same time, for example, the vibration frequency caused by external construction and artificial cutting is relatively similar, the event information corresponding to several distribution features is screened out. In order to judge the security event information more accurately, the environment provided by the pre-stored environmental data and the real-time environmental data is combined with the distribution of the feature segment for comprehensive judgment. The environment can be used more accurately to identify the probability or possibility of various event information, and then the event name in the security event information is generated, and the judgment is more accurate.

[0016] Optionally, the event information with the highest matching degree with the pre-stored environment data and the real-time environment data is selected from the filtered multiple event information as the event name in the security event information, including: Acquire several formation conditions pre-associated with the filtered event information, where a single event information is associated with one or more formation conditions; Match all the formation conditions of the event information with the pre-stored environment data and the real-time environment data one by one, and record the number of matches of the formation conditions in each event information; The event information with the largest number of matching conditions with the pre-stored environmental data and the real-time environmental data is selected as the event name in the security event information.

[0017] By adopting the above technical solution, in the process of matching the event information corresponding to the feature segment with the pre-stored environment data and the real-time environment data, the formation conditions of the event information are matched with the pre-stored environment data and the real-time environment data to determine whether the formation conditions can be achieved on the basis of the pre-stored environment and the real-time environment. If so, it is determined to be a match; if it cannot be achieved or the probability of achieving it is very small, it is determined to be a mismatch. By judging the formation conditions of several event information, the event information with the highest probability of occurring in the pre-stored environment and the real-time environment is screened out as the event name of the security event information.

[0018] Optionally, the event information whose screening conditions match the largest number of pre-stored environment data and real-time environment data is used as the event name in the security event information, including: When the event information with the largest number of matching conditions with the pre-stored environment data and the real-time environment data includes two or more, the monitoring information in the real-time environment data is obtained; Event information whose formation conditions match the monitoring information is obtained as the event name in the security event information.

[0019] By adopting the above technical solution, monitoring equipment is installed at preset positions on the optical cable line, such as locations where risks frequently occur, so that when the judgment is vague, monitoring information can be retrieved to obtain the most intuitive on-site images, thereby improving the accuracy of security event information judgment.

[0020] The second object of the invention is achieved by the following technical solutions: An optical cable early warning system based on DAS, comprising: The data acquisition module is used for the DAS monitoring terminal to collect the monitoring data of the optical cable in real time and send the monitoring data to the pre-trained optical cable early warning model. The monitoring data includes vibration and sound signals along the optical fiber in the optical cable; The risk judgment module is used for the optical cable early warning model to extract feature information from the monitoring data in real time, and to perform feature recognition on the feature information to determine whether there is a safety risk in the optical cable, and output the judgment result, which includes whether there is a risk or not; The positioning calculation module is used for the optical cable early warning model to obtain the time information of the current monitoring data and calculate the positioning information when the judgment result is that there is a risk; An environmental identification module is used to obtain pre-stored environmental data and real-time environmental data of the location where the positioning information is located and send them to the optical cable early warning model; An event generation module is used for the optical cable early warning model to retrieve security event judgment rules and generate security event information based on pre-stored environmental data, real-time environmental data and feature information. The security event information includes event name and location information. The information sending module is used to send security event information to the operation and maintenance terminal.

[0021] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned DAS-based optical cable early warning management and control method when executing the computer program.

[0022] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned DAS-based optical cable early warning management and control method are implemented.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: 1. Combine environmental conditions, fiber vibration, and sound signals to comprehensively judge specific security event information at the risk point of the optical cable, and push the security event information to the operation and maintenance terminal. The judgment of security event information is completed by independent analysis of a single model, and a comprehensive judgment is made using multi-source data such as sound, vibration, pre-stored environmental data, real-time environmental data, and machine learning. The output security event information is more accurate, which improves the efficiency and accuracy of risk identification and positioning of security events in optical cable monitoring, so as to achieve the purpose of optimizing the early warning and control performance of optical cables; 2. Through machine learning to compare image features, the characteristics of amplitude, frequency and waveform that pose safety risks can be identified. The optical cable early warning model continuously receives monitoring data, segments the feature information according to the pulsed light signal emitted by the DAS, and uses segmented comparison to accurately identify the feature segment with risks, thereby accurately locating the feature segment. 3. The pre-stored information can be used to obtain the buried environment and depth of the optical cable at the location of the risky characteristic segment, providing background information for the judgment of security event information. The climate temperature and humidity at the location of the characteristic segment can be obtained through real-time environmental data, providing real-time judgment factors for the judgment of security event information, thereby improving the accuracy of security event information judgment; 4. Match the formation conditions of the event information with the pre-stored environment data and real-time environment data to determine whether the formation conditions can be achieved based on the pre-stored environment and real-time environment, and filter out the event information with the highest probability of occurring in the pre-stored environment and real-time environment as the event name of the security event information. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of an embodiment of an optical cable early warning control method based on DAS of the present application; Figure 2 This is a flowchart for implementing step S20 in an embodiment of a DAS-based optical cable early warning control method of the present application; Figure 3 This is a flowchart for implementing step S50 in an embodiment of a DAS-based optical cable early warning control method of the present application; Figure 4 This is a principle block diagram of a computer device of the present application. DETAILED DESCRIPTION

[0025] The following is combined with Figure 1-4 This application is described in further detail.

[0026] In one embodiment, if Figure 1 As shown, the present application discloses a DAS-based optical cable early warning control method, which specifically includes the following steps: S10: The DAS monitoring terminal collects monitoring data of the optical cable in real time and sends the monitoring data to the pre-trained optical cable early warning model. The monitoring data includes vibration and sound signals along the optical fiber in the optical cable. In this embodiment, the DAS monitoring terminal refers to a device that uses distributed acoustic wave sensing technology to perform remote monitoring, and the monitoring data is the vibration of the optical fiber line in the optical cable, the sound signal and the corresponding propagation time information.

[0027] The optical cable early warning model is a model trained by the CNN neural network to determine whether there are risks in the monitoring data and generate security event information based on the risks.

[0028] S20: The optical cable early warning model extracts feature information from the monitoring data in real time, performs feature recognition on the feature information, determines whether there is a safety risk in the optical cable, and outputs a judgment result, which includes whether there is a risk or not; In this embodiment, the feature information refers to the vibration features and sound features of the vibration and sound signals, and the feature recognition is the feature pair recognition performed by the optical cable warning model through machine learning.

[0029] Safety risks include external damage, natural environmental impacts, and human interference. External damage includes the destruction and theft of optical cables, such as the sound and vibration caused by digging, cutting or other physical actions. Natural environmental impacts include natural disasters, such as the vibration caused by landslides, earthquakes, and tsunamis. Human interference includes vibration and sound generated by construction activities near optical cables.

[0030] Specifically, refer to Figure 2 , step S20 comprises the steps of: S21: The optical cable early warning model extracts characteristic information from the monitoring data in real time, including amplitude information, frequency information and waveform information of vibration and sound signals; S22: Obtain the distribution of amplitude information, frequency information, and waveform information and perform segment cutting to obtain a number of feature segments; S23: performing a similarity comparison between each feature segment and a pre-stored distribution feature image representing the risk; S24: When the similarity between the feature segment and the pre-stored distribution feature is greater than or equal to the similarity threshold, the feature segment is judged to have a security risk; when the similarity between the feature segment and the stored distribution feature is less than the similarity threshold, the feature segment is judged to have no risk.

[0031] In this embodiment, segment cutting is based on the frequency of the detection pulse light emitted by the DAS monitoring terminal to the optical fiber, and when comparing features, the distribution of amplitude, frequency, and waveform trend is compared. The similarity threshold is a custom setting.

[0032] S30: When the judgment result is that there is a risk, the optical cable early warning model obtains the time information of the current monitoring data and calculates the positioning information; In this embodiment, the time information is the time propagation information, including the acquisition time and the receiving time of the current characteristic segment. The acquisition time is the time when the DAS monitoring terminal sends the pulse light signal of this segment, and the receiving time is the time when the pulse light reflected by the characteristic segment reaches the DAS monitoring terminal. The difference between the two is the time difference information. Specifically, when the judgment result of the characteristic segment is that there is a risk, the speed of light is known. Based on the time difference information, the distance between the DAS monitoring terminal and the optical cable where the risk exists is obtained by multiplying the speed of light by the time difference information and then dividing by 2, and the positioning information of the current characteristic segment can be calculated.

[0033] S40: obtaining pre-stored environmental data and real-time environmental data of the location where the positioning information is located and sending them to the optical cable early warning model; In this embodiment, the pre-stored environmental data refers to the environmental information of the buried environment of the optical cable segment and the buried information of the optical cable pre-stored in the database. The real-time environmental data refers to the real-time environmental information of the buried environment of the optical cable segment.

[0034] Specifically, step S40 includes the following steps: S41: Based on the positioning information, obtain pre-stored environmental data of the location of the positioning information from a preset database, where the pre-stored environmental data includes topographic information, optical cable laying information, etc.; S42: Send a query request to the cloud server to obtain real-time environmental data of the location of the positioning information, where the real-time environmental data includes real-time climate information, real-time environmental change information, and monitoring information.

[0035] In this embodiment, the topographic information can be used to obtain the soil conditions of the location where the optical cable is buried and the surrounding topographic conditions. The optical cable burial information refers to the depth and method of burial of the optical cable.

[0036] Climate information refers to information such as temperature, humidity and wind speed at the location where the optical cable is buried. Real-time environmental change information refers to environmental changes at the location where the optical cable is buried, such as natural disasters such as earthquakes, landslides, and tsunamis. Monitoring information is to set up monitoring equipment at the preset location of the buried optical cable to obtain monitoring images of the location of the optical cable. Monitoring equipment is usually set up at optical cable sections with large population flow, easy access to the optical cable location, and where external forces often affect or damage the optical cable.

[0037] In the optical cable sections where monitoring equipment is not installed, timely judgments can be made based on climate information and real-time environmental change information, and subsequent judgments can be viewed and further judged by retrieving map information from the cloud server.

[0038] S50: The optical cable early warning model retrieves the security event judgment rule, and generates security event information based on the pre-stored environmental data, the real-time environmental data and the feature information, where the security event information includes the event name and location information; In this embodiment, the security event judgment rule is used to judge the specific risk cause of the optical cable location that is currently at risk. The judgment rule needs to input three judgment factors, namely, characteristic information, pre-stored environmental data, and real-time environmental data, and make a comprehensive judgment based on the distribution of amplitude information, frequency information, and waveform information, as well as pre-stored environment, real-time environment and other data. The event name and the specific cause of the risk at the optical cable location at risk include external force damage, natural factors, or human interference.

[0039] Specifically, refer to Figure 3 , step S50 comprises the steps of: S51: the optical cable early warning model retrieves the security event judgment rule, obtains several distribution features with the highest similarity to the feature segment, and identifies and selects the event information pre-bound to the distribution features; S52: Selecting, from among the selected event information, event information that has the highest degree of matching with the pre-stored environment data and the real-time environment data as the event name in the security event information; S53: Generate security event information by combining the event name and location information.

[0040] In this embodiment, since the feature segment only displays the distribution of amplitude information, frequency information and waveform information, there may be event information with similar amplitude information, frequency information and waveform information. For example, the vibration generated by external construction and the vibration frequencies caused by artificial cutting and some earthquakes are relatively similar. Therefore, based on the existence of multiple distribution features with high similarity in the feature segment, several event information are screened out.

[0041] The step of selecting the event information that has the highest matching degree with the pre-stored environment data and the real-time environment data includes: S521: Acquire several formation conditions pre-associated with the screened event information, where a single event information is associated with one or more formation conditions; S522: Match all the formation conditions of the event information with the pre-stored environment data and the real-time environment data one by one, and record the number of matches of the formation conditions in each event information; S523: Filter the event information whose formation conditions match the largest number of pre-stored environment data and real-time environment data, and use it as the event name in the security event information.

[0042] In this embodiment, each event information includes one or more formation conditions, which can improve the accuracy of matching the pre-stored environmental data, real-time environmental data and the formation conditions. For example, for the same feature segment distribution, the filtered event information includes human interference and natural factors, and the real-time environmental data shows that there is no natural disaster in the area at present. It is known through the pre-stored environmental data that it is at the bottom of the road, and it is known through the real-time environmental data that there is a construction notice in the area at present, and it can be judged that it is in the construction stage by the time when the abnormality occurs, then it is judged that the event information is human-influenced.

[0043] Among them, when it is judged that there is a risk in the optical cable section where the monitoring equipment is installed, when the event information whose formation conditions match the pre-stored environmental data and the real-time environmental data in the largest number includes two or more, the monitoring information in the real-time environmental data is obtained; the event information whose formation conditions match the monitoring information is obtained as the event name in the security event information.

[0044] S60: Send the security event information to the operation and maintenance terminal.

[0045] In one embodiment, the operation and maintenance terminal is a PC or mobile terminal used by the staff responsible for operating and maintaining the optical cable.

[0046] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0047] In one embodiment, a DAS-based optical cable early warning system is provided, and the DAS-based optical cable early warning system corresponds to the DAS-based optical cable early warning management and control method in the above embodiment. The DAS-based optical cable early warning system includes: The data acquisition module is used for the DAS monitoring terminal to collect the monitoring data of the optical cable in real time and send the monitoring data to the pre-trained optical cable early warning model. The monitoring data includes vibration and sound signals along the optical fiber in the optical cable; The risk judgment module is used for the optical cable early warning model to extract feature information from the monitoring data in real time, and to perform feature recognition on the feature information to determine whether there is a safety risk in the optical cable, and output the judgment result, which includes whether there is a risk or not; The positioning calculation module is used for the optical cable early warning model to obtain the time information of the current monitoring data and calculate the positioning information when the judgment result is that there is a risk; An environmental identification module is used to obtain pre-stored environmental data and real-time environmental data of the location where the positioning information is located and send them to the optical cable early warning model; An event generation module is used for the optical cable early warning model to retrieve security event judgment rules and generate security event information based on pre-stored environmental data, real-time environmental data and feature information. The security event information includes event name and location information. The information sending module is used to send security event information to the operation and maintenance terminal.

[0048] Optionally, the risk assessment module includes: The feature acquisition submodule is used for the optical cable early warning model to extract feature information from the monitoring data in real time, including the amplitude information, frequency information and waveform information of vibration and sound signals; The segmentation submodule is used to obtain the distribution of amplitude information, frequency information and waveform information and perform segment cutting processing to obtain several feature segments; A comparison submodule, used for comparing the similarity of each feature segment with a pre-stored distribution feature image representing the risk; The judgment submodule is used to judge that the feature segment has a security risk when the similarity between the feature segment and the pre-stored distribution feature is greater than or equal to the similarity threshold, and to judge that the feature segment does not have a risk when the similarity between the feature segment and the stored distribution feature is less than the similarity threshold.

[0049] Optionally, the positioning calculation module includes: The time difference acquisition module is used for the optical cable early warning model to obtain the collection time and reception time of the current feature segment to obtain the time difference information when the judgment result of the feature segment is that there is a risk; The calculation submodule is used to calculate the positioning information of the current feature segment based on the time difference information.

[0050] Optionally, the environment recognition module includes: The pre-stored environment acquisition submodule is used to obtain the pre-stored environment data of the location of the positioning information from the preset database based on the positioning information, and the pre-stored environment data includes topographic information, optical cable laying information, etc.; The real-time environment acquisition submodule is used to send a query request to the cloud server to obtain real-time environment data at the location of the positioning information. The real-time environment data includes real-time climate information, real-time environment change information and monitoring information.

[0051] Optionally, the event generation module includes: The event acquisition submodule is used for the optical cable early warning model to retrieve the security event judgment rules, obtain several distribution features with the highest similarity to the feature segment, and identify and filter out the event information pre-bound to the distribution features; The event matching submodule is used to select the event information with the highest matching degree with the pre-stored environment data and the real-time environment data from the screened several event information as the event name in the security event information; The event generation submodule is used to generate security event information by combining the event name and location information.

[0052] Optionally, the event matching submodule includes: A condition acquisition unit, used to acquire a number of formation conditions pre-associated with the screened event information, where a single event information is associated with one or more formation conditions; A condition matching unit, used to match all the formation conditions of the event information with the pre-stored environment data and the real-time environment data one by one, and record the number of matches of the formation conditions in each event information; The event name unit is used to filter the event information whose formation conditions match the largest number of pre-stored environmental data and real-time environmental data as the event name in the security event information.

[0053] Optionally, the event matching submodule also includes: A monitoring acquisition unit, configured to acquire monitoring information in the real-time environment data when the event information with the largest number of matching conditions with the pre-stored environment data and the real-time environment data includes two or more; The monitoring judgment unit is used to obtain event information whose formation conditions match the monitoring information as the event name in the security event information.

[0054] For the specific definition of a DAS-based optical cable early warning system, please refer to the definition of a DAS-based optical cable early warning control method above, which will not be repeated here. Each module in the above-mentioned DAS-based optical cable early warning system can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0055] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a DAS-based optical cable early warning management and control method is implemented.

[0056] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a DAS-based optical cable early warning management and control method is implemented.

[0057] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, a DAS-based optical cable early warning management and control method is implemented.

[0058] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0059] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0060] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A DAS-based optical cable early warning control method, characterized in that: Includes steps: The DAS monitoring terminal collects the monitoring data of the optical cable in real time and sends the monitoring data to the pre-trained optical cable early warning model. The monitoring data includes vibration and sound signals along the optical fiber in the optical cable. The optical cable early warning model extracts feature information from the monitoring data in real time, performs feature recognition on the feature information, determines whether there is a safety risk in the optical cable, and outputs the judgment result, which includes whether there is a risk or not; When the judgment result is that there is a risk, the optical cable early warning model obtains the time information of the current monitoring data and calculates the positioning information; Obtaining pre-stored environmental data and real-time environmental data at the location of the positioning information and sending them to the optical cable early warning model; The optical cable early warning model retrieves the security event judgment rules and generates security event information based on pre-stored environmental data, real-time environmental data and feature information. The security event information includes the event name and location information. Send security event information to the operation and maintenance terminal.

2. According to the DAS-based optical cable early warning management method of claim 1, it is characterized in that: The optical cable early warning model extracts feature information from the monitoring data in real time, performs feature recognition on the feature information, determines whether there is a safety risk in the optical cable, and outputs a judgment result, which includes whether there is a risk or not, including: The optical cable early warning model extracts characteristic information from the monitoring data in real time, including amplitude information, frequency information and waveform information of vibration and sound signals; Obtain the distribution of amplitude information, frequency information and waveform information and perform segment cutting to obtain several feature segments; Compare each feature segment with a pre-stored distribution feature image representing the risk; When the similarity between the feature segment and the pre-stored distribution feature is greater than or equal to the similarity threshold, the feature segment is judged to have a security risk. When the similarity between the feature segment and the stored distribution feature is less than the similarity threshold, the feature segment is judged to have no risk.

3. The optical cable early warning control method based on DAS according to claim 2 is characterized in that: When the judgment result is that there is a risk, the optical cable early warning model obtains the time information of the current monitoring data and calculates the positioning information, including: When the judgment result of the characteristic segment is that there is a risk, the optical cable early warning model obtains the collection time and reception time of the current characteristic segment to obtain time difference information; The positioning information of the current feature segment is calculated based on the time difference information.

4. The optical cable early warning control method based on DAS according to claim 1 is characterized in that: The method of obtaining the pre-stored environmental data and the real-time environmental data of the location where the positioning information is located and sending them to the optical cable early warning model includes: Based on the positioning information, the pre-stored environmental data of the location of the positioning information is obtained from a preset database, the pre-stored environmental data including topographic information, optical cable laying information, etc.; Send a query request to the cloud server to obtain real-time environmental data at the location of the positioning information. The real-time environmental data includes real-time climate information, real-time environmental change information, and monitoring information.

5. The optical cable early warning control method based on DAS according to claim 4 is characterized in that: The optical cable early warning model retrieves the security event judgment rules and generates security event information based on the pre-stored environmental data, the real-time environmental data and the characteristic information. The security event information includes the event name and location information, including: The optical cable early warning model retrieves the security event judgment rules, obtains several distribution features with the highest similarity to the feature segment, and identifies and selects the event information pre-bound to the distribution features; From the selected event information, select the event information with the highest matching degree with the pre-stored environment data and the real-time environment data as the event name in the security event information; Generate security event information by combining event name and location information.

6. The optical cable early warning control method based on DAS according to claim 5 is characterized in that: The event information having the highest matching degree with the pre-stored environment data and the real-time environment data is selected from the filtered plurality of event information as the event name in the security event information, including: Acquire several formation conditions pre-associated with the filtered event information, where a single event information is associated with one or more formation conditions; Match all the formation conditions of the event information with the pre-stored environment data and the real-time environment data one by one, and record the number of matches of the formation conditions in each event information; The event information with the largest number of matching conditions with the pre-stored environmental data and the real-time environmental data is selected as the event name in the security event information.

7. The optical cable early warning control method based on DAS according to claim 6 is characterized in that: The event information whose screening conditions match the largest number of pre-stored environment data and real-time environment data is used as the event name in the security event information, including: When the event information with the largest number of matching conditions with the pre-stored environment data and the real-time environment data includes two or more, the monitoring information in the real-time environment data is obtained; Event information whose formation conditions match the monitoring information is obtained as the event name in the security event information.

8. A DAS-based optical cable early warning system, characterized in that: include: The data acquisition module is used for the DAS monitoring terminal to collect the monitoring data of the optical cable in real time and send the monitoring data to the pre-trained optical cable early warning model. The monitoring data includes vibration and sound signals along the optical fiber in the optical cable; The risk judgment module is used for the optical cable early warning model to extract feature information from the monitoring data in real time, and to perform feature recognition on the feature information to determine whether there is a safety risk in the optical cable, and output the judgment result, which includes whether there is a risk or not; The positioning calculation module is used for the optical cable early warning model to obtain the time information of the current monitoring data and calculate the positioning information when the judgment result is that there is a risk; An environmental identification module is used to obtain pre-stored environmental data and real-time environmental data of the location where the positioning information is located and send them to the optical cable early warning model; An event generation module is used for the optical cable early warning model to retrieve security event judgment rules and generate security event information based on pre-stored environmental data, real-time environmental data and feature information. The security event information includes event name and location information. The information sending module is used to send security event information to the operation and maintenance terminal.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the DAS-based optical cable early warning management and control method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a DAS-based optical cable early warning management and control method are implemented as described in any one of claims 1 to 7.

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