Optical fiber sensing ghost point signal identification method, apparatus and device, and storage medium

Abstract

The invention provides an optical fiber sensing ghost point signal identification method, apparatus and device, and a storage medium. The method comprises the steps of respectively storing a historical optical fiber sensing vibration signal and a real-time optical fiber sensing vibration signal to a first hash table and a second hash table; whether the real-time interval length of the real-time continuous vibration interval in the second hash table is contained in the historical interval length of any historical continuous vibration interval in the first hash table or not and whether the difference value between the real-time interval length and the historical interval length is larger than a length threshold value or not are judged; if yes, marking the real-time optical fiber sensing vibration signal as a ghost point candidate signal, and obtaining the occurrence frequency of a target historical continuous vibration interval consistent with the real-time continuous vibration interval in the first hash table; and when the occurrence frequency is greater than the threshold frequency, determining that the real-time optical fiber sensing vibration signal is an actual ghost point signal. According to the invention, accurate identification of ghost point signals is realized.

Description

Technical Field

[0001] This invention relates to the field of signal sensing and signal processing technology, specifically to a method, apparatus, device, and storage medium for identifying ghost point signals from fiber optic sensing. Background Technology

[0002] In the field of intelligent transportation, with the continuous maturation of active traffic management technologies, the demand for intelligent highways and vehicle-road cooperation is rapidly increasing. Fiber optic sensing technology, as a new distributed fiber optic sensing technology, is gradually being applied to road monitoring. This technology, by laying specially designed direct-buried vibration detection optical cables beneath the asphalt pavement, can achieve "all-time, all-area, all-weather" vibration signal sensing. It has significant advantages in signal-to-noise ratio, ease of construction and networking, passive characteristics, and resistance to electromagnetic interference, and is particularly suitable for long-distance vibration signal monitoring. It accurately reconstructs the vibration excitation signals generated by moving objects such as vehicles, exhibiting excellent performance in both the time and frequency domains of signal sensing. Furthermore, the detection and positioning accuracy can be flexibly controlled by adjusting the fiber spacing, enabling real-time trajectory tracking of moving objects even in poor lighting conditions and extreme weather.

[0003] However, in practical applications, it has been found that fiber optic sensing devices can randomly exhibit abnormalities: even without the influence of moving objects, some fibers may produce noticeable vibration response signals, and the location and duration of these signals are not fixed, a phenomenon known as the "ghost point" problem. The presence of ghost points severely interferes with the recognition of vibration signal responses caused by moving objects, leading to misjudgments by the device and affecting the accuracy and reliability of functions such as intelligent highway vehicle-road coordination and traffic flow analysis.

[0004] Because the generation mechanism of ghost point signals is complex and may be affected by various factors such as changes in ambient temperature, slight displacement of the foundation, and noise of the equipment itself, and because their characteristics are somewhat similar to the vibration signals of real moving objects, they are difficult to distinguish using traditional signal processing methods. This has become a key bottleneck restricting the further promotion and application of fiber optic sensing technology in the field of smart highways.

[0005] Therefore, there is an urgent need to provide a method, device, equipment, and storage medium for identifying ghost point signals in fiber optic sensing, so as to achieve accurate identification of ghost point signals. Summary of the Invention

[0006] In view of this, it is necessary to provide a method, device, equipment and storage medium for identifying ghost point signals of fiber optic sensing, so as to solve the technical problem that ghost point signals and vibration signals of real moving objects have certain similarities in the prior art, making it difficult to distinguish them by traditional signal processing methods, that is, making it impossible to accurately identify ghost point signals.

[0007] To address the aforementioned technical problems, in a first aspect, the present invention provides a method for identifying ghost point signals in fiber optic sensing, comprising: Historical fiber optic sensing vibration signals and real-time fiber optic sensing vibration signals are stored in a first hash table and a second hash table, respectively. The first hash table uses timestamps as keys and multiple historical continuous vibration intervals as values, while the second hash table uses timestamps as keys and real-time continuous vibration intervals as values. Determine whether the real-time interval length of the real-time continuous vibration interval is included within the historical interval length of any of the historical continuous vibration intervals, and whether the difference between the real-time interval length and the historical interval length is greater than a length threshold. If so, the real-time fiber optic sensing vibration signal is marked as a ghost point candidate signal, and the frequency of occurrence of the target historical continuous vibration interval that is consistent with the real-time continuous vibration interval in the first hash table is obtained. When the frequency of occurrence is greater than the threshold frequency, the real-time fiber optic sensing vibration signal is determined to be an actual ghost point signal.

[0008] In one possible implementation, the historical fiber optic sensing vibration signal includes multiple original vibration intervals at the same timestamp; before determining whether the real-time interval length of the real-time continuous vibration interval is included within the historical interval length of any of the historical continuous vibration intervals, the method further includes: Identify at least two vibration intervals among the plurality of original vibration intervals that have an intersection, and take the union of the at least two vibration intervals to be merged as the historical continuous vibration interval.

[0009] In one possible implementation, the method further includes: The real-time fiber optic sensing vibration signal is stored as a historical fiber optic sensing vibration signal in the first hash table; The step of storing the real-time fiber optic sensing vibration signal as a historical fiber optic sensing vibration signal in the first hash table further includes: Determine whether the length of the data queue stored in the first hash table has reached the capacity limit, wherein the capacity limit is the product of the maximum queue length and a preset percentage; If so, the storage space of the first hash table is expanded by a preset expansion factor, and the real-time fiber optic sensing vibration signal is stored in the expanded first hash table.

[0010] In one possible implementation, the method further includes: When the occurrence frequency is less than or equal to the threshold frequency, it is determined whether the length of the data queue stored in the first hash table has reached the maximum queue length. If not, the real-time fiber optic sensing vibration signal is stored in the first hash table; If so, the data to be eliminated in the first hash table is determined and removed, and the real-time fiber optic sensing vibration signal is stored in the first hash table after the data to be eliminated is removed.

[0011] In one possible implementation, determining and removing the data to be evicted from the first hash table includes: The low-frequency signals in the historical fiber optic sensing vibration signals whose historical access frequencies are less than the access frequency threshold are identified, and these low-frequency signals are used as the elimination signals.

[0012] In one possible implementation, determining and removing the data to be evicted from the first hash table further includes: The historical fiber optic sensing vibration signals are identified as having timeout signals with durations exceeding a time threshold, and these timeout signals are designated as the elimination signals.

[0013] In one possible implementation, the method further includes: When the first hash table triggers the capacity reset condition, the first hash table is shrunk to the maximum queue length.

[0014] Secondly, the present invention also provides a device for identifying ghost point signals from fiber optic sensing, comprising: The vibration signal separation storage unit is used to store historical fiber optic sensing vibration signals and real-time fiber optic sensing vibration signals into a first hash table and a second hash table, respectively. The first hash table uses timestamps as keys and multiple historical continuous vibration intervals as values, while the second hash table uses timestamps as keys and real-time continuous vibration intervals as values. The interval length comparison unit is used to determine whether the real-time interval length of the real-time continuous vibration interval is included within the historical interval length of any of the historical continuous vibration intervals, and whether the difference between the real-time interval length and the historical interval length is greater than a length threshold. The ghost point candidate signal determination unit is used to mark the real-time fiber optic sensing vibration signal as a ghost point candidate signal when the real-time interval length of the real-time continuous vibration interval is included within the historical interval length of any of the historical continuous vibration intervals, and the difference between the real-time interval length and the historical interval length is greater than a length threshold, and to obtain the occurrence frequency of the target historical continuous vibration interval that is consistent with the real-time continuous vibration interval in the first hash table. The actual ghost point signal determination unit is used to determine the real-time fiber optic sensing vibration signal as an actual ghost point signal when the occurrence frequency is greater than the threshold frequency.

[0015] Thirdly, the present invention also provides an optical fiber sensing signal analysis device, comprising a sensing optical fiber, a memory, and a processor, wherein, The sensing optical fiber is used to collect real-time fiber optic sensing vibration signals. The memory is used to store programs; The processor, coupled to the memory and the image acquisition unit, is used to execute the program stored in the memory to implement the steps in the fiber optic sensing ghost point signal identification method described in any of the above possible implementations.

[0016] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the fiber optic sensing ghost point signal identification method described in any of the above possible implementations.

[0017] The beneficial effects of this invention are as follows: The method for identifying phantom point signals in fiber optic sensing provided by this invention first determines whether a real-time fiber optic sensing vibration signal is a candidate phantom point signal based on the difference between the real-time interval length and the historical interval length, as well as the difference between the real-time interval length and the historical interval length. This utilizes the characteristic that moving signal objects have longer signal lengths while phantom point signals have shorter signal lengths, thus achieving a preliminary judgment of phantom point signals. Then, due to the persistence of environmental interference or equipment noise, the existence period of a true phantom point is relatively long. That is, only when the frequency of occurrence of a target historical continuous vibration interval consistent with the real-time continuous vibration interval in the first hash table exceeds a threshold frequency is the real-time fiber optic sensing vibration signal determined to be an actual phantom point signal. This ensures that the actual phantom point signal at the identification point conforms to its characteristic distribution, further ensuring the accuracy of the identification of the actual phantom point signal.

[0018] Furthermore, this invention stores historical and real-time fiber optic sensing vibration signals by setting up two independent hash tables, a first hash table and a second hash table. This decouples the historical and real-time fiber optic sensing vibration signals, facilitating independent management of them. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A schematic flowchart of an embodiment of the fiber optic sensing ghost point signal identification method provided by the present invention; Figure 2 This is a schematic flowchart of an embodiment of the present invention for merging the original vibration intervals of historical fiber optic sensing vibration signals. Figure 3 This is a schematic diagram of an embodiment of the present invention that stores real-time fiber optic sensing vibration signals as historical fiber optic sensing vibration signals into a first hash table. Figure 4 A schematic diagram of an embodiment of the present invention is provided, in which the real-time fiber optic sensing vibration signal is stored as a historical fiber optic sensing vibration signal in a first hash table when the frequency of occurrence is less than or equal to a threshold frequency. Figure 5 A schematic diagram of an embodiment of the fiber optic sensing ghost point signal identification device provided by the present invention; Figure 6 This is a schematic diagram of an embodiment of the fiber optic sensing signal analysis device provided by the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0022] It should be understood that the illustrative drawings are not drawn to scale. The flowcharts used in this invention illustrate operations implemented according to some embodiments of the invention. It should be understood that the operations in the flowcharts may be implemented out of order, and steps without logical contextual relationships may be reversed or performed simultaneously. Furthermore, those skilled in the art, guided by the content of this invention, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor systems and / or microcontroller systems.

[0023] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0024] This invention provides a method, apparatus, device, and storage medium for identifying ghost point signals in fiber optic sensing, which will be described below.

[0025] Figure 1 This is a schematic flowchart of an embodiment of the fiber optic sensing ghost point signal identification method provided by the present invention, as shown below. Figure 1 As shown, the method for identifying ghost point signals in fiber optic sensing includes: S101. Store the historical fiber optic sensing vibration signal and the real-time fiber optic sensing vibration signal into the first hash table and the second hash table respectively; the first hash table uses the timestamp as the key and multiple historical continuous vibration intervals as the value, and the second hash table uses the timestamp as the key and the real-time continuous vibration interval as the value.

[0026] Specifically, the real-time fiber optic sensing vibration signal is the current timestamp, i.e. the vibration signal of the current frame, while the historical fiber optic sensing signal includes multiple historical timestamps, i.e. the vibration signals corresponding to multiple historical frames.

[0027] It should be noted that the sensing fiber includes multiple measuring points, and both the historical continuous vibration interval and the real-time continuous vibration interval refer to the vibration interval composed of vibration signals corresponding to multiple measuring points.

[0028] For example, a sensing fiber includes measuring points numbered 1-200. When a vibration source passes by, measuring points numbered 100-150 in the sensing fiber generate vibration signals. The continuous vibration interval is [100, 150], indicating that there is a continuous vibration signal between measuring point numbered 100 and measuring point numbered 150.

[0029] S102. Determine whether the real-time interval length of the real-time continuous vibration interval is included within the historical interval length of any historical continuous vibration interval, and whether the difference between the real-time interval length and the historical interval length is greater than the length threshold.

[0030] The length threshold can be set or adjusted according to the actual situation, and no specific limit is set here.

[0031] S103. If so, mark the real-time fiber optic sensing vibration signal as a ghost point candidate signal, and obtain the frequency of occurrence of the target historical continuous vibration interval whose interval length is consistent with the real-time continuous vibration interval in the first hash table.

[0032] For example, if the real-time continuous vibration interval is [1,3], then the target continuous vibration interval is the historical continuous vibration interval of all historical fiber optic sensing vibration signals in the first hash table that is [1,3]. Specifically, if the historical continuous vibration intervals of the historical fiber optic sensing vibration signals are [1,2], [1,4], [1,4], [1,3], and [1,3], then the frequency of occurrence is 2.

[0033] S104. When the frequency of occurrence exceeds the threshold frequency, the real-time fiber optic sensing vibration signal is determined to be the actual ghost point signal.

[0034] To ensure the accuracy of identifying actual ghost point signals, the threshold frequency should be based on experimental support. Specifically, multiple vibration energy data points are collected for experiments, and the frequency of signals marked as actual ghost points is statistically analyzed. The threshold frequency is then determined based on this signal frequency.

[0035] In a specific embodiment of the present invention, the threshold frequency is 7.

[0036] It should be noted that the first hash table stores candidate ghost point signals and normal movement signals.

[0037] It should also be noted that: at the next moment, or at the next timestamp, the real-time fiber optic sensing vibration signal can be regarded as the historical fiber optic sensing vibration signal, which can be used to identify whether the real-time fiber optic sensing vibration signal collected at the next timestamp is a ghost signal. Therefore, in some embodiments of the present invention, after step S104, the following is also included: The real-time fiber optic sensing vibration signal is stored as a historical fiber optic sensing vibration signal in the first hash table.

[0038] In other words, the second hash table always stores the real-time fiber optic sensing vibration signal at the current moment.

[0039] It should be understood that the method for identifying phantom point signals in fiber optic sensing according to the embodiments of the present invention can be implemented in any device based on the method for identifying phantom point signals in fiber optic sensing, such as demodulation equipment or host computers or other electronic devices for the purpose of eliminating phantom point signals. Specifically, the method for identifying phantom point signals in fiber optic sensing is stored in the aforementioned device as a pre-programmed program. When the device is started, the program is invoked, and the method for identifying phantom point signals in fiber optic sensing is implemented.

[0040] Compared with existing technologies, the fiber optic sensing ghost signal identification method provided in this invention first determines whether the real-time fiber optic sensing vibration signal is a candidate ghost signal based on the difference between the real-time interval length and the historical interval length. This utilizes the characteristic that moving signal objects have longer signal lengths while ghost signal lengths are shorter, enabling a preliminary judgment of ghost signal. Then, due to the persistence of environmental interference or equipment noise, the existence period of a true ghost point is relatively long. That is, only when the frequency of occurrence of the target historical continuous vibration interval consistent with the real-time continuous vibration interval in the first hash table exceeds a threshold frequency is the real-time fiber optic sensing vibration signal determined to be an actual ghost signal. This ensures that the actual ghost signal at the identification point conforms to its characteristic distribution, further ensuring the accuracy of actual ghost signal identification.

[0041] Furthermore, in this embodiment of the invention, by setting up two independent hash tables, a first hash table and a second hash table, the historical fiber optic sensing vibration signal and the real-time fiber optic sensing vibration signal can be stored. This can decouple the historical fiber optic sensing vibration signal and the real-time fiber optic sensing vibration signal, and facilitate independent management of the historical fiber optic sensing vibration signal and the real-time fiber optic sensing vibration signal.

[0042] In practical applications, due to differences in vehicle size or overlapping measurement areas of adjacent measuring points, historical fiber optic sensing vibration signals may contain signals that should belong to a single vibration source but instead show multiple discrete vibration intervals. To avoid this situation negatively impacting the determination of ghost point candidate signals, in some embodiments of the present invention, such as... Figure 2 As shown, before step S102, the procedure further includes: S201. Identify at least two vibration intervals to be merged from multiple original vibration intervals that have an intersection. S202, take the union of at least two vibration intervals to be merged as the historical continuous vibration interval.

[0043] Specifically, if the original vibration intervals include [1,5], [3,7] and [9,20], then since the intervals between [1,5] and [3,7] intersect at [3,5], the vibration intervals to be merged are [1,5] and [3,7], and the historical continuous vibration interval is [1,7].

[0044] The present invention provides a technical solution for obtaining historical continuous vibration intervals by merging vibration intervals based on interval intersection. This reduces data redundancy and integrates vibration signals generated by the same vibration source to improve the accuracy of subsequent ghost point candidate signal determination, thereby further ensuring the accuracy of ghost point signal identification.

[0045] When a real-time fiber optic sensing vibration signal is identified as an actual ghost point signal, the real-time fiber optic sensing vibration signal is stored in the first hash table as the signal that is temporally closest to the real-time fiber optic sensing vibration signal acquired at the next moment. This signal is crucial for the subsequent identification of ghost point signals. To avoid signal loss due to hash collisions or in complex scenarios, in some embodiments of the present invention, such as... Figure 3 As shown, storing the real-time fiber optic sensing vibration signal as a historical fiber optic sensing vibration signal in the first hash table also includes: S301. Determine whether the length of the data queue stored in the first hash table has reached the capacity limit. The capacity limit is the product of the maximum queue length and the preset percentage.

[0046] The maximum queue length is a parameter configured when the first hash table is built, with a preset percentage of 0.7 to 0.9.

[0047] S302. If so, the storage space of the first hash table is expanded by the preset expansion factor, and the real-time fiber optic sensing vibration signal is stored in the expanded first hash table.

[0048] The preset expansion factor can be set or adjusted according to the actual application scenario. For example, the preset expansion factor can be 1.2 times, which means expanding the storage space of the first hash table to 1.2 times the original storage space.

[0049] This invention expands the first hash table to store high-frequency new signal points, i.e., real-time fiber optic sensing vibration signals that occur more frequently than a threshold frequency, into the expanded first hash table. This maintains a low collision rate in the hash table, ensuring reliable data storage in complex scenarios, avoiding data loss, and improving the robustness of data storage.

[0050] It should be noted that, in order to save storage resources, the maximum expansion factor of the first hash table is less than or equal to 4 times the initial capacity.

[0051] The aforementioned expansion process is to ensure that the latest and most frequent ghost point candidate signals are reliably stored in the first hash table. When the frequency of occurrence is less than or equal to the threshold frequency, in order to balance storage resources and the accuracy of subsequent ghost point signal identification, in some embodiments of the present invention, such as... Figure 4 As shown, the method for identifying ghost point signals in fiber optic sensing also includes: S401. When the frequency of occurrence is less than or equal to the threshold frequency, determine whether the length of the data queue stored in the first hash table has reached the maximum queue length. S402. If not, store the real-time fiber optic sensing vibration signal in the first hash table. S403. If so, determine and remove the data to be eliminated from the first hash table, and store the real-time fiber optic sensing vibration signal into the first hash table after removing the data to be eliminated.

[0052] In this embodiment of the invention, when the frequency of occurrence is less than or equal to the threshold frequency, the maximum queue length is used as the judgment criterion to maximize the utilization of storage space. When the maximum queue length is reached, automatic data rotation is achieved through a culling strategy, thereby achieving the goal of balancing resource consumption and cache efficiency while accurately identifying ghost point signals.

[0053] In a specific embodiment of the present invention, step S403, determining and removing data to be eliminated from the first hash table, includes: Identify low-frequency signals in historical fiber optic sensing vibration signals whose historical access frequency is less than the access frequency threshold, and use these low-frequency signals as signals to be eliminated. And / or, The system identifies timeout signals in historical fiber optic sensing vibration signals whose duration exceeds a time threshold and designates these timeout signals as to be phased out.

[0054] The embodiments of the present invention determine the signal to be eliminated by using parameters based on two dimensions: access frequency and / or existence time. This can adapt to the characteristics of ghost point signals, such as "unfixed occurrence location" and "unfixed duration", avoid storing a large number of redundant signals, avoid the adverse effects of redundant signals on the frequency determination process, and provide storage space for storing real-time fiber optic sensing vibration signals.

[0055] Both the access frequency threshold and the time threshold can be set or adjusted according to the actual application scenario. For example, the time threshold can be 60 seconds.

[0056] Since ghost point signals are dynamically changing, when ghost point signals in the first hash table are accessed too frequently or exist for too long, they become less meaningful and need to be deleted. This results in a situation where the amount of data in the first hash table occupies a relatively low proportion of the total storage space. In this case, to save storage resources, in some embodiments of the present invention, the method for identifying ghost point signals in fiber optic sensing further includes: When the capacity reset condition of the first hash table is triggered, the first hash table will be shrunk to the maximum queue length.

[0057] Specifically, the capacity reset conditions may include, but are not limited to: the storage space occupancy rate of the first hash table is less than the preset occupancy rate, the data access frequency of the first hash table is less than the preset frequency, and the data existence time is greater than the preset time.

[0058] This invention allows for the scaling down of the expanded first hash table, reducing its storage space when the amount of data to be stored is not large, thus avoiding waste of storage resources.

[0059] In summary, the fiber optic sensing ghost point signal identification method proposed in this embodiment of the invention firstly, accurately distinguishes between real moving object signals and ghost point false signals through historical data statistics, interval feature analysis, and threshold determination, reducing the false judgment rate. Secondly, it manages ghost point data by combining access frequency and timestamps to adapt to the "intermittent presence and fluctuating life cycle" characteristics of ghost points, ensuring that the characteristics of currently active ghost points are always retained in the cache, improving the interference identification accuracy in dynamic environments. Thirdly, by identifying ghost points, it can provide data basis for subsequent ghost point elimination, thereby ensuring the accuracy of fiber optic sensing systems in functions such as vehicle trajectory tracking and traffic flow analysis, and providing reliable data support for vehicle-road cooperation and proactive traffic management.

[0060] On the other hand, embodiments of the present invention also provide a device for identifying phantom point signals from fiber optic sensing, such as... Figure 5 As shown, the fiber optic ghost point signal identification device 500 includes: The vibration signal separation storage unit 501 is used to store historical fiber optic sensing vibration signals and real-time fiber optic sensing vibration signals into a first hash table and a second hash table, respectively. The first hash table uses timestamps as keys and multiple historical continuous vibration intervals as values, while the second hash table uses timestamps as keys and real-time continuous vibration intervals as values. The interval length comparison unit 502 is used to determine whether the real-time interval length of the real-time continuous vibration interval is included within the historical interval length of any historical continuous vibration interval, and whether the difference between the real-time interval length and the historical interval length is greater than the length threshold. The ghost point candidate signal determination unit 503 is used to mark the real-time fiber optic sensing vibration signal as a ghost point candidate signal when the real-time interval length of the real-time continuous vibration interval is included within the historical interval length of any historical continuous vibration interval, and the difference between the real-time interval length and the historical interval length is greater than the length threshold, and obtain the occurrence frequency of the target historical continuous vibration interval that is consistent with the real-time continuous vibration interval in the first hash table. The actual ghost point signal determination unit 504 is used to determine the real-time fiber optic sensing vibration signal as the actual ghost point signal when the frequency of occurrence is greater than the threshold frequency.

[0061] The fiber optic ghost point signal identification device 500 provided in the above embodiments can realize the technical solutions described in the above fiber optic ghost point signal identification method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above fiber optic ghost point signal identification method embodiments, and will not be repeated here.

[0062] like Figure 6 As shown, the present invention also provides a fiber optic sensor signal analysis device 600. The fiber optic sensor signal analysis device 600 includes a sensing fiber 601, a processor 602, a memory 603, and a display 604. Figure 6 Only some components of the fiber optic sensing signal analysis device 600 are shown; however, it should be understood that implementation of all shown components is not required, and more or fewer components may be implemented instead.

[0063] The sensing fiber optic cable 601 is used to acquire real-time fiber optic sensing vibration signals.

[0064] In some embodiments, processor 602 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in memory 603 or process data, such as the fiber optic sensing ghost point signal identification method of the present invention.

[0065] In some embodiments, memory 603 may be an internal storage unit of the fiber optic sensor signal analysis device 600, such as a hard disk or memory of the fiber optic sensor signal analysis device 600. In other embodiments, memory 603 may also be an external storage device of the fiber optic sensor signal analysis device 600, such as a pluggable hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the fiber optic sensor signal analysis device 600.

[0066] Furthermore, the memory 603 may include both internal storage units of the fiber optic sensor signal analysis device 600 and external storage devices. The memory 603 is used to store the application software and various types of data of the fiber optic sensor signal analysis device 600.

[0067] In some embodiments, display 604 may be an LED display, a liquid crystal display, a touch-screen liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 604 is used to display information from the fiber optic sensor signal analysis device 600 and to display a visual user interface. Components 601-604 of the fiber optic sensor signal analysis device 600 communicate with each other via a device bus.

[0068] In some embodiments of the present invention, when the processor 602 executes the identification program for fiber optic sensing ghost point signals in the memory 603, the following steps can be implemented: Historical fiber optic sensing vibration signals and real-time fiber optic sensing vibration signals are stored in a first hash table and a second hash table, respectively. The first hash table uses timestamps as keys and multiple historical continuous vibration intervals as values, while the second hash table uses timestamps as keys and real-time continuous vibration intervals as values. Determine whether the real-time interval length of the real-time continuous vibration interval is included within the historical interval length of any historical continuous vibration interval, and whether the difference between the real-time interval length and the historical interval length is greater than the length threshold. If so, the real-time fiber optic sensing vibration signal is marked as a ghost point candidate signal, and the occurrence frequency of the target historical continuous vibration interval that is consistent with the real-time continuous vibration interval is obtained from the first hash table. When the frequency of occurrence exceeds the threshold frequency, the real-time fiber optic sensing vibration signal is determined to be the actual ghost point signal.

[0069] It should be understood that when the processor 602 executes the identification program for the fiber optic sensing ghost point signal in the memory 603, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.

[0070] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0071] The present invention provides a detailed description of a method, apparatus, device, and storage medium for identifying ghost point signals in fiber optic sensing. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for identifying ghost point signals in fiber optic sensing, characterized in that, include: Historical fiber optic sensing vibration signals and real-time fiber optic sensing vibration signals are stored in a first hash table and a second hash table, respectively. The first hash table uses timestamps as keys and multiple historical continuous vibration intervals as values, while the second hash table uses timestamps as keys and real-time continuous vibration intervals as values. Determine whether the real-time interval length of the real-time continuous vibration interval is included within the historical interval length of any of the historical continuous vibration intervals, and whether the difference between the real-time interval length and the historical interval length is greater than a length threshold. If so, the real-time fiber optic sensing vibration signal is marked as a ghost point candidate signal, and the frequency of occurrence of the target historical continuous vibration interval that is consistent with the real-time continuous vibration interval in the first hash table is obtained. When the frequency of occurrence is greater than the threshold frequency, the real-time fiber optic sensing vibration signal is determined to be an actual ghost point signal.

2. The method for identifying ghost point signals in fiber optic sensing according to claim 1, characterized in that, The historical fiber optic sensing vibration signal includes multiple original vibration intervals at the same timestamp; before determining whether the real-time interval length of the real-time continuous vibration interval is included within the historical interval length of any of the historical continuous vibration intervals, the method further includes: Identify at least two vibration intervals to be merged that have an intersection among the plurality of original vibration intervals; The union of the at least two vibration intervals to be merged is taken as the historical continuous vibration interval.

3. The method for identifying ghost point signals in fiber optic sensing according to claim 1, characterized in that, The method further includes: The real-time fiber optic sensing vibration signal is stored as a historical fiber optic sensing vibration signal in the first hash table; The step of storing the real-time fiber optic sensing vibration signal as a historical fiber optic sensing vibration signal in the first hash table further includes: Determine whether the length of the data queue stored in the first hash table has reached the capacity limit, wherein the capacity limit is the product of the maximum queue length and a preset percentage; If so, the storage space of the first hash table is expanded by a preset expansion factor, and the real-time fiber optic sensing vibration signal is stored in the expanded first hash table.

4. The method for identifying ghost point signals in fiber optic sensing according to claim 3, characterized in that, The method further includes: When the occurrence frequency is less than or equal to the threshold frequency, it is determined whether the length of the data queue stored in the first hash table has reached the maximum queue length. If not, the real-time fiber optic sensing vibration signal is stored in the first hash table; If so, the data to be eliminated in the first hash table is determined and removed, and the real-time fiber optic sensing vibration signal is stored in the first hash table after the data to be eliminated is removed.

5. The method for identifying phantom point signals in fiber optic sensing according to claim 4, characterized in that, The process of determining and removing data to be eliminated from the first hash table includes: The low-frequency signals in the historical fiber optic sensing vibration signals whose historical access frequencies are less than the access frequency threshold are identified, and these low-frequency signals are used as the elimination signals.

6. The method for identifying ghost point signals in fiber optic sensing according to claim 4, characterized in that, The process of determining and removing the data to be eliminated from the first hash table further includes: The historical fiber optic sensing vibration signals are identified as having timeout signals with durations exceeding a time threshold, and these timeout signals are designated as the elimination signals.

7. The method for identifying phantom point signals in fiber optic sensing according to claim 3, characterized in that, The method further includes: When the first hash table triggers the capacity reset condition, the first hash table is shrunk to the maximum queue length.

8. A device for identifying ghost point signals from fiber optic sensing, characterized in that, include: The vibration signal separation storage unit is used to store historical fiber optic sensing vibration signals and real-time fiber optic sensing vibration signals into a first hash table and a second hash table, respectively. The first hash table uses timestamps as keys and multiple historical continuous vibration intervals as values, while the second hash table uses timestamps as keys and real-time continuous vibration intervals as values. The interval length comparison unit is used to determine whether the real-time interval length of the real-time continuous vibration interval is included within the historical interval length of any of the historical continuous vibration intervals, and whether the difference between the real-time interval length and the historical interval length is greater than a length threshold. The ghost point candidate signal determination unit is used to mark the real-time fiber optic sensing vibration signal as a ghost point candidate signal when the real-time interval length of the real-time continuous vibration interval is included within the historical interval length of any of the historical continuous vibration intervals, and the difference between the real-time interval length and the historical interval length is greater than a length threshold, and to obtain the occurrence frequency of the target historical continuous vibration interval that is consistent with the real-time continuous vibration interval in the first hash table. The actual ghost point signal determination unit is used to determine the real-time fiber optic sensing vibration signal as an actual ghost point signal when the occurrence frequency is greater than the threshold frequency.

9. A fiber optic sensor signal analysis device, characterized in that, Includes sensing optical fibers, memory, and a processor, among which, The sensing optical fiber is used to collect real-time fiber optic sensing vibration signals. The memory is used to store programs; The processor, coupled to the memory and the image acquisition unit, is used to execute the program stored in the memory to implement the steps in the fiber optic sensing ghost point signal identification method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the fiber optic sensing ghost point signal identification method according to any one of claims 1 to 7.

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