Unattended optical fiber early warning information checking method

Abstract

The invention discloses an unattended optical fiber early warning information checking method, and relates to the technical field of optical fiber early warning protection, and the method comprises the following steps: receiving and analyzing alarm information, and extracting the position, risk level and trigger time of an alarm point; acquiring real-time state data of the camera and the unmanned aerial vehicle, wherein the real-time state data comprises equipment online state, electric quantity, coverage range and environmental factors; dynamically calculating an alarm emergency degree according to the alarm risk type and the triggering time, wherein the emergency degree is attenuated along with time; respectively calculating the view coverage efficiency of the camera and the operation coverage efficiency of the unmanned aerial vehicle based on the spatial relationship between the equipment position and the alarm point; estimating consumed time for adjusting the preset position by the camera and time for the unmanned aerial vehicle to fly to the alarm point; integrating the alarm emergency degree, the equipment coverage efficiency and the arrival time, dynamically scoring and sequencing the equipment priority; scheduling the highest-score equipment to check according to the priority, and if the equipment is not available, turning to manual work; checking results are recorded, scores are optimized, and the scheduling decision-making efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of optical fiber early warning protection, and in particular relates to an unmanned optical fiber early warning alarm information verification method. Background Art

[0002] As an advanced monitoring method, fiber optic early warning has extremely high sensitivity and can respond quickly to interference or damage and issue alarms, but there is a problem of high false alarms; 202210206573.4 discloses a distributed linkage system for fiber optic early warning systems and cameras, which uses video monitoring methods to verify fiber optic early warning alarm information, but this method is difficult to apply in engineering and requires full coverage of cameras; 202110568624.3 discloses a drone oil and gas pipeline emergency inspection method and system. After the fiber optic early warning alarm, the drone is equipped with a dual-optical pod to verify the alarm point of the fiber optic alarm, but this method is limited by the flight of the drone and cannot be used. In addition, the verification of fiber optic alarm information currently relies mainly on manual labor. When a fiber optic early warning alarm occurs, personnel will be notified immediately to go to the site for verification. This process involves multiple links from alarm notification to personnel response and then to on-site verification. The length of time for single-point verification depends largely on the distance between the personnel and the location of the alarm. Under ideal circumstances, if the distance is short, personnel may only need 10 minutes to reach the site. However, if the distance is far or transportation is inconvenient, the verification time may be extended to 1 hour or even longer. This verification method that relies on manual labor and physical distance is not only inefficient, but also easily affected by multiple uncontrollable factors. Summary of the Invention

[0003] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0004] The present invention provides an unmanned optical fiber early warning alarm information verification method, comprising the following steps:

[0005] Step S1: Receive and analyze alarm information to extract the location, risk level, and trigger time of the alarm point;

[0006] Step S2: Collect real-time status data of the camera and drone, including device online status, power level, coverage, and environmental factors;

[0007] Step S3: Dynamically calculate the alarm urgency based on the alarm risk type and trigger time, and the urgency decays over time;

[0008] Step S4: Based on the spatial relationship between the device location and the alarm point, the camera's field of view coverage efficiency and the drone's operation coverage efficiency are calculated respectively;

[0009] Step S5: estimating the time it takes for the camera to adjust the preset position and the time it takes for the drone to fly to the alarm point;

[0010] Step S6: Dynamically score and prioritize devices based on the alarm urgency, device coverage efficiency, and arrival time;

[0011] Step S7: dispatch the highest-scoring device to perform the inspection according to priority. If the device is unavailable, manual handling is performed.

[0012] Step S8: Record the verification results and optimize the scoring parameters to improve the efficiency of subsequent scheduling decisions.

[0013] Furthermore, the step S1 includes the following steps:

[0014] Step S11: The optical fiber warning host pushes alarm information in real time through the API interface. The alarm information includes a timestamp, longitude and latitude coordinates, and a risk type code.

[0015] Step S12: The inspection platform analyzes the alarm information and extracts the alarm point location La=(lat, lng), risk level Ra and trigger time Ta.

[0016] Furthermore, step S2 includes the following steps:

[0017] Step S21: Acquire real-time status data of the camera, including online status Sd, power Bd, and preset field of view Vd = {(lati, lngi, radius)};

[0018] Step S22: Acquire the real-time status data of the drone, including the online status Su, the remaining battery Bu, the current position Lu, and the maximum operating radius Ru;

[0019] Step S23: Collect environmental factor data and generate an environmental weight factor We.

[0020] Furthermore, step S3 includes the following steps:

[0021] Step S31, mapping the initial emergency value E0 according to the risk type;

[0022] Step S32: Dynamically adjust the alarm urgency and introduce a time decay function to calculate the dynamic urgency. The formula is as follows:

[0023]

[0024] Where Ea is the dynamic alarm urgency, ranging from 0 to 1, which decays with the unprocessed time. E0 is the initial urgency value obtained by risk type mapping. λ is the decay coefficient. Tc is the current system time. Ta is the alarm trigger time. e is the base of the natural logarithm.

[0025] Furthermore, step S4 includes the following steps:

[0026] Step S41: Ensure that the cameras that are close to the alarm point and have good field of view are preferentially dispatched. The coverage efficiency is calculated based on the distance between the alarm point and the center of the camera's preset field of view. When the spherical distance di is less than radius i, the coverage efficiency is: Otherwise Cd=0;

[0027] Where di is the spherical distance between the alarm point and the center of the camera's i-th preset position field of view, Cd is the camera coverage efficiency, and radiusi is the radius of the camera's i-th preset position field of view;

[0028] Step S42: Evaluate the coverage capability of the UAV’s operating range to the warning point, and calculate the coverage efficiency based on the distance between the current position and the warning point. If du<Ru, the coverage efficiency is: Otherwise Cu=0;

[0029] Where Cu is the UAV coverage efficiency, Ru is the maximum operating radius of the UAV, and du is the Euclidean distance from the current position of the UAV to the warning point.

[0030] Furthermore, step S5 includes the following steps:

[0031] Step S51: The camera arrival time Dd is the fixed time taken to adjust the preset position;

[0032] Step S52: The drone arrival time Du is calculated using the flight speed vu, take-off and landing time, and distance du using the following formula:

[0033]

[0034] Where Du is the estimated total time it takes for the drone to reach the warning point, vu is the flight speed of the drone, ttakeoff is the takeoff time of the drone, and tlanding is the landing time of the drone.

[0035] Furthermore, step S6 includes the following steps:

[0036] Step S61: Implement multi-dimensional dynamic scheduling decisions, comprehensively consider alarm urgency, equipment availability, coverage efficiency, and environmental factors, and calculate the equipment priority score Sp. The formula is as follows:

[0037]

[0038] Where Sp is the device priority score, α, β, γ, and δ are dynamic adjustment coefficients, which are the weights of controlling alarm urgency, camera efficiency, drone efficiency, and environmental factors, respectively. Ea is the dynamic alarm urgency, Ad is the camera availability status, which takes a value of 0 or 1, Au is the drone availability status, and We is the environmental weight factor, which is affected by weather conditions.

[0039] Step S62: Sort by score and select the device with the highest Sp to perform the verification task.

[0040] Furthermore, step S7 includes the following steps:

[0041] Step S71: Set the highest-scoring device as a camera, control it to jump to a matching preset position and return a video stream;

[0042] Step S72: Assume that the highest-scoring device is a drone, plan the optimal flight path, and hover to take photos;

[0043] Step S73: If the scores of all devices are lower than the threshold, mark that manual intervention is required and push the alarm to the disposal queue.

[0044] Furthermore, step S8 includes the following steps:

[0045] Step S81: Record the device execution time, verification results, and environmental data;

[0046] Step S82: Optimize the dynamic adjustment coefficients α, β, γ, and δ through machine learning.

[0047] The present invention has the following beneficial effects:

[0048] The verification method of the present invention greatly improves the verification efficiency by introducing multiple sensing and defense means such as drones, video surveillance, and fiber optic early warning to communicate with each other. This method can quickly process large amounts of alarm data, reduce the tediousness and errors of manual operations, and make the verification process more efficient and accurate.

[0049] At the same time, the new method has strong execution capabilities, which can ensure the timeliness and accuracy of verification results, providing strong guarantees for pipeline operation safety.

[0050] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1 The present invention is a flowchart of an unattended optical fiber early warning alarm information verification method. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0054] See also Figure 1 As shown, the present invention is an unattended optical fiber early warning alarm information verification method, comprising the following steps:

[0055] Step S1: Receive and analyze alarm information to extract the location, risk level, and trigger time of the alarm point;

[0056] Step S2: Collect real-time status data of the camera and drone, including device online status, power level, coverage, and environmental factors;

[0057] Step S3: Dynamically calculate the alarm urgency based on the alarm risk type and trigger time, and the urgency decays over time;

[0058] Step S4: Based on the spatial relationship between the device location and the alarm point, the camera's field of view coverage efficiency and the drone's operation coverage efficiency are calculated respectively;

[0059] Step S5: estimating the time it takes for the camera to adjust the preset position and the time it takes for the drone to fly to the alarm point;

[0060] Step S6: Dynamically score and prioritize devices based on the alarm urgency, device coverage efficiency, and arrival time;

[0061] Step S7: dispatch the highest-scoring device to perform the inspection according to priority. If the device is unavailable, manual handling is performed.

[0062] Step S8: Record the verification results and optimize the scoring parameters to improve the efficiency of subsequent scheduling decisions.

[0063] Step S1 includes the following steps:

[0064] Step S11: The optical fiber warning host pushes alarm information in real time through the API interface. The alarm information includes timestamp, longitude and latitude coordinates, and risk type code;

[0065] Step S12: The inspection platform analyzes the alarm information and extracts the alarm point location La=(lat, lng), risk level Ra and trigger time Ta.

[0066] Step S2 includes the following steps:

[0067] Step S21: Acquire real-time status data of the camera, including online status Sd, power Bd, and preset field of view Vd = {(lati, lngi, radius)};

[0068] Step S22: Acquire the real-time status data of the drone, including the online status Su, the remaining battery Bu, the current position Lu, and the maximum operating radius Ru;

[0069] Step S23: Collect environmental factor data and generate an environmental weight factor We.

[0070] Step S3 includes the following steps:

[0071] Step S31, mapping the initial emergency value E0 according to the risk type;

[0072] Step S32: Dynamically adjust the alarm urgency to avoid unreasonable reduction in the priority of alarms that have not been processed for a long time. A time decay function is introduced to calculate the dynamic urgency. The formula is as follows:

[0073]

[0074] Where Ea is the dynamic alarm urgency, ranging from 0 to 1, which decays with the unprocessed time. E0 is the initial urgency value obtained by risk type mapping. λ is the decay coefficient. Tc is the current system time. Ta is the alarm trigger time. e is the base of the natural logarithm.

[0075] Step S4 includes the following steps:

[0076] Step S41: Ensure that the cameras that are close to the alarm point and have good field of view are preferentially dispatched. The coverage efficiency is calculated based on the distance between the alarm point and the center of the camera's preset field of view. When the spherical distance di is less than radius i, the coverage efficiency is: Otherwise Cd=0;

[0077] Where di is the spherical distance between the alarm point and the center of the camera's i-th preset position field of view, Cd is the camera coverage efficiency, and radiusi is the radius of the camera's i-th preset position field of view;

[0078] Step S42: Evaluate the coverage capability of the UAV’s operating range to the warning point, and calculate the coverage efficiency based on the distance between the current position and the warning point. If du<Ru, the coverage efficiency is: Otherwise Cu=0;

[0079] Where Cu is the UAV coverage efficiency, Ru is the maximum operating radius of the UAV, and du is the Euclidean distance from the current position of the UAV to the warning point.

[0080] Step S5 includes the following steps:

[0081] Step S51: The camera arrival time Dd is the fixed time taken to adjust the preset position;

[0082] Step S52: The drone arrival time Du is calculated using the flight speed vu, take-off and landing time, and distance du using the following formula:

[0083]

[0084] Where Du is the estimated total time it takes for the drone to reach the warning point, vu is the flight speed of the drone, ttakeoff is the takeoff time of the drone, and tlanding is the landing time of the drone.

[0085] Step S6 includes the following steps:

[0086] Step S61: Implement multi-dimensional dynamic scheduling decisions, comprehensively consider alarm urgency, equipment availability, coverage efficiency, and environmental factors, and calculate the equipment priority score Sp. The formula is as follows:

[0087]

[0088] Where Sp is the device priority score, α, β, γ, and δ are dynamic adjustment coefficients, which are the weights of controlling alarm urgency, camera efficiency, drone efficiency, and environmental factors, respectively. Ea is the dynamic alarm urgency, Ad is the camera availability status, which takes a value of 0 or 1, Au is the drone availability status, and We is the environmental weight factor, which is affected by weather conditions.

[0089] Step S62: Sort by score and select the device with the highest Sp to perform the verification task.

[0090] Step S7 includes the following steps:

[0091] Step S71: Set the highest-scoring device as a camera, control it to jump to a matching preset position and return a video stream;

[0092] Step S72: Assume that the highest-scoring device is a drone, plan the optimal flight path, and hover to take photos;

[0093] Step S73: If the scores of all devices are lower than the threshold, mark that manual intervention is required and push the alarm to the disposal queue.

[0094] Step S8 includes the following steps:

[0095] Step S81: Record the device execution time, verification results, and environmental data;

[0096] Step S82: Optimize the dynamic adjustment coefficients α, β, γ, and δ through machine learning.

[0097] A specific application of this embodiment is:

[0098] Step 1:

[0099] The fiber optic early warning host pushes alarm information to the inspection platform in real time through the API interface. The alarm information includes timestamp, longitude and latitude coordinates and risk type code;

[0100] The inspection platform analyzes the alarm information and extracts the alarm point location La = (lat, lng), risk level Ra and trigger time Ta;

[0101] Step 2: Obtain the real-time status data of the camera, including the online status Sd (0 / 1 indicates offline / online), the battery level Bd, and the preset field of view Vd = {(lati,lngi,radiusi)} (circular coverage area with a radius of 200 meters);

[0102] Obtain real-time status data of the drone, including online status Su, remaining battery Bu, current location Lu and maximum operating radius Ru (five kilometers);

[0103] Collect environmental factor data (such as wind speed, rainfall, and light intensity) and generate an environmental weight factor We (ranging from 0.1 to 1.0, with the weight reduced in harsh environments) through a fuzzy logic algorithm;

[0104] Step 3:

[0105] Map the initial emergency value E0 according to the risk type, such as: high risk alarm E0 = 10, medium risk E0 = 6, low risk E0 = 3;

[0106] The time decay function is introduced to calculate the dynamic urgency:

[0107]

[0108] Step 4: Calculate the camera coverage efficiency. Take the spherical distance di between the alarm point and the camera preset position as an example. If di < radius i, then the coverage efficiency is:

[0109]

[0110] For example: di = 150m, radius i = 200m, then Cd = 0.25;

[0111] Evaluate the coverage capability of the UAV’s operating range to the warning point. Calculate the coverage efficiency based on the distance between the current position and the warning point. If du < Ru, the coverage efficiency is:

[0112]

[0113] For example: du = 3km, Ru = 5km, then Cu = 0.4;

[0114] Step 5:

[0115] The time required to adjust the camera preset position is fixed at Dd = 30s;

[0116] The arrival time Du of the drone is calculated by the flight speed vu = 10m / s, takeoff and landing time ttakeoff = tlanding = 60s and distance, and the formula is as follows:

[0117]

[0118] For example: du = 3km, then Du = 300 + 60 + 60 = 420s;

[0119] Step 6:

[0120] Taking into account alarm urgency, equipment availability, coverage efficiency, and environmental factors, the priority scoring formula is:

[0121]

[0122] Among them, α=0.5, β=0.3, γ=0.2, δ=0.1;

[0123] Sort by score. For example, if a camera has Sp=8.2 and a drone has Sp=6.5, the camera will be scheduled first.

[0124] Step 7

[0125] If the camera has the highest score, it will be controlled to jump to the matching preset position (for example, the PTZ will rotate to 30° azimuth of the alarm point) and transmit the real-time video stream back to the inspection platform;

[0126] If the drone scores the highest, it will plan the optimal flight path (e.g., using the A* algorithm to avoid obstacles) and hover 50 meters above the warning point to capture high-definition images;

[0127] If the scores of all devices are lower than the threshold (e.g. Sp < 5), manual intervention is required, an alarm is pushed to the handling queue, and the operation and maintenance personnel are notified;

[0128] Step 8

[0129] Record equipment execution time, verification results (such as "confirmed mechanical damage") and environmental data, and store them in a cloud database;

[0130] Optimize the dynamic adjustment coefficients α, β, γ, and δ through machine learning.

[0131] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0132] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An unattended optical fiber early warning alarm information verification method, characterized by: The following steps are involved: Step S1: Receive and analyze alarm information to extract the location, risk level, and trigger time of the alarm point; Step S2: Collect real-time status data of the camera and drone, including device online status, power level, coverage, and environmental factors; Step S3: Dynamically calculate the alarm urgency based on the alarm risk type and trigger time, and the urgency decays over time; Step S4: Based on the spatial relationship between the device location and the alarm point, the camera's field of view coverage efficiency and the drone's operation coverage efficiency are calculated respectively; Step S5: estimating the time it takes for the camera to adjust the preset position and the time it takes for the drone to fly to the alarm point; Step S6: Dynamically score and prioritize devices based on the alarm urgency, device coverage efficiency, and arrival time; Step S7: dispatch the highest-scoring device to perform the inspection according to priority. If the device is unavailable, manual handling is performed. Step S8: Record the verification results and optimize the scoring parameters to improve the efficiency of subsequent scheduling decisions.

2. The unattended optical fiber early warning alarm information verification method according to claim 1, characterized in that: The step S1 includes the following steps: Step S11: The optical fiber warning host pushes alarm information in real time through the API interface. The alarm information includes a timestamp, longitude and latitude coordinates, and a risk type code. Step S12: The inspection platform analyzes the alarm information and extracts the alarm point location La=(lat, lng), risk level Ra and trigger time Ta.

3. The unmanned optical fiber early warning alarm information verification method according to claim 1, characterized in that: The step S2 includes the following steps: Step S21: Acquire real-time status data of the camera, including online status Sd, power Bd, and preset field of view Vd = {(lati, lngi, radius)}; Step S22: Acquire the real-time status data of the drone, including the online status Su, the remaining battery Bu, the current position Lu, and the maximum operating radius Ru; Step S23: Collect environmental factor data and generate an environmental weight factor We.

4. The unmanned optical fiber early warning alarm information verification method according to claim 1, characterized in that: The step S3 includes the following steps: Step S31, mapping the initial emergency value E0 according to the risk type; Step S32: Dynamically adjust the alarm urgency to prevent the unreasonable decrease in the priority of alarms that have not been processed for a long time. A time decay function is introduced to calculate the dynamic urgency. The formula is as follows: Ea=E0×e-λ×(T c -T a ); Where Ea is the dynamic alarm urgency, ranging from 0 to 1, which decays with the unprocessed time. E0 is the initial urgency value obtained by risk type mapping. λ is the decay coefficient. Tc is the current system time. Ta is the alarm trigger time. e is the base of the natural logarithm.

5. The unattended optical fiber early warning alarm information verification method according to claim 1, characterized in that: The step S4 includes the following steps: Step S41: Ensure that the cameras that are close to the alarm point and have good field of view are preferentially dispatched. The coverage rate is calculated based on the distance between the alarm point and the center of the camera's preset field of view. When the spherical distance di is less than radius i, the coverage rate is: Otherwise Cd=0; Where di is the spherical distance between the alarm point and the center of the camera's i-th preset position field of view, Cd is the camera coverage efficiency, and radiusi is the radius of the camera's i-th preset position field of view; Step S42: Evaluate the coverage capability of the UAV’s operating range to the warning point, and calculate the coverage rate based on the distance between the current position and the warning point. If du<Ru, the coverage rate is: Otherwise Cu=0; Where Cu is the UAV coverage efficiency, Ru is the maximum operating radius of the UAV, and du is the Euclidean distance from the current position of the UAV to the warning point.

6. The unmanned optical fiber early warning alarm information verification method according to claim 1, characterized in that: The step S5 includes the following steps: Step S51: The camera arrival time Dd is the fixed time taken to adjust the preset position; Step S52: The drone arrival time Du is calculated using the flight speed vu, take-off and landing time, and distance du using the following formula: Where Du is the estimated total time it takes for the drone to reach the warning point, vu is the flight speed of the drone, ttakeoff is the takeoff time of the drone, and tlanding is the landing time of the drone.

7. The unattended optical fiber early warning alarm information verification method according to claim 1, characterized in that: The step S6 includes the following steps: Step S61: Implement multi-dimensional dynamic scheduling decisions, comprehensively consider alarm urgency, equipment availability, coverage efficiency, and environmental factors, and calculate the equipment priority score Sp. The formula is as follows: Where Sp is the device priority score, α, β, γ, and δ are dynamic adjustment coefficients, which are the weights of controlling alarm urgency, camera efficiency, drone efficiency, and environmental factors, respectively. Ea is the dynamic alarm urgency, Ad is the camera availability status, which takes a value of 0 or 1, Au is the drone availability status, and We is the environmental weight factor, which is affected by weather conditions. Step S62: Sort by score and select the device with the highest Sp to perform the verification task.

8. The unmanned optical fiber early warning alarm information verification method according to claim 1, characterized in that: The step S7 includes the following steps: Step S71: Set the highest-scoring device as a camera, control it to jump to a matching preset position and return a video stream; Step S72: Assume that the highest-scoring device is a drone, plan the optimal flight path, and hover to take photos; Step S73: If the scores of all devices are lower than the threshold, mark that manual intervention is required and push the alarm to the disposal queue.

9. The unattended optical fiber early warning alarm information verification method according to claim 1, characterized in that: The step S8 includes the following steps: Step S81: Record the device execution time, verification results, and environmental data; Step S82: Optimize the dynamic adjustment coefficients α, β, γ, and δ through machine learning.

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