Hydrological cableway obstacle avoidance early warning method, system, equipment and medium

Abstract

The invention discloses a hydrological cableway obstacle avoidance early warning method, system and device and a medium. The method comprises the following steps: acquiring initial movement track information of a target fish lead; acquiring a track monitoring area according to the initial motion track information; acquiring obstacle movement track information in the track monitoring area in real time; acquiring a relative speed, a relative distance and a track included angle between the target fish lead and the obstacle according to the initial movement track information and the obstacle movement track information; inputting the relative speed, the relative distance and the track included angle into a preset early warning evaluation model to obtain an early warning evaluation value; comparing the early warning evaluation value with a preset risk threshold value to obtain a comparison result; and obtaining a grading early warning mechanism of the target lead fish according to the comparison result, and the method has the advantage that the collision risk of the lead fish in a complex environment can be accurately evaluated.

Description

Technical Field

[0001] This application relates to the technical field of hydrological flow measurement, and particularly to a method, system, device and medium for obstacle avoidance and early warning of a hydrological cableway. Background Technique

[0002] The hydrological cableway flow measurement system is an advanced flow measurement technology that accurately transports a measuring instrument (i.e., a lead fish) to a designated position through a cross-river cableway. The lead fish is equipped with a variety of key components, such as a radar speed measurement sensor for measuring the flow velocity, a water level sensor for measuring the water level, a data acquisition device responsible for collecting and processing data, and a wired or wireless transmission system for data transmission. In the field of water conservancy, its importance is self-evident, and it can accurately measure water flow information, providing key data support for water conservancy project planning, water resource management, flood control and disaster reduction, etc.

[0003] Currently, when the hydrological cableway flow measurement system conducts flow measurement operations, the lead fish moves in the water body through the cableway system, and often has a collision risk due to rapid water flow, sudden changes in wind speed and direction, and floating obstacles (such as branches, garbage, etc.). The existing hydrological cableway obstacle avoidance and early warning methods rely on manual observation or a single sensor (such as ultrasonic) to detect obstacles, and there are defects in accurately assessing the collision risk of the lead fish in a complex environment, resulting in late warning and low safety of flow measurement operations. Summary of the Invention

[0004] The main purpose of this application is to provide a method, system, device and medium for obstacle avoidance and early warning of a hydrological cableway, aiming to solve the technical problem that the existing hydrological cableway flow measurement obstacle avoidance and early warning methods are difficult to accurately assess the collision risk of the lead fish in a complex environment.

[0005] To achieve the above object, this application provides a method for obstacle avoidance and early warning of a hydrological cableway, including the following steps:

[0006] Obtain the initial motion trajectory information of the target lead fish;

[0007] Obtain the trajectory monitoring area according to the initial motion trajectory information;

[0008] Obtain the obstacle motion trajectory information in the trajectory monitoring area in real time;

[0009] Obtain the relative velocity, relative distance and trajectory angle between the target lead fish and the obstacle according to the initial motion trajectory information and the obstacle motion trajectory information;

[0010] Input the relative velocity, relative distance and trajectory angle into a preset early warning evaluation model to obtain an early warning evaluation value;

[0011] Compare the early warning evaluation value with a preset risk threshold to obtain a comparison result;

[0012] According to the comparison result, obtain the grading warning mechanism of the target lead fish.

[0013] Optionally, the warning evaluation model expression is:

[0014] R = K1·V + K2·D -1 + K3·θ;

[0015] In the formula, R is the warning evaluation value, V is the relative velocity between the target lead fish and the obstacle, D is the relative distance between the target lead fish and the obstacle, θ is the trajectory angle between the target lead fish and the obstacle, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

[0016] Optionally, obtaining the relative velocity between the target lead fish and the obstacle includes:

[0017] Obtain the driving velocity vector of the target lead fish;

[0018] Obtain the water flow velocity and equivalent wind speed near the obstacle;

[0019] Output the sum of the velocity vectors of the water flow velocity and the equivalent wind speed as the actual velocity vector of the obstacle;

[0020] Output the difference between the driving velocity vector and the actual velocity vector as the relative velocity vector between the target lead fish and the obstacle;

[0021] Convert the relative velocity vector into relative velocity.

[0022] Optionally, let the equivalent wind speed be Vr, and the expression of Vr is:

[0023] When the obstacle is a branch, Vr = μ·Va;

[0024] When the obstacle is garbage, Vr = C·ρ·S·Va 2 / 2;

[0025] In the formula, μ is the proportionality coefficient, Va is the measured wind speed, C is the wind resistance coefficient, ρ is the air density, and S is the area of the garbage exposed above the water surface.

[0026] Optionally, obtain the obstacle movement trajectory information in the trajectory monitoring area in real time, including:

[0027] Obtain the first movement trajectory data of the obstacle collected by radar;

[0028] Obtain the second movement trajectory data of the obstacle collected by machine vision;

[0029] Assign weights to the first movement trajectory data and the second movement trajectory data through confidence to obtain the obstacle movement trajectory information after data fusion.

[0030] Optionally, the risk threshold includes a low risk threshold and a high risk threshold;

[0031] According to the comparison result, obtain the grading warning mechanism of the target lead fish, including:

[0032] If the comparison result is that the warning evaluation value is less than the low risk threshold, maintain the current flow measurement operation of the target lead fish;

[0033] If the comparison result is that the warning evaluation value is between the low risk threshold and the high risk threshold, send an audible and visual alarm message, and obtain the deceleration adjustment information of the target lead fish;

[0034] If the comparison result is that the warning evaluation value is greater than the high risk threshold, obtain the emergency braking control information of the target lead fish, and according to the initial movement trajectory information and the obstacle movement trajectory information, obtain the optimal obstacle avoidance path.

[0035] Optionally, obtaining the optimal obstacle avoidance path according to the initial movement trajectory information and the obstacle movement trajectory information includes:

[0036] According to the initial movement trajectory information and the obstacle movement trajectory information, predict the collision risk area within a preset future time, and generate a local risk heat map;

[0037] Taking the current position of the target lead fish as the starting point, and generating multiple candidate obstacle avoidance paths according to the local risk heat map;

[0038] Obtain the comprehensive cost function of each candidate obstacle avoidance path, and output the candidate obstacle avoidance path with the smallest value corresponding to the comprehensive cost function as the optimal obstacle avoidance path; where, the expression of the comprehensive cost function is:

[0039] C = W1·K4·L + W2·K5·ΔV + W3·K6·R max + W4·K7·E;

[0040] In the formula, C is the comprehensive cost value of the path, L is the total length of the candidate obstacle avoidance path, ΔV is the speed change amount required for obstacle avoidance, R max is the maximum risk evaluation value corresponding in the local risk heat map, E represents the motor energy consumption required for the candidate obstacle avoidance path, W1 is the first weight coefficient, W2 is the second weight coefficient, W3 is the third weight coefficient, W4 is the fourth weight coefficient, K4 is the fourth adjustment coefficient, K5 is the fifth adjustment coefficient, K6 is the sixth adjustment coefficient, and K7 is the seventh adjustment coefficient.

[0041] To achieve the above object, the present application also provides a hydrological cableway obstacle avoidance warning system, including:

[0042] The first information acquisition module is used to acquire the initial movement trajectory information of the target lead fish;

[0043] A monitoring area acquisition module, configured to acquire a trajectory monitoring area according to initial motion trajectory information;

[0044] A second information acquisition module, configured to acquire, in real time, obstacle motion trajectory information within the trajectory monitoring area;

[0045] A data processing module, configured to acquire a relative speed, a relative distance, and a trajectory angle between a target lead fish and an obstacle according to the initial motion trajectory information and the obstacle motion trajectory information;

[0046] An evaluation module, configured to input the relative speed, the relative distance, and the trajectory angle into a preset warning evaluation model to obtain a warning evaluation value;

[0047] A comparison module, configured to compare the warning evaluation value with a preset risk threshold to obtain a comparison result;

[0048] A warning module, configured to obtain a hierarchical warning mechanism for the target lead fish according to the comparison result.

[0049] To achieve the above object, the present application further provides a computer device, which includes a memory and a processor. A computer program is stored in the memory, and the processor executes the computer program to implement the above method.

[0050] To achieve the above object, the present application further provides a computer-readable storage medium, on which a computer program is stored, and the processor executes the computer program to implement the above method.

[0051] The beneficial effects that the present application can achieve are as follows:

[0052] Based on the initial motion trajectory information of the target lead fish, the present application can acquire a trajectory monitoring area, which is equivalent to a risk area where there is a high probability of collision between the target lead fish and obstacles within the area. Then, it acquires, in real time, the obstacle motion trajectory information within the trajectory monitoring area. According to the initial motion trajectory information and the obstacle motion trajectory information, it can acquire, in real time, the relative speed, the relative distance, and the trajectory angle between the target lead fish and the obstacle. Then, by inputting the relative speed, the relative distance, and the trajectory angle into a preset warning evaluation model, a warning evaluation value can be obtained through quantitative calculation. Since the relative speed, the relative distance, and the trajectory angle are all dynamic values, the calculated warning evaluation value is also a dynamic value, thereby quantitatively and dynamically evaluating the collision risk of the target lead fish. Then, by comparing the warning evaluation value with a preset risk threshold, a comparison result can be obtained, thereby accurately evaluating the collision risk of the lead fish in a complex environment, and a hierarchical warning mechanism for the target lead fish can be obtained according to the comparison result, and the warning is more reasonable. Description of the Drawings

[0053] To more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0054] Figure 1 It is a schematic flow chart of a method for obstacle avoidance and early warning of a hydrological cableway in an embodiment of the present application;

[0055] Figure 2 It is a schematic diagram of a trajectory monitoring area in an embodiment of the present application;

[0056] Figure 3 It is a schematic structural diagram of a hydrological cableway in an embodiment of the present application.

[0057] The realization of the purpose of the present application, the functional features and advantages will be further described with reference to the embodiments and the drawings. Specific Embodiments

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0059] It should be noted that all the directional indications (such as up, down, left, right, front, back...) in the embodiments of the present application are only used to explain the relative position relationship and movement conditions between the components in a specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0060] In the present application, unless otherwise clearly defined and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements or the interaction relationship between two elements, unless otherwise clearly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific situations.

[0061] In addition, if there are descriptions such as "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the meaning of "and / or" that appears throughout the text includes three parallel scenarios. Taking "A and / or B" as an example, it includes Scenario A, or Scenario B, or the scenario where both A and B are satisfied simultaneously. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0062] Embodiment 1

[0063] Referring to Figure 1 , this embodiment provides a method for obstacle avoidance warning of a hydrological cableway, including the following steps:

[0064] Obtain the initial motion trajectory information of the target lead fish;

[0065] According to the initial motion trajectory information, obtain the trajectory monitoring area;

[0066] Obtain the motion trajectory information of the obstacles within the trajectory monitoring area in real time;

[0067] According to the initial motion trajectory information and the motion trajectory information of the obstacles, obtain the relative velocity, relative distance, and trajectory angle between the target lead fish and the obstacles;

[0068] Input the relative velocity, relative distance, and trajectory angle into a preset warning evaluation model to obtain a warning evaluation value;

[0069] Compare the warning evaluation value with a preset risk threshold to obtain a comparison result;

[0070] According to the comparison result, obtain the hierarchical warning mechanism of the target lead fish.

[0071] In this embodiment, based on the initial motion trajectory information of the target lead fish, a trajectory monitoring area can be obtained. This trajectory monitoring area is equivalent to a risk area where there is a high probability of collision between the target lead fish and obstacles within this area. Then, the motion trajectory information of the obstacles within the trajectory monitoring area is obtained in real time. According to the initial motion trajectory information and the motion trajectory information of the obstacles, the relative velocity, relative distance, and trajectory angle between the target lead fish and the obstacles can be obtained in real time. Then, the relative velocity, relative distance, and trajectory angle are input into a preset warning evaluation model, and a warning evaluation value can be quantitatively calculated. Since the relative velocity, relative distance, and trajectory angle are all dynamic values, the calculated warning evaluation value is also a dynamic value. Thus, the collision risk of the target lead fish is quantitatively and dynamically evaluated. Then, the warning evaluation value is compared with a preset risk threshold to obtain a comparison result, so as to accurately evaluate the collision risk of the lead fish in a complex environment, and a hierarchical warning mechanism for the target lead fish can be obtained according to the comparison result, making the warning more reasonable.

[0072] It should be noted that, as Figure 2 shown, when determining the initial motion trajectory of the target lead fish (i.e., the preliminary formulated moving route of the lead fish for flow measurement), then a corresponding width is extended on both sides of the initial motion trajectory (the specific extended width can be obtained through training with historical big data), so as to form a trajectory monitoring area that needs to be monitored. By reasonably planning a certain range of the trajectory monitoring area, areas outside the range do not need to be monitored, which can improve the monitoring accuracy, reduce the data processing pressure, improve the data feedback efficiency, and thus improve the timeliness of warning information.

[0073] As an alternative embodiment, the expression of the warning evaluation model is:

[0074] R = K1·V + K2·D -1 + K3·θ;

[0075] In the formula, R is the warning evaluation value, V is the relative velocity between the target lead fish and the obstacle, D is the relative distance between the target lead fish and the obstacle, θ is the trajectory angle between the target lead fish and the obstacle, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

[0076] In this embodiment, the relative velocity, relative distance, and trajectory angle between the target lead fish and the obstacle are cited as influencing parameter factors for the warning evaluation value. Among them, the greater the relative velocity and trajectory angle, and the smaller the relative distance, the greater the risk of collision, that is, the corresponding warning evaluation value is greater. It is highly targeted and can truly represent the magnitude of the collision risk. At the same time, considering the different unit attributes of the above three influencing parameter factors, corresponding adjustment coefficients are used for conversion and adjustment, so as to perform quantitative superposition. Finally, the quantitative calculation of the warning evaluation value is realized, which has strong guidance.

[0077] It should be noted that the trajectory angle θ = 0°, which means the target lead fish and the obstacle are moving in the same direction, with a relatively low risk. The trajectory angle θ = 180°, which means the target lead fish and the obstacle are moving towards each other, with a relatively high risk.

[0078] As an alternative implementation, obtaining the relative velocity between the target lead fish and the obstacle includes:

[0079] Obtaining the driving velocity vector of the target lead fish;

[0080] Obtaining the water flow velocity and the equivalent wind speed near the obstacle;

[0081] Outputting the sum of the velocity vectors of the water flow velocity and the equivalent wind speed as the actual velocity vector of the obstacle;

[0082] Outputting the difference between the driving velocity vector and the actual velocity vector as the relative velocity vector between the target lead fish and the obstacle;

[0083] Converting the relative velocity vector into a relative velocity.

[0084] In this implementation, when calculating the relative velocity between the target lead fish and the obstacle, since the directions of movement may be different, the calculation should be carried out using velocity vectors. Decompose the velocity vectors into the X and Y directions. Since the running speed of the lead fish is mainly controlled by the system, it is less affected by environmental factors. Here, according to the flow measurement system, the driving velocity vector of the target lead fish is known, denoted as The actual velocity of the obstacle is mainly affected by the water flow velocity and the equivalent wind speed. The equivalent wind speed refers to the equivalent driving velocity of the obstacle. Therefore, here, by calculating the water flow velocity V2 and the equivalent wind speed V3 near the obstacle, their corresponding velocity vectors are respectively Then the relative velocity vector Finally, the relative velocity can be calculated

[0085] Such as Figure 3 As shown, the hydrological cableway includes steel towers on both sides. There are multiple working cables driven by motors between the tops of the two steel towers. A traveling frame is slidably arranged on the working cables. A lead fish is suspended at the bottom of the traveling frame by a suspension rope. The lead fish can move synchronously with the traveling frame. By driving different working cables to slide and controlling their sliding speeds, the moving direction and speed of the lead fish can be adjusted, thereby controlling the moving trajectory of the lead fish. Here, a camera and various sensors can be arranged on the traveling frame, and various sensors (such as current meters, gyroscopes, etc.) are also carried in the target lead fish, so that the information of the target lead fish and its surrounding environment (such as water flow velocity, wind speed, obstacle type, obstacle moving speed and direction, etc.) can be collected in real time.

[0086] As an alternative implementation, let the equivalent wind speed be Vr, and the expression of Vr is:

[0087] When the obstacle is a branch, Vr = μ·Va;

[0088] When the obstacle is garbage, Vr = C·ρ·S·Va 2 / 2;

[0089] In the formula, μ is the proportionality coefficient, Va is the measured wind speed, C is the wind resistance coefficient, ρ is the air density, and S is the area of the garbage exposed above the water surface.

[0090] In this embodiment, to improve the calculation accuracy and efficiency of the equivalent wind speed, the different calculation difficulties of the equivalent wind speed caused by the obstacle type are also considered here. When the obstacle is a branch, it is difficult to calculate the area of the branch exposed above the water surface and the area is small. Therefore, the measured wind speed has little impact on the branch, and the branch is mainly driven by the water flow. Therefore, when the obstacle is a branch, the equivalent wind speed can be calculated based on the calculation formula Vr = μ·Va to improve the calculation efficiency. The proportionality coefficient μ can represent the correlation degree between the measured wind speed and the equivalent wind speed and can be obtained by training based on historical big data. If the obstacle is garbage, since the garbage is generally in a lump, its area exposed above the water surface can be effectively calculated, and the measured wind speed also has a great impact on the flow speed and direction of the garbage. Therefore, based on the calculation formula Vr = C·ρ·S·Va 2 / 2, the corresponding equivalent wind speed can be accurately calculated. In summary, this embodiment provides different calculation methods for the equivalent wind speed based on the obstacle type, taking into account both the calculation accuracy and the calculation efficiency.

[0091] It should be noted that both the identification of the obstacle type and the calculation of the area of the garbage exposed above the water surface can be realized by machine vision recognition technology.

[0092] As an optional embodiment, the movement trajectory information of the obstacle in the trajectory monitoring area is obtained in real time, including:

[0093] Obtain the first movement trajectory data of the obstacle collected by radar;

[0094] Obtain the second movement trajectory data of the obstacle collected by machine vision;

[0095] Assign weights to the first movement trajectory data and the second movement trajectory data through confidence levels to obtain the movement trajectory information of the obstacle after data fusion.

[0096] In this embodiment, since the accuracy of the obstacle movement trajectory information is crucial for subsequent accurate early warning assessment, considering the data distortion caused by data collection using a single sensor, the first movement trajectory data of the obstacle is collected by radar, and the second movement trajectory data of the obstacle is collected by machine vision (using a vision camera). Then, the confidence levels of the two collection methods can be obtained through training based on historical big data, and weights are assigned to fuse the two sets of data, finally obtaining more accurate obstacle movement trajectory information.

[0097] As an alternative embodiment, the risk threshold includes a low-risk threshold and a high-risk threshold;

[0098] According to the comparison result, obtain the hierarchical early warning mechanism for the target lead fish, including:

[0099] If the comparison result shows that the early warning evaluation value is less than the low-risk threshold, maintain the current flow measurement operation of the target lead fish;

[0100] If the comparison result shows that the early warning evaluation value is between the low-risk threshold and the high-risk threshold, send out acoustic and optical alarm information, and obtain the deceleration adjustment information of the target lead fish;

[0101] If the comparison result shows that the early warning evaluation value is greater than the high-risk threshold, obtain the emergency braking control information of the target lead fish, and according to the initial movement trajectory information and the obstacle movement trajectory information, obtain the optimal obstacle avoidance path.

[0102] In this embodiment, the risk threshold is divided into a low-risk threshold and a high-risk threshold. When the early warning evaluation value is less than the low-risk threshold, that is, the risk is relatively low, the current flow measurement operation of the target lead fish can be maintained, that is, no adjustment is required. When the early warning evaluation value is between the low-risk threshold and the high-risk threshold, it indicates that there is a certain collision risk. Therefore, acoustic and optical alarm information can be sent to remind the back-end management personnel to pay continuous attention so that adjustments can be made at any time. At the same time, the target lead fish is automatically decelerated and adjusted for buffer observation. When the early warning evaluation value is greater than the high-risk threshold, it indicates that the collision risk is very high. At this time, emergency braking is required, and at the same time, the optimal obstacle avoidance path is automatically planned. Therefore, this embodiment not only realizes hierarchical early warning according to the risk level, but also can plan a new path subsequently, providing accurate empirical data support for the management personnel to make decisions to maximize the reduction of the collision risk.

[0103] As an alternative embodiment, according to the initial movement trajectory information and the obstacle movement trajectory information, obtain the optimal obstacle avoidance path, including:

[0104] According to the initial movement trajectory information and the obstacle movement trajectory information, predict the collision risk area within a preset future time, and generate a local risk heat map;

[0105] Taking the current position of the target lead fish as the starting point, and generating multiple candidate obstacle avoidance paths according to the local risk heat map;

[0106] Obtaining the comprehensive cost function of each candidate obstacle avoidance path, and outputting the candidate obstacle avoidance path with the smallest value corresponding to the comprehensive cost function as the optimal obstacle avoidance path; wherein, the expression of the comprehensive cost function is:

[0107] C = W1·K4·L + W2·K5·ΔV + W3·K6·R max + W4·K7·E;

[0108] In the formula, C is the comprehensive path cost value, L is the total length of the candidate obstacle avoidance path, ΔV is the speed change amount required for obstacle avoidance, and R max is the maximum risk assessment value corresponding in the local risk heat map, E represents the motor energy consumption required for the candidate obstacle avoidance path, W1 is the first weight coefficient, W2 is the second weight coefficient, W3 is the third weight coefficient, W4 is the fourth weight coefficient, K4 is the fourth adjustment coefficient, K5 is the fifth adjustment coefficient, K6 is the sixth adjustment coefficient, and K7 is the seventh adjustment coefficient.

[0109] In this embodiment, when performing path planning, first, according to the initial motion trajectory information and the obstacle motion trajectory information, the collision risk area within a preset future time can be predicted, and a local risk heat map is generated. Then, taking the current position of the target lead fish as the starting point, and combining with the local risk heat map, multiple candidate obstacle avoidance paths can be generated. Then, the relevant parameters of each candidate obstacle avoidance path are input into the constructed comprehensive cost function to obtain the corresponding value. The smaller the value, the better the path. Finally, the candidate obstacle avoidance path with the smallest value corresponding to the comprehensive cost function is output as the optimal obstacle avoidance path. At this time, the target lead fish can move forward according to the new optimal obstacle avoidance path, so as to realize automatically planning a better obstacle avoidance path according to the complex environment. Compared with manual planning, the planning accuracy is improved, and the planning efficiency is also improved. At the same time, this embodiment improves the calculation formula of the comprehensive cost function. In the formula, the comprehensive path cost value C is used to evaluate the preference degree of the path. The smaller the value, that is, the lower the cost paid, the better the selected path can be represented. And this embodiment introduces the total length L of the candidate obstacle avoidance path, the speed change amount ΔV required for obstacle avoidance, and the maximum risk assessment value R corresponding in the local risk heat map maxFour relatively targeted and representative influencing parameter factors, namely the total length L of the candidate obstacle avoidance path, the speed change amount ΔV, the maximum risk assessment value R, and the motor energy consumption E required for the candidate obstacle avoidance path, are comprehensively considered to evaluate the obstacle avoidance path, so as to effectively evaluate the comprehensive path cost value C. Considering the different attributes of the above four influencing parameter factors, corresponding adjustment coefficients are used for transformation and adjustment respectively, so that the four influencing parameter factors can be superimposed on each other. Considering the different influencing degrees of the four influencing parameter factors, corresponding weight coefficients are also assigned. The weight coefficients can be calibrated according to the task priority. For example, W1 = 0.25, W2 = 0.25, W3 = 0.35, W4 = 0.15.

[0110] It should be noted that in the above formula, the total length L can be calculated by the path planning algorithm, the speed change amount ΔV is the amplitude of the motor acceleration and deceleration required for obstacle avoidance, and the maximum risk assessment value R max can be directly extracted from the local risk heat map, reflecting the risk level of the area passed by the path. The motor energy consumption E is the power consumed by the target lead fish for the motor torque, running time, etc. during the movement on the corresponding path.

[0111] Embodiment 2

[0112] Based on the same inventive concept as the foregoing embodiment, this embodiment also provides a hydrological cableway obstacle avoidance early warning system, including:

[0113] A first information acquisition module, configured to acquire the initial movement trajectory information of the target lead fish;

[0114] A monitoring area acquisition module, configured to acquire a trajectory monitoring area according to the initial movement trajectory information;

[0115] A second information acquisition module, configured to acquire the movement trajectory information of the obstacles in the trajectory monitoring area in real time;

[0116] A data processing module, configured to acquire the relative speed, relative distance, and trajectory angle between the target lead fish and the obstacles according to the initial movement trajectory information and the movement trajectory information of the obstacles;

[0117] An evaluation module, configured to input the relative speed, relative distance, and trajectory angle into a preset early warning evaluation model to obtain an early warning evaluation value;

[0118] A comparison module, configured to compare the early warning evaluation value with a preset risk threshold to obtain a comparison result;

[0119] An early warning module, configured to obtain a hierarchical early warning mechanism for the target lead fish according to the comparison result.

[0120] For the relevant explanations and examples of each module in the system of this embodiment, reference can be made to the method of the foregoing embodiment, which will not be elaborated here.

[0121] Embodiment 3

[0122] Based on the same inventive concept as the foregoing embodiments, this embodiment provides a computer device, which includes a memory and a processor. A computer program is stored in the memory, and the processor executes the computer program to implement the above method.

[0123] Embodiment 4

[0124] Based on the same inventive concept as the foregoing embodiments, this embodiment provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and the processor executes the computer program to implement the above method.

[0125] The foregoing are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied to other related technical fields, shall be equally included in the patent protection scope of the present application.

Claims

1. A hydrological cableway obstacle avoidance warning method, characterized in that: The following steps are involved: Obtaining the initial motion trajectory information of the target lead fish; According to the initial motion trajectory information, the trajectory monitoring area is obtained; Obtain the obstacle movement trajectory information in the trajectory monitoring area in real time; According to the initial motion trajectory information and the obstacle motion trajectory information, the relative speed, relative distance and trajectory angle between the target lead fish and the obstacle are obtained; Inputting the relative speed, relative distance and trajectory angle into a preset early warning assessment model to obtain an early warning assessment value; Compare the early warning assessment value with the preset risk threshold to obtain a comparison result; According to the comparison results, a graded early warning mechanism for target lead fish is obtained.

2. A hydrological cableway obstacle avoidance warning method as claimed in claim 1, characterized in that: The early warning evaluation model expression is: R=K1·V+K2·D -1 +K3·θ; Where R is the warning evaluation value, V is the relative speed between the target lead fish and the obstacle, D is the relative distance between the target lead fish and the obstacle, θ is the trajectory angle between the target lead fish and the obstacle, K1 is the first adjustment coefficient, K2 is the second adjustment coefficient, and K3 is the third adjustment coefficient.

3. A hydrological cableway obstacle avoidance warning method as claimed in claim 1 or 2, characterized in that: Get the relative speed between the target lead fish and the obstacle, including: Obtain the driving speed vector of the target lead fish; Obtain water velocity and equivalent wind speed near obstacles; The sum of the velocity vectors of the water velocity and the equivalent wind speed is output as the actual velocity vector of the obstacle; The difference between the driving speed vector and the actual speed vector is output as the relative speed vector between the target lead fish and the obstacle; Convert the relative velocity vector to relative speed.

4. A hydrological cableway obstacle avoidance warning method as claimed in claim 3, characterized in that: Assume the equivalent wind speed is Vr, the expression of Vr is: When the obstacle is a tree branch, Vr = μ·Va; When the obstacle is garbage, Vr=C·ρ·S·Va 2 / 2; In the formula, μ is the proportionality coefficient, Va is the measured wind speed, C is the drag coefficient, ρ is the air density, and S is the area of ​​garbage exposed above the water surface.

5. A hydrological cableway obstacle avoidance warning method as claimed in claim 1, characterized in that: Obtain obstacle movement trajectory information in the trajectory monitoring area in real time, including: Acquire first motion trajectory data of the obstacle collected by the radar; Acquire second motion trajectory data of the obstacle collected by machine vision; The first motion trajectory data and the second motion trajectory data are weighted by confidence to obtain obstacle motion trajectory information after data fusion.

6. A hydrological cableway obstacle avoidance warning method as claimed in claim 1, characterized in that: The risk threshold includes a low risk threshold and a high risk threshold; According to the comparison results, a graded early warning mechanism for target lead fish is obtained, including: If the comparison result is that the warning assessment value is less than the low risk threshold, the current flow measurement operation of the target lead fish is maintained; If the comparison result is that the warning assessment value is between the low risk threshold and the high risk threshold, an audible and visual alarm message is sent, and deceleration adjustment information of the target lead fish is obtained; If the comparison result is that the warning evaluation value is greater than the high-risk threshold, the emergency braking control information of the target lead fish is obtained, and the optimal obstacle avoidance path is obtained according to the initial motion trajectory information and the obstacle motion trajectory information.

7. A hydrological cableway obstacle avoidance warning method as claimed in claim 6, characterized in that: According to the initial motion trajectory information and the obstacle motion trajectory information, the optimal obstacle avoidance path is obtained, including: Based on the initial motion trajectory information and obstacle motion trajectory information, the collision risk area within the preset time in the future is predicted and a local risk heat map is generated; Taking the current position of the target lead fish as the starting point, multiple candidate obstacle avoidance paths are generated based on the local risk heat map; Obtain the comprehensive cost function of each candidate obstacle avoidance path, and output the candidate obstacle avoidance path with the smallest value corresponding to the comprehensive cost function as the optimal obstacle avoidance path; wherein, the expression of the comprehensive cost function is: C=W1·K4·L+W2·K5·ΔV+W3·K6·R max +W4·K7·E; Where C is the comprehensive cost of the path, L is the total length of the candidate obstacle avoidance path, ΔV is the speed change required for obstacle avoidance, and R max is the corresponding maximum risk assessment value in the local risk heat map, E represents the motor energy consumption required for the candidate obstacle avoidance path, W1 is the first weight coefficient, W2 is the second weight coefficient, W3 is the third weight coefficient, W4 is the fourth weight coefficient, K4 is the fourth adjustment coefficient, K5 is the fifth adjustment coefficient, K6 is the sixth adjustment coefficient, and K7 is the seventh adjustment coefficient.

8. A hydrological cableway obstacle avoidance warning system, characterized in that: include: The first information acquisition module is used to obtain the initial motion trajectory information of the target lead fish; A monitoring area acquisition module is used to acquire a trajectory monitoring area according to the initial motion trajectory information; The second information acquisition module is used to obtain the obstacle movement trajectory information in the trajectory monitoring area in real time; The data processing module is used to obtain the relative speed, relative distance and trajectory angle between the target lead fish and the obstacle according to the initial motion trajectory information and the obstacle motion trajectory information; An evaluation module, used for inputting the relative speed, relative distance and trajectory angle into a preset early warning evaluation model to obtain an early warning evaluation value; A comparison module, used to compare the warning assessment value with a preset risk threshold to obtain a comparison result; The early warning module is used to obtain a graded early warning mechanism for target lead fish according to the comparison results.

9. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 7.

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