CN113207826A - Intelligent buoy anti-interference method, device and system and storage medium - Google Patents
Intelligent buoy anti-interference method, device and system and storage medium Download PDFInfo
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Abstract
The application discloses an intelligent buoy anti-interference method, device, system and computer readable storage medium, the method comprises: collecting various environmental data, collecting float movement data, judging whether the float enters a vertical stable period, calculating an environmental reference threshold value according to underwater data or the underwater data and the environmental data after the float enters the vertical stable period, and judging whether the fish mouth is effective according to the environmental reference threshold value. Through the method and the device, the influence of various environmental factors on the fish mouth validity detection at the current moment can be predicted in a vertical stability period in real time, the problem that the acceleration is not accurate enough when being used as a fish mouth validity judgment standard can be effectively solved, water wave disturbance can be effectively filtered, interference caused by natural environments such as water flow and wind speed is eliminated, and the true condition of the fish mouth signal is guaranteed to the maximum extent. After the effective fish mouth is obtained, the mcu drives the floating tail light-emitting unit to alarm in a color changing mode, and the real-time performance of signals is guaranteed.
Description
Technical Field
The application relates to the technical field of outdoor sports application, in particular to an intelligent buoy anti-interference method, device, system and storage medium.
Background
At present, electronic floats with a hook-biting color-changing reminding function on the market generally have two modes, one mode is that a special water-sensitive material is coated at a certain position of a float tail, an electric signal is triggered when the float tail is contacted with water, and then the electronic float has an alarm function by changing the color of light of the float tail; the other is that a circuit board with an acceleration sensor is assembled in the float body, and when the sensor senses that the float moves up and down, the light at the float tail changes color to give an alarm. The first solution is easily affected by the environment or operation habit, such as raining, or the contamination of water when the user throws the toy easily triggers false alarm. The second scheme is mainly characterized in that the algorithm is insufficient, the alarm is triggered only by the acceleration change of the acceleration sensor, the second scheme is easily influenced by wind waves, the buoy inclination and the like, and the use experience is poor.
Generally, the electronic buoy based on the acceleration sensor judges whether a fish bites the hook or not by considering whether the acceleration value in the direction vertical to the water surface is larger than or smaller than the acceleration value in the static state by a certain range as a standard. The obvious drawback of this method is that the interference rejection capability is not feasible. When the water surface has wind waves or the buoy has slight defects to do work, the acceleration value perpendicular to the water surface direction is not constant, so that the fish biting hook is judged to be inaccurate according to the change value of the threshold value, and meanwhile, the change of the acceleration when the casting rod or the buoy enters the water to turn over can also cause the false triggering of an alarm signal.
Disclosure of Invention
The application provides an intelligent buoy anti-interference method, device, system and computer storage medium.
The embodiment of the application provides an intelligent buoy anti-interference method, which comprises the following steps: collecting various environmental data, collecting float movement data, judging whether the float enters a vertical stable period, calculating an environmental reference threshold value according to an underwater data change value or the underwater data change value and the environmental data after the float enters the vertical stable period, and judging whether the fish mouth is effective or not according to the environmental reference threshold value.
The embodiment of the application provides an intelligence is cursory anti jamming unit, includes: the buoy state judging module 301 is used for judging whether the buoy enters a vertical stable period or not and whether the buoy exits the vertical stable period or not; the environmental data acquisition module 302 comprises a water flow environmental data acquisition unit and a wind power environmental data acquisition unit, and is used for acquiring various underwater environmental data and water surface environmental data; the buoy movement data acquisition module 303 is used for acquiring buoy movement data; the environment reference threshold calculation module 304 comprises an environment threshold calculation unit 1 and an environment threshold calculation unit 2, and is used for calculating an environment reference threshold according to the floating movement data and the environment data; the fish-mouth validity judging module 305 is configured to judge the validity of the fish-mouth, and includes an acceleration judging unit and an inclination judging unit, and is configured to judge the validity of the fish-mouth according to the change value of the real-time underwater data z and the environmental reference threshold.
An embodiment of the present application provides an apparatus, including: the intelligent floating anti-interference device comprises a sensor, a processor, a memory, an input device, an output device, a light-emitting module and an intelligent floating anti-interference device; wherein the processor comprises one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method of the intelligent floating anti-jamming method according to any one of the embodiments of the application.
The embodiment of the application provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is executed by a processor to implement the method for the intelligent buoy anti-interference method according to any one of the embodiments of the application.
The embodiment of the application provides an anti-interference method of intelligence cursory, device, equipment and storage medium, through gathering cursory removal data, or cursory removal data and multiple environmental data, obtain environment benchmark threshold value, and judge the validity of fish mouth based on environment benchmark threshold value, when judging that the fish mouth is effective, the drive floats the light emitting module of tail and carries out the warning that discolours, in this application, influence that environmental factor such as stormy waves brought can be got rid of in the demonstration of intelligence cursory, guarantee authenticity and the real-time of signal.
With regard to the above embodiments and other aspects of the present application and implementations thereof, further description is provided in the accompanying drawings description, detailed description and claims.
Drawings
FIG. 1 is a flow chart of an anti-interference method for an intelligent buoy in the embodiment of the present application;
FIG. 2 is a flow chart of another intelligent buoy anti-interference method in the embodiment of the present application;
FIG. 3 is a schematic structural diagram of an intelligent floating anti-jamming device in the embodiment of the present application;
fig. 4 is a schematic structural diagram of an intelligent floating anti-interference device in the embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the present application more apparent, embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that the embodiments and features of the embodiments in the present application may be arbitrarily combined with each other without conflict.
Fig. 1 is a flowchart of an intelligent buoy anti-interference method in an embodiment of the present application, where the embodiment may be applicable to a situation where an intelligent buoy identifies whether a fish bites, and the method may be executed by an intelligent buoy anti-interference device in an embodiment of the present application, where the device may be implemented by software and/or hardware, and may be generally integrated in an intelligent system, and the method in the embodiment of the present application includes:
step 101, after the buoy enters water, a vertical stabilization period is added, and whether the buoy enters the vertical stabilization period is judged.
Specifically, the method comprises the following steps: setting a minimum value a0 and a maximum value b0 of a vertical judgment standard interval, after the floating is in water, acquiring underwater data w by mcu according to a preset frequency in real time, for example, acquiring the underwater data w once every 100 milliseconds in real time, increasing 1 for a stable counter m when the underwater data w in the z-axis direction is greater than a0 and w is less than b0 every time, and resetting and restarting the calculation for the stable counter if the underwater data w is not continuous in the (a0, b0) interval; when m acquired continuously is larger than a vertical stable period threshold value v1, the method considers that the vertical stable period is entered and clears an unstable counter n; wherein the z-axis is perpendicular to the direction of the water surface. And the underwater data w is the buoy acceleration value acquired on the z axis after the buoy enters water and before the buoy enters a vertical stable period.
And 102, acquiring an environment reference threshold after entering a vertical stable period.
Specifically, the method comprises the steps of continuously acquiring underwater data u of the buoy in the z-axis direction according to a preset frequency, wherein the underwater data u is the buoy acceleration value acquired on the z-axis before an environment reference threshold value is obtained after the buoy enters a vertical stable period. And calculating the average variation value of the acceleration of the buoy in a preset period according to the underwater data u, taking the average variation value of the acceleration as an environment reference threshold value h, and continuously calculating the average variation value of the acceleration according to the preset period to update the environment reference threshold value h immediately.
Illustratively, the difference between the acceleration value at the current time and the acceleration value after 100 milliseconds is calculated as the acceleration change value in 100 milliseconds, 9 acceleration change values are acquired in 100 milliseconds within 1 second, the 9 acceleration change values are added and divided by 9 to obtain the average acceleration change value within 1 second, the average acceleration change value within 1 second is used as the environment reference threshold value h, and the environment reference threshold value is calculated and updated every 1 second as above.
And 103, judging the validity of the fish mouth.
Specifically, the method comprises the following steps: and after entering a vertical stable period and obtaining an environment reference threshold, acquiring underwater data x, y and z in the directions of an x axis, a y axis and a z axis, wherein the x, y and z are acceleration values of the buoy in the directions of the x axis, the y axis and the z axis after obtaining the environment reference threshold respectively, and further obtaining three-axis acceleration change values delta x, delta y and delta z. The X axis, the Y axis and the Z axis adopt a standard coordinate system or a terrestrial coordinate system, and the X axis and the Y axis are parallel to the water surface direction; if the change value of the underwater data z in the z-axis direction is larger than the environmental reference threshold value h, preliminarily considering that the fish mouth is effective;
specifically, the z-axis direction acceleration change value is used as the criterion for determining the effective fish signal, and the change value of the acceleration value acquired twice before and after the effective fish signal is compared with the environmental reference threshold. Because the acceleration change brought by the fish mouth is not uniform in general, the change value of the acceleration is greatly changed when the fish mouth signal is actually present.
Illustratively, the speed values of the buoy are acquired twice continuously, the speed value of the buoy acquired for the first time is z1, the speed value of the buoy acquired for the second time is z2, the time interval between z2 and z1 can be set by self, for example, the time interval between z2 and z1 can be set to 100 milliseconds, and the change value Δ z of the speed value z of the buoy in the z-axis direction is calculated to be z2-z 1. The starting time of the acquisition may also be set by itself, for example, the data of z1 is acquired immediately after the environmental reference threshold is determined to be obtained, and the specific embodiment is not limited herein. This is considered to be an effective fishmouth signal when the acceleration change Δ z in the z-axis direction, which is the change in the underwater data in the z-axis direction, is greater than the environment reference threshold h, which is the most recently updated acceleration average change acquired during the vertical stabilization period.
Further optionally, when the validity of the fishmouth is judged in the step, the detection of the inclination state of the float can be added, and the inclination of the float can cause the deviation of the acceleration value in the vertical direction, so that the influence of the inclination of the float needs to be filtered again after the valid fishmouth signal is obtained. The acceleration change Δ x in the x-axis, the acceleration change Δ y in the y-axis are calculated, and the x-axis deviation ranges a1, b1 and the y-axis deviation ranges a2, b2 are set. When Δ x > a1 and Δ x < b1, while Δ y > a2 and Δ y < b2, then the fishmouth signal is finally considered valid. Wherein the method of calculation of Δ x and Δ y is the same as the method of calculating Δ z.
And step 104, immediately driving the light emitting module of the drift tail to alarm in a color changing way after the effective fish mouth is obtained.
And 105, judging whether the buoy exits the vertical stable period.
Specifically, the method comprises the following steps: and setting non-vertical stabilization period standard intervals a3 and b3, counting n, and increasing n by 1 when the vertical direction acceleration value z of the floating on the water surface is greater than a3 and z is less than b 3. If the underwater data is not continuous in the (a3, b3) interval, clearing the instability counter and restarting the calculation; and when the unstable count n is greater than the vertical unstable period threshold v2, exiting the vertical stable period, and resetting the stable counter m to zero to start counting again.
In the embodiment, by setting the vertical stabilization period, the vibration interference generated when the casting rod or the float enters water and turns over can be shielded in advance. The average change value of the float acceleration in the direction vertical to the water surface is collected to be used as an environment reference threshold value, the problem that the judgment standard of the fish-mouth validity only by adopting the acceleration is not accurate enough can be effectively solved, and the real condition of the fish-mouth signal is guaranteed to the maximum extent. After the effective fish mouth is obtained, the mcu drives the floating tail light-emitting unit to alarm in a color changing mode, and the real-time performance of signals is guaranteed.
FIG. 2 is another flow chart of the intelligent buoy anti-interference method; according to the embodiment, an interference elimination model is constructed by collecting environmental data and combining a machine learning algorithm, instant data of water flow acceleration and wind acceleration are predicted, an interference value caused by an environmental variable is predicted, the detected interference value is corrected in time, and the influence of the interference value is considered in the step of determining the validity of the fish mouth. The method of the present embodiment includes:
step 201, after the buoy enters water, a vertical stabilization period is added, and whether the buoy enters the vertical stabilization period is judged.
Specifically, the method comprises the following steps: setting a minimum value a0 and a maximum value b0 of a vertical judgment standard interval, after the vertical judgment standard interval floats into water, mcu collects underwater data w in real time according to a preset frequency, if the underwater data w can be collected once every 100 milliseconds, when the underwater data w in the z-axis direction is greater than a0 and w is less than b0 every time, a stable counter m is increased by 1, and if the underwater data are not continuous in the (a0, b0) interval, the stable counter is cleared and starts to calculate again; when m acquired continuously is larger than a vertical stable period threshold value v1, the method considers that the vertical stable period is entered and clears an unstable counter n; wherein the z-axis is perpendicular to the direction of the water surface. And the underwater data w is the buoy acceleration value acquired on the z axis after the buoy enters water and before the buoy enters a vertical stable period.
Step 202, after entering a vertical stable period, acquiring an environment reference threshold.
Specifically, acquiring underwater data u of a buoy in the z-axis direction, wherein the underwater data u is a buoy acceleration value acquired on the z-axis before an environment reference threshold value is obtained after the buoy enters a vertical stable period, and calculating an acceleration change value Δ u of the buoy on the z-axis for the first time according to the underwater data u, wherein the time interval between u2-u1 and u2 and u1 can be set by self, for example, is set to 100 milliseconds; the starting time of the acquisition can also be set by itself, for example, data of u1 is acquired immediately after entering the vertical stable period, and the specific embodiment is not limited herein. And obtaining a delta u value by calculating the acceleration change value for the first time, wherein the delta u value is used as the initial environment reference threshold value h 1. And after entering a vertical stable period, continuously acquiring various underwater environment data and water surface environment data. Underwater environmental data includes water flow velocity, water flow acceleration, and the like; the water surface environment data comprises wind speed, wind acceleration, wind direction and the like. And obtaining an environmental interference value i through an environmental learning model after fusion based on the collected underwater environmental data and the collected water surface environmental data. Training a low-speed motion model of the buoy under water through the underwater environment data to obtain an acceleration average change value of water flow on a z axis; obtaining a wind speed vector according to the wind speed and the wind direction, obtaining the component of the wind speed vector in the z-axis direction, training a low-speed motion model of the water surface according to the water surface environment data, and obtaining an acceleration average change value of the wind power on the z-axis; and combining a machine learning algorithm, constructing an interference elimination model by using a long-short term memory artificial neural network (LSTM), training and predicting the instant data of the water flow acceleration average change value and the wind force acceleration average change value, and outputting an instant environment interference value i.
Further, acquiring average change values of water flow and wind force acceleration, constructing an LSTM neural network model, and predicting interference data in the z-axis direction at the current moment;
a) acquiring data of wind acceleration and water acceleration in a preset period according to a preset frequency, calculating an average change value of the acceleration on a z axis, and performing normalization processing; if the underwater environment data and the water surface environment data can be acquired once at intervals of 100 milliseconds, 10 times of acquisition within 1 second can be performed;
b) establishing a first-layer neural network by taking the normalized initial data as a training data set;
c) adopting unsupervised learning to start layering training from bottom to top to obtain initial weight parameters connected with all hidden layers;
d) and performing inverse normalization on the output result of the deep neural network to obtain an instant acceleration change value in the z-axis direction, and taking the instant acceleration change value as an environmental interference value i at the current moment.
And performing weighted fusion on the initial environment reference threshold h1 and the environment interference value i to obtain a latest environment reference threshold h at the current moment, wherein a weight parameter can be set according to historical data, and a specific implementation manner is not limited herein.
And step 203, judging the validity of the fish mouth.
Specifically, the method comprises the following steps: and after entering a vertical stable period and obtaining an environment reference threshold, acquiring underwater data x, y and z in the directions of an x axis, a y axis and a z axis, wherein the x, y and z are acceleration values of the buoy in the directions of the x axis, the y axis and the z axis after obtaining the environment reference threshold respectively, and further obtaining three-axis acceleration change values delta x, delta y and delta z. The X axis, the Y axis and the Z axis adopt a standard coordinate system or a terrestrial coordinate system, and the X axis and the Y axis are parallel to the direction of the water surface. If the change value of the underwater data z in the z-axis direction is larger than the environmental reference threshold value h, preliminarily considering that the fish mouth is effective;
preferably, in this example, the z-axis direction acceleration variation is used as a criterion for determining the effective fish signal, and the variation of the acceleration acquired twice before and after the comparison is compared with the environmental reference threshold. Because the acceleration change brought by the fish mouth is not uniform in general, the change value of the acceleration is greatly changed when the fish mouth signal is actually present. And continuously acquiring the speed value of the buoy twice, wherein the speed value of the buoy acquired for the first time is z1, the speed value of the buoy acquired for the second time is z2, the time interval between z2 and z1 can be set by self, and the change value delta z of the speed value z of the buoy in the z-axis direction is calculated to be z2-z 1. Such as set to 100 milliseconds. The starting time of the acquisition may also be set by itself, for example, the data of z1 is acquired immediately after the environmental reference threshold is determined to be obtained, and the specific embodiment is not limited herein. This is considered to be an effective fishmouth signal when the float acceleration change Δ z in the z-axis direction, which is the change in the underwater data in the z-axis direction, is greater than the most recently updated environmental reference threshold h acquired during the stabilization period.
Further optionally, when the validity of the fishmouth is judged in the step, the detection of the inclination state of the float can be added, and the inclination of the float can cause the deviation of the acceleration value in the vertical direction, so that the influence of the inclination of the float needs to be filtered again after the valid fishmouth signal is obtained. The acceleration change Δ x in the x-axis, the acceleration change Δ y in the y-axis are calculated, and the x-axis deviation ranges a1, b1 and the y-axis deviation ranges a2, b2 are set. When Δ x > a1 and Δ x < b1, while Δ y > a2 and Δ y < b2, then the fishmouth signal is finally considered valid. Wherein the method of calculation of Δ x and Δ y is the same as the method of calculating Δ z.
And step 204, immediately driving a light emitting module of the drift tail to alarm in a color changing way after the effective fish mouth is obtained.
And step 205, judging whether the buoy exits the vertical stable period.
The method comprises the following specific steps: non-vertical stationary phase standard intervals a3 and b3 are set and n is counted, increasing by 1 when z > a3 and z < b 3. And when n is larger than the non-vertical stable period threshold value v2, exiting the vertical stable period and clearing the stable counter m.
In the embodiment, by setting the vertical stabilization period, the vibration interference generated when the casting rod or the float enters water and turns over can be shielded in advance. The method has the advantages that the method collects the cursory acceleration change value delta z2-z1, collects environmental data such as water flow speed, water flow acceleration, wind speed, wind acceleration and wind direction, constructs an environmental data model through an LSTM neural network, can instantly predict the influence of various environmental factors at the current moment on the fishmouth validity detection in a vertical stable period, can effectively improve the problem that the judgment standard is not accurate enough only by adopting the acceleration as the fishmouth validity, can effectively filter water wave disturbance, eliminates the interference caused by natural environments such as water flow and wind speed, and ensures the real condition of fishmouth signals to the maximum extent. After the effective fish mouth is obtained, the mcu drives the floating tail light-emitting unit to alarm in a color changing mode, and the real-time performance of signals is guaranteed.
Fig. 3 is a schematic structural diagram of an intelligent buoy anti-interference operation device in the embodiment of the present application, and the intelligent buoy anti-interference operation device provided in the embodiment of the present application can execute an operation method provided in any embodiment of the present application, and has corresponding modules and beneficial effects of the execution method. The device can be implemented by software and/or hardware, and specifically comprises: the device comprises a float state judgment module 301, an environment data acquisition module 302, a float movement data acquisition module 303, an environment reference threshold value calculation module 304 and a fishmouth validity judgment module 305.
The buoy state determination module 301 is configured to determine whether the buoy enters a vertical stability period or exits the vertical stability period;
an environment data acquisition module 302, configured to acquire multiple types of environment data;
the buoy movement data acquisition module 303 is used for acquiring buoy movement data;
an environment reference threshold calculation module 304, configured to obtain an environment reference threshold according to a variation Δ z of the underwater data z in the z-axis direction, or a variation Δ z of the underwater data z and the environment data;
the fishmouth validity determination module 305 determines the validity of the fishmouth according to the change value of the real-time underwater data z and the environmental reference threshold.
According to the technical scheme of the embodiment of the application, the influence caused by environmental factors such as wind waves and the like can be eliminated, and the authenticity and the real-time performance of signals are ensured.
In one embodiment, the float state determination module 301 includes:
entering a vertical stable period judging unit: used for judging whether the buoy enters a vertical stable period;
exiting the vertical stabilization phase judging unit: used for judging whether the buoy exits the vertical stable period;
in one embodiment, the environmental data acquisition module 302 includes:
and the water flow environment data acquisition unit is used for acquiring underwater environment data comprising water flow speed and acceleration of water flow on a z axis, and acquiring an acceleration average change value of the water flow on the z axis by training a low-speed motion model floating underwater.
The wind environment data acquisition unit is used for acquiring water surface environment data including wind speed, wind acceleration and wind direction, acquiring a wind speed vector according to the wind speed and the wind direction, acquiring a component of the wind speed vector in the z-axis direction, and acquiring an acceleration average change value of wind power on the z-axis direction by training a low-speed motion model of the water surface.
In one embodiment, the float movement data acquiring module 303 is configured to continuously acquire a certain amount of data in the x-axis, y-axis and z-axis directions, including the speed of the float movement, the acceleration of the float movement and the variation value of the acceleration of the float on the x-axis, y-axis and z-axis.
In one embodiment, the environment threshold calculation module 304 includes:
after entering a vertical stabilization period, the environment threshold calculation unit 1 calculates an average variation value of acceleration in a preset period, and takes the average variation value of acceleration as an environment reference threshold h.
After entering a vertical stability period, the environment threshold value calculation unit 2 calculates the acceleration change value for the first time to obtain an initial environment reference threshold value, after acquiring the average change value of the water flow and wind acceleration, constructs an LSTM neural network model based on the acceleration change value data, predicts interference data in the z-axis direction as an environment interference value, and obtains a final environment reference threshold value h by considering the interference of an instant environment interference value on the basis of the initial environment reference threshold value.
In one embodiment, the fishmouth validity determination module 305 includes:
and the acceleration judging unit is used for judging whether the fish mouth is effective or not by comparing the change value of the acceleration in the z-axis direction, which is used as the judgment standard of the effective fish signal, with the environmental threshold value after entering the vertical stable period.
And the inclination judging unit is used for filtering the influence of the inclination of the floating body by calculating the acceleration change values on the x axis and the y axis.
Fig. 4 is a schematic structural diagram of the anti-jamming operating system of the intelligent buoy in the embodiment of the present application, as shown in fig. 4, the apparatus includes a sensor 40, an input device 41, a memory 42, a processor 43, an intelligent buoy anti-jamming device 44, an output device 45, and a light module 46; the above components may be connected by a bus or other means, and fig. 4 illustrates the connection by a bus as an example.
In one embodiment, the sensor 40 is used as a sensing medium for acquiring the speed, acceleration and environmental parameters of the float, and the acquiring manner is not limited herein.
In one embodiment, the input device 41 is responsible for processing the data acquired by the sensor.
In one embodiment, the memory 42 is used as a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as the modules corresponding to the intelligent float anti-jamming operation device in the embodiment of the present application (the float state determination module 301, the environment data acquisition module 302, the float movement data acquisition module 303, the environment reference threshold calculation module 304, and the fishmouth validity determination module 305). The processor 43 executes various functional applications and data processing of the device by executing software programs, instructions and modules stored in the memory 42, so as to realize the above-mentioned intelligent buoy anti-interference method.
In one embodiment, the memory 42 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 42 may include high-speed random access memory, and may also include non-volatile memory, such as at least one cache memory device, flash memory device, or other non-volatile solid-state memory device.
In one embodiment, the number of the processors 43 may be one or more, and one processor 43 is taken as an example in fig. 4;
in one embodiment, the output device 45 is responsible for the output of data;
in one embodiment, the light module 46 immediately receives a valid fish mouth and provides a color change alarm.
For example, the apparatus in this embodiment of the present application may include a processing device (e.g., a central processing unit, etc.), a read-only memory, an input/output (I/O) interface, and the like, where the apparatus in this embodiment of the present application further includes the following modules: the device comprises a float state judgment module 301, an environment data acquisition module 302, a float movement data acquisition module 303, an environment reference threshold value calculation module 304 and a fishmouth validity judgment module 305. The equipment of the embodiment of the application can realize the intelligent buoy anti-interference method, and the specific steps can be as follows:
(1) collecting underwater data after the floater enters water;
and after the buoy enters water, a vertical stabilization period is added, whether the buoy enters the vertical stabilization period is judged, and if the buoy enters the vertical stabilization period, the underwater data w in the z-axis direction is continuously acquired. Wherein the z-axis is perpendicular to the direction of the water surface; and the underwater data w is the buoy acceleration value acquired on the z axis after the buoy enters water and before the buoy enters a vertical stable period. (ii) a
(2) And (4) judging the floating state:
a) setting a minimum value a0 and a maximum value b0 of a vertical judgment standard interval, acquiring underwater data w in real time by mcu according to a preset frequency, for example, acquiring the underwater data once every 100 milliseconds in real time, increasing 1 to a stable counter m when the underwater data w in the z-axis direction is greater than a0 and w is less than b0 every time, and resetting and restarting the calculation of the stable counter if the underwater data are not continuous in the (a0, b0) interval; when m of continuous acquisition is larger than a vertical stable period threshold value v1, the vertical stable period is considered to be entered and an instability counter n is cleared.
b) Non-vertical stationary phase standard intervals a3 and b3 are set and n is counted, increasing by 1 when z > a3 and z < b 3. And when n is greater than the non-stable period threshold value v2, exiting the vertical stable period and clearing the stable counter m.
(3) Collecting environmental data;
acquiring underwater environment data including water flow speed and water flow acceleration, training a low-speed motion model of the buoy under water through the underwater environment data, and obtaining an acceleration average change value of water flow on a z axis;
acquiring water surface environment data including wind speed, wind acceleration, wind direction and the like, acquiring a wind speed vector according to the wind speed and the wind direction, acquiring a component of the wind speed vector in the z-axis direction, training a low-speed motion model of the water surface through the water surface environment data, and acquiring an acceleration average change value of the wind power on the z-axis.
(4) Computing environment benchmark threshold
After entering a vertical stable period, calculating an acceleration average change value of underwater data u of the buoy in a preset period, and taking the acceleration average change value as an environment reference threshold value h; the underwater data u is a buoy acceleration value collected on the z axis before the environment reference threshold value is obtained after the buoy enters a vertical stable period;
or after entering a vertical stable period, acquiring an acceleration change value delta u, which floats on the z axis, from u2-u1, and obtaining a value delta u by calculating the acceleration change value for the first time, wherein the value delta u is used as an initial environment reference threshold h 1; constructing an LSTM neural network model according to the acquired average variation value of the acceleration of the water flow on the z axis and the average variation value of the acceleration of the wind force on the z axis, and predicting interference data i in the z axis direction; and after weighting and fusing the initial environment reference threshold h1 and the environment interference value i, outputting an instant environment interference value.
(5) Fish-mouth validity determination
The change value of the acceleration of the buoy acquired twice before and after is compared with the environmental reference threshold, and the change value of the acceleration is greatly changed when the fish mouth is really existed because the acceleration change brought by the fish mouth is not uniform under the general condition. The acceleration value acquired for the first time is z1, the acceleration value acquired for the second time is z2, when the change value of the buoyancy acceleration in the z-axis direction (delta z) is z2-z1 and is greater than the latest environment reference threshold value h acquired in the stable period, the change value is considered to be an effective fishmouth signal preliminarily, and the delta z is the change value of underwater data in the z-axis direction.
The acceleration change Δ x in the x-axis, the acceleration change Δ y in the y-axis are calculated, and the x-axis deviation ranges a1, b1 and the y-axis deviation ranges a2, b2 are set. When Δ x > a1 and Δ x < b1, while Δ y > a2 and Δ y < b2, then the fishmouth signal is finally considered valid.
Of course, the storage medium provided in the embodiments of the present application contains computer-executable instructions, and the computer-executable instructions are not limited to the method operations described above, and may also be used to execute the intelligent floating anti-jamming operation method provided in any embodiment of the present application.
From the above description of the embodiments, it is obvious for those skilled in the art that the present application can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a processor, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), and the like, and performs the methods described in the embodiments of the present application.
It is to be noted that, in the embodiment of the above-mentioned intelligent buoy anti-interference operation device, each included unit and module are only divided according to functional logic, but are not limited to the above division, as long as the corresponding function can be realized; in addition, specific names of the functional units are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the application.
The above description is only exemplary embodiments of the present application, and is not intended to limit the scope of the present application.
In general, the various embodiments of the application may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. For example, some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the application is not limited thereto.
Embodiments of the application may be implemented by a data processor of an apparatus executing computer program instructions, for example in a processor entity, or by hardware, or by a combination of software and hardware. The computer program instructions may be assembly instructions, instruction set architecture (isa) instructions, machine related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages.
Any logic flow block diagrams in the figures of this application may represent program steps, or may represent interconnected logic circuits, modules, and functions, or may represent a combination of program steps and logic circuits, modules, and functions. The computer program may be stored on a memory. The memory may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as, but not limited to, Read Only Memory (ROM), Random Access Memory (RAM), optical storage devices and systems, and the like. The computer readable medium may include a non-transitory storage medium. The data processor may be of any type suitable to the local technical environment, such as but not limited to general purpose computers, special purpose computers, microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), programmable logic devices (FGPAs), and processors based on a multi-core processor architecture.
The foregoing has provided by way of exemplary and non-limiting examples a detailed description of exemplary embodiments of the present application. Various modifications and adaptations to the foregoing embodiments may become apparent to those skilled in the relevant arts in view of the following drawings and the appended claims without departing from the scope of the invention. Therefore, the proper scope of the invention is to be determined according to the claims.
Claims (11)
1. An intelligent buoy anti-interference method comprises the following steps:
s1, after the buoy enters water, adding a vertical stabilization period, and judging whether the buoy enters the vertical stabilization period;
s2, calculating an environment reference threshold h after entering the vertical stable period;
s3, after the environment reference threshold h is obtained, continuously acquiring underwater data z of the buoy in the z-axis direction, and further obtaining a change value delta z of the underwater data z; wherein the z-axis is perpendicular to the water surface direction;
s4, if the change value delta z of the underwater data z of the buoy in the z-axis direction is larger than the environment reference threshold value h, preliminarily considering that the fish mouth is effective;
s5, continuously judging underwater data x and y in the horizontal direction, thereby confirming the validity of the fish mouth again, wherein the x axis and the y axis are parallel to the water surface direction;
s6, immediately driving the light-emitting module of the drift tail to alarm by changing color after the effective fish mouth is obtained;
and s7, judging whether the buoy exits the vertical stable period.
2. The method of claim 1, wherein the step s1 of determining whether the float enters a vertical stabilization period comprises: setting a minimum value a0 and a maximum value b0 of a vertical stable interval; after the buoy floats into water, acquiring underwater data w in real time according to a preset frequency, and increasing 1 by a stable counter m when the underwater data w in the z-axis direction is in a vertical stable interval (a0, b0) each time; if the underwater data w is not continuous in the (a0, b0) interval, the stability counter is cleared and the calculation is restarted; when m acquired continuously is larger than a threshold v1 in the vertical stable period, judging to enter the vertical stable period and resetting an unstable counter n; and the underwater data w is a buoy acceleration value acquired on the z axis before the buoy enters a vertical stable period after entering water.
3. The method of claim 1, the calculating of the environmental reference threshold h in step s2 comprising: after entering a vertical stabilization period, continuously acquiring underwater data u of a buoy in the z-axis direction according to a preset frequency, calculating an average variation value of the acceleration of the buoy in a preset period according to the underwater data u, taking the average variation value of the acceleration as an environment reference threshold value h, and continuously calculating the average variation value of the acceleration according to the preset period to update the environment reference threshold value h in real time; and the underwater data u is the buoy acceleration value acquired on the z axis before the environment reference threshold value is obtained after the buoy enters the vertical stable period.
4. The method of claim 1, the calculating of the environmental reference threshold h in step s2 comprising: after entering a vertical stabilization period, acquiring underwater data u of the buoy in the z-axis direction, calculating a first acceleration change value according to the underwater data u to obtain a delta u value, and taking the delta u value as an initial environment reference threshold value h 1; continuously acquiring various underwater environment data and water surface environment data, wherein the underwater environment data comprises water flow speed, water flow acceleration and the like; the water surface environment data comprises wind speed, wind acceleration, wind direction and the like; training a low-speed motion model of the buoy under water through the underwater environment data to obtain an acceleration average change value of water flow on a z axis; obtaining a wind speed vector according to the wind speed and the wind direction, obtaining the component of the wind speed vector in the z-axis direction, training a low-speed motion model of the water surface according to the water surface environment data, and obtaining an acceleration average change value of the wind power on the z-axis; combining a machine learning algorithm, constructing an interference elimination model by using a long-short term memory artificial neural network (LSTM), training and predicting instant data of a water flow acceleration average change value and a wind force acceleration average change value, and outputting an instant environment interference value i; obtaining a final environment reference threshold h according to the initial environment reference threshold h1 and the environment interference value i; and the underwater data u is a buoy acceleration value acquired on the z axis before the buoy obtains the environment reference threshold after entering the vertical stable period.
5. The method according to claim 4, wherein the method for calculating the environment reference threshold comprises the following steps;
s21, acquiring data of wind acceleration and water acceleration in a preset period according to a preset frequency, calculating an average acceleration change value of the data on a z axis, and performing normalization processing;
s22, establishing a first-layer neural network by taking the normalized initial data as a training data set;
s23, performing bottom-up layered training by adopting unsupervised learning to obtain initial weight parameters connected with hidden layers;
s24, performing inverse normalization on the output result of the deep neural network to obtain an instant acceleration change value in the z-axis direction, and taking the instant acceleration change value as an environmental interference value i at the current moment;
s25, carrying out weighted fusion on the obtained initial environment reference threshold value h1 and the environment interference value i to obtain the final environment reference threshold value h at the current moment.
6. The method of claim 1, said underwater data z in step s3 being acceleration values in the z-axis of a float acquired after obtaining an environmental reference threshold; the continuously acquiring underwater data z of the buoy in the z-axis direction comprises the following steps: and continuously acquiring the speed value of the buoy twice, wherein the speed value of the buoy on the z axis acquired for the first time is z1, the speed value of the buoy on the z axis acquired for the second time is z2, the time interval between z2 and z1 can be preset, and the change value delta z of the speed value z of the buoy in the z axis direction is calculated to be z2-z 1.
7. The method of claim 1, wherein step s5 comprises: calculating an acceleration value of the buoy on an x axis to obtain an acceleration change value delta x, calculating an acceleration value on a y axis to obtain an acceleration change value delta y, and confirming the validity of the fishmouth again if the underwater data change value delta x in the horizontal direction is within a threshold interval (a1, b1) and delta y is within a threshold interval (a2, b 2); the X axis, the Y axis and the Z axis adopt a standard coordinate system or a terrestrial coordinate system, and the X axis and the Y axis are parallel to the water surface direction.
8. The method according to claim 2, wherein step s7 specifically comprises: acquiring underwater data in real time according to a preset frequency, and increasing an unstable count n by 1 when the underwater data z in the z-axis direction is in a vertical unstable interval (a3, b3) each time; if the underwater data is not continuous in the (a3, b3) interval, clearing the instability counter n and restarting the calculation; and when the unstable count n is greater than the vertical unstable period threshold v2, exiting the vertical stable period, and resetting the stable counter m to zero to start counting again.
9. An intelligent float anti-interference device for executing the method of claims 1-8, comprising a float state determination module 301, an environmental data acquisition module 302, a float movement data acquisition module 303, an environmental reference threshold value calculation module 304, a fishmouth validity determination module 305; the float state determining module 301 comprises a unit for determining whether the float enters the vertical stabilization period and a unit for determining whether the float exits the vertical stabilization period, and is used for determining whether the float enters the vertical stabilization period or not; the environmental data acquisition module 302 comprises a water flow environmental data acquisition unit and a wind power environmental data acquisition unit and is used for acquiring various environmental data; the float movement data acquisition module 303 is configured to acquire float movement data; the environment reference threshold calculation module 304 includes an environment threshold calculation unit 1 and an environment threshold calculation unit 2, configured to calculate an environment reference threshold; the fishmouth validity determination module 305 includes an acceleration determination unit and an inclination determination unit, and is configured to determine validity of a fishmouth according to a difference of the real-time underwater data z and an environmental reference threshold.
10. An intelligent fishing system comprising a sensor, a processor, a memory, an input device and an output device, a light emitting module and an intelligent buoy anti-jamming device provided with the intelligent buoy anti-jamming device of claim 9.
11. A non-transitory computer readable storage medium having stored thereon a computer program for executing the method of claims 1-8.
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