Control system for treble and bass sound equipment

Abstract

The invention relates to the technical field of sound equipment intelligent control, in particular to a treble and bass sound equipment control system. Carrying out biological recognition on a target in a monitoring area to obtain target parameter data and member motion data, and carrying out recognition through a clustering algorithm to obtain a target aggregation group and target aggregation parameters; a target aggregation danger coefficient is obtained through measurement and calculation according to the target aggregation parameters, and a critical aggregation group is determined according to the target aggregation danger coefficient; identifying an environment condition between the critical aggregation group and the monitoring point to obtain monitoring environment data; constructing a sound mode recognition model, recognizing target aggregation parameters and monitoring environment data of the critical aggregation group to obtain sound mode data and sound parameter data, and expelling; and according to the front and back changes of the critical aggregation group motion data, an expelling effect coefficient is measured and calculated, and the sound mode identification model is fed back and re-identified. According to the invention, through identification, expelling and feedback of the target organisms in the monitoring area, the organism expelling effect is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent audio control, in particular to a high-low audio equipment control system. Background Art

[0002] With the advancement of science and technology, sound expulsion technology has gradually attracted attention as an environmentally friendly, safe and efficient biological expulsion technology. Sound expulsion technology can disrupt the normal behavior of organisms and keep them away from specific areas by using sound stimulation of specific frequencies, volumes and waveforms.

[0003] Compared with traditional expulsion methods, sound expulsion technology has significant advantages. For example, non-invasiveness: sound expulsion does not involve direct harm to organisms, avoiding damage to the ecosystem. High efficiency: By precisely adjusting the sound frequency and intensity, a strong expulsion effect can be produced for specific types of organisms, avoiding unnecessary effects on other harmless organisms. Sustainability: Unlike chemical expulsion methods, sound expulsion does not cause environmental pollution or biological resistance problems.

[0004] However, the existing sound expulsion technology has a single mode of sound expulsion, and cannot accurately set the expulsion parameters according to the biological aggregation, movement and environmental conditions; it also fails to identify and feedback the expulsion effect, resulting in the sound biological expulsion effect being not accurate enough.

[0005] Therefore, a high and low frequency audio equipment control system is proposed. Summary of the invention

[0006] The purpose of the present invention is to provide a control system for high and low-pitched sound equipment, which obtains target parameter data and member motion data by identifying target organisms in the monitoring area, and obtains target clusters and target cluster parameters through clustering algorithm identification; obtains target cluster risk coefficients based on target cluster parameters, and determines critical clusters based on target cluster risk coefficients; identifies the environmental conditions between the critical clusters and the monitoring points to obtain monitoring environment data; constructs an audio pattern recognition model to identify the target cluster parameters and monitoring environment data of the critical clusters, obtains audio pattern data and audio parameter data, and expels them; calculates the expulsion effect coefficient based on the before and after changes in the critical cluster motion data, feeds back and re-identifies the audio pattern recognition model, and effectively improves the biological expulsion effect.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A high-low frequency audio equipment control system, comprising:

[0009] The target distribution recognition module uses the location of the audio equipment as the monitoring point, and then obtains the monitoring area according to the set monitoring radius; identifies the target organisms entering the monitoring area, and collects the target parameter data and member movement data;

[0010] The aggregation situation recognition module recognizes the target parameter data and member motion data through the clustering algorithm, and divides the target organisms into target aggregation groups according to the recognition results; and obtains the target aggregation parameters according to the target parameter data and member motion data of the target organisms in the target aggregation groups;

[0011] The danger level measurement module calculates the target aggregation danger coefficient according to the target aggregation parameters; and determines the most dangerous critical aggregation group according to the target aggregation danger coefficient;

[0012] The sound parameter acquisition module identifies the environmental conditions between the critical cluster and the monitoring point and obtains the monitoring environment data; constructs a sound pattern recognition model to identify the target aggregation parameters and monitoring environment data of the critical cluster, obtains the sound pattern data and sound parameter data, and performs biological expulsion; the sound pattern data includes bass mode and treble mode; the sound parameter data includes sound frequency, sound pressure level, sound waveform, sound duration and sound direction;

[0013] The sound effect feedback module obtains the sound wave time point according to the target aggregation parameters and the monitoring environment data; the expulsion effect coefficient is calculated according to the changes in the movement data of the critical aggregation group before and after the sound wave time point; and the sound pattern recognition model is fed back and re-identified according to the expulsion effect coefficient.

[0014] The target parameter data includes: biological type, biological quantity, biological size coefficient and biological risk coefficient; wherein the biological risk coefficient is obtained based on the data of loss caused by biological types to the protection target in historical data;

[0015] The member movement data includes: the relative position of the organism, the movement speed of the organism, the movement direction of the organism and the relative distance of the organism; the relative position of the organism is the position and orientation data of the organism relative to the monitoring point; the relative distance of the organism is the distance between the organism and the monitoring point.

[0016] The target aggregation parameters include the type of aggregated organisms, the biological hazard coefficient, the number of aggregated organisms, the size coefficient of aggregated organisms, the relative position of aggregated organisms, the movement speed of aggregated organisms, the movement direction of aggregated organisms, the relative distance of aggregated organisms and the density of aggregated organisms;

[0017] The process of obtaining the relative position of the aggregated organisms is as follows: obtaining the position data of the target organisms in the target aggregate group, constructing a minimum circumscribed circle according to the position data; and using the relative position data of the center of the minimum circumscribed circle and the monitoring point as the relative position of the aggregated organisms;

[0018] The movement speed of the aggregated organisms is the average movement speed of the target organisms in the target aggregate group;

[0019] The movement direction of the aggregated organisms is the average of the movement directions of the target organisms in the target aggregate group;

[0020] The relative distance of the aggregated organisms is the average value of the relative distances between the target organisms in the target aggregate group and the monitoring point;

[0021] The process of acquiring the density of aggregated organisms is as follows: constructing a minimum circumscribed circle according to the position data of the target organisms in the target aggregate group, and obtaining the density of aggregated organisms according to the volume of the minimum circumscribed circle and the number of aggregated organisms.

[0022] The calculation formula of the target aggregation risk coefficient is:

[0023]

[0024] Among them, Dans represents the target aggregation risk factor; cla i Indicates the biological hazard coefficient corresponding to the type of aggregated organisms; T i represents the size coefficient of aggregated organisms; n represents the number of aggregated organisms in the target aggregate group; θ() represents the angle function of the direction in space; dir represents the movement direction of aggregated organisms; D1 represents the direction of the line connecting the target aggregate group and the monitoring point; DT represents the angle threshold; vel represents the movement speed of aggregated organisms; VT represents the movement speed threshold; tan represents the relative distance of aggregated organisms; TT represents the relative distance threshold; den represents the density of aggregated organisms; ET represents the density threshold; α1, α2, α3 and α4 represent the risk coefficient; exp represents the exponential function with natural constants as the base.

[0025] The monitoring environment data includes environmental medium composition data, medium movement data, environmental temperature data and environmental noise data; the environmental medium composition data is the medium data between the animal and the monitoring point.

[0026] The sound pattern recognition model includes a parameter data input layer, a parameter data recognition layer, a sound pattern recognition layer and a sound parameter output layer; the parameter data input layer inputs the target aggregation parameters and the monitoring environment data into the model; the parameter data recognition layer extracts and recognizes the features of the target aggregation parameters and the monitoring environment data; the sound pattern recognition layer recognizes and obtains the sound pattern data, including the bass mode and the treble mode; the sound parameter output layer is used to recognize the sound parameter data, including the sound frequency, the sound pressure level, the sound waveform, the sound duration and the sound direction;

[0027] The sound pattern recognition model training process is as follows:

[0028] Conduct biological expulsion tests based on the sound, and obtain expulsion test data sets and expulsion test labels;

[0029] The expulsion test data set includes test target aggregation parameters, test monitoring environment data, test sound mode data and test sound parameter data; the expulsion test label includes a test expulsion effect coefficient;

[0030] The acoustic pattern recognition model is trained by expelling the test data set and expelling the test labels.

[0031] The sound wave time effect point is the moment when the sound emitted by the speaker has an effect on the organism;

[0032] The acoustic time point is calculated by determining the time when the sound emitted by the sound system intersects with the motion trajectory of the critical gathering group according to the environmental medium composition data, medium movement data, environmental temperature data and environmental noise data in the monitoring environment data, as the first time period; the motion trajectory of the critical gathering group is obtained by identifying the target gathering parameters;

[0033] The reaction time of the agglomerated organisms to the sound expulsion is obtained according to the experimental test as the second time period;

[0034] The sound wave time point is calculated based on the time point when the sound is emitted, the first time period and the second time period.

[0035] The process of acquiring the motion data of the critical cluster before and after the acoustic wave time point is as follows:

[0036] Setting a time threshold; acquiring movement data of the critical cluster within the time threshold range before the acoustic wave time-effect point to obtain first movement data, wherein the first movement data includes movement speed of the clustered organisms, movement direction of the clustered organisms, relative distance of the clustered organisms, and density of the clustered organisms;

[0037] The movement data of the critical cluster within the time threshold range after the acoustic wave time effect point is obtained to obtain the second movement data, wherein the second movement data includes the movement speed of the expelled organisms, the movement direction of the expelled organisms, the relative distance of the expelled organisms and the density of the expelled organisms.

[0038] The expulsion effect coefficient is calculated based on the first movement data and the second movement data:

[0039]

[0040] Among them, Eff represents the expulsion effect coefficient; tce represents the relative distance of the expelled organisms; tan represents the relative distance of the aggregated organisms; θ() represents the angle function of the direction in space; dir represents the movement direction of the aggregated organisms; D1 represents the direction of the line connecting the target cluster and the monitoring point; qir represents the movement direction of the expelled organisms; D2 represents the direction of the line connecting the target cluster and the monitoring point after the acoustic wave time point; f() represents the confusion degree function of the directional data; qsd represents the movement speed of the expelled organisms; vel represents the movement speed of the aggregated organisms; smd represents the density of the expelled organisms; den represents the density of the aggregated organisms; β1, β2, β3 and β4 represent the expulsion coefficients.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. The present invention obtains the target aggregation parameters of the target cluster, and obtains the target aggregation hazard coefficient of the target cluster from four dimensions, including the biological hazard coefficient and body weight of the target organisms in the target cluster, the movement direction and movement speed of the target organisms in the target cluster, the relative distance between the target cluster and the monitoring point, and the density of the target cluster; the hazard level of the target cluster is accurately and comprehensively measured through these four dimensions; and the target aggregation hazard coefficient is then used to accurately determine the most critical cluster with the highest hazard level.

[0043] 2. The present invention performs biological expulsion tests based on sound to obtain an expulsion test data set and an expulsion test label; the expulsion test data set includes test target aggregation parameters, test monitoring environment data, test sound pattern data and test sound parameter data; the expulsion test label includes a test expulsion effect coefficient; an audio pattern recognition model is obtained by training the expulsion test data set and the expulsion test label; the audio pattern recognition model can accurately identify according to the target aggregation parameters and the monitoring environment data to obtain the audio pattern data and audio parameter data with the best expulsion effect.

[0044] 3. The present invention obtains a sound wave time point according to the time when the sound emitted by the speaker intersects with the movement trajectory of the critical gathering group, and the reaction time of the gathering biological species to the sound; obtains the movement data of the critical gathering group within the time threshold range before and after the sound wave time point as the first movement data and the second movement data; calculates and obtains the expulsion effect coefficient according to the relative distance change, movement angle, movement speed change and biological density change between the first movement data and the second movement data; and accurately measures the biological expulsion effect through the expulsion effect coefficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a structural schematic diagram of a high and low-pitched audio equipment control system of the present invention;

[0046] Figure 2is a structural schematic diagram of the sound pattern recognition model of the present invention;

[0047] Figure 3 It is a schematic diagram of the structure of the audio equipment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Embodiment 1

[0050] A control system for high and low-pitched audio equipment, the structure of which is as follows Figure 1 As shown, including:

[0051] The target distribution recognition module uses the location of the audio equipment as the monitoring point, and then obtains the monitoring area based on the set monitoring radius; identifies the target organisms entering the monitoring area, and collects target parameter data and member movement data.

[0052] The target parameter data include: biological type, biological quantity, biological body size coefficient and biological hazard coefficient; wherein, the biological hazard coefficient is obtained based on the loss data caused by the biological type to the protection target in historical data, including economic losses, quantity losses, etc.; the biological body size coefficient is obtained by comparing the actual body size data of the target organism with the standard body size data; the standard body size data is obtained by averaging the samples collected from the target organism.

[0053] The member movement data includes: the relative position of the organism, the movement speed of the organism, the movement direction of the organism and the relative distance of the organism; the relative position of the organism is the position and orientation data of the organism relative to the monitoring point; the relative distance of the organism is the distance between the organism and the monitoring point.

[0054] The aggregation situation recognition module recognizes the target parameter data and member motion data through the clustering algorithm, and divides the target organisms into target aggregation groups according to the recognition results; and obtains the target aggregation parameters according to the target parameter data and member motion data of the target organisms in the target aggregation groups.

[0055] The target aggregation parameters include the type of aggregated organisms, the biological hazard coefficient, the number of aggregated organisms, the size coefficient of aggregated organisms, the relative position of aggregated organisms, the movement speed of aggregated organisms, the movement direction of aggregated organisms, the relative distance of aggregated organisms and the density of aggregated organisms;

[0056] The process of obtaining the relative position of the aggregated organisms is as follows: obtaining the position data of the target organisms in the target aggregate group, constructing a minimum circumscribed circle according to the position data; and using the relative position data of the center of the minimum circumscribed circle and the monitoring point as the relative position of the aggregated organisms;

[0057] The movement speed of the aggregated organisms is the average movement speed of the target organisms in the target aggregate group;

[0058] The movement direction of the aggregated organisms is the average of the movement directions of the target organisms in the target aggregate group, which is measured by the direction vector;

[0059] The relative distance of the aggregated organisms is the average value of the relative distances between the target organisms in the target aggregate group and the monitoring point;

[0060] The process of acquiring the density of aggregated organisms is as follows: constructing a minimum circumscribed circle according to the position data of the target organisms in the target aggregate group, and obtaining the density of aggregated organisms according to the volume of the minimum circumscribed circle and the number of aggregated organisms.

[0061] The present invention identifies target parameter data and member motion data through a clustering algorithm, and divides the target organisms into target clusters according to the identification results; obtains target cluster parameters according to the target parameter data and member motion data of the target organisms in the target clusters; accurately divides the target organisms to provide a basis for subsequent danger identification.

[0062] The danger level measurement module calculates the target aggregation danger coefficient based on the target aggregation parameters; and determines the critical aggregation group with the highest danger level based on the target aggregation danger coefficient.

[0063] The calculation formula of the target aggregation risk coefficient is:

[0064]

[0065] Among them, Dans represents the target aggregation risk factor; cla i Indicates the biological hazard coefficient corresponding to the type of aggregated organisms; T i represents the size coefficient of aggregated organisms; n represents the number of organisms in the target aggregate group; θ() represents the angle function of the direction in space; dir represents the movement direction of aggregated organisms; D1 represents the direction of the line connecting the target aggregate group and the monitoring point; DT represents the angle threshold; vel represents the movement speed of aggregated organisms; VT represents the movement speed threshold; tan represents the relative distance of aggregated organisms; TT represents the relative distance threshold; den represents the density of aggregated organisms; ET represents the density threshold; α1, α2, α3 and α4 represent the risk coefficient; exp represents an exponential function with a natural constant as the base.

[0066] The present invention obtains the target aggregation parameters of the target cluster, and obtains the target aggregation risk coefficient of the target cluster from four dimensions, including the biological risk coefficient and body weight of the target organisms in the target cluster, the movement direction and movement speed of the target organisms in the target cluster, the relative distance between the target cluster and the monitoring point, and the density of the target cluster; the risk level of the target cluster is accurately and comprehensively measured through these four dimensions; and the target aggregation risk coefficient is then used to accurately determine the critical cluster with the highest risk level.

[0067] The sound parameter acquisition module identifies the environmental conditions between the critical cluster and the monitoring point to obtain the monitoring environment data; constructs a sound pattern recognition model to identify the target aggregation parameters and monitoring environment data of the critical cluster, obtains the sound pattern data and sound parameter data, and performs biological expulsion; the sound pattern data includes bass mode and treble mode; the sound parameter data includes sound frequency, sound pressure level, sound waveform, sound duration and sound direction.

[0068] The monitoring environment data includes environmental medium composition data, medium movement data, environmental temperature data and environmental noise data; the environmental medium composition data is the medium data between the animal and the monitoring point, including gas, liquid, solid, etc.

[0069] The acoustic pattern recognition model is constructed based on a deep neural network model, and its structure is as follows: Figure 2 As shown; including a parameter data input layer, a parameter data recognition layer, an audio mode recognition layer and an audio parameter output layer;

[0070] The parameter data input layer inputs the target aggregation parameters and monitoring environment data into the model;

[0071] The parameter data recognition layer extracts and recognizes features of target aggregation parameters and monitoring environment data;

[0072] The sound mode recognition layer recognizes and obtains sound mode data, including a bass mode and a treble mode;

[0073] The sound parameter output layer is used to identify sound parameter data, including sound frequency, sound pressure level, sound waveform, sound duration and sound direction.

[0074] The training process of the acoustic pattern recognition model is:

[0075] Conduct biological expulsion tests based on the sound, and obtain expulsion test data sets and expulsion test labels;

[0076] The expulsion test data set includes test target aggregation parameters, test monitoring environment data, test sound mode data and test sound parameter data; the expulsion test label includes a test expulsion effect coefficient;

[0077] The acoustic pattern recognition model is trained by expelling the test data set and expelling the test labels.

[0078] The present invention performs biological expulsion test according to sound, and obtains an expulsion test data set and an expulsion test label; the expulsion test data set includes test target aggregation parameters, test monitoring environment data, test sound pattern data and test sound parameter data; the expulsion test label includes a test expulsion effect coefficient; an sound pattern recognition model is obtained by training the expulsion test data set and the expulsion test label; the sound pattern recognition model can accurately identify according to the target aggregation parameters and the monitoring environment data, and obtain the sound pattern data and sound parameter data with the best expulsion effect.

[0079] The sound effect feedback module obtains the sound wave time point according to the target aggregation parameters and the monitoring environment data; the expulsion effect coefficient is calculated according to the changes in the movement data of the critical aggregation group before and after the sound wave time point; and the sound pattern recognition model is fed back and re-identified according to the expulsion effect coefficient.

[0080] The sound wave time effect point is the moment when the sound emitted by the speaker has an effect on the organism;

[0081] The acoustic time point is calculated by determining the time when the sound emitted by the sound system intersects with the motion trajectory of the critical gathering group according to the environmental medium composition data, medium movement data, environmental temperature data and environmental noise data in the monitoring environment data, as the first time period; the motion trajectory of the critical gathering group is obtained by identifying the target gathering parameters;

[0082] According to experimental tests, the reaction time of the agglomerated biological species to the sound is obtained as the second time period;

[0083] The sound wave time point is calculated based on the time point when the sound is emitted, the first time period and the second time period.

[0084] The process of acquiring the movement data of the critical cluster before and after the acoustic wave time effect point is as follows: setting a time threshold;

[0085] Acquire the motion data of the target cluster within the time threshold range before the acoustic wave time effect point to obtain the first motion data, wherein the first motion data includes the motion speed of the clustered organisms, the motion direction of the clustered organisms, the relative distance of the clustered organisms and the density of the clustered organisms;

[0086] The motion data of the target cluster within the time threshold range after the acoustic wave time effect point is obtained to obtain the second motion data, wherein the second motion data includes the movement speed of the expelled organisms, the movement direction of the expelled organisms, the relative distance of the expelled organisms and the density of the expelled organisms.

[0087] The expulsion effect coefficient is calculated based on the first movement data and the second movement data:

[0088]

[0089] Among them, Eff represents the expulsion effect coefficient; tce represents the relative distance of the expelled organisms; tan represents the relative distance of the aggregated organisms; θ() represents the angle function of the direction in space; dir represents the movement direction of the aggregated organisms; D1 represents the direction of the line connecting the target cluster and the monitoring point; qir represents the movement direction of the expelled organisms; D2 represents the direction of the line connecting the target cluster and the monitoring point after the acoustic wave time point; f() represents the confusion degree function of the directional data, which is realized by the covariance matrix; qsd represents the movement speed of the expelled organisms; vel represents the movement speed of the aggregated organisms; smd represents the density of the expelled organisms; den represents the density of the aggregated organisms; β1, β2, β3 and β4 represent the expulsion coefficients.

[0090] The present invention obtains a sound wave time effect point according to the time when the sound emitted by the sound system intersects with the movement trajectory of the critical gathering group, and the reaction time of the gathering biological species to the sound; obtains the movement data of the critical gathering group within the time threshold range before and after the sound wave time effect point as the first movement data and the second movement data; calculates and obtains the expulsion effect coefficient according to the relative distance change, movement angle, movement speed change and biological density change between the first movement data and the second movement data; and accurately measures the biological expulsion effect through the expulsion effect coefficient.

[0091] The present invention identifies target organisms in a monitoring area, obtains target parameter data and member motion data, obtains target clusters and target cluster parameters through clustering algorithm identification; obtains target cluster risk coefficients based on target cluster parameters, and determines critical clusters based on target cluster risk coefficients; identifies environmental conditions between critical clusters and monitoring points to obtain monitoring environment data; constructs an audio pattern recognition model to identify target cluster parameters and monitoring environment data of critical clusters, obtains audio pattern data and audio parameter data, and expels them; calculates an expulsion effect coefficient based on changes before and after the critical cluster motion data, feeds back and re-identifies the audio pattern recognition model, and effectively improves the biological expulsion effect.

[0092] Embodiment 2

[0093] With the in-depth exploration of the ocean, the development and maintenance of transportation routes, the construction of offshore resource exploitation platforms, and the development of tourism models, human society is increasingly intersecting with the ocean.

[0094] When humans are active at sea, they often encounter interference from biological gatherings; for example, birds gathering on or flying around ships may collide with the ships, disrupting the navigator's vision and causing damage to the ships; at the same time, the droppings left by birds on ships contain acidic substances, which may corrode the hull over a long period of time. When fish gather in large numbers, it may interfere with the clarity of the waterway, affect the propulsion of the ship or the working efficiency of underwater equipment, etc.; even when large fish appear in crowded areas, it is easy to cause safety problems.

[0095] In addition, when organisms over-gather, they may have a negative impact on certain marine ecosystems and cause local ecological imbalance. For example, some birds affect the population of certain fish or marine organisms through excessive foraging, which in turn affects the marine food chain and requires the expulsion of organisms.

[0096] Expelling organisms by sound is a relatively convenient and environmentally friendly method. Using the high and low pitch sound equipment control system of the present invention can effectively achieve the effect of expelling marine organisms.

[0097] The structure of the control system of the high and low frequency audio equipment is as follows: Figure 1 As shown, including:

[0098] The target distribution recognition module uses the location of the audio equipment as the monitoring point, and then obtains the monitoring area based on the set monitoring radius; identifies the target organisms entering the monitoring area, and collects target parameter data and member movement data.

[0099] The target parameter data includes: biological type, biological quantity, biological size coefficient and biological risk coefficient; wherein the biological risk coefficient is obtained based on the data of loss caused by biological types to the protection target in historical data;

[0100] The member movement data includes: the relative position of the organism, the movement speed of the organism, the movement direction of the organism and the relative distance of the organism; the relative position of the organism is the position and orientation data of the organism relative to the monitoring point; the relative distance of the organism is the distance between the organism and the monitoring point.

[0101] The aggregation situation recognition module recognizes the target parameter data and member motion data through the clustering algorithm, and divides the target organisms into target aggregation groups according to the recognition results; and obtains the target aggregation parameters according to the target parameter data and member motion data of the target organisms in the target aggregation groups.

[0102] The target aggregation parameters include the type of aggregated organisms, the biological hazard coefficient, the number of aggregated organisms, the size coefficient of aggregated organisms, the relative position of aggregated organisms, the movement speed of aggregated organisms, the movement direction of aggregated organisms, the relative distance of aggregated organisms and the density of aggregated organisms;

[0103] The process of obtaining the relative position of the aggregated organisms is as follows: obtaining the position data of the target organisms in the target aggregate group, constructing a minimum circumscribed circle according to the position data; and using the relative position data of the center of the minimum circumscribed circle and the monitoring point as the relative position of the aggregated organisms;

[0104] The movement speed of the aggregated organisms is the average movement speed of the target organisms in the target aggregate group;

[0105] The movement direction of the aggregated organisms is the average of the movement directions of the target organisms in the target aggregate group;

[0106] The relative distance of the aggregated organisms is the average value of the relative distances between the target organisms in the target aggregate group and the monitoring point;

[0107] The process of acquiring the density of aggregated organisms is as follows: constructing a minimum circumscribed circle according to the position data of the target organisms in the target aggregate group, and obtaining the density of aggregated organisms according to the volume of the minimum circumscribed circle and the number of aggregated organisms.

[0108] The biological aggregation at sea was identified and data Table 1 was obtained.

[0109] Table 1 Data table of marine biological aggregation parameters

[0110]

[0111] The target clusters in Table 1 are sorted in descending order based on the relative distance of the clustered organisms; and the density of the clustered organisms is retained as an integer during the measurement process.

[0112] The present invention identifies target parameter data and member motion data through a clustering algorithm, and divides the target organisms into target clusters according to the identification results; obtains target cluster parameters according to the target parameter data and member motion data of the target organisms in the target clusters; accurately divides the target organisms to provide a basis for subsequent danger identification.

[0113] The danger level measurement module calculates the target aggregation danger coefficient based on the target aggregation parameters; and determines the critical aggregation group with the highest danger level based on the target aggregation danger coefficient.

[0114] The calculation formula of the target aggregation risk coefficient is:

[0115]

[0116] Among them, Dans represents the target aggregation risk factor; cla i Indicates the biological hazard coefficient corresponding to the type of aggregated organisms; T i represents the size coefficient of aggregated organisms; n represents the number of organisms in the target aggregate group; θ() represents the angle function of the direction in space; dir represents the movement direction of aggregated organisms; D1 represents the direction of the line connecting the target aggregate group and the monitoring point; DT represents the angle threshold; vel represents the movement speed of aggregated organisms; VT represents the movement speed threshold; tan represents the relative distance of aggregated organisms; TT represents the relative distance threshold; den represents the density of aggregated organisms; ET represents the density threshold; α1, α2, α3 and α4 represent the risk coefficient; exp represents the exponential function with natural constants as the base; wherein, the risk coefficient is obtained through data verification optimization.

[0117] The present invention obtains the target aggregation parameters of the target cluster, and obtains the target aggregation risk coefficient of the target cluster from four dimensions, including: 1. the biological risk coefficient and body weight of the target organisms in the target cluster; 2. the movement direction and movement speed of the target organisms in the target cluster; 3. the relative distance between the target cluster and the monitoring point; 4. the density of the target cluster; the risk level of the target cluster is accurately and comprehensively measured through these four dimensions; and the target aggregation risk coefficient is then used to accurately determine the critical cluster with the highest risk level.

[0118] The sound parameter acquisition module identifies the environmental conditions between the critical cluster and the monitoring point to obtain the monitoring environment data; constructs a sound pattern recognition model to identify the target aggregation parameters and monitoring environment data of the critical cluster, obtains the sound pattern data and sound parameter data, and performs biological expulsion; the sound pattern data includes bass mode and treble mode; the sound parameter data includes sound frequency, sound pressure level, sound waveform, sound duration and sound direction.

[0119] The monitoring environment data includes environmental medium composition data, medium movement data, environmental temperature data and environmental noise data; the environmental medium composition data is the medium data between the animal and the monitoring point.

[0120] The environmental medium composition data of the target clusters were identified and Table 2 was obtained.

[0121] Table 2 Data table of environmental media composition of target clusters

[0122]

[0123] The sound pattern recognition model includes a parameter data input layer, a parameter data recognition layer, a sound pattern recognition layer and a sound parameter output layer; the parameter data input layer inputs the target aggregation parameters and the monitoring environment data into the model; the parameter data recognition layer extracts and recognizes the features of the target aggregation parameters and the monitoring environment data; the sound pattern recognition layer recognizes and obtains the sound pattern data, including the bass mode and the treble mode; the sound parameter output layer is used to recognize the sound parameter data, including the sound frequency, the sound pressure level, the sound waveform, the sound duration and the sound direction;

[0124] The training process of the acoustic pattern recognition model is:

[0125] Conduct biological expulsion tests based on the sound, and obtain expulsion test data sets and expulsion test labels;

[0126] The expulsion test data set includes test target aggregation parameters, test monitoring environment data, test sound mode data and test sound parameter data; the expulsion test label includes a test expulsion effect coefficient;

[0127] The acoustic pattern recognition model is trained by expelling the test data set and expelling the test labels.

[0128] The present invention performs biological expulsion test according to sound, and obtains an expulsion test data set and an expulsion test label; the expulsion test data set includes test target aggregation parameters, test monitoring environment data, test sound pattern data and test sound parameter data; the expulsion test label includes a test expulsion effect coefficient; an sound pattern recognition model is obtained by training the expulsion test data set and the expulsion test label; the sound pattern recognition model can accurately identify according to the target aggregation parameters and the monitoring environment data, and obtain the sound pattern data and sound parameter data with the best expulsion effect.

[0129] The sound effect feedback module obtains the sound wave time point according to the target aggregation parameters and the monitoring environment data; the expulsion effect coefficient is calculated according to the changes in the movement data of the critical aggregation group before and after the sound wave time point; and the sound pattern recognition model is fed back and re-identified according to the expulsion effect coefficient.

[0130] The feedback and re-identification include adjusting the sound pattern recognition model according to the expulsion effect coefficient; and re-identifying the critical cluster using the adjusted sound pattern recognition model.

[0131] The sound wave time effect point is the moment when the sound emitted by the speaker has an effect on the organism;

[0132] The acoustic wave time point is calculated by determining the time when the sound emitted by the speaker intersects with the movement trajectory of the emergency gathering group according to the environmental medium composition data, medium movement data, environmental temperature data and environmental noise data in the monitoring environment data, as the first time period;

[0133] According to experimental tests, the reaction time of the agglomerated biological species to the sound is obtained as the second time period;

[0134] The sound wave time point is calculated based on the time point when the sound is emitted, the first time period and the second time period.

[0135] The process of acquiring the movement data of the critical cluster before and after the acoustic wave time effect point is as follows: setting a time threshold;

[0136] Acquire the motion data of the target cluster within the time threshold range before the acoustic wave time effect point to obtain the first motion data, wherein the first motion data includes the motion speed of the clustered organisms, the motion direction of the clustered organisms, the relative distance of the clustered organisms and the density of the clustered organisms;

[0137] The motion data of the target cluster within the time threshold range after the acoustic wave time effect point is obtained to obtain the second motion data, wherein the second motion data includes the movement speed of the expelled organisms, the movement direction of the expelled organisms, the relative distance of the expelled organisms and the density of the expelled organisms.

[0138] The data acquisition method of the second motion data set is consistent with that of the first motion data set.

[0139] The expulsion effect coefficient is calculated based on the first movement data and the second movement data:

[0140]

[0141] Among them, Eff represents the expulsion effect coefficient; tce represents the relative distance of the expelled organisms; tan represents the relative distance of the aggregated organisms; θ() represents the angle function of the direction in space; dir represents the movement direction of the aggregated organisms; D1 represents the direction of the line connecting the target cluster and the monitoring point; qir represents the movement direction of the expelled organisms; D2 represents the direction of the line connecting the target cluster and the monitoring point after the acoustic wave time point; f() represents the confusion degree function of the directional data; qsd represents the movement speed of the expelled organisms; vel represents the movement speed of the aggregated organisms; smd represents the density of the expelled organisms; den represents the density of the aggregated organisms; β1, β2, β3 and β4 represent the expulsion coefficients.

[0142] The expulsion coefficient is obtained through data verification optimization.

[0143] The present invention obtains a sound wave time effect point according to the time when the sound emitted by the sound system intersects with the movement trajectory of the critical gathering group, and the reaction time of the gathering biological species to the sound; obtains the movement data of the critical gathering group within the time threshold range before and after the sound wave time effect point as the first movement data and the second movement data; calculates and obtains the expulsion effect coefficient according to the relative distance change, movement angle, movement speed change and biological density change between the first movement data and the second movement data; and accurately measures the biological expulsion effect through the expulsion effect coefficient.

[0144] The high and low-pitched audio equipment control system is applied to audio equipment; the structure of the audio equipment is as follows Figure 3 As shown; it includes a data processing unit, a treble control unit and a bass control unit; in the data processing unit, the target organism is identified and warned, and the sound mode data and the sound parameter data are obtained; the treble control unit and the bass control unit are controlled by the sound mode data and the sound parameter data; at the same time, the data processing unit can also identify and feedback the expulsion effect.

[0145] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control system for high and low frequency sound equipment, characterized in that: include: The target distribution recognition module uses the location of the audio equipment as the monitoring point, and then obtains the monitoring area based on the set monitoring radius; Identify target organisms that enter the monitoring area and collect target parameter data and member movement data; The aggregation situation recognition module recognizes the target parameter data and member motion data through the clustering algorithm, and divides the target organisms into target aggregation groups according to the recognition results; and obtains the target aggregation parameters according to the target parameter data and member motion data of the target organisms in the target aggregation groups; The danger level measurement module calculates the target aggregation danger coefficient based on the target aggregation parameters; Determine the most dangerous critical cluster based on the target cluster risk factor; The sound parameter acquisition module identifies the environmental conditions between the critical cluster and the monitoring point and obtains the monitoring environment data; constructs a sound pattern recognition model to identify the target aggregation parameters and monitoring environment data of the critical cluster, obtains the sound pattern data and sound parameter data, and performs biological expulsion; the sound pattern data includes bass mode and treble mode; the sound parameter data includes sound frequency, sound pressure level, sound waveform, sound duration and sound direction; The sound effect feedback module obtains the sound wave time point according to the target aggregation parameters and the monitoring environment data; the expulsion effect coefficient is calculated according to the changes in the movement data of the critical aggregation group before and after the sound wave time point; and the sound pattern recognition model is fed back and re-identified according to the expulsion effect coefficient.

2. A high and low frequency audio equipment control system according to claim 1, characterized in that: The target parameter data includes: biological type, biological quantity, biological size coefficient and biological risk coefficient; wherein the biological risk coefficient is obtained based on the data of loss caused by biological types to the protection target in historical data; The member movement data includes: the relative position of the organism, the movement speed of the organism, the movement direction of the organism and the relative distance of the organism; the relative position of the organism is the position and orientation data of the organism relative to the monitoring point; the relative distance of the organism is the distance between the organism and the monitoring point.

3. A high and low frequency audio equipment control system according to claim 1, characterized in that: The target aggregation parameters include the type of aggregated organisms, the biological hazard coefficient, the number of aggregated organisms, the size coefficient of aggregated organisms, the relative position of aggregated organisms, the movement speed of aggregated organisms, the movement direction of aggregated organisms, the relative distance of aggregated organisms and the density of aggregated organisms; The process of obtaining the relative position of the aggregated organisms is as follows: obtaining the position data of the target organisms in the target aggregate group, constructing a minimum circumscribed circle according to the position data; and using the relative position data of the center of the minimum circumscribed circle and the monitoring point as the relative position of the aggregated organisms; The movement speed of the aggregated organisms is the average movement speed of the target organisms in the target aggregate group; The movement direction of the aggregated organisms is the average of the movement directions of the target organisms in the target aggregate group; The relative distance of the aggregated organisms is the average value of the relative distances between the target organisms in the target aggregate group and the monitoring point; The process of acquiring the density of aggregated organisms is as follows: constructing a minimum circumscribed circle according to the position data of the target organisms in the target aggregate group, and obtaining the density of aggregated organisms according to the volume of the minimum circumscribed circle and the number of aggregated organisms.

4. A high and low frequency audio equipment control system according to claim 3, characterized in that: The calculation formula of the target aggregation risk coefficient is: Among them, Dans represents the target aggregation risk factor; cla i Indicates the biological hazard coefficient corresponding to the type of aggregated organisms; T i represents the size coefficient of aggregated organisms; n represents the number of aggregated organisms in the target aggregate group; θ() represents the angle function of the direction in space; dir represents the movement direction of aggregated organisms; D1 represents the direction of the line connecting the target aggregate group and the monitoring point; DT represents the angle threshold; vel represents the movement speed of aggregated organisms; VT represents the movement speed threshold; tan represents the relative distance of aggregated organisms; TT represents the relative distance threshold; den represents the density of aggregated organisms; ET represents the density threshold; α1, α2, α3 and α4 represent the risk coefficient; exp represents the exponential function with natural constants as the base.

5. A high and low frequency audio equipment control system according to claim 4, characterized in that: The monitoring environment data includes environmental medium composition data, medium movement data, environmental temperature data and environmental noise data; the environmental medium composition data is the medium data between the animal and the monitoring point.

6. A high and low frequency audio equipment control system according to claim 1, characterized in that: The sound pattern recognition model includes a parameter data input layer, a parameter data recognition layer, a sound pattern recognition layer and a sound parameter output layer; the parameter data input layer inputs the target aggregation parameters and the monitoring environment data into the model; the parameter data recognition layer extracts and recognizes the features of the target aggregation parameters and the monitoring environment data; the sound pattern recognition layer recognizes and obtains the sound pattern data, including the bass mode and the treble mode; the sound parameter output layer is used to recognize the sound parameter data, including the sound frequency, the sound pressure level, the sound waveform, the sound duration and the sound direction; The sound pattern recognition model training process is as follows: Conduct biological expulsion tests based on the sound, and obtain expulsion test data sets and expulsion test labels; The expulsion test data set includes test target aggregation parameters, test monitoring environment data, test sound mode data and test sound parameter data; the expulsion test label includes a test expulsion effect coefficient; The acoustic pattern recognition model is trained by expelling the test data set and expelling the test labels.

7. A high and low frequency audio equipment control system according to claim 1, characterized in that: The sound wave time effect point is the moment when the sound emitted by the speaker has an effect on the organism; The acoustic time point is calculated by determining the time when the sound emitted by the sound system intersects with the motion trajectory of the critical gathering group according to the environmental medium composition data, medium movement data, environmental temperature data and environmental noise data in the monitoring environment data, as the first time period; the motion trajectory of the critical gathering group is obtained by identifying the target gathering parameters; The reaction time of the agglomerated organisms to the sound expulsion is obtained according to the experimental test as the second time period; The sound wave time point is calculated based on the time point when the sound is emitted, the first time period and the second time period.

8. A high and low frequency audio equipment control system according to claim 1, characterized in that: The process of acquiring the motion data of the critical cluster before and after the acoustic wave time point is as follows: Setting a time threshold; acquiring movement data of the critical cluster within the time threshold range before the acoustic wave time-effect point to obtain first movement data, wherein the first movement data includes movement speed of the clustered organisms, movement direction of the clustered organisms, relative distance of the clustered organisms, and density of the clustered organisms; The movement data of the critical cluster within the time threshold range after the acoustic wave time effect point is obtained to obtain the second movement data, wherein the second movement data includes the movement speed of the expelled organisms, the movement direction of the expelled organisms, the relative distance of the expelled organisms and the density of the expelled organisms.

9. A high and low frequency audio equipment control system according to claim 8, characterized in that: The expulsion effect coefficient is calculated based on the first movement data and the second movement data: Among them, Eff represents the expulsion effect coefficient; tce represents the relative distance of the expelled organisms; tan represents the relative distance of the aggregated organisms; θ() represents the angle function of the direction in space; dir represents the movement direction of the aggregated organisms; D1 represents the direction of the line connecting the target cluster and the monitoring point; qir represents the movement direction of the expelled organisms; D2 represents the direction of the line connecting the target cluster and the monitoring point after the acoustic wave time point; f() represents the confusion degree function of the directional data; qsd represents the movement speed of the expelled organisms; vel represents the movement speed of the aggregated organisms; smd represents the density of the expelled organisms; den represents the density of the aggregated organisms; β1, β2, β3 and β4 represent the expulsion coefficients.

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