Phishing decision-making method and device, vehicle, equipment, storage medium and program product

Abstract

The invention provides a fishing decision-making method and device, a vehicle, equipment, a storage medium and a program product, and the method is applied to a vehicle-mounted terminal, and comprises the steps: receiving fishing point recommendation information generated by a cloud end based on a fishing demand of a user; under the condition that a target water area indicated by the fishing point recommendation information is reached, multi-dimensional underwater data of the target water area and vehicle body posture data of the vehicle-mounted terminal are acquired; based on the multi-dimensional underwater data and the vehicle body posture data, fish school distribution condition prediction is carried out, and a fish condition thermodynamic diagram of the target water area is obtained; based on the fish condition thermodynamic diagram, fishing point decision making is carried out, and an optimal fishing point is obtained and displayed, through the mode of combining cloud recommendation and terminal decision making, the problems that traditional fishing depends on subjective experience of fishermen, consequently, fishing point selection blindness is large, and the success rate is low are solved, an actual fish school gathering area can be accurately positioned, and the fishing efficiency is improved. Therefore, the fishing efficiency and success rate are greatly improved.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and the Internet of Things, and in particular to a fishing decision-making method, apparatus, vehicle, equipment, storage medium, and program product. Background Technology

[0002] Fishing, a popular outdoor recreational activity, offers both enjoyment and challenge in terms of judging and responding to the natural environment. Traditional fishing relies on personal experience, requiring anglers to consider factors such as weather, wind direction, and terrain to deduce where fish are hiding. However, this method is highly dependent on the angler's subjective experience, making it difficult to standardize and pass on knowledge. Novices often return empty-handed due to lack of experience, resulting in a poor fishing experience.

[0003] Although existing auxiliary tools such as weather forecast software and fishing enthusiast communication platforms can provide some information, these tools only provide scattered, macro-level data. Users still need to integrate the information themselves and rely on experience to make decisions. In essence, they are still information-posting-style assistance with a low success rate, making it difficult to escape the predicament of relying on luck. Summary of the Invention

[0004] This invention provides a fishing decision-making method, device, vehicle, equipment, storage medium, and program product to solve the problem that fishing in the prior art relies on subjective experience and has a poor success rate, thereby realizing the transformation from experience-driven to data-driven and improving fishing efficiency and success rate.

[0005] This invention provides a fishing decision-making method, comprising: Receive fishing spot recommendations generated by the cloud based on the user's fishing needs; Upon arriving at the target water area indicated by the fishing spot recommendation information, acquire multi-dimensional underwater data of the target water area and vehicle attitude data of the vehicle terminal; Based on the multidimensional underwater data and the vehicle posture data, the distribution of fish schools is predicted to obtain a fish heat map of the target water area. Based on the fish activity heatmap, fishing spot decisions are made to obtain and display the best fishing spot.

[0006] According to a fishing decision-making method provided by the present invention, the multidimensional underwater data includes multidimensional water quality data and underwater sonar data; The process of predicting fish distribution based on the multidimensional underwater data and the vehicle posture data to obtain a fish heat map of the target water area includes: Acquire water area images of the target water area, and identify fish species based on the water area images to obtain fish species categories; Based on the vehicle body attitude data, the underwater sonar data is dynamically compensated to obtain corrected sonar data; Historical fish catch data for the target waters are extracted from the fishing spot recommendation information. Based on the environmental meteorological data of the target water area, the fish species, the multidimensional water quality data, the corrected sonar data, and the historical fish catch data, the fish distribution is predicted to obtain a fish heat map of the target water area.

[0007] According to a fishing decision-making method provided by the present invention, the step of predicting the fish distribution based on environmental meteorological data of the target water area, the fish species category, the multidimensional water quality data, the corrected sonar data, and the historical fish catch data, to obtain a fish heat map of the target water area, includes: Based on the light intensity data and meteorological data in the environmental meteorological data, as well as the dissolved oxygen data and water temperature data in the multidimensional water quality data, the fish activity status is predicted to obtain the fish activity index of the fish species. Based on the fish activity index, the historical fish catch data, and the substrate type and water flow velocity in the corrected sonar data, the distribution of fish schools is predicted to obtain a fish heat map of the target water area.

[0008] According to a fishing decision-making method provided by the present invention, the step of making a fishing spot decision based on the fish activity heatmap to obtain the optimal fishing spot includes: Based on the fish activity heat map, multiple candidate fishing spots are identified in the target water area; Based on the fish heat map, the fish aggregation score of each candidate fishing spot is determined. Based on the multidimensional underwater data, at least one of the following is determined for each candidate fishing spot: terrain suitability score, water quality score, and fishing spot safety score. The optimal fishing spot is determined from the candidate fishing spots based on the fish aggregation score, the terrain suitability score, the water quality score, and the fishing spot safety score.

[0009] According to a fishing decision-making method provided by the present invention, the step of obtaining and displaying the optimal fishing spot further includes: Upon reaching the optimal fishing spot, obtain an image of the water area at the optimal fishing spot; Fish species analysis is performed on the water area images of the fishing spot to obtain the fish species category and fish density of the optimal fishing spot. Based on the fish species type, fish density, and multidimensional underwater data at the fishing spot, a fishing tackle configuration suggestion is generated, which includes a suggested line rig specification and a suggested bait type.

[0010] According to a fishing decision-making method provided by the present invention, the display of the optimal fishing spot includes: Based on the optimal fishing spot, an augmented reality display command is generated, and based on the augmented reality display command, the augmented reality display device connected to the vehicle terminal is controlled to overlay the fish heat map and the optimal fishing spot on the water view of the target water area.

[0011] According to a fishing decision-making method provided by the present invention, the step of obtaining and displaying the optimal fishing spot further includes: When a user is fishing at the optimal fishing spot, the fishing spot environment data of the optimal fishing spot is acquired in real time. The fishing spot environment data includes at least one of millimeter-wave radar data, fishing spot sonar data, and fishing spot water temperature data. Water entry detection is performed based on the environmental data of the fishing spot to obtain the water entry detection results; If the water entry detection result indicates that a person has entered the water, an emergency water entry control command is generated, and based on the emergency water entry control command, the actuator connected to the vehicle terminal is controlled to retrieve the fishing gear and send a distress signal to the rescue terminal.

[0012] According to a fishing decision-making method provided by the present invention, the step of obtaining and displaying the optimal fishing spot further includes: When a user is fishing at the optimal fishing spot, the meteorological data of the optimal fishing spot is acquired in real time. The meteorological data of the fishing spot includes at least one of the following: wind speed data, precipitation data, and air pressure data. When extreme weather conditions are indicated by the meteorological data at the fishing spot, a weather warning control command is generated. Based on the weather warning control command, the actuator connected to the vehicle terminal is controlled to retract the fishing tackle and release the anchor to fix the vehicle's position.

[0013] The present invention also provides a fishing decision-making device, applied to a vehicle-mounted terminal, comprising: The information receiving unit is used to receive fishing spot recommendation information generated by the cloud based on the user's fishing needs; The data acquisition unit is used to acquire multi-dimensional underwater data of the target water area and vehicle attitude data of the vehicle terminal when the target water area indicated by the fishing spot recommendation information is reached. The fish situation prediction unit is used to predict the distribution of fish based on the multidimensional underwater data and the vehicle posture data, and obtain a fish situation heat map of the target water area. The decision display unit is used to make fishing spot decisions based on the fish activity heatmap, and to obtain and display the best fishing spot.

[0014] The present invention also provides a vehicle, including an on-board terminal, a sensor array, and a display device; the sensor array includes a water quality sensor group, sonar, and attitude sensor; The vehicle-mounted terminal is used to receive fishing spot recommendation information generated by the cloud based on the user's fishing needs. Upon arriving at the target water area indicated by the fishing spot recommendation information, it acquires multi-dimensional underwater data of the target water area collected by the sensor array and vehicle body posture data of the vehicle-mounted terminal. Based on the multi-dimensional underwater data and the vehicle body posture data, it predicts the distribution of fish schools to obtain a fish heat map of the target water area. Based on the fish heat map, it makes a fishing spot decision to obtain the best fishing spot and controls the display device to display the best fishing spot.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the fishing decision method as described above.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the fishing decision method as described above.

[0017] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the fishing decision method as described above.

[0018] The fishing decision-making method, device, vehicle, equipment, storage medium, and program products provided by this invention solve the problem of traditional fishing relying on the angler's subjective experience, leading to blind selection of fishing spots and low success rate, by combining cloud recommendation with terminal decision-making. It introduces vehicle posture data and multi-dimensional underwater data for real-time fish situation prediction, and performs correlation analysis between underwater environmental conditions and the vehicle's own posture. This allows the prediction of fish distribution to comprehensively consider the complex working conditions of the vehicle-mounted mobile environment, thereby greatly improving the authenticity and reliability of the fish heat map. Based on this fish heat map, fishing spot decisions can more accurately locate the actual areas where fish gather, thus effectively improving fishing efficiency and success rate. Attached Figure Description

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

[0020] Figure 1 This is a flowchart illustrating the fishing decision-making method provided by the present invention; Figure 2 This is a schematic diagram of the fishing decision-making device provided by the present invention; Figure 3 This is a structural schematic diagram of the vehicle provided by the present invention; Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] Fishing, a popular outdoor recreational activity, offers core enjoyment and challenge through the understanding and interaction with the natural environment. For a long time, success in fishing has heavily relied on the angler's personal experience. An experienced angler can determine potential hiding places by observing the weather, water color, and wind direction, combined with knowledge of fish habits and memory of specific water features. However, this method is fraught with uncertainty, and its knowledge system is highly personalized, difficult to replicate or pass on, making it extremely challenging for beginners and often resulting in a failed attempt, significantly impacting the overall experience.

[0023] While technological advancements have led to the emergence of auxiliary tools, such as software applications providing weather information or platforms for anglers to connect, the information provided by these tools is often fragmented, macroscopic, and independent. Users still need to piece together this information with their own vague experiences, much like piecing together scattered map fragments, in an attempt to deduce reliable conclusions. In other words, these current tools merely serve as information dissemination tools; the decision-making process still relies on the angler's subjective experience. This approach is not only inefficient but also fails to liberate fishing from the traditional constraints of "luck."

[0024] In response, this invention provides a fishing decision-making method that aims to transform fishing activities from a traditional experience-driven model to a data-driven intelligent decision-making model. This breaks through the limitations of traditional fishing activities that rely on personal subjective experience, and constructs a complete closed-loop logic from cloud recommendation to terminal perception and intelligent decision-making. This achieves a fundamental shift from experience-driven to data-driven approaches, enabling scientific and efficient fishing decisions and greatly improving fishing efficiency and success rate.

[0025] Figure 1 This is a flowchart illustrating the fishing decision-making method provided by the present invention, as shown below. Figure 1As shown, this method is applied to an in-vehicle terminal. The in-vehicle terminal can be a computing and control system integrated within the vehicle, such as an in-vehicle infotainment system, an in-vehicle computer, or a dedicated edge computing unit. This in-vehicle terminal typically includes a processor, memory, a communication module, and interfaces for connecting to various sensors and human-machine interface devices on the vehicle. Alternatively, the in-vehicle terminal can be a portable, standalone device that can be installed in the vehicle, such as a smartphone or tablet, which can work in conjunction with the vehicle control system via a dedicated in-vehicle mount and adapter. The method includes: Step 110: Receive fishing spot recommendation information generated by the cloud based on the user's fishing needs; Step 120: Upon arriving at the target water area indicated by the fishing spot recommendation information, acquire multi-dimensional underwater data of the target water area and vehicle attitude data of the vehicle terminal. Step 130: Based on multidimensional underwater data and vehicle posture data, predict the distribution of fish schools to obtain a fish heat map of the target water area. Step 140: Decision on fishing spots based on fish activity heatmap, and obtain and display the best fishing spot.

[0026] Specifically, in practical applications, when a user has the idea of ​​fishing, they can transmit / send their fishing request to the cloud through a user terminal, such as a smartphone or tablet, or through the human-computer interaction interface on an in-vehicle terminal. This fishing request can be an explicit command input by the user, such as saying "I want to fish for crucian carp in a nearby reservoir," or it can be an implicit request inferred by the terminal based on information such as the user's historical behavior, habits, current season, and geographical location.

[0027] After receiving a user's fishing request, the cloud platform generates fishing spot recommendations based on its stored big data and pre-set intelligent decision-making models. Specifically, the cloud platform can comprehensively analyze historical fishing data (such as other users successfully catching fish under similar conditions, even the location and environmental data of crucian carp), macro-meteorological data (such as air pressure, wind force, and temperature trends in the next few hours), and geographic information data (such as water type, area, and accessibility). Through deep learning models, such as LSTM (Long Short-Term Memory), it performs calculations to predict fishing areas with a high probability of success and generates fishing spot recommendations, which are then sent to the vehicle terminal or the vehicle terminal bound to the user's terminal.

[0028] Here, the fishing spot recommendation information is not simply a geographical coordinate point, but rather a suggested range of target water areas. Multiple target water areas can be suggested, and the recommendation information can also include reasons for recommending each target water area, predicted fishing success rates, historical catch data, and other auxiliary decision-making information. After generating the fishing spot recommendation information, the cloud can send it to the vehicle terminal via a wireless communication network.

[0029] After receiving the fishing spot recommendation information, the vehicle terminal can display the recommendation information so that the user can select and confirm the final target water area. After the user confirms, the vehicle terminal can start the vehicle navigation function to guide the user to the target water area.

[0030] Once the vehicle arrives at the target water area, such as when it stops at the reservoir bank, the onboard terminal will control various sensors connected to the vehicle to begin collecting data on the surrounding environment. This includes collecting water quality data, water flow velocity, underwater structure, and the vehicle's attitude, thereby obtaining multi-dimensional underwater data of the target water area and vehicle attitude data.

[0031] Here, multidimensional underwater data refers to data reflecting multiple dimensions of the actual underwater environment of the target water area. This data can include information reflecting the physical or chemical properties of the water, such as water temperature, turbidity, pH, dissolved oxygen, and conductivity; information reflecting underwater structure or topography, such as water depth, current velocity, and substrate type; and information reflecting underwater biological activity. This data can be collected by one or more sensors installed on the vehicle (such as an integrated underwater detector, or distributed water quality sensor arrays, attitude sensors, sonar, etc.) and transmitted to the onboard terminal.

[0032] Vehicle attitude data reflects the current attitude of the vehicle where the onboard terminal is located, such as the vehicle's tilt angle, pitch angle, roll angle, and the frequency and amplitude of minor vibrations caused by engine operation or uneven road surfaces. This data can be collected by attitude sensors installed on the vehicle, such as inertial measurement units, gyroscopes, and accelerometers, and sent to the onboard terminal.

[0033] Furthermore, after obtaining multidimensional underwater data and vehicle posture data, in this embodiment of the invention, fish situation prediction can be performed based on this data to predict the distribution of fish in the target water area, thereby generating a fish situation heat map of the target water area.

[0034] Specifically, this can be achieved by first using vehicle attitude data to dynamically compensate and correct multi-dimensional underwater data. For example, when a vehicle is parked on a sloping bank, the attitude sensor detects the vehicle's tilt angle. The onboard terminal can then use this angle to correct the sensor's detection reference surface, such as that of a sonar, thereby obtaining accurate water depth data. Similarly, when the vehicle vibrates, the onboard terminal can use the vehicle attitude data as input and employ algorithms such as Kalman filtering to filter out noise caused by vibration in the collected multi-dimensional underwater data, thus ensuring the accuracy and stability of the detection data.

[0035] After obtaining accurate, compensated, multi-dimensional underwater data, the vehicle-mounted terminal invokes a pre-built fish school distribution prediction model for calculation. This model can be a machine learning model, pre-trained in the cloud and sent to the vehicle-mounted terminal for deployment. Specifically, the vehicle-mounted terminal can input the corrected multi-dimensional underwater data into the fish school distribution prediction model. This model has pre-learned the correlation between various underwater environmental factors and fish aggregation behavior, thus enabling it to output a prediction of the probability of fish school distribution in the target waters based on the input multi-dimensional underwater data.

[0036] To present the complex prediction results intuitively to users, the in-vehicle terminal renders them into a fish population heatmap. This fish population heatmap is a visual data map that uses different colors or brightness levels to represent the density or activity intensity of fish at different locations within the target water area. For example, green represents high-value areas with dense fish populations, yellow represents medium-density areas, and red or blue represents low-value areas with sparse fish populations.

[0037] After generating a fish activity heatmap, the vehicle-mounted terminal can use this heatmap to make fishing spot decisions, selecting the optimal fishing location. That is, the vehicle-mounted terminal can analyze the fish activity heatmap to determine the most suitable spot for casting within the target waters covered by the heatmap; this spot is the optimal fishing point. For example, it can search for the pixel with the highest fish concentration or the center point of the area in the fish activity heatmap and determine it as the optimal fishing point; alternatively, it can combine environmental meteorological data of the target waters, historical fish catch data, and the fish concentration reflected in the fish activity heatmap to determine the optimal fishing point within the target waters.

[0038] Ultimately, the vehicle-mounted terminal will obtain the precise location of the best fishing spot and can control the connected display device to show the location information of the best fishing spot to the user.

[0039] Here, the best fishing spot can be displayed in various ways. For example, it can be marked with special icons (such as star marks) on the electronic map of the vehicle's central control screen, or it can be projected onto the real-time water surface image on the vehicle's windshield or mobile phone screen using augmented reality technology, such as AR-HUD (Augmented Reality Head-Up Display).

[0040] The fishing decision-making method provided by this invention combines cloud-based recommendations with terminal-based decision-making. This solves the problem of traditional fishing relying on the angler's subjective experience, leading to blind selection of fishing spots and low success rates. By introducing vehicle posture data and multi-dimensional underwater data for real-time fish situation prediction, and by correlating the underwater environment with the vehicle's own posture, the prediction of fish distribution comprehensively considers the complex working conditions of the vehicle-mounted mobile environment. This greatly improves the authenticity and reliability of the fish heat map. Based on this fish heat map, fishing spot decisions can more accurately locate the actual areas where fish gather, thereby effectively improving fishing efficiency and success rate.

[0041] Based on the above embodiments, multidimensional underwater data includes multidimensional water quality data and underwater sonar data; step 130 includes: Acquire water images of the target water area and identify fish species based on the water images to obtain the fish species category; Based on vehicle attitude data, underwater sonar data is dynamically compensated to obtain corrected sonar data; Extract historical fish catch data for the target waters from the fishing spot recommendation information; Based on environmental meteorological data, fish species, multidimensional water quality data, corrected sonar data, and historical fish catch data of the target water area, the distribution of fish schools is predicted, and a fish heat map of the target water area is obtained.

[0042] Specifically, multidimensional underwater data includes multidimensional water quality data and underwater sonar data. Multidimensional water quality data can include pH, dissolved oxygen, water temperature, turbidity, and conductivity data, among others. This data can be acquired by a water quality sensor array (including pH electrodes, dissolved oxygen sensors, water temperature sensors, turbidity meters, and conductivity sensors).

[0043] Underwater sonar data is underwater environmental data acquired through acoustic detection. Specifically, it can be collected by a dual-frequency sonar system. For example, high-frequency sound waves (such as above 200kHz) can be used to finely depict underwater topographic structures (such as steep slopes, gullies, weed beds, and rock piles) and substrate types (such as rocky bottoms, silty bottoms, and sandy bottoms), while low-frequency sound waves (such as 50-200kHz) can detect the distribution, size, and density of fish over a wider range.

[0044] Based on this, the process of predicting fish distribution based on multidimensional underwater data and vehicle posture data to obtain a fish heat map of the target water area can specifically include: First, images of the target water area can be acquired. This means the vehicle-mounted terminal can control a connected biometric camera, for example, an extended camera on a jib arm, to capture real-time images of the water surface or near the water's edge. Next, a fish recognition model, such as one trained on deep learning object detection algorithms like YOLO (You Only Look Once) v8, can be invoked to analyze the water images, identify fish targets, and output their species classification. For example, it can identify whether the main fish species present in the target water area are carp, grass carp, or crucian carp. The fish recognition model can be trained in the cloud and then sent to the vehicle-mounted terminal.

[0045] Meanwhile, the vehicle-mounted terminal can utilize vehicle attitude data to dynamically compensate and correct underwater sonar data, obtaining corrected sonar data. For example, the vehicle-mounted terminal can perform trigonometric function correction on the sonar detection depth data based on the tilt angle in the vehicle attitude data to obtain the true vertical water depth; it can perform spatial coordinate transformation on the sonar scanning sector based on the roll angle and azimuth angle in the vehicle attitude data to construct a distortion-free underwater topographic map; and it can use Kalman filtering to remove motion blur or shaking noise caused by vehicle swaying based on the acceleration and angular velocity in the vehicle attitude data.

[0046] Subsequently, based on environmental meteorological data, fish species, multidimensional water quality data, corrected sonar data, and historical fish catch data of the target water area, the distribution of fish schools can be predicted to obtain a fish activity heat map of the target water area. The environmental meteorological data can be local micro-meteorological data collected in real time by sensors on the vehicle, such as barometers and light sensors, or real-time or forecast meteorological information for the area obtained from cloud-based weather services, such as current air pressure values ​​and their trends, light intensity, wind speed and direction, etc. This embodiment of the invention does not specifically limit the specific data collection methods used.

[0047] Historical catch data is obtained from fishing spot recommendation information. That is, in addition to the geographical location of the target water area, the fishing spot recommendation information sent from the cloud can also include historical catch data related to that water area. This data is compiled by the cloud based on statistics from a large number of users' past fishing records, and can include the frequency of fish catches, main fish species, and average size in different seasons, times, and weather conditions.

[0048] Specifically, the vehicle-mounted terminal takes all the aforementioned dimensions of information as input and feeds them into the fish distribution prediction model. During the prediction process, the model comprehensively analyzes multidimensional water quality data representing the physiological environment of the water body, such as the favorable effect of high dissolved oxygen; corrected sonar data representing the underwater space, such as fish tending to congregate in areas with underwater rock structures; historical fish catch data representing historical experience, such as this area historically being a high-yield area; and environmental meteorological data affecting fish feeding activity, such as fish feeding more actively when air pressure rises. Finally, it outputs a fish activity heatmap representing the fish density or activity intensity at multiple coordinate points within the target water body.

[0049] Based on the above embodiments, using environmental meteorological data, fish species, multidimensional water quality data, corrected sonar data, and historical fish catch data of the target water area, the fish distribution is predicted to obtain a fish heat map of the target water area, including: Based on light intensity and meteorological data from environmental meteorological data, as well as dissolved oxygen and water temperature data from multidimensional water quality data, fish activity status is predicted to obtain the fish activity index for each fish species. Based on fish activity index, historical fish catch data, and corrected sonar data on substrate type and water flow velocity, fish distribution is predicted to obtain a fish heat map of the target water area.

[0050] Specifically, the process of predicting the fish distribution and obtaining a fish heatmap of the target water area can include: First, fish activity status can be predicted to estimate the intensity of fish's foraging and activity intentions under the current environment, thereby obtaining the fish activity index for each fish species.

[0051] Specifically, the vehicle-mounted terminal can predict fish activity based on light intensity and meteorological data from environmental meteorological data, as well as multidimensional water quality data such as dissolved oxygen and water temperature data. That is, it analyzes the activity level of fish in the target water area based on the above data, and outputs the activity index of the identified fish species, i.e., the fish activity index of the fish species, such as "In the current environment, the activity index of crucian carp is 85 points (very active)".

[0052] Subsequently, fish distribution can be predicted to obtain a fish activity heatmap of the target waters. That is, based on the fish activity index, combined with historical catch data, and corrected sonar data on substrate type and water flow velocity, fish distribution can be predicted.

[0053] Specifically, this involves inputting fish activity index, historical catch data, and sediment type and water flow velocity from calibrated sonar data into a fish distribution prediction model. The model comprehensively analyzes the input data. For example, a high fish activity index indicates strong fish activity and a higher probability of aggregation; conversely, a low index indicates poor activity and a lower probability of aggregation. For instance, carp prefer to feed in slow-moving, muddy bottoms, while some fish prefer to inhabit rocky areas with flowing water. A high historical catch frequency indicates a high probability of fish aggregation, while a low catch frequency indicates a lower probability. By comprehensively analyzing the above data and gridding the target water area, the model outputs a fish aggregation probability score for each grid point within the target water area. These scores are then visualized to obtain the final fish heatmap.

[0054] Based on the above embodiments, in step 140, the optimal fishing spot is determined based on the fish activity heatmap, including: Based on fish activity heatmaps, multiple candidate fishing spots were identified in the target waters. Based on the fish activity heat map, the fish aggregation score of each candidate fishing spot is determined. Based on multidimensional underwater data, at least one of the following is determined for each candidate fishing spot: terrain suitability score, water quality score, and fishing spot safety score. The best fishing spot is determined from among the candidate fishing spots based on the fish aggregation score, as well as at least one of the terrain suitability score, water quality score, and fishing spot safety score.

[0055] Specifically, the process of making fishing spot decisions based on fish activity heatmaps includes the following: First, based on the fish activity heatmap, multiple potential fishing spots, or candidate fishing spots, can be selected from the target waters. Specifically, this can be done by identifying all areas with heat values ​​higher than a set threshold, such as 75, from the fish activity heatmap, and using the center point, geometric centroid, or peak heat value point of these areas as candidate fishing spots.

[0056] Next, the fish aggregation score for each candidate fishing spot can be determined based on the fish heat map. This fish aggregation score can be understood as a score that directly reflects the density and activity intensity of the fish. The most direct implementation is that the vehicle-mounted terminal directly reads the specific heat value of each candidate fishing spot's location on the fish heat map and uses that heat value as its fish aggregation score. For example, if candidate fishing spot A is located in an area with a heat value of 92, then its fish aggregation score is 92.

[0057] Simultaneously, based on multi-dimensional underwater data, at least one of the following scores can be determined for each candidate fishing spot: terrain suitability score, water quality score, and fishing spot safety score. The terrain suitability score indicates whether the terrain structure of the fishing spot is conducive to fish hiding and fishing; it can be obtained through underwater sonar data assessment. The water quality score indicates the quality of the water at the fishing spot; it can be obtained through multi-dimensional underwater data assessment (such as dissolved oxygen, water temperature, turbidity, pH value, etc.). The fishing spot safety score indicates the safety of fishing at the fishing spot; it can be determined through assessment of water depth, water flow velocity, etc., from underwater sonar data.

[0058] Then, based on the fish aggregation score, and at least one of the following scores—topography suitability score, water quality score, and fishing spot safety score—the best fishing spot can be determined from among the candidate spots. That is, the vehicle-mounted terminal can comprehensively calculate the above scores, such as by averaging or weighted averaging (weights can be preset), to obtain the final fishing spot score for each candidate spot. Then, based on the fishing spot score, the candidate fishing spot with the highest score can be selected and determined as the best fishing spot ultimately recommended to the user.

[0059] Based on the above embodiments, the optimal fishing spot is obtained and displayed, and then the process further includes: Once you reach the optimal fishing spot, obtain an image of the water area at that spot. Fish species analysis is performed based on images of the fishing spot waters to determine the fish species type and fish density at the optimal fishing spot. Based on the type of fish species, fish density, and multi-dimensional underwater data at the fishing spot, fishing tackle configuration suggestions are generated, including suggested line specifications and suggested bait types.

[0060] Specifically, after obtaining and displaying the optimal fishing spot, the user can proceed to the displayed optimal fishing spot to fish. Furthermore, upon arriving at the optimal fishing spot, this embodiment of the invention can also provide tackle suggestions to further improve the success rate of fishing.

[0061] In detail, when the user arrives at the optimal fishing spot according to the guidance, the vehicle terminal will control the biometric camera to precisely align its field of view with the water area where the optimal fishing spot is located, and capture one or more video streams to form an image of the water area at the fishing spot. This arrival can be a physical arrival, that is, the vehicle stops at the shore of the fishing spot, or an operational arrival, that is, the user's fishing gear (such as bait and float) has reached the underwater coordinates.

[0062] Next, fish species analysis can be performed based on the images of the fishing spot's waters. This involves calling a fish recognition model to analyze the images of the fishing spot's waters to identify the types of fish species within the fishing spot's waters, as well as the size of the fish schools, such as the number of fish, their size, and their movement patterns. This allows for the determination of the optimal fishing spot's fish species and fish density.

[0063] After this, tackle recommendations can be generated based on the fish species, fish density, and multi-dimensional underwater data at the fishing spot. Specifically, the analysis can consider the fish species' size, feeding habits, and struggling strength; the fish density can determine which tackle and bait will attract fish more quickly; and underwater sonar data can determine the weight of the sinker and the hook type. For example, in rocky areas, hooks less prone to snagging are recommended. Combining the information from these analyses, a final tackle configuration recommendation can be generated. This recommendation includes suggested line specifications and suggested bait types.

[0064] In this embodiment of the invention, the fishing tackle recommendation can provide users with one-stop intelligent fishing advice. It not only tells users where to cast their lines, but also guides them on what equipment and bait to use. This seamless connection from decision-making to execution greatly reduces the reliance on personal experience in fishing activities, thereby significantly improving the fishing success rate and experience for users, especially beginners.

[0065] Based on the above embodiments, step 140, displaying the optimal fishing spot, includes: Based on the optimal fishing spot, an augmented reality display command is generated, and based on the augmented reality display command, the augmented reality display device connected to the vehicle terminal is controlled to overlay the fish heat map and the optimal fishing spot on the water view of the target water area.

[0066] Specifically, in this embodiment of the invention, the display of the best fishing spot can be achieved through augmented reality technology, which can greatly improve the intuitiveness of information presentation and the interactive experience compared to the traditional display on the central control screen.

[0067] In detail, after the vehicle-mounted terminal determines the optimal fishing spot, its built-in graphics processing unit or dedicated AR (Augmented Reality) engine generates augmented reality display instructions based on the optimal fishing spot. These augmented reality display instructions are not the images themselves, but rather computer commands used to render and position virtual objects. That is, the vehicle-mounted terminal uses the vehicle's real-time positioning information, the heading angle information provided by the electronic compass, and camera calibration parameters to perform spatial coordinate system transformation. This transforms the two-dimensional map coordinates used for internal calculations—namely, the fish heatmap and the coordinates of the optimal fishing spot—into three-dimensional spatial coordinates centered on the vehicle and aligned with the real world, and generates augmented reality display instructions based on these coordinates. This instruction can include data for virtual objects, such as the mesh model data and color texture data of the fish heat map to be rendered, and the marker model data of the best fishing spot to be rendered (e.g., a 3D star or light column model). It can also include the precise coordinates, scaling and orientation of the virtual objects in real-world 3D space, as well as the display effects of the virtual objects, such as the transparency of the fish heat map (to ensure that it does not completely obscure the real water surface view), and the glowing and flashing effects of the marker model of the best fishing spot, to attract the user's attention.

[0068] Next, the in-vehicle terminal can send the generated augmented reality display instructions to the augmented reality display device connected to it. This augmented reality display device can be an in-vehicle augmented reality head-up display (AR-HUD) system, which projects virtual images onto the vehicle's windshield and uses optical design to make the user feel that the image is floating several meters or even tens of meters in front of the vehicle, accurately blending with the real environment; or it can be a personal AR device connected to the in-vehicle terminal wirelessly or wiredly, such as AR glasses, a smartphone / tablet running a specific app, etc. This embodiment of the invention does not specifically limit the type of device.

[0069] Once the augmented reality display device receives the augmented reality display command from the in-vehicle terminal, it can execute the command. At this time, the user can observe a water view through the device, which is the real target water area. At the same time, a fish heat map and a virtual image of the best fishing spot are precisely overlaid on the water view. The user will see that the colorful fish heat map appears to be laid on the real water surface, while a prominent virtual marker is accurately placed on the best fishing spot in the distance.

[0070] In this embodiment of the invention, by adopting an augmented reality display method, abstract two-dimensional decision data is transformed into three-dimensional visual guidance information that is integrated with the real world, thereby greatly reducing the cognitive load of users. In particular, in AR-HUD, users do not need to take their eyes off the environment in front of them, which significantly improves the convenience and safety of operation when searching for fishing spots or fine-tuning the position of vehicles.

[0071] Based on the above embodiments, in step 140, after obtaining and displaying the optimal fishing spot, the method further includes: When a user is fishing at the best fishing spot, the fishing spot environment data of the best fishing spot is obtained in real time. The fishing spot environment data includes at least one of millimeter-wave radar data, fishing spot sonar data, and fishing spot water temperature data. Water entry detection is performed based on fishing spot environmental data to obtain water entry detection results; If the water entry detection result indicates that a person has entered the water, an emergency water entry control command is generated. Based on the emergency water entry control command, the actuator connected to the vehicle terminal is controlled to retrieve the fishing gear and send a distress signal to the rescue terminal.

[0072] Specifically, when a user is already fishing at the optimal spot, the vehicle's sensors can monitor the environment of that spot in real time to obtain real-time environmental data. This environmental data can include at least one of millimeter-wave radar data, sonar data, and water temperature data. The millimeter-wave radar data is collected by the vehicle's onboard millimeter-wave radar, which continuously scans the area in front of the vehicle, particularly the water area. The sonar data is collected continuously during fishing. The water temperature data is collected by a water temperature sensor.

[0073] Next, water entry detection can be performed based on the fishing spot environmental data to obtain the water entry detection results. That is, the vehicle-mounted terminal will perform fusion analysis on the aforementioned fishing spot environmental data to determine whether anyone has entered the water, thus obtaining the water entry detection results. For example, if millimeter-wave radar data detects a human-shaped target rapidly moving towards the water surface and disappearing, if sonar data analysis at the fishing spot confirms a strong acoustic shock wave from entering the water, or if local water temperature data detects a momentary anomaly in the water temperature at the fishing spot, it can be determined that someone has entered the water.

[0074] Following this, if the water entry detection indicates that someone has entered the water, the vehicle-mounted terminal will immediately generate and execute an emergency water entry control command to control the actuator to retrieve the fishing tackle. The actuator here can be an automatic rod-retrieval robotic arm electrically connected to the vehicle-mounted terminal, or an electric fishing reel; this embodiment of the invention does not specifically limit this. The vehicle-mounted terminal will control the mechanism to immediately retrieve the fishing tackle at maximum speed to prevent secondary injury to the person who has fallen into the water from the fishing rod, fishing line, etc., while also protecting the user's property.

[0075] Simultaneously, the vehicle-mounted terminal's communication module immediately sends a distress signal to a pre-set rescue terminal. To ensure successful transmission even in remote areas without mobile phone signal, the distress signal is preferably sent via the vehicle's BeiDou satellite communication module in short message format. This distress signal includes at least the event type (e.g., a person falling into the water alarm), geographical coordinates, and a timestamp of the event.

[0076] In this embodiment of the invention, water entry detection is performed using multi-sensor data, and emergency intervention is initiated upon confirmation of water entry, greatly improving the accuracy of identifying dangerous events and avoiding false alarms. More importantly, it represents a leap from traditional passive alarms to proactive physical intervention, especially in situations where anglers may lose their self-rescue ability due to emergencies, enabling automatic emergency response and distress calls, thus providing safety assurance for outdoor fishing.

[0077] Based on the above embodiments, in step 140, after obtaining and displaying the optimal fishing spot, the method further includes: When a user is fishing at the best fishing spot, the weather data of the best fishing spot is obtained in real time. The weather data of the fishing spot includes at least one of the following: wind speed data, precipitation data and air pressure data of the fishing spot. When extreme weather conditions are indicated by meteorological data at the fishing spot, a weather warning control command is generated. Based on the weather warning control command, the actuator connected to the vehicle terminal is controlled to retrieve the fishing tackle and release the anchor to fix the vehicle's position.

[0078] Specifically, when a user is fishing at the best fishing spot, the vehicle terminal can continuously obtain real-time weather data of the fishing spot location through sensors on the vehicle or remote weather services. This weather data can include at least one of the following: wind speed data, precipitation data, and air pressure data.

[0079] Next, the vehicle-mounted terminal compares the acquired meteorological data of the fishing spot with preset safety thresholds. This includes comparing wind speed data with the wind speed threshold, precipitation data with the precipitation conditions, and air pressure data with the air pressure threshold. If the wind speed exceeds the threshold (e.g., exceeding level 8, approximately 17.2 m / s), the precipitation data is at the "heavy rain" or "torrential rain" level, or the air pressure drops above the threshold (e.g., 5 hPa) within a short period (e.g., within one hour), extreme weather can be identified. In this case, the vehicle-mounted terminal immediately generates and executes a weather warning control command to control the actuator to retrieve the fishing tackle. Specifically, the vehicle-mounted terminal controls the automatic rod-retrieval robotic arm or electric fishing reel to immediately retrieve the tackle. In strong winds or thunderstorms, long fishing rods are easily broken by the wind or become lightning rods, posing a significant safety hazard.

[0080] Simultaneously, the anchor can be released to secure the vehicle's position. This anchor is a mechanical device specifically designed for field parking and electrically connected to the vehicle's terminal. For example, it could be an electrically controlled ground anchor that can be inserted into the ground, or an electrically launched heavy-duty ship anchor. When strong winds are detected, especially on soft or sloping ground, there is a risk that the vehicle may be blown away or even slip. The vehicle's terminal will control the immediate release or activation of the anchor to secure the vehicle in its current position, preventing accidental displacement due to severe weather and ensuring the safety of the occupants and the vehicle itself.

[0081] In addition, the vehicle terminal's display screen and audio system will issue clear audio and visual alarms, such as "Warning: Level 8 gale detected. The fishing rod has been automatically retrieved and the vehicle has been secured. Please remain vigilant!"

[0082] In this embodiment of the invention, the passive process of relying on personal experience to judge the weather and manually pack equipment in traditional fishing is transformed into an automated, proactive intervention process based on accurate data and preset thresholds, which greatly improves the safety of outdoor fishing activities.

[0083] Based on the above embodiments, in order to further optimize the fishing function, data sharing can also be performed after the user finishes fishing.

[0084] Specifically, after completing a fishing trip, users can choose to share their catch record to the cloud. This catch record can include images of the catch, a capture timestamp automatically associated with the vehicle terminal, precise geographical location, and various environmental data of the fishing spot at the moment of capture (such as water temperature, water depth, dissolved oxygen concentration, air pressure, etc.).

[0085] Users can choose whether to upload the fishing record through the human-machine interface of the in-vehicle terminal. During the upload process, privacy protection options can be selected, such as allowing users to blur precise geographical coordinates or share anonymously.

[0086] After receiving and aggregating fishing records uploaded from one or more vehicle terminals, the cloud platform uses these records as new training samples to periodically iteratively optimize the intelligent decision-making model deployed in the cloud. For example, this data, marked with success and containing rich contextual information, can be used to train deep learning models (such as LSTM models) to generate fishing spot recommendations, fish distribution prediction models, and fish recognition models, thereby continuously improving their prediction and recognition accuracy and generalization capabilities. The optimized new model can then be distributed from the cloud to each vehicle terminal for updates, allowing the intelligence level of the fishing decision-making process to continuously improve with the accumulation of user data.

[0087] In addition, this embodiment of the invention also provides a user feedback mechanism. That is, users can rate the various functions provided throughout the fishing activity, such as the accuracy of fishing spot recommendation information, the timeliness of safety warnings, or the user experience of AR display.

[0088] In-vehicle terminals or cloud-based systems can collect this rating data. On one hand, this data can be used for statistical analysis of the overall performance of various functions, providing data support for subsequent version iterations. On the other hand, analyzing the historical rating data of specific users can identify their personal preferences and adjust the weights of the algorithm model for that user accordingly. For example, if it is identified that a user consistently gives high scores to recommended deep water areas, the weights of features related to "water depth" can be increased for that user in subsequent recommendation processes, thereby achieving personalized optimization of the recommendation logic and providing decisions that better match the user's fishing habits.

[0089] The fishing decision-making device provided by the present invention is described below. The fishing decision-making device described below can be referred to in correspondence with the fishing decision-making method described above.

[0090] Figure 2 This is a schematic diagram of the fishing decision-making device provided by the present invention, as shown below. Figure 2 As shown, the device is used in an in-vehicle terminal, and the device includes: Information receiving unit 210 is used to receive fishing spot recommendation information generated by the cloud based on the user's fishing needs; The data acquisition unit 220 is used to acquire multi-dimensional underwater data of the target water area and vehicle attitude data of the vehicle terminal when the target water area indicated by the fishing spot recommendation information is reached. Fish situation prediction unit 230 is used to predict the distribution of fish based on the multidimensional underwater data and the vehicle posture data, and obtain a fish situation heat map of the target water area. The decision display unit 240 is used to make a fishing spot decision based on the fish heat map, and to obtain and display the best fishing spot.

[0091] The fishing decision-making device provided by this invention solves the problem of traditional fishing relying on the angler's subjective experience, which leads to blind selection of fishing spots and low success rate, by combining cloud recommendation and terminal decision-making. It introduces vehicle posture data and multi-dimensional underwater data for real-time fish situation prediction, and analyzes the correlation between the underwater environment and the vehicle's own posture. This allows the prediction of fish distribution to comprehensively consider the complex working conditions of the vehicle-mounted mobile environment, thereby greatly improving the authenticity and reliability of the fish heat map. Based on this fish heat map, fishing spot decisions can be made more accurately to locate the actual area where fish gather, thus effectively improving the efficiency and success rate of fishing.

[0092] Based on the above embodiments, the multidimensional underwater data includes multidimensional water quality data and underwater sonar data; Fish Prediction Unit 230 is used for: Acquire water area images of the target water area, and identify fish species based on the water area images to obtain fish species categories; Based on the vehicle body attitude data, the underwater sonar data is dynamically compensated to obtain corrected sonar data; Historical fish catch data for the target waters are extracted from the fishing spot recommendation information. Based on the environmental meteorological data of the target water area, the fish species, the multidimensional water quality data, the corrected sonar data, and the historical fish catch data, the fish distribution is predicted to obtain a fish heat map of the target water area.

[0093] Based on the above embodiments, the fish behavior prediction unit 230 is used for: Based on the light intensity data and meteorological data in the environmental meteorological data, as well as the dissolved oxygen data and water temperature data in the multidimensional water quality data, the fish activity status is predicted to obtain the fish activity index of the fish species. Based on the fish activity index, the historical fish catch data, and the substrate type and water flow velocity in the corrected sonar data, the distribution of fish schools is predicted to obtain a fish heat map of the target water area.

[0094] Based on the above embodiments, the decision display unit 240 is used for: Based on the fish activity heat map, multiple candidate fishing spots are identified in the target water area; Based on the fish heat map, the fish aggregation score of each candidate fishing spot is determined. Based on the multidimensional underwater data, at least one of the following is determined for each candidate fishing spot: terrain suitability score, water quality score, and fishing spot safety score. The optimal fishing spot is determined from the candidate fishing spots based on the fish aggregation score, the terrain suitability score, the water quality score, and the fishing spot safety score.

[0095] Based on the above embodiments, the decision display unit 240 is further configured to: Upon reaching the optimal fishing spot, obtain an image of the water area at the optimal fishing spot; Fish species analysis is performed on the water area images of the fishing spot to obtain the fish species category and fish density of the optimal fishing spot. Based on the fish species type, fish density, and multidimensional underwater data at the fishing spot, a fishing tackle configuration suggestion is generated, which includes a suggested line rig specification and a suggested bait type.

[0096] Based on the above embodiments, the decision display unit 240 is used for: Based on the optimal fishing spot, an augmented reality display command is generated, and based on the augmented reality display command, the augmented reality display device connected to the vehicle terminal is controlled to overlay the fish heat map and the optimal fishing spot on the water view of the target water area.

[0097] Based on the above embodiments, the device further includes an emergency warning unit, used for: When a user is fishing at the optimal fishing spot, the fishing spot environment data of the optimal fishing spot is acquired in real time. The fishing spot environment data includes at least one of millimeter-wave radar data, fishing spot sonar data, and fishing spot water temperature data. Water entry detection is performed based on the environmental data of the fishing spot to obtain the water entry detection results; If the water entry detection result indicates that a person has entered the water, an emergency water entry control command is generated, and based on the emergency water entry control command, the actuator connected to the vehicle terminal is controlled to retrieve the fishing gear and send a distress signal to the rescue terminal.

[0098] Based on the above embodiments, the emergency warning unit is also used for: When a user is fishing at the optimal fishing spot, the meteorological data of the optimal fishing spot is acquired in real time. The meteorological data of the fishing spot includes at least one of the following: wind speed data, precipitation data, and air pressure data. When extreme weather conditions are indicated by the meteorological data at the fishing spot, a weather warning control command is generated. Based on the weather warning control command, the actuator connected to the vehicle terminal is controlled to retract the fishing tackle and release the anchor to fix the vehicle's position.

[0099] The present invention also provides a vehicle, Figure 3 This is a structural schematic diagram of the vehicle provided by the present invention, such as... Figure 3 As shown, the vehicle includes an on-board terminal 310, a sensor array 320, and a display device 330; the sensor array 320 includes a water quality sensor group, sonar, and attitude sensor. The vehicle-mounted terminal 310 is used to receive fishing spot recommendation information generated by the cloud based on the user's fishing needs, and upon arriving at the target water area indicated by the fishing spot recommendation information, acquire multi-dimensional underwater data of the target water area collected by the sensor array 320 and vehicle body posture data of the vehicle-mounted terminal, predict the distribution of fish based on the multi-dimensional underwater data and the vehicle body posture data, obtain a fish heat map of the target water area, make a fishing spot decision based on the fish heat map, obtain the best fishing spot, and control the display device 330 to display the best fishing spot.

[0100] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include a processor 410, a communications interface 420, a memory 430, and a communication bus 440, wherein the processor 410, communications interface 420, and memory 430 communicate with each other via the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a fishing decision-making method. This method is applied to a vehicle-mounted terminal and includes receiving fishing spot recommendation information generated by the cloud based on the user's fishing needs; upon arriving at the target water area indicated by the fishing spot recommendation information, acquiring multi-dimensional underwater data of the target water area and vehicle attitude data of the vehicle-mounted terminal; based on the multi-dimensional underwater data and the vehicle attitude data, predicting the distribution of fish schools to obtain a fish heat map of the target water area; and based on the fish heat map, making a fishing spot decision to obtain and display the best fishing spot.

[0101] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0102] On the other hand, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is able to execute the fishing decision-making method provided by the above methods, the method being applied to a vehicle-mounted terminal, the method comprising receiving fishing spot recommendation information generated by the cloud based on the user's fishing needs; upon arriving at the target water area indicated by the fishing spot recommendation information, acquiring multi-dimensional underwater data of the target water area and vehicle body posture data of the vehicle-mounted terminal; based on the multi-dimensional underwater data and the vehicle body posture data, predicting the distribution of fish schools to obtain a fish heat map of the target water area; and based on the fish heat map, making a fishing spot decision to obtain and display the best fishing spot.

[0103] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the fishing decision-making method provided by the above methods. This method is applied to a vehicle-mounted terminal and includes receiving fishing spot recommendation information generated by the cloud based on the user's fishing needs; upon arriving at a target water area indicated by the fishing spot recommendation information, acquiring multi-dimensional underwater data of the target water area and vehicle body posture data of the vehicle-mounted terminal; based on the multi-dimensional underwater data and the vehicle body posture data, predicting the distribution of fish schools to obtain a fish heat map of the target water area; and based on the fish heat map, making a fishing spot decision to obtain and display the optimal fishing spot.

[0104] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0105] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A fishing decision-making method, characterized in that, Applications in vehicle-mounted terminals include: Receive fishing spot recommendations generated by the cloud based on the user's fishing needs; Upon arriving at the target water area indicated by the fishing spot recommendation information, acquire multi-dimensional underwater data of the target water area and vehicle attitude data of the vehicle terminal; Based on the multidimensional underwater data and the vehicle posture data, the distribution of fish schools is predicted to obtain a fish heat map of the target water area. Based on the fish activity heatmap, fishing spot decisions are made to obtain and display the best fishing spot.

2. The fishing decision-making method according to claim 1, characterized in that, The multidimensional underwater data includes multidimensional water quality data and underwater sonar data; The process of predicting fish distribution based on the multidimensional underwater data and the vehicle posture data to obtain a fish heat map of the target water area includes: Acquire water area images of the target water area, and identify fish species based on the water area images to obtain fish species categories; Based on the vehicle body attitude data, the underwater sonar data is dynamically compensated to obtain corrected sonar data; Historical fish catch data for the target waters are extracted from the fishing spot recommendation information. Based on the environmental meteorological data of the target water area, the fish species, the multidimensional water quality data, the corrected sonar data, and the historical fish catch data, the fish distribution is predicted to obtain a fish heat map of the target water area.

3. The fishing decision-making method according to claim 2, characterized in that, The method of predicting fish distribution based on environmental meteorological data of the target water area, fish species, multidimensional water quality data, corrected sonar data, and historical fish catch data, to obtain a fish situation heat map of the target water area, includes: Based on the light intensity data and meteorological data in the environmental meteorological data, as well as the dissolved oxygen data and water temperature data in the multidimensional water quality data, the fish activity status is predicted to obtain the fish activity index of the fish species. Based on the fish activity index, the historical fish catch data, and the substrate type and water flow velocity in the corrected sonar data, the distribution of fish schools is predicted to obtain a fish heat map of the target water area.

4. The fishing decision-making method according to any one of claims 1 to 3, characterized in that, The process of determining the optimal fishing spot based on the fish activity heatmap includes: Based on the fish activity heat map, multiple candidate fishing spots are identified in the target water area; Based on the fish heat map, the fish aggregation score of each candidate fishing spot is determined. Based on the multidimensional underwater data, at least one of the following is determined for each candidate fishing spot: terrain suitability score, water quality score, and fishing spot safety score. The optimal fishing spot is determined from the candidate fishing spots based on the fish aggregation score, the terrain suitability score, the water quality score, and the fishing spot safety score.

5. The fishing decision-making method according to any one of claims 1 to 3, characterized in that, The process of obtaining and displaying the optimal fishing spot also includes: Upon reaching the optimal fishing spot, obtain an image of the water area at the optimal fishing spot; Fish species analysis is performed on the water area images of the fishing spot to obtain the fish species category and fish density of the optimal fishing spot. Based on the fish species type, fish density, and multidimensional underwater data at the fishing spot, a fishing tackle configuration suggestion is generated, which includes a suggested line rig specification and a suggested bait type.

6. The fishing decision-making method according to any one of claims 1 to 3, characterized in that, The display of the optimal fishing spot includes: Based on the optimal fishing spot, an augmented reality display command is generated, and based on the augmented reality display command, the augmented reality display device connected to the vehicle terminal is controlled to overlay the fish heat map and the optimal fishing spot on the water view of the target water area.

7. The fishing decision-making method according to any one of claims 1 to 3, characterized in that, The process of obtaining and displaying the optimal fishing spot also includes: When a user is fishing at the optimal fishing spot, the fishing spot environment data of the optimal fishing spot is acquired in real time. The fishing spot environment data includes at least one of millimeter-wave radar data, fishing spot sonar data, and fishing spot water temperature data. Water entry detection is performed based on the environmental data of the fishing spot to obtain the water entry detection results; If the water entry detection result indicates that a person has entered the water, an emergency water entry control command is generated, and based on the emergency water entry control command, the actuator connected to the vehicle terminal is controlled to retrieve the fishing gear and send a distress signal to the rescue terminal.

8. The fishing decision-making method according to any one of claims 1 to 3, characterized in that, The process of obtaining and displaying the optimal fishing spot also includes: When a user is fishing at the optimal fishing spot, the meteorological data of the optimal fishing spot is acquired in real time. The meteorological data of the fishing spot includes at least one of the following: wind speed data, precipitation data, and air pressure data. When extreme weather conditions are indicated by the meteorological data at the fishing spot, a weather warning control command is generated. Based on the weather warning control command, the actuator connected to the vehicle terminal is controlled to retract the fishing tackle and release the anchor to fix the vehicle's position.

9. A fishing decision-making device, characterized in that, Applications in vehicle-mounted terminals include: The information receiving unit is used to receive fishing spot recommendation information generated by the cloud based on the user's fishing needs; The data acquisition unit is used to acquire multi-dimensional underwater data of the target water area and vehicle attitude data of the vehicle terminal when the target water area indicated by the fishing spot recommendation information is reached. The fish situation prediction unit is used to predict the distribution of fish based on the multidimensional underwater data and the vehicle posture data, and obtain a fish situation heat map of the target water area. The decision display unit is used to make fishing spot decisions based on the fish activity heatmap, and to obtain and display the best fishing spot.

10. A vehicle, characterized in that, It includes an on-board terminal, a sensor array, and a display device; the sensor array includes a water quality sensor group, sonar, and attitude sensor. The vehicle-mounted terminal is used to receive fishing spot recommendation information generated by the cloud based on the user's fishing needs. Upon arriving at the target water area indicated by the fishing spot recommendation information, it acquires multi-dimensional underwater data of the target water area collected by the sensor array and vehicle body posture data of the vehicle-mounted terminal. Based on the multi-dimensional underwater data and the vehicle body posture data, it predicts the distribution of fish schools to obtain a fish heat map of the target water area. Based on the fish heat map, it makes a fishing spot decision to obtain the best fishing spot and controls the display device to display the best fishing spot.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the fishing decision-making method as described in any one of claims 1 to 8.

12. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the fishing decision-making method as described in any one of claims 1 to 8.

13. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the fishing decision-making method as described in any one of claims 1 to 8.

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