Water area sand excavation identification method and device, terminal and medium

Abstract

The invention discloses a water area sand excavation identification method and device, a terminal and a medium. The method comprises the steps that target area information corresponding to a target area, sand excavation operation area information corresponding to a sand excavation operation area in the target area information, equipment information of a target ship and navigation information of the target ship in the target area information are obtained; obtaining a first evaluation result through a preset first model according to the target area information, the sand excavation operation area information and the navigation information; a second evaluation result is obtained through a preset second model according to the equipment information and pre-stored sand excavation operation equipment information; and determining a target recognition result based on the first evaluation result, the second evaluation result and a preset evaluation result threshold. According to the method, the target ship is comprehensively evaluated from multiple angles by identifying the navigation information of the target ship and the equipment on the ship, and then whether the target ship carries out sand excavation operation or not is accurately judged.

Description

Technical Field

[0001] The present application relates to the field of sand mining management technology, and in particular to a method, device, terminal and medium for identifying sand mining in water areas. Background Art

[0002] According to relevant laws and regulations, water administration departments at all levels bear the main responsibility for managing sand mining in waters. Sand mining vessels usually operate in designated sand mining areas. These areas are generally places with rich sand and gravel resources determined by geological surveys, such as the inner side of the river bend, near the river island or at the estuary. Because the water flow dynamics in these areas make it easier for sand and gravel to deposit, sand mining ships will frequently operate in these specific locations, and their navigation tracks are also concentrated in these areas. Common sand mining equipment for sand mining ships include sand mining pumps and grab buckets. Sand mining pumps are a type of equipment that uses strong suction to suck underwater sand and gravel onto the ship. They are suitable for waters with finer sand and deeper water sources. Grabs use mechanical arms to control the opening and closing of grab buckets to grab sand and gravel. This type of equipment is more effective for blocky, larger-grained sand and gravel or sand mining operations in shallow waters.

[0003] However, some existing methods for monitoring sand mining in waters include those that rely on manual inspections, simple video surveillance, or methods for identifying sand mining in waters based only on a certain type of sensor data. Traditional manual inspections may miss sand mining activities due to factors such as limited vision, poor weather conditions, or long inspection intervals. At the same time, abnormal trajectories in navigation information may suggest that a vessel is conducting sand mining operations, but this evidence alone may not be sufficient; similarly, the vessel's equipment alone cannot determine whether the vessel is conducting sand mining operations. Summary of the invention

[0004] The main purpose of this application is to provide a method, device, terminal and medium for identifying sand mining in water areas, aiming to evaluate the target ship from multiple angles and comprehensively by identifying the navigation information and equipment on board the target ship, and then accurately determine whether the target ship is conducting sand mining operations.

[0005] To achieve the above objectives, the present application provides a method for identifying sand mining in waters, the method comprising:

[0006] Acquire target area information corresponding to the target area, sand mining operation area information corresponding to the sand mining operation area in the target area information, equipment information of the target vessel, and navigation information of the target vessel in the target area information, wherein the target area information is used to characterize the condition of the monitored waters, and the sand mining operation area is used to characterize the condition of the sand mining operation area in the target area;

[0007] By presetting a first model, a first evaluation result is obtained according to the target area information, the sand mining operation area information and the navigation information, wherein the first evaluation result is used to characterize the evaluation of the navigation status of the target vessel in the target area;

[0008] By presetting the second model, a second evaluation result is obtained according to the equipment information and pre-stored sand mining equipment information, wherein the second evaluation result is used to characterize the evaluation of the equipment status on the target vessel;

[0009] Based on the first evaluation result, the second evaluation result and a preset evaluation result threshold, a target recognition result is determined, wherein the target recognition result is used to characterize a determination result of whether the target vessel is carrying out sand mining operations in a target area.

[0010] Specifically, the target area information includes target area coordinate data, the sand mining operation area information includes sand mining operation area coordinate data, the navigation information includes navigation track data and navigation speed data, and the preset first model includes a first input layer, a first convolutional layer, a first pooling layer, a first fully connected layer and a first output layer;

[0011] The first evaluation result is obtained by presetting the first model according to the target area information, the sand mining operation area information and the navigation information, including:

[0012] Obtaining a first input vector through the first input layer according to the target area coordinate data, the sand mining operation area coordinate data, the navigation track data and the navigation speed data;

[0013] Obtaining a first convolutional feature map according to the first input vector through the first convolutional layer;

[0014] Obtaining a first tensor according to the first convolutional feature map through the first pooling layer;

[0015] Obtaining a first feature vector according to the first tensor through the first fully connected layer;

[0016] The first evaluation result is obtained through the first output layer according to the first feature vector.

[0017] Specifically, the first output layer includes three first neurons, the first evaluation result is a three-dimensional output vector, the three-dimensional output vector includes three first output probabilities, and the first output probabilities correspond to the first neurons one by one;

[0018] The obtaining the first evaluation result according to the first feature vector through the first output layer includes:

[0019] The first feature vector is input into the first output layer, and the three-dimensional output vector is output, wherein the three first output probabilities are respectively used to characterize the probability of the target ship sailing normally, the probability that the target ship is close to the sand mining operation area but does not carry out sand mining operations, and the probability that the target ship carries out sand mining operations in the sand mining operation area.

[0020] Specifically, the first output layer includes one second neuron, and the first evaluation result is a behavior probability corresponding to the target vessel;

[0021] The obtaining the first evaluation result according to the first feature vector through the first output layer includes:

[0022] The first feature vector is input into the first output layer, and the behavior probability is outputted, wherein the behavior probability is used to characterize the probability that the target vessel performs sand mining operations in the sand mining operation area.

[0023] Specifically, the device information includes device type information, device power data, and device size data, and the preset second model includes a second input layer, a second convolutional layer, a second pooling layer, a second fully connected layer, and a second output layer;

[0024] The second evaluation result is obtained by presetting the second model according to the equipment information and the pre-stored sand mining equipment information, including:

[0025] Obtaining a second input vector through the second input layer according to the equipment type information, the equipment power data, the equipment size data and the sand mining equipment information;

[0026] Obtaining a second convolutional feature map according to the second input vector through the second convolutional layer;

[0027] Obtaining a second tensor according to the second convolutional feature map through the second pooling layer;

[0028] Obtaining a second feature vector according to the second tensor through the second fully connected layer;

[0029] The second evaluation result is obtained through the second output layer according to the second feature vector.

[0030] Specifically, the first output layer includes one third neuron, and the second evaluation result is a probability of equipment matching corresponding to the target vessel;

[0031] The obtaining the second evaluation result according to the second feature vector through the second output layer includes:

[0032] The second feature vector is input into the second output layer, and the equipment matching probability is outputted, wherein the equipment matching probability is used to characterize the probability that the equipment on the target vessel is sand mining equipment.

[0033] Specifically, the preset evaluation result threshold includes a first evaluation result threshold and a second evaluation result threshold;

[0034] The determining of the target recognition result based on the first evaluation result, the second evaluation result and a preset evaluation result threshold includes:

[0035] If the first behavior probability is not less than the first evaluation result threshold and the device matching probability is not less than the second evaluation result threshold, the target recognition result is determined as the target vessel performing sand mining operations in the sand mining operation area.

[0036] To achieve the above-mentioned purpose, the present application also provides a water area sand mining identification device, the device comprising:

[0037] The first unit is used to obtain target area information corresponding to the target area, sand mining operation area information corresponding to the sand mining operation area in the target area information, equipment information of the target vessel, and navigation information of the target vessel in the target area information, wherein the target area information is used to characterize the conditions of the monitored waters, and the sand mining operation area is used to characterize the conditions of the sand mining operation area in the target area;

[0038] The second unit is used to obtain a first evaluation result according to the target area information, the sand mining operation area information and the navigation information by presetting a first model, wherein the first evaluation result is used to represent the evaluation of the navigation status of the target vessel in the target area;

[0039] A third unit is used to obtain a second evaluation result according to the equipment information and pre-stored sand mining equipment information by presetting a second model, wherein the second evaluation result is used to represent the evaluation of the equipment status on the target vessel;

[0040] The fourth unit is used to determine a target recognition result based on the first evaluation result, the second evaluation result and a preset evaluation result threshold, wherein the target recognition result is used to characterize the determination result of whether the target vessel is carrying out sand mining operations in the target area.

[0041] To achieve the above-mentioned purpose, the present application also provides a terminal, including a memory storing multiple instructions; the processor loads instructions from the memory to execute the steps in any one of the methods provided in the present application.

[0042] To achieve the above objectives, the present application also provides a medium, wherein the medium stores a plurality of instructions, wherein the instructions are suitable for a processor to load to execute the steps in any one of the methods provided in the present application.

[0043] The present application provides a method, device, terminal and medium for identifying sand mining in waters, which can first obtain target area information corresponding to the target area, sand mining operation area information corresponding to the sand mining operation area in the target area information, equipment information of the target ship and navigation information of the target ship in the target area information, wherein the target area information is used to characterize the condition of the monitored waters, and the sand mining operation area is used to characterize the condition of the sand mining operation area in the target area; then, by presetting a first model, a first evaluation result is obtained according to the target area information, the sand mining operation area information and the navigation information, wherein the first evaluation result is used to characterize the evaluation of the navigation condition of the target ship in the target area; then, by presetting a second model, a second evaluation result is obtained according to the equipment information and pre-stored sand mining operation equipment information, wherein the second evaluation result is used to characterize the evaluation of the equipment condition on the target ship; finally, based on the first evaluation result, the second evaluation result and the preset evaluation result threshold, the target identification result is accurately determined, wherein the target identification result is used to characterize the accurate judgment result of whether the target ship implements sand mining operations in the target area.

[0044] This application can evaluate the target ship from multiple angles and comprehensively by identifying the navigation information and equipment on board the target ship, and then accurately determine whether the target ship is conducting sand mining operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of a process for a method provided in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of the structure of the device provided in the embodiment of the present application;

[0047] Figure 3 A schematic diagram of the structure of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

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

[0049] Some existing methods of supervising sand mining in waters include relying on manual inspections, simple video surveillance, or methods of identifying sand mining in waters based only on a certain type of sensor data. Traditional manual inspections may miss sand mining activities due to factors such as limited vision, poor weather conditions, or long inspection intervals. At the same time, abnormal trajectories in navigation information may suggest that the ship is conducting sand mining operations, but this evidence alone may not be sufficient; similarly, the ship's equipment alone cannot determine whether the ship is conducting sand mining operations.

[0050] Therefore, the embodiments of the present application provide a method, device, terminal and medium for identifying sand mining in water areas to solve practical technical problems.

[0051] In some embodiments, the device may be specifically integrated into an electronic device, which may be a terminal, a server or other device.

[0052] In some embodiments, the server may also be implemented in the form of a terminal.

[0053] Among them, the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), as well as big data and artificial intelligence platforms.

[0054] The terminal may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server may be directly or indirectly connected via wired or wireless communication, and this application does not limit this.

[0055] It should be noted that the serial numbers of the following embodiments are not intended to limit the preferred order of the embodiments.

[0056] The present application embodiment provides a method for identifying sand mining in a water area, such as Figure 1 As shown, the specific process of the method can be as follows:

[0057] S110. Obtain target area information corresponding to the target area, sand mining operation area information corresponding to the sand mining operation area in the target area information, equipment information of the target vessel, and navigation information of the target vessel in the target area information, wherein the target area information is used to characterize the condition of the monitored waters, and the sand mining operation area is used to characterize the condition of the sand mining operation area in the target area.

[0058] In some embodiments, the target area information may include the geographic scope information of the target area, and the boundary coordinates of the target area can be clarified through a geographic information system (GIS), for example, longitude and latitude coordinates are used to accurately define its scope, such as a section of water area of ​​a river, the coordinates of the upstream end point are (longitude X1, latitude Y1), and the coordinates of the downstream end point are (longitude X2, latitude Y2), so as to define the approximate scope of the target area. At the same time, basic geometric data such as the area and perimeter of the area can also be obtained, which helps to grasp the overall size of the water area.

[0059] Satellite remote sensing images, combined with image processing technology, can also be used to further clearly present the water and land boundaries, surrounding topography and other conditions of the target area. For example, it can be determined whether the river bank is steep or flat, and whether there are special terrains such as river islands and river branches. These topographical features are of reference value for subsequent judgments on the rationality of ship navigation and sand mining activities.

[0060] In some embodiments, the sand mining operation area information can be obtained with the help of GIS technology. By accurately determining the boundary coordinates of the sand mining operation area, its shape and range are clearly presented in digital form, which is convenient for subsequent comparison and analysis with the real-time position of the ship. For example, the area may be a polygon, and the coordinates of each vertex can be accurately recorded in the database.

[0061] In some embodiments, the equipment information can be obtained by manually checking a detailed list of equipment on board, including operating equipment (such as model, power, capacity, opening and closing angle and other parameters), power equipment (engine power, type, etc.), auxiliary operating equipment (screening specifications of screening equipment, conveying capacity of conveying equipment, etc.) and safety equipment (life-saving equipment, fire-fighting equipment, etc.).

[0062] In some embodiments, the navigation information can be obtained using an automatic identification system (AIS), which can transmit the vessel's position coordinates (latitude and longitude), heading, speed and other information in real time. By receiving these data, the dynamic position changes of the target vessel in the target area can be clearly grasped. For example, it can be seen whether the vessel is sailing downstream along the river bank or approaching the sand mining operation area within a certain period of time.

[0063] S120. By presetting a first model, a first evaluation result is obtained according to the target area information, the sand mining operation area information and the navigation information, wherein the first evaluation result is used to characterize the evaluation of the navigation status of the target vessel in the target area.

[0064] In some embodiments, the target area information includes target area coordinate data, the sand mining operation area information includes sand mining operation area coordinate data, the navigation information includes navigation trajectory data and navigation speed data, and the preset first model includes a first input layer, a first convolutional layer, a first pooling layer, a first fully connected layer and a first output layer.

[0065] Specifically, the first evaluation result is obtained by presetting the first model according to the target area information, the sand mining operation area information and the navigation information, including the steps S121 to S125 as shown below:

[0066] S121. Obtain a first input vector through the first input layer according to the target area coordinate data, the sand mining operation area coordinate data, the navigation track data and the navigation speed data.

[0067] In some embodiments, a water area near a port can be used as a target area, and the area range of the target area is a rectangle, represented by the coordinates of the upper left corner and the lower right corner, such as (x1, y1) and (x2, y2). There is a designated sand mining operation area in the target area, and its coordinate range is (x3, y3) and (x4, y4). The target ship can be monitored by radar, and the movement trajectory (one position coordinate is recorded every 10 seconds) and speed (unit: m / s) of the target ship in the target area can be recorded.

[0068] Specifically, the first input layer may have 4 neurons corresponding to the coordinates x1, y1, x2, y2 of the target area, and another 4 neurons corresponding to the coordinates x3, y3, x4, y4 of the sand mining area. In addition, n neurons are used to input the coordinates of the target ship's motion trajectory in the time series (assuming that the coordinates of n time points are recorded), and n neurons are used to input the corresponding speed value. The total number of input neurons is 8+2n.

[0069] Specifically, the first input layer normalizes the target area coordinate data, the sand mining operation area coordinate data, the navigation track data, and the navigation speed data to obtain the first input vector.

[0070] S122. Obtain a first convolutional feature map according to the first input vector through the first convolutional layer.

[0071] In some embodiments, the first convolution layer may be a convolution layer with a convolution kernel size of 3*3, a step size of 1, and 4 convolution kernels. The target area, the sand mining operation area, and the ship motion trajectory can be regarded as a special "image" form of data, and the data of the input layer is input to the first convolution layer according to a certain spatial layout (for example, the coordinates and speed values ​​are arranged into a two-dimensional matrix).

[0072] Specifically, after the convolution operation, four feature maps are obtained, namely the first convolution feature map, each of which highlights the local features in the input data, such as the spatial relationship between the ship trajectory and the boundary of the operating area, the local direction change of the trajectory, etc. The size of each feature map is m*m (the value of m depends on the layout of the input data and the convolution kernel parameters).

[0073] S123. Obtain a first tensor according to the first convolutional feature map through the first pooling layer.

[0074] In some embodiments, the first pooling layer can be a maximum pooling layer with a pooling window size of 2*2 and a step size of 2. The four feature maps are pooled by the first pooling layer to output a 4*m / 2*m / 2 tensor, i.e., the first tensor.

[0075] S124. Obtain a first feature vector according to the first tensor through the first fully connected layer.

[0076] In some embodiments, the first fully connected layer can be a fully connected layer with 128 neurons, which flattens the first tensor after pooling into a vector input, and obtains a 128-dimensional vector after weighted summation of neurons and activation function (such as ReLU function). This vector is used to further integrate and abstract the previously extracted features to obtain intermediate features related to navigation status evaluation. The 128-dimensional vector is the first feature vector.

[0077] S125. Obtain the first evaluation result according to the first feature vector through the first output layer.

[0078] In some embodiments, the first output layer includes three first neurons, the first evaluation result is a three-dimensional output vector, the three-dimensional output vector includes three first output probabilities, and the first output probabilities correspond one-to-one to the first neurons.

[0079] Specifically, obtaining the first evaluation result according to the first feature vector through the first output layer includes the following specific implementation process:

[0080] The first feature vector is input into the first output layer, and the three-dimensional output vector is output, wherein the three first output probabilities are respectively used to characterize the probability of the target ship sailing normally, the probability that the target ship is close to the sand mining operation area but does not carry out sand mining operations, and the probability that the target ship carries out sand mining operations in the sand mining operation area.

[0081] Specifically, the output layer has 3 neurons, corresponding to 3 navigation status evaluation categories: normal navigation, entering the sand mining area but not operating, and sand mining in progress. After the first output layer, the three-dimensional output vector is obtained, and the three-dimensional output vector represents the probability that the target ship belongs to each navigation status evaluation category. For example, the three-dimensional output vector is (0.8, 0.1, 0.1), indicating that the target ship has an 80% probability of normal navigation, a 10% probability of entering the sand mining area but not operating, and a 10% probability of sand mining in progress.

[0082] In some embodiments, the first output layer includes one second neuron, and the first evaluation result is a behavior probability corresponding to the target vessel.

[0083] Specifically, obtaining the first evaluation result according to the first feature vector through the first output layer includes the following specific implementation process:

[0084] The first feature vector is input into the first output layer, and the behavior probability is outputted, wherein the behavior probability is used to characterize the probability that the target vessel performs sand mining operations in the sand mining operation area.

[0085] S130. By presetting a second model, a second evaluation result is obtained according to the equipment information and pre-stored sand mining equipment information, wherein the second evaluation result is used to characterize the evaluation of the equipment status on the target vessel.

[0086] In some embodiments, the device information includes device type information, device power data, and device size data, and the preset second model includes a second input layer, a second convolutional layer, a second pooling layer, a second fully connected layer, and a second output layer.

[0087] Specifically, the second evaluation result is obtained by presetting the second model according to the equipment information and the pre-stored sand mining equipment information, including the steps S131 to S135 as shown below:

[0088] S131. Obtain a second input vector through the second input layer according to the equipment type information, equipment power data, equipment size data and the sand mining equipment information.

[0089] In some embodiments, the equipment type information can be represented by a numerical value; the equipment power data can include two numerical values, namely, the upper limit and the lower limit of the power range; the equipment size data can include two numerical values, namely, the volume and the capacity; in contrast, the sand mining equipment information can be the standard range of the sand mining equipment, and the standard range is represented by an interval. Thus, the second input layer can be provided with 15 neurons, corresponding to each numerical value.

[0090] Specifically, each numerical value is input into the second input layer, and the second input vector is obtained after normalization.

[0091] S132. Obtain a second convolutional feature map according to the second input vector through the second convolutional layer.

[0092] Continuing with the above embodiment, the second convolution layer can be a convolution layer with a convolution kernel size of 2×2, a step size of 1, and 3 convolution kernels. Specifically, the values ​​corresponding to the 15 neurons of the input layer are arranged in a certain order into a 3×5 (hypothetical two-dimensional layout form, a more reasonable layout can be adjusted according to actual conditions) matrix form, and input into the convolution layer as image-like data, so that the convolution kernel can be used to mine the local correlation features between different parameters and between actual equipment and standard equipment. After the second convolution layer, 3 feature maps are obtained, namely the second convolution feature maps, and the size of each feature map is assumed to be 2×4 (the specific size depends on factors such as the input matrix and the convolution kernel parameters).

[0093] S133. Obtain a second tensor according to the second convolutional feature map through the second pooling layer.

[0094] Continuing with the above embodiment, the second pooling layer can be a maximum pooling layer with a pooling window size of 2×2 and a step size of 2. Specifically, after the three feature maps are pooled, the size of the feature maps becomes 1×2, and the output is a 3×1×2 tensor, i.e., the second tensor, which effectively reduces the data dimension while retaining important information such as the main difference features after comparing various parameters of the device with the standard.

[0095] S134. Obtain a second feature vector according to the second tensor through the second fully connected layer.

[0096] Continuing with the above embodiment, the second fully connected layer can be a fully connected layer with 32 neurons. Specifically, the second fully connected layer flattens the pooled 3×1×2 tensor into a vector of length 6, and after weighted summation of neurons and activation function (such as ReLU function), outputs a 32-dimensional vector, i.e., the second feature vector, which further integrates and abstracts the previously extracted device-related features, and extracts intermediate features that are more relevant to the device status evaluation.

[0097] S135. Obtain the second evaluation result according to the second feature vector through the second output layer.

[0098] In some embodiments, the first output layer includes one third neuron, and the second evaluation result is a probability of equipment matching corresponding to the target vessel.

[0099] Specifically, obtaining the second evaluation result according to the second feature vector through the second output layer includes the following specific implementation process:

[0100] The second feature vector is input into the second output layer, and the equipment matching probability is outputted, wherein the equipment matching probability is used to characterize the probability that the equipment on the target vessel is sand mining equipment.

[0101] Continuing with the above embodiment, the 32-dimensional vector is weighted and summed with the weight vector in the third neuron, and then processed by an activation function (such as a sigmoid function, etc., which can map the output value to a suitable function in the range of 0-1), and finally a value between 0 and 1 is obtained, which is the equipment matching probability. For example, if the equipment matching probability finally calculated is 0.8, it means that according to the judgment of the preset second model, there is an 80% probability that the equipment on the target ship is sand mining equipment.

[0102] S140. Determine a target recognition result based on the first evaluation result, the second evaluation result and a preset evaluation result threshold, wherein the target recognition result is used to characterize a determination result of whether the target vessel is carrying out sand mining operations in a target area.

[0103] Continuing with the above embodiment, the preset evaluation result threshold includes a first evaluation result threshold and a second evaluation result threshold.

[0104] Specifically, determining the target recognition result based on the first evaluation result, the second evaluation result and a preset evaluation result threshold includes the following specific implementation process:

[0105] If the first behavior probability is not less than the first evaluation result threshold and the device matching probability is not less than the second evaluation result threshold, the target recognition result is determined as the target vessel performing sand mining operations in the sand mining operation area.

[0106] In some embodiments, the first evaluation result threshold can be set based on a large amount of actual monitoring data, law enforcement experience, and analysis of the rules of sand mining operations in the waters. For example, after statistical analysis of many confirmed sand mining cases in history, it is found that when the probability that the behavior characteristics of the target ship in the sand mining area meet the sand mining operation is 0.6 (this is an example value) or above, there is a high probability that sand mining is indeed being carried out, and the first evaluation result threshold can be set to 0.6. The first evaluation result threshold is equivalent to giving a reference limit for judging whether sand mining is taking place from the perspective of ship behavior performance.

[0107] At the same time, the second evaluation result threshold is determined based on the standard conditions of legal sand mining ship equipment, past experience in identifying different types of equipment, etc. For example, by analyzing the characteristic parameters of various types of legal sand mining equipment and the results of a large number of actual ship equipment tests, it is found that when the equipment matching probability reaches 0.5 (example value) or above, the equipment basically meets the requirements of sand mining equipment. At this time, the second evaluation result threshold is set to 0.5, setting a standard limit for judging whether it is sand mining equipment from the perspective of the equipment.

[0108] Assume that the behavior probability of a target ship is 0.7 and the equipment matching probability is 0.6. It is known that the first evaluation result threshold is set to 0.6 and the second evaluation result threshold is set to 0.5. At this time, since 0.7 (behavior probability) is not less than 0.6 (first evaluation result threshold), and 0.6 (equipment matching probability) is not less than 0.5 (second evaluation result threshold), the judgment condition is met, then it can be determined that the target recognition result is that the target ship is performing sand mining operations in the sand mining operation area; on the contrary, if the behavior probability of the target ship is 0.5 and the equipment matching probability is 0.4, even if one of the probabilities is close to the threshold, as long as one does not meet the corresponding threshold condition (such as the behavior probability is less than the first evaluation result threshold), it cannot be determined that the ship is performing sand mining operations in the sand mining operation area, and such a target recognition result will not be determined.

[0109] In summary, the present application provides a method for identifying sand mining in waters. By identifying the navigation information and equipment on board of the target ship, the target ship is evaluated comprehensively from multiple angles, and then it is accurately determined whether the target ship is conducting sand mining operations.

[0110] In order to better implement the above method, the embodiment of the present application also provides a water area sand mining identification device, which can be integrated in an electronic device, and the electronic device can be a terminal, a server, etc. Among them, the terminal can be a mobile phone, a tablet computer, a smart Bluetooth device, a laptop, a personal computer, etc.; the server can be a single server or a server cluster composed of multiple servers.

[0111] For example, in this embodiment, the method of the embodiment of the present application will be described in detail by taking the specific integration of the water area sand mining identification device in the terminal as an example.

[0112] For example, Figure 2 As shown, the water area sand mining identification device 300 may include a first unit 301, a second unit 302, a third unit 303 and a fourth unit 304, and the device includes:

[0113] The first unit is used to obtain target area information corresponding to the target area, sand mining operation area information corresponding to the sand mining operation area in the target area information, equipment information of the target vessel, and navigation information of the target vessel in the target area information, wherein the target area information is used to characterize the conditions of the monitored waters, and the sand mining operation area is used to characterize the conditions of the sand mining operation area in the target area;

[0114] The second unit is used to obtain a first evaluation result according to the target area information, the sand mining operation area information and the navigation information by presetting a first model, wherein the first evaluation result is used to represent the evaluation of the navigation status of the target vessel in the target area;

[0115] A third unit is used to obtain a second evaluation result according to the equipment information and pre-stored sand mining equipment information by presetting a second model, wherein the second evaluation result is used to represent the evaluation of the equipment status on the target vessel;

[0116] The fourth unit is used to determine a target recognition result based on the first evaluation result, the second evaluation result and a preset evaluation result threshold, wherein the target recognition result is used to characterize the determination result of whether the target vessel is carrying out sand mining operations in the target area.

[0117] In specific implementation, the above units can be implemented as independent entities, or can be arbitrarily combined to be implemented as the same or several entities. The specific implementation of the above units can refer to the previous method embodiments, which will not be repeated here.

[0118] It can be seen from the above that the embodiments of the present application can evaluate the target ship from multiple angles and comprehensively by identifying the navigation information and equipment on board the target ship, and then accurately determine whether the target ship is performing sand mining operations.

[0119] The embodiment of the present application also provides an electronic device, which can be a terminal, a server, etc. The terminal can be a mobile phone, a tablet computer, a smart Bluetooth device, a laptop, a personal computer, etc. The server can be a single server or a server cluster composed of multiple servers, etc.

[0120] In some embodiments, the product processing device can also be integrated into multiple electronic devices. For example, the product processing device can be integrated into multiple servers, and the water area sand mining identification method of the present application can be implemented by multiple servers.

[0121] In this embodiment, the electronic device of this embodiment is a terminal as an example for detailed description, for example, Figure 3 As shown, it shows a schematic diagram of the structure of the terminal 300 involved in the embodiment of the present application, specifically:

[0122] The terminal 300 may include one or more processors 301 of processing cores, one or more storage media 302, a power supply 303, an input module 304, and a communication module 305. Those skilled in the art will appreciate that Figure 3 The structure of the terminal 300 shown in the figure does not constitute a limitation on the terminal 300, and the terminal 300 may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0123] The processor 301 is the control center of the terminal 300. It uses various interfaces and lines to connect various parts of the entire terminal 300. It executes various functions of the terminal 300 and processes data by running or executing software programs and / or modules stored in the memory 302, and calling data stored in the memory 302, so as to monitor the terminal 300 as a whole. In some embodiments, the processor 301 may include one or more processing cores; in some embodiments, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 301.

[0124] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and data processing by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the terminal 300, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0125] The terminal 300 also includes a power supply 303 for supplying power to various components. In some embodiments, the power supply 303 can be logically connected to the processor 301 through a power management system, so as to manage charging, discharging, and power consumption through the power management system. The power supply 303 can also include any components such as one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators.

[0126] The terminal 300 may further include an input module 304, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0127] The terminal 300 may also include a communication module 305. In some embodiments, the communication module 305 may include a wireless module. The terminal 300 may perform short-range wireless transmission through the wireless module of the communication module 305, thereby providing the user with wireless broadband Internet access. For example, the communication module 305 may be used to help the user send and receive emails, browse web pages, and access streaming media.

[0128] Although not shown, the terminal 300 may also include a display unit, etc., which will not be described in detail here. Specifically in this embodiment, the processor 301 in the terminal 300 will load the executable files corresponding to the processes of one or more applications into the memory 302 according to the following instructions, and the processor 301 will run the applications stored in the memory 302, thereby realizing various functions, as follows:

[0129] Acquire target area information corresponding to the target area, sand mining operation area information corresponding to the sand mining operation area in the target area information, equipment information of the target vessel, and navigation information of the target vessel in the target area information, wherein the target area information is used to characterize the condition of the monitored waters, and the sand mining operation area is used to characterize the condition of the sand mining operation area in the target area;

[0130] By presetting a first model, a first evaluation result is obtained according to the target area information, the sand mining operation area information and the navigation information, wherein the first evaluation result is used to characterize the evaluation of the navigation status of the target vessel in the target area;

[0131] By presetting the second model, a second evaluation result is obtained according to the equipment information and pre-stored sand mining equipment information, wherein the second evaluation result is used to characterize the evaluation of the equipment status on the target vessel;

[0132] Based on the first evaluation result, the second evaluation result and a preset evaluation result threshold, a target recognition result is determined, wherein the target recognition result is used to characterize a determination result of whether the target vessel is carrying out sand mining operations in a target area.

[0133] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0134] It can be seen from the above that the embodiments of the present application can evaluate the target ship from multiple angles and comprehensively by identifying the navigation information and equipment on board the target ship, and then accurately determine whether the target ship is performing sand mining operations.

[0135] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a medium and loaded and executed by a processor.

[0136] To this end, an embodiment of the present application provides a medium in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any of the water area sand mining identification methods provided in the embodiment of the present application. For example, the instructions can execute the following steps:

[0137] Acquire target area information corresponding to the target area, sand mining operation area information corresponding to the sand mining operation area in the target area information, equipment information of the target vessel, and navigation information of the target vessel in the target area information, wherein the target area information is used to characterize the condition of the monitored waters, and the sand mining operation area is used to characterize the condition of the sand mining operation area in the target area;

[0138] By presetting a first model, a first evaluation result is obtained according to the target area information, the sand mining operation area information and the navigation information, wherein the first evaluation result is used to characterize the evaluation of the navigation status of the target vessel in the target area;

[0139] By presetting the second model, a second evaluation result is obtained according to the equipment information and pre-stored sand mining equipment information, wherein the second evaluation result is used to characterize the evaluation of the equipment status on the target vessel;

[0140] Based on the first evaluation result, the second evaluation result and a preset evaluation result threshold, a target recognition result is determined, wherein the target recognition result is used to characterize a determination result of whether the target vessel is carrying out sand mining operations in a target area.

[0141] The medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0142] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes a computer instruction, the computer instruction is stored in a medium. A processor of a computer device reads the computer instruction from the medium, and the processor executes the computer instruction, so that the computer device executes the method provided in various optional implementations provided in the above embodiments.

[0143] Since the instructions stored in the medium can execute the steps in any one of the methods for identifying sand mining in water areas provided in the embodiments of the present application, the beneficial effects that can be achieved by any one of the methods for identifying sand mining in water areas provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0144] The above is a detailed introduction to a method, device, terminal and medium for identifying sand mining in water areas provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for identifying sand mining in waters, characterized in that: The method comprises: Acquire target area information corresponding to the target area, sand mining operation area information corresponding to the sand mining operation area in the target area information, equipment information of the target vessel, and navigation information of the target vessel in the target area information, wherein the target area information is used to characterize the condition of the monitored waters, and the sand mining operation area is used to characterize the condition of the sand mining operation area in the target area; By presetting a first model, a first evaluation result is obtained according to the target area information, the sand mining operation area information and the navigation information, wherein the first evaluation result is used to characterize the evaluation of the navigation status of the target vessel in the target area; By presetting the second model, a second evaluation result is obtained according to the equipment information and pre-stored sand mining equipment information, wherein the second evaluation result is used to characterize the evaluation of the equipment status on the target vessel; Based on the first evaluation result, the second evaluation result and a preset evaluation result threshold, a target recognition result is determined, wherein the target recognition result is used to characterize a determination result of whether the target vessel is carrying out sand mining operations in a target area.

2. The method according to claim 1, characterized in that The target area information includes target area coordinate data, the sand mining operation area information includes sand mining operation area coordinate data, the navigation information includes navigation track data and navigation speed data, and the preset first model includes a first input layer, a first convolutional layer, a first pooling layer, a first fully connected layer and a first output layer; The first evaluation result is obtained by presetting the first model according to the target area information, the sand mining operation area information and the navigation information, including: Obtaining a first input vector through the first input layer according to the target area coordinate data, the sand mining operation area coordinate data, the navigation track data and the navigation speed data; Obtaining a first convolutional feature map according to the first input vector through the first convolutional layer; Obtaining a first tensor according to the first convolutional feature map through the first pooling layer; Obtaining a first feature vector according to the first tensor through the first fully connected layer; The first evaluation result is obtained through the first output layer according to the first feature vector.

3. The method according to claim 2, characterized in that The first output layer includes three first neurons, the first evaluation result is a three-dimensional output vector, the three-dimensional output vector includes three first output probabilities, and the first output probabilities correspond to the first neurons one by one; The obtaining the first evaluation result according to the first feature vector through the first output layer includes: The first feature vector is input into the first output layer, and the three-dimensional output vector is output, wherein the three first output probabilities are respectively used to characterize the probability of the target ship sailing normally, the probability that the target ship is close to the sand mining operation area but does not carry out sand mining operations, and the probability that the target ship carries out sand mining operations in the sand mining operation area.

4. The method according to claim 2, characterized in that The first output layer includes one second neuron, and the first evaluation result is a behavior probability corresponding to the target vessel; The obtaining the first evaluation result according to the first feature vector through the first output layer includes: The first feature vector is input into the first output layer, and the behavior probability is outputted, wherein the behavior probability is used to characterize the probability that the target vessel performs sand mining operations in the sand mining operation area.

5. The method according to claim 4, characterized in that The device information includes device type information, device power data, and device size data, and the preset second model includes a second input layer, a second convolutional layer, a second pooling layer, a second fully connected layer, and a second output layer; The second evaluation result is obtained by presetting the second model according to the equipment information and the pre-stored sand mining equipment information, including: Obtaining a second input vector through the second input layer according to the equipment type information, the equipment power data, the equipment size data and the sand mining equipment information; Obtaining a second convolutional feature map according to the second input vector through the second convolutional layer; Obtaining a second tensor according to the second convolutional feature map through the second pooling layer; Obtaining a second feature vector according to the second tensor through the second fully connected layer; The second evaluation result is obtained through the second output layer according to the second feature vector.

6. The method according to claim 5, characterized in that The first output layer includes one third neuron, and the second evaluation result is a probability of equipment matching corresponding to the target vessel; The obtaining the second evaluation result according to the second feature vector through the second output layer includes: The second feature vector is input into the second output layer, and the equipment matching probability is outputted, wherein the equipment matching probability is used to characterize the probability that the equipment on the target vessel is sand mining equipment.

7. The method according to claim 6, characterized in that The preset evaluation result threshold includes a first evaluation result threshold and a second evaluation result threshold; The determining of the target recognition result based on the first evaluation result, the second evaluation result and a preset evaluation result threshold includes: If the first behavior probability is not less than the first evaluation result threshold and the device matching probability is not less than the second evaluation result threshold, the target recognition result is determined as the target vessel performing sand mining operations in the sand mining operation area.

8. A device for identifying sand mining in waters, characterized in that: The device comprises: The first unit is used to obtain target area information corresponding to the target area, sand mining operation area information corresponding to the sand mining operation area in the target area information, equipment information of the target vessel, and navigation information of the target vessel in the target area information, wherein the target area information is used to characterize the conditions of the monitored waters, and the sand mining operation area is used to characterize the conditions of the sand mining operation area in the target area; The second unit is used to obtain a first evaluation result according to the target area information, the sand mining operation area information and the navigation information by presetting a first model, wherein the first evaluation result is used to represent the evaluation of the navigation status of the target vessel in the target area; A third unit is used to obtain a second evaluation result according to the equipment information and pre-stored sand mining equipment information by presetting a second model, wherein the second evaluation result is used to represent the evaluation of the equipment status on the target vessel; The fourth unit is used to determine a target recognition result based on the first evaluation result, the second evaluation result and a preset evaluation result threshold, wherein the target recognition result is used to characterize the determination result of whether the target vessel is carrying out sand mining operations in the target area.

9. A terminal, characterized in that: The method comprises a processor and a memory, wherein the memory stores a plurality of instructions; the processor loads instructions from the memory to execute the steps in the method according to any one of claims 1 to 7.

10. A medium, characterized in that The medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the steps in the method according to any one of claims 1 to 7.

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