Waterlogged area processing method, device and storage medium based on swarm intelligence

Abstract

The present invention discloses a method, device, and storage medium for processing waterlogged areas based on swarm intelligence. The method comprises: performing waterlogging detection on an input image to determine whether a waterlogged area exists within the road section displayed in the input image; when a waterlogged area exists in the input image, obtaining target group data from the input image; performing behavioral analysis on the target group data to determine the degree of waterlogging in the waterlogged area; when a flooded road area is determined to exist in the input image based on the waterlogging degree information, performing behavioral recognition on the target object, and issuing an alarm to the target object when the recognition result meets preset conditions. The present invention solves the technical problem of low waterlogging detection accuracy in existing waterlogging detection methods, which results in inaccurate alarms.

Description

Technical Field

[0001] The present invention relates to the field of intelligent identification technology, and in particular to a method, device and storage medium for processing waterlogged areas based on swarm intelligence. Background Art

[0002] During rainy weather, flooded roads are often widespread in cities, making it difficult for traffic police to dispatch personnel to patrol everywhere immediately. Rainy weather also causes traffic congestion and frequent accidents. Therefore, how to accurately identify vehicles that do not slow down on flooded roads based on intelligent analysis technologies such as video acquisition to reduce manpower deployment has become an urgent problem to be solved.

[0003] Identifying vehicles that don't slow down on flooded roads based on intelligent analysis technology involves both vehicle identification and tracking technology and water accumulation detection technology. Vehicle identification and tracking technology currently has relatively mature algorithms within the industry, ensuring high accuracy. However, current computer vision-based water accumulation detection algorithms are still immature, making it difficult to distinguish the extent of water accumulation and determine whether the road is flooded enough to require deceleration. On rainy days, slippery surfaces or roads with only shallow water accumulation can be mistakenly identified as flooded, resulting in false alarms for vehicles traveling normally on the road.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The embodiments of the present invention provide a method, device and storage medium for processing waterlogged areas based on swarm intelligence, so as to at least solve the technical problem that the waterlogging detection method in the prior art has a low waterlogging detection accuracy rate, resulting in inaccurate alarm prompts.

[0006] According to one aspect of an embodiment of the present invention, a method for processing waterlogged areas based on swarm intelligence is provided, including: performing waterlogging detection on an input image to determine whether a waterlogged area exists in a road section displayed in the input image; when the waterlogged area exists in the input image, obtaining target group data from the input image; performing behavioral analysis on the target group data to determine information on the degree of waterlogging in the waterlogged area; when it is determined that a flooded road area exists in the input image based on the waterlogging degree information, performing behavioral recognition on the target object, and issuing an alarm to the target object when the recognition result meets a preset condition.

[0007] According to another aspect of an embodiment of the present invention, a waterlogged area processing device based on swarm intelligence is also provided, including: a detection module, used to perform waterlogging detection on an input image, and determine whether there is a waterlogged area in the road section displayed in the above input image; an acquisition module, used to obtain target group data from the above input image when the above waterlogged area exists in the above input image; a determination module, used to perform behavioral analysis on the above target group data, and determine the waterlogging degree information of the above waterlogged area; a processing module, used to perform behavioral recognition on the target object when it is determined that there is a flooded road area in the above input image based on the above waterlogging degree information, and to issue an alarm prompt to the above target object when the recognition result meets the preset conditions.

[0008] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is also provided, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned waterlogged area processing methods based on swarm intelligence.

[0009] According to another aspect of an embodiment of the present invention, an electronic device is also provided, characterized in that it includes: a processor; and a memory, connected to the above-mentioned processor, for providing the above-mentioned processor with instructions for processing the following processing steps: performing water accumulation detection on the input image to determine whether there is a water accumulation area in the road section displayed in the above-mentioned input image; when the above-mentioned water accumulation area exists in the above-mentioned input image, obtaining target group data from the above-mentioned input image; performing behavior analysis on the above-mentioned target group data to determine the water accumulation degree information of the above-mentioned water accumulation area; when it is determined that there is a flooded road area in the above-mentioned input image based on the above-mentioned water accumulation degree information, performing behavior recognition on the target object, and when the recognition result meets the preset conditions, issuing an alarm prompt to the above-mentioned target object.

[0010] In an embodiment of the present invention, a method of processing waterlogged areas based on swarm intelligence is adopted. By performing waterlogging detection on an input image, it is determined whether there is a waterlogged area in the road section displayed in the above input image; when the above-mentioned waterlogged area exists in the above-mentioned input image, target group data is obtained from the above-mentioned input image; behavior analysis is performed on the above-mentioned target group data to determine the waterlogging degree information of the above-mentioned waterlogged area; when it is determined that there is a flooded road area in the above-mentioned input image based on the above-mentioned waterlogging degree information, behavior recognition is performed on the target object, and when the recognition result meets the preset conditions, an alarm prompt is issued to the above-mentioned target object, thereby achieving the purpose of judging the waterlogging degree of the waterlogged area based on swarm intelligence and issuing an alarm prompt, thereby realizing the technical effect of improving the accuracy of waterlogging detection and the accuracy of alarm prompts, and further solving the technical problem that the waterlogging detection method in the prior art has a low waterlogging detection accuracy resulting in inaccurate alarm prompts. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0012] Figure 1 This is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for treating a waterlogged area based on swarm intelligence according to an embodiment of the present invention;

[0013] Figure 2 is a flow chart of a method for processing a waterlogged area based on swarm intelligence according to an embodiment of the present invention;

[0014] Figure 3 1 is a schematic diagram of a scenario of an optional method for processing a waterlogged area using swarm intelligence according to an embodiment of the present invention;

[0015] Figure 4 is a flow chart of an optional method for processing a waterlogged area based on swarm intelligence according to an embodiment of the present invention;

[0016] Figure 5 is a flowchart of another optional method for processing a waterlogged area based on swarm intelligence according to an embodiment of the present invention;

[0017] Figure 6 is a flowchart of another optional method for processing a waterlogged area based on swarm intelligence according to an embodiment of the present invention;

[0018] Figure 7 A schematic structural diagram of a waterlogged area treatment device based on swarm intelligence according to an embodiment of the present invention;

[0019] Figure 8 It is a structural block diagram of a computer terminal according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] First, some nouns or terms that appear in the description of the embodiments of the present invention are subject to the following explanations:

[0023] Swarm Intelligence: Swarm Intelligence is a cutting-edge machine intelligence method that leverages the collective wisdom of the group to collaboratively solve large-scale, complex problems. It provides new technologies and approaches for solving large-scale, complex problems that are difficult to address with traditional methods. It has been widely applied in fields such as transportation, crowdsourcing, and software development. For specific application scenarios, domestic and international scholars have designed various swarm intelligence methods from the perspectives of individual evaluation mechanisms, individual encoding and decoding strategies, and swarm organizational structures.

[0024] Crowd Simulation: Crowd simulation is the process of simulating the movement of a large number of entities or characters. It is commonly used in crisis training, architectural and urban planning, and evacuation simulations. It can also be used to create virtual scenes in movies or video games.

[0025] Object Detection: Object detection is a branch of computer technology closely related to computer vision and image processing. Its goal is to detect specific semantic target entities, such as people, buildings, and cars, in digital images and videos, and output the result as a rectangular box that tightly encloses the target entity. Object detection has applications in many computer vision fields, such as image retrieval and video acquisition.

[0026] Computer Vision: Computer vision is the study of how machines can "see." Specifically, it refers to the use of cameras and computers to replace the human eye in identifying, tracking, and measuring objects. This is followed by image processing, where computers create images more suitable for human observation or for transmission to instruments. As a scientific discipline, computer vision studies related theories and technologies, aiming to build artificial intelligence systems capable of extracting information from images or multidimensional data.

[0027] Example 1

[0028] According to an embodiment of the present invention, an embodiment of a method for processing a waterlogged area based on swarm intelligence is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] The method embodiment provided in Embodiment 1 of the present invention may be executed in a mobile terminal, a computer terminal, or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for treating a flooded area based on swarm intelligence is shown. Figure 1 As shown, the computer terminal 10 (or mobile device 10) may include one or more (illustrated as 102a, 102b, ..., 102n) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0030] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be fully or partially integrated into any of the other components of the computer terminal 10 (or mobile device). As described in the embodiments of the present invention, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0031] The memory 104 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the swarm intelligence-based waterlogged area treatment method in the embodiment of the present invention. The processor 102 executes the software programs and modules stored in the memory 104 to perform various functional applications and data processing, thereby implementing the above-mentioned swarm intelligence-based waterlogged area treatment method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0032] The transmission device 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0033] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0034] On rainy days, roads are often flooded due to uneven paving, aging, and damage. When a motor vehicle passes through a flooded road or bridge, it should stop to check the water conditions and pass at a low speed after confirming it is safe. However, in reality, motor vehicle drivers often ignore this rule and continue to drive at high speeds when facing flooded roads. This may not only pose a danger to the driver due to nails, wires, etc. in the water, but will also splash water and affect nearby pedestrians and cyclists. Punishing illegal vehicles that do not slow down when passing through flooded roads not only protects the rights and interests of pedestrians and non-motor vehicle drivers, but also ensures the safety of motor vehicle operations. In the foreseeable future, it will become an emerging regulatory direction for transportation departments.

[0035] During rainy weather, flooded roads are often widespread in cities, making it difficult for traffic police to dispatch personnel to patrol everywhere immediately. Rainy weather also causes traffic congestion and frequent accidents. Accurately identifying vehicles that do not slow down on flooded roads based on intelligent analysis technologies such as video acquisition to reduce manpower deployment has become a pressing issue.

[0036] Currently, the technology for identifying vehicles that do not slow down on flooded roads primarily involves two aspects: vehicle identification and tracking, and water detection. Existing technologies already offer mature methods for vehicle identification and tracking, accurately identifying information such as vehicle location and speed. Flooded roads are primarily identified through methods such as object detection, semantic segmentation, and background subtraction. When the algorithm determines that a road is flooded, it identifies vehicles whose speed exceeds a set threshold as violating traffic rules and issues an alarm.

[0037] The main drawback of these existing solutions is that they struggle to accurately distinguish flooded roads. From a purely visual perspective, even a human observing a flooded road through video cannot discern the depth of the water. Therefore, on rainy days, these solutions can easily misidentify slippery surfaces or shallowly flooded roads as flooded, generating false alarms for vehicles traveling normally on the roads.

[0038] In view of the above problems, the present invention provides the following based on the above operating environment: Figure 2 The waterlogged area treatment method based on swarm intelligence is shown in the figure. Figure 2 FIG. 1 is a flow chart of a method for processing a waterlogged area based on swarm intelligence according to an embodiment of the present invention. Figure 2 As shown, the method includes:

[0039] Step S202: performing water accumulation detection on the input image to determine whether there is a water accumulation area within the road section displayed in the input image;

[0040] Step S204: when the waterlogged area exists in the input image, obtaining target group data from the input image;

[0041] Step S206: Performing behavioral analysis on the target group data to determine the degree of waterlogging in the waterlogged area.

[0042] Step S208: When it is determined based on the water accumulation level information that a flooded road area exists in the input image, behavior recognition is performed on the target object, and when the recognition result meets a preset condition, an alarm is issued to the target object.

[0043] Optionally, the above-mentioned input image is an image obtained by real-time video capture of the target road section, for example, it is obtained by real-time video capture of the target road section using a camera or other video capture equipment. After receiving the above-mentioned input image, it can be detected whether there is a waterlogged area in the road section displayed in the input image.

[0044] Optionally, the target group may include, but is not limited to, people and / or vehicles, etc.; wherein, the target group may include, but is not limited to, pedestrians on the road, two-wheeled / three-wheeled vehicle riders, etc. The target group data may be, but is not limited to, obtained based on the location and driving trajectory of the target group (i.e., the group of people and / or vehicles), vehicle speed, and other related information, and the data type of the target group data is spatially structured data.

[0045] Optionally, when the water level information of the waterlogged area reaches a certain preset value, the waterlogged area is determined to be a flooded road area. Optionally, the target object may be, but is not limited to, a vehicle traveling in the flooded area; and whether the recognition result satisfies the preset condition may be, but is not limited to, whether the speed of the vehicle traveling in the flooded road area exceeds a preset value.

[0046] Figure 3 is a scene diagram of an optional swarm intelligence method for processing waterlogged areas according to an embodiment of the present invention. The input image may be as follows: Figure 3 In the embodiment of the present invention, a waterlogging area processing method based on swarm intelligence is adopted to detect waterlogging on the input image to determine whether there is a waterlogging area in the road section shown in the input image; when the waterlogging area exists in the input image, the following method is used: Figure 3 The detection frame shown marks the waterlogged area and obtains the target group data from the above-mentioned input image; the above-mentioned target group data is subjected to behavioral analysis to determine the water level information of the above-mentioned waterlogged area; when it is determined that there is a flooded road area in the above-mentioned input image based on the above-mentioned water level information, the target object is subjected to behavioral recognition, and when the recognition result meets the preset conditions (for example, passing through the waterlogged area without slowing down, etc.), an alarm prompt is issued to the above-mentioned target object, thereby achieving the purpose of judging the water level of the waterlogged area based on group intelligence and issuing an alarm prompt, thereby realizing the technical effect of improving the accuracy of waterlogging detection and the accuracy of alarm prompts, and thus solving the technical problem that the waterlogging detection method in the prior art has a low waterlogging detection accuracy and causes inaccurate alarm prompts.

[0047] It should be noted that, in order to address the shortcoming that it is difficult to visually determine the degree of water accumulation in a scene, an embodiment of the present invention proposes to use the feedback behavior of the target group (people and / or vehicles) in the scene regarding the water accumulation in the scene to assist in analyzing the degree of water accumulation in the scene, thereby improving the accuracy of determining flooded roads. The embodiment of the present invention is based on the existing and relatively mature target group recognition and tracking method in the field of computer vision, extracts spatial structured data of the target group's position trajectory, and combines it with the crowd and vehicle group simulation method based on swarm intelligence to realize the behavior analysis of the target group in the scene to estimate the degree of water accumulation in the scene. This avoids the reliance on the water accumulation recognition algorithm based on computer vision, improves the ability to distinguish between flooded roads and shallow water accumulation roads, and effectively avoids excessive warnings for speeding vehicles driving on rainy days.

[0048] In an optional embodiment, a water accumulation area processing method based on swarm intelligence is used to detect water accumulation on the input image, and determining whether the water accumulation area exists in the input image includes: using a first neural network model to detect water accumulation on the input image, and determining whether the water accumulation area exists in the input image.

[0049] Optionally, the first neural network model is a pre-trained target detection model, which is used to detect whether the water accumulation area exists in the input image, and if so, record the position coordinates of the water accumulation area.

[0050] It should be noted that, in addition to using the first neural network model to determine whether there is a water accumulation area in the above input image, it is also possible to determine whether there is a water accumulation area in the input image through semantic segmentation, frame difference, etc.

[0051] As an optional embodiment, Figure 4 is a flow chart of an optional method for processing a waterlogged area based on swarm intelligence according to an embodiment of the present invention, such as Figure 4 As shown, the method for processing waterlogged areas based on swarm intelligence, obtaining the target group data from the input image, includes:

[0052] Step S302: using a second neural network model to identify and track the target group in the input image to obtain a first processing result;

[0053] Step S304, determining a second processing result based on the first processing result and the position information of the image acquisition device preset in the road section;

[0054] Step S306: convert the second processing result into spatial structured data to obtain the target group data.

[0055] Optionally, the second neural network model is a pre-trained target detection and tracking model, which is used to identify and track the target group in the input image, wherein the target group may include but is not limited to people and / or vehicles, etc.; wherein the people may include but are not limited to pedestrians, two-wheeled / three-wheeled vehicle riders and other road users.

[0056] Optionally, the first processing result may include, but is not limited to, relevant information such as the location, driving trajectory, and vehicle speed of the target group (i.e., people and / or vehicles); the second processing result is the specific location of the target group in the first processing result, wherein the specific location is obtained based on the location information of the image acquisition device preset in the road section; the target group data is spatially structured data, wherein the spatially structured data is obtained based on the second processing result, and the spatially structured data is stored in a group simulation queue (i.e., a group of people / vehicles simulation queue).

[0057] In an optional embodiment, a method for processing waterlogged areas based on swarm intelligence performs behavioral analysis on the target group data to determine the waterlogging degree information, including:

[0058] Step S402: Analyze the behavior of the target group data using a swarm intelligence model to obtain a waterlogging degree score for the waterlogged area.

[0059] Step S404: When the water accumulation degree score is greater than a first preset threshold, it is determined that the flooded road area exists in the input image.

[0060] Optionally, the above-mentioned swarm intelligence model can be but is not limited to a swarm intelligence human / vehicle group simulation model; the above-mentioned swarm intelligence model is used to characterize the relationship between the water accumulation situation in the above-mentioned water accumulation area and the behavior of the target group in the water accumulation area, and the actual behavior data of the target group is fitted by iterative optimization of model parameters to estimate the degree of water accumulation in the water accumulation area. The above-mentioned water accumulation degree score is used to represent the degree of water accumulation in the above-mentioned water accumulation area, wherein the above-mentioned group can be but is not limited to a group of people and / or vehicles.

[0061] Optionally, the above-mentioned swarm intelligence model (i.e., swarm intelligence model) is initially modeled based on the collected data, wherein the above-mentioned collected data may be, but is not limited to, public data sets, or target group (people and / or vehicles) trajectory data in any scenario collected by the user.

[0062] As an optional embodiment, Figure 5 is a flow chart of another optional method for processing a waterlogged area based on swarm intelligence according to an embodiment of the present invention, such as Figure 5As shown, the method for processing waterlogged areas based on swarm intelligence uses the swarm intelligence model to perform behavioral analysis on the target group data to obtain the waterlogging degree scores, including:

[0063] Step S502: Analyze the behavior of the target group data using the swarm intelligence model to obtain a predicted trajectory of the target group.

[0064] Step S504, determining the actual trajectory of the target group based on the target group data;

[0065] Step S506: Calculate the difference between the actual trajectory and the predicted trajectory, and perform multiple iterative calculations based on the difference and a preset traffic rate variation pattern to determine the actual traffic rate of the flooded area.

[0066] Step S508: Calculate the waterlogging degree score using the actual traffic rate.

[0067] Optionally, the above difference result may be, but is not limited to, a spatial distance, a speed difference, a distance difference, etc. between the actual trajectory and the predicted trajectory.

[0068] Optionally, in the above step S506, multiple iterative calculations are performed based on the above difference results and the preset traffic rate variation law, and determining the actual traffic rate of the above waterlogged area refers to searching for the traffic rate of the waterlogged area closest to the actual situation through multiple iterations. For example, in each iterative calculation process, the traffic rate at each location in the waterlogged scene is first assumed; the trajectory of the target group is predicted based on the assumed result to obtain a predicted trajectory, and the difference result (difference) between the predicted trajectory and the actual trajectory is calculated; based on the difference result and the preset traffic rate variation law, the traffic rate and other parameters of the next iteration are set; the preset traffic rate corresponding to the difference result when the search difference result is the smallest is used as the actual traffic rate of the waterlogged area.

[0069] Optionally, the accessibility of the flooded area can be set via an explicit parameter. The accessibility range can be, but is not limited to, 0-1, where a accessibility of 1 indicates no obstruction, including, but not limited to, obstructions such as accumulated water, potholes, and stairs; a accessibility of 0 indicates an impassable obstacle; a accessibility between 0 and 1 indicates a certain degree of obstruction, with the closer the accessibility value is to 1, the less obstruction there is. Optionally, the waterlogging degree score can be represented by the inverse of the actual accessibility, or (1-actual accessibility).

[0070] In an optional embodiment, the method for processing a waterlogged area based on swarm intelligence, performing behavior recognition on the target object includes:

[0071] Step S602, performing behavior recognition on the target object to determine speed data of the target object;

[0072] Step S604: When the speed data exceeds a second preset threshold, the speed data and the corresponding occurrence time of the speed data are recorded.

[0073] Optionally, the target object may be, but is not limited to, a vehicle traveling in a flooded area, and the speed data may be, but is not limited to, the instantaneous speed or average speed of the target object. Optionally, the instantaneous speed or average speed of the target object may be calculated using a parameter-free method based on the actual trajectory of the target object, for example, the spatial center position of the target object at each moment.

[0074] Optionally, before using the swarm intelligence model to perform behavioral analysis on the above-mentioned target group data, the start time t0 and end time t1 of the above-mentioned target group data in the swarm simulation queue, as well as the time range (i.e., storage duration) (t1-t0) corresponding to the above-mentioned target group data are recorded, and when the above-mentioned time range (t1-t0) reaches a second preset threshold, the above-mentioned swarm intelligence model is trained.

[0075] Optionally, when the speed data of the target object (i.e., a vehicle traveling in a flooded area) exceeds a second preset threshold, the speed data of the target object and the occurrence time corresponding to the speed data are saved in a specific queue, wherein the specific queue is used to record relevant information of the target object that may have speeding phenomenon.

[0076] In an optional embodiment, the method for processing a waterlogged area based on swarm intelligence, when the recognition result satisfies the preset conditions, issuing an alarm to the target object includes:

[0077] Step S702, determining the time range corresponding to the target group data;

[0078] Step S704: When the occurrence time is within the time range and the target object is located in the flooded road area, an alarm is issued to the target object.

[0079] Optionally, the above time range is the difference (t1-t0) between the start time t0 and the end time t1 of the target group data in the group simulation queue.

[0080] Optionally, when the speed of the target object exceeds a second preset threshold (i.e., there is speeding), and the time for the target object to pass through the flooded area is within the time range, and it is determined that the flooded area where the target object is located is a flooded road area, an alarm is issued to the target object.

[0081] As an optional embodiment, when it is detected that the speed data of the target object in the flooded road area exceeds the second preset value, the target object will receive an alarm prompt, wherein, Figure 3 As shown, the above-mentioned alarm prompt may include but is not limited to the real-time image of video acquisition and the analysis results output by the swarm intelligence model, wherein the above-mentioned analysis results may include but are not limited to: the speed data of the target object (for example, 53km / h), the distribution of the accumulated water area, the trajectory of people / vehicles, and the display result that the target object did not slow down when passing through the flooded road.

[0082] In an optional embodiment, the above method further includes the following method steps:

[0083] Step S802, obtaining an input image;

[0084] Step S804: Performing water accumulation detection on the input image. When a water accumulation area exists within the road section displayed in the input image, obtaining target group data from the input image. Performing behavior analysis on the target group data. When it is determined that a flooded road area exists in the input image based on the water accumulation level information of the flooded area, performing behavior recognition on the target object. When the recognition result meets preset conditions, issuing an alarm prompt to the target object.

[0085] Step S806: reporting the above identification result to the server.

[0086] Optionally, when there is a waterlogged area in the road section displayed in the above input image, it is determined whether the number of consecutive input image frames without waterlogging meets the preset conditions. If so, the group simulation queue is cleared and the next round of image input is entered.

[0087] It should be noted that, in this embodiment, it is first determined whether there is a flooded road area in the input image based on the target group data, and then the behavior of the target object is identified. When the identification result meets the preset conditions, for example, when the speed data of the above-mentioned target object exceeds the second preset threshold, it is confirmed that the above-mentioned target object is speeding in the flooded road area, an alarm is issued to the above-mentioned target object, and the above-mentioned identification result is reported to the server, thereby achieving the purpose of judging the degree of water accumulation in the flooded area based on group intelligence and issuing an alarm, thereby realizing the technical effect of improving the accuracy of water accumulation detection and the accuracy of alarm prompts, and thus solving the technical problem of low water accumulation detection accuracy and inaccurate alarm prompts in the water accumulation detection method in the prior art.

[0088] As an optional embodiment, Figure 6 is a flow chart of another optional method for processing a waterlogged area based on swarm intelligence according to an embodiment of the present invention, such as Figure 6As shown, the input image information is obtained, and it is determined whether the number of frames of the continuous input image without water accumulation meets the preset conditions. If so, the human / vehicle group simulation queue is cleared and the next round of image input is entered. Otherwise, the human / vehicle targets in the input image are continuously detected; the above-mentioned human / vehicle targets are structured, and the spatial structured data of the above-mentioned human / vehicle targets are extracted and saved in the human / vehicle group simulation queue. The speed of each vehicle is calculated and the speeding vehicles are recorded, and the next round of image input is continued.

[0089] Read relevant data information in the human / vehicle group simulation queue to determine whether the length of the above human / vehicle group simulation queue meets the preset conditions. If so, train the swarm intelligent human / vehicle group simulation model based on the data in the above human / vehicle group simulation queue; otherwise, continue to read relevant data information in the human / vehicle group simulation queue; determine the degree of water accumulation in the waterlogged area based on the trained swarm intelligent human / vehicle group simulation model, that is, assign a water accumulation degree score to the waterlogged area; when the above water accumulation degree score is greater than the threshold, determine that the above waterlogged area is a flooded area; calculate the speed of each vehicle in the human / vehicle group simulation queue and record the speeding vehicles, search and record the vehicles passing through the above flooded area from the above speeding vehicles, and return the above recording results to the above human / vehicle group simulation queue to continue the next round of swarm intelligent human / vehicle group simulation model training.

[0090] It should be noted that determining whether the length of the simulated human / vehicle queue has reached a threshold is primarily accomplished by determining whether the time range (i.e., storage duration) corresponding to the relevant data in the simulated human / vehicle queue has reached a threshold. If the time range (t1-t0) reaches the threshold, the swarm intelligence human / vehicle simulation model is trained. Furthermore, to conserve storage space, data stored for longer than the time range (t1-t0) is deleted.

[0091] In an optional embodiment, the above method further includes the following method steps:

[0092] Step S902, receiving an input image from a client;

[0093] Step S904: performing waterlogging detection on the input image. When a waterlogged area exists within the road section displayed in the input image, acquiring target group data from the input image. Performing behavior analysis on the target group data. When it is determined that a flooded road area exists within the input image based on the waterlogging level information of the flooded area, performing behavior recognition on the target object.

[0094] Step S906: When the recognition result meets the preset conditions, the recognition result is returned to the client, so that the recognition result is displayed on the graphical user interface of the client and an alarm is sent to the target object through the client.

[0095] In this embodiment, an input image is received from a client; water accumulation detection is performed on the input image, and when a waterlogged area exists in the road section displayed in the input image, target group data is obtained from the input image; behavior analysis is performed on the target group data, and when it is determined that a flooded road area exists in the input image based on the water accumulation level information of the waterlogged area, behavior recognition is performed on the target object; when the recognition result meets the preset conditions, the recognition result is returned to the client, so that the recognition result is displayed on the graphical user interface of the client and an alarm prompt is issued to the target object through the client, thereby achieving the purpose of judging the water accumulation level of the waterlogged area based on group intelligence and issuing an alarm prompt, thereby achieving the technical effect of improving the accuracy of water accumulation detection and the accuracy of alarm prompts, and further solving the technical problem of low water accumulation detection accuracy and inaccurate alarm prompts in the water accumulation detection method in the prior art.

[0096] It should be noted that for the aforementioned method embodiments, the algorithm can be developed through but not limited to the open source deep learning algorithm framework pytorch and ported to the C++ framework; all newly developed codes in the aforementioned method embodiments can be but not limited to python and C++ codes.

[0097] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0098] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0099] Example 2

[0100] According to an embodiment of the present invention, there is also provided an embodiment of a device for implementing the above-mentioned method for treating a waterlogged area based on swarm intelligence. Figure 7 According to an embodiment of the present invention, a schematic structural diagram of a waterlogged area processing device based on swarm intelligence is shown in FIG7 . The device includes: a detection module 20, an acquisition module 22, a determination module 24, and a processing module 26, wherein:

[0101] The above-mentioned detection module 20 is used to detect water accumulation in the input image and determine whether there is a waterlogged area in the road section displayed in the above-mentioned input image; the above-mentioned acquisition module 22 is used to obtain the target group data from the above-mentioned input image when the above-mentioned waterlogged area exists in the above-mentioned input image; the above-mentioned determination module 24 is used to perform behavioral analysis on the above-mentioned target group data and determine the water accumulation level information of the above-mentioned waterlogged area; the above-mentioned processing module 26 is used to perform behavioral recognition of the target object when it is determined that there is a flooded road area in the above-mentioned input image based on the above-mentioned water accumulation level information, and to issue an alarm prompt to the above-mentioned target object when the recognition result meets the preset conditions.

[0102] It should be noted that the detection module 20, acquisition module 22, determination module 24, and processing module 26 described above correspond to steps S202 to S208 in Example 1. The examples and application scenarios implemented by the two modules and the corresponding steps are the same, but are not limited to the contents disclosed in Example 1. It should be noted that the above modules, as part of the device, can be run in the computer terminal 10 provided in Example 1.

[0103] It should be noted that the preferred implementation of this embodiment can be found in the relevant description in Example 1 and will not be repeated here.

[0104] Example 3

[0105] According to an embodiment of the present invention, an embodiment of an electronic device is also provided, which can be any computing device in a computing device group. The electronic device includes: a processor and a memory, wherein:

[0106] The above-mentioned memory is connected to the above-mentioned processor and is used to provide the above-mentioned processor with instructions for processing the following processing steps: performing water accumulation detection on the input image to determine whether there is a water accumulation area in the road section displayed in the above-mentioned input image; when the above-mentioned water accumulation area exists in the above-mentioned input image, obtaining target group data from the above-mentioned input image; performing behavior analysis on the above-mentioned target group data to determine the water accumulation level information of the above-mentioned water accumulation area; when it is determined that there is a flooded road area in the above-mentioned input image based on the above-mentioned water accumulation level information, performing behavior recognition on the target object, and when the recognition result meets the preset conditions, issuing an alarm prompt to the above-mentioned target object.

[0107] It should be noted that the preferred implementation of this embodiment can be found in the relevant description in Example 1 and will not be repeated here.

[0108] Example 4

[0109] According to an embodiment of the present invention, a computer terminal embodiment can also be provided, and the computer terminal can be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal can also be replaced by a terminal device such as a mobile terminal.

[0110] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of a computer network.

[0111] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the waterlogged area processing method based on swarm intelligence: performing waterlogging detection on the input image to determine whether there is a waterlogged area in the road section displayed in the above-mentioned input image; when the above-mentioned waterlogged area exists in the above-mentioned input image, obtaining the target group data from the above-mentioned input image; performing behavioral analysis on the above-mentioned target group data to determine the waterlogging degree information of the above-mentioned waterlogged area; when it is determined that there is a flooded road area in the above-mentioned input image based on the above-mentioned waterlogging degree information, performing behavioral recognition on the target object, and when the recognition result meets the preset conditions, issuing an alarm prompt to the above-mentioned target object.

[0112] Optionally, Figure 8 1 is a block diagram of a computer terminal according to an embodiment of the present invention. Figure 8 As shown, the computer terminal may include: one or more (only one is shown in the figure) processors 32, a memory 34, and a program stored in the memory and executable on the processor, and may also include a peripheral interface 36. The memory 34 is connected to the processor 32 for providing instructions for the processor to process the following processing steps: performing water accumulation detection on the input image to determine whether there is a water accumulation area in the road section displayed in the input image; when the water accumulation area exists in the input image, obtaining target group data from the input image; performing behavior analysis on the target group data to determine the water accumulation degree information of the water accumulation area; when it is determined that there is a flooded road area in the input image based on the water accumulation degree information, performing behavior recognition on the target object, and issuing an alarm to the target object when the recognition result meets the preset conditions.

[0113] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and device for treating waterlogged areas based on swarm intelligence in the embodiments of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method for treating waterlogged areas based on swarm intelligence. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0114] The processor can call the information and application stored in the memory through the transmission device to execute the following steps: perform water accumulation detection on the input image to determine whether there is a water accumulation area in the road section displayed in the above input image; when the above water accumulation area exists in the above input image, obtain the target group data from the above input image; perform behavior analysis on the above target group data to determine the water accumulation level information of the above water accumulation area; when it is determined that there is a flooded road area in the above input image based on the above water accumulation level information, perform behavior recognition on the target object, and when the recognition result meets the preset conditions, issue an alarm prompt to the above target object.

[0115] Optionally, the processor may also execute the program code of the following steps: performing water accumulation detection on the input image using a first neural network model to determine whether the water accumulation area exists in the input image, wherein the first neural network model is a pre-trained target detection model.

[0116] Optionally, the processor may also execute the program code of the following steps: using a second neural network model to identify and track the target group in the input image to obtain a first processing result, wherein the second neural network model is a pre-trained target detection and tracking model; determining a second processing result based on the first processing result and the position information of the image acquisition device preset in the road section; converting the second processing result into spatially structured data to obtain the target group data.

[0117] Optionally, the processor may also execute the program code of the following steps: using a swarm intelligence model to perform behavioral analysis on the target group data to obtain a water accumulation degree score for the waterlogged area, wherein the swarm intelligence model is used to characterize the relationship between the waterlogging condition in the waterlogged area and the behavior of the target group in the waterlogged area, and the actual behavior data of the target group are fitted by iteratively optimizing the model parameters to estimate the water accumulation degree in the waterlogged area, and the water accumulation degree score is used to represent the water accumulation degree in the waterlogged area; when the water accumulation degree score is greater than a first preset threshold, it is determined that the flooded road area exists in the input image.

[0118] Optionally, the processor may also execute the program code of the following steps: using the swarm intelligence model to perform behavioral analysis on the target group data to obtain a predicted trajectory of the target group; determining the actual trajectory of the target group based on the target group data; calculating the difference between the actual trajectory and the predicted trajectory, and performing multiple iterative calculations based on the difference and a preset traffic rate change rule to determine the actual traffic rate of the waterlogged area; and using the actual traffic rate to calculate the waterlogging degree score.

[0119] Optionally, the processor may also execute the program code of the following steps: performing behavior recognition on the target object to determine the speed data of the target object; when the speed data exceeds a second preset threshold, recording the speed data and the corresponding occurrence time of the speed data.

[0120] Optionally, the processor may also execute the program code of the following steps: determining the time range corresponding to the target group data; and issuing an alarm to the target object when the occurrence time is within the time range and the target object is located in the flooded road area.

[0121] Optionally, the processor may also execute the program code of the following steps: obtaining an input image; performing water accumulation detection on the input image, and when a water accumulation area exists in the road section displayed in the input image, obtaining target group data from the input image; performing behavior analysis on the target group data, and when it is determined that a flooded road area exists in the input image based on the water accumulation level information of the water accumulation area, performing behavior recognition on the target object, and issuing an alarm to the target object when the recognition result meets the preset conditions; and reporting the recognition result to the server.

[0122] Optionally, the processor may also execute the program code of the following steps: receiving an input image from a client; performing water accumulation detection on the input image, and when a water accumulation area exists in the road section displayed in the input image, obtaining target group data from the input image; performing behavior analysis on the target group data, and when it is determined that a flooded road area exists in the input image based on the water accumulation level information of the water accumulation area, performing behavior recognition on the target object; when the recognition result meets a preset condition, returning the recognition result to the client, so that the recognition result is displayed on the graphical user interface of the client and an alarm is issued to the target object through the client.

[0123] By adopting an embodiment of the present invention, a solution for processing waterlogged areas based on swarm intelligence is provided. By performing waterlogging detection on an input image, it is determined whether there is a waterlogged area in the road section displayed in the input image; when the waterlogged area exists in the input image, target group data is obtained from the input image; behavior analysis is performed on the target group data to determine the waterlogging degree information of the waterlogged area; when it is determined based on the waterlogging degree information that there is a flooded road area in the input image, behavior recognition is performed on the target object, and when the recognition result meets the preset conditions, an alarm prompt is issued to the target object, thereby achieving the purpose of judging the waterlogging degree of the waterlogged area based on swarm intelligence and issuing an alarm message, thereby achieving the technical effect of improving the accuracy of waterlogging degree judgment and alarm message, and further solving the technical problem of low waterlogging detection accuracy leading to inaccurate alarm prompts in the existing waterlogging detection method.

[0124] It can be understood by those skilled in the art that Figure 8 The structure shown is for illustration only, and the computer terminal may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a handheld computer, a mobile Internet device (MID), a PAD, or other terminal devices. Figure 8 It does not limit the structure of the above electronic device. For example, the computer terminal may also include Figure 8 More or fewer components (such as network interfaces, display devices, etc.) shown in, or with Figure 8 Different configurations shown.

[0125] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0126] Example 5

[0127] According to an embodiment of the present invention, a storage medium embodiment is further provided. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the waterlogged area processing method based on swarm intelligence provided in the first embodiment.

[0128] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0129] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: performing water accumulation detection on the input image to determine whether there is a water accumulation area in the road section displayed in the above input image; when the above water accumulation area exists in the above input image, obtaining target group data from the above input image; performing behavior analysis on the above target group data to determine the water accumulation degree information of the above water accumulation area; when it is determined that there is a flooded road area in the above input image based on the above water accumulation degree information, performing behavior recognition on the target object, and when the recognition result meets the preset conditions, issuing an alarm prompt to the above target object.

[0130] Optionally, in this embodiment, the storage medium is configured to store program code for executing the following steps: using a first neural network model to detect water accumulation on the above-mentioned input image, and determining whether the above-mentioned water accumulation area exists in the above-mentioned input image, wherein the above-mentioned first neural network model is a pre-trained target detection model.

[0131] Optionally, in this embodiment, the storage medium is configured to store program code for executing the following steps: using a second neural network model to identify and track the target group in the above-mentioned input image to obtain a first processing result, wherein the above-mentioned second neural network model is a pre-trained target detection and tracking model; determining a second processing result based on the above-mentioned first processing result and the position information of the image acquisition device preset in the above-mentioned road section; converting the above-mentioned second processing result into spatially structured data to obtain the above-mentioned target group data.

[0132] Optionally, in this embodiment, the storage medium is configured to store program code for executing the following steps: using a swarm intelligence model to perform behavioral analysis on the above-mentioned target group data to obtain a water accumulation degree score of the above-mentioned water accumulation area, wherein the above-mentioned swarm intelligence model is used to characterize the relationship between the water accumulation situation in the water accumulation area and the behavior of the target group in the water accumulation area, and the actual behavioral data of the target group are fitted by iterative optimization of model parameters to estimate the water accumulation degree of the water accumulation area, and the above-mentioned water accumulation degree score is used to represent the water accumulation degree of the above-mentioned water accumulation area; when the above-mentioned water accumulation degree score is greater than a first preset threshold value, it is determined that the above-mentioned flooded road area exists in the above-mentioned input image.

[0133] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: performing behavioral analysis on the target group data using the swarm intelligence model to obtain a predicted trajectory of the target group; determining the actual trajectory of the target group based on the target group data; calculating the difference between the actual trajectory and the predicted trajectory, and performing multiple iterative calculations based on the difference and a preset traffic rate change rule to determine the actual traffic rate of the waterlogged area; and obtaining the waterlogging degree score using the actual traffic rate.

[0134] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: performing behavior recognition on the target object to determine the speed data of the target object; when the speed data exceeds a second preset threshold, recording the speed data and the corresponding occurrence time of the speed data.

[0135] Optionally, in this embodiment, the storage medium is configured to store program codes for executing the following steps: determining the time range corresponding to the above-mentioned target group data; when the above-mentioned occurrence time is within the above-mentioned time range and the area where the above-mentioned target object is located is the above-mentioned flooded road area, issuing an alarm prompt to the above-mentioned target object.

[0136] Optionally, in this embodiment, the storage medium is configured to store program code for executing the following steps: obtaining an input image; performing water accumulation detection on the input image, and when there is a water accumulation area in the road section displayed in the input image, obtaining target group data from the input image; performing behavior analysis on the target group data, and when it is determined that there is a flooded road area in the input image based on the water accumulation level information of the water accumulation area, performing behavior recognition on the target object, and when the recognition result meets the preset conditions, issuing an alarm prompt to the target object; and reporting the recognition result to the server.

[0137] Optionally, in this embodiment, the storage medium is configured to store program code for executing the following steps: receiving an input image from a client; performing water accumulation detection on the input image, and when a water accumulation area exists in the road section displayed in the input image, obtaining target group data from the input image; performing behavior analysis on the target group data, and when it is determined that a flooded road area exists in the input image based on the water accumulation level information of the water accumulation area, performing behavior recognition on the target object; when the recognition result meets the preset conditions, returning the recognition result to the client, so that the recognition result is displayed on the graphical user interface of the client and an alarm prompt is issued to the target object through the client.

[0138] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0139] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0140] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, and can be electrical or other forms.

[0141] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0142] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0143] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it 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 all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0144] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for treating waterlogged areas based on swarm intelligence, characterized in that: include: Performing water accumulation detection on the input image to determine whether there is a water accumulation area within the road section displayed in the input image; When the water accumulation area exists in the input image, acquiring target group data from the input image; Performing behavioral analysis on the target group data using a swarm intelligence model to obtain a predicted trajectory of the target group; determining an actual trajectory of a target group based on the target group data; Calculating a difference between the actual trajectory and the predicted trajectory, and performing multiple iterative calculations based on the difference and a preset traffic rate variation rule to determine an actual traffic rate in the flooded area; Determining the degree of waterlogging in the waterlogged area according to the actual traffic rate; When it is determined based on the water accumulation degree information that there is a flooded road area in the input image, behavior recognition is performed on the target object, and when the recognition result meets the preset conditions, an alarm prompt is issued to the target object.

2. The method for processing waterlogged areas based on swarm intelligence according to claim 1, wherein performing waterlogging detection on the input image to determine whether the waterlogged area exists in the input image comprises: Perform water accumulation detection on the input image using a first neural network model to determine whether the water accumulation area exists in the input image, wherein the first neural network model is a pre-trained target detection model.

3. The method for processing waterlogged areas based on swarm intelligence according to claim 1, wherein obtaining the target group data from the input image comprises: Using a second neural network model to identify and track the target group in the input image to obtain a first processing result, wherein the second neural network model is a pre-trained target detection and tracking model; determining a second processing result based on the first processing result and position information of an image acquisition device preset in the road section; The second processing result is converted into spatially structured data to obtain the target group data.

4. The method for processing a flooded area based on swarm intelligence according to claim 1, wherein the swarm intelligence model is used to characterize the relationship between the flooding situation in the flooded area and the behavior of a target group within the flooded area, and the degree of flooding in the flooded area is estimated by iteratively optimizing model parameters to fit the actual behavior data of the target group. The method for determining the degree of flooding based on the actual traffic rate includes: Calculating a waterlogging degree score using the actual traffic rate, wherein the waterlogging degree score is used to represent the waterlogging degree of the waterlogged area; When the water accumulation degree score is greater than a first preset threshold, it is determined that the flooded road area exists in the input image.

5. According to the method for processing waterlogged areas based on swarm intelligence in claim 1, the step of performing behavior recognition on the target object comprises: Performing behavior recognition on the target object to determine speed data of the target object; When the speed data exceeds a second preset threshold, the speed data and the occurrence time corresponding to the speed data are recorded.

6. The method for processing waterlogged areas based on swarm intelligence according to claim 5, wherein when the recognition result satisfies the preset condition, issuing an alarm to the target object comprises: Determine the time range corresponding to the target group data; When the occurrence time is within the time range and the area where the target object is located is the flooded road area, an alarm prompt is issued to the target object.

7. A method for treating waterlogged areas based on swarm intelligence, characterized in that: include: Get the input image; Performing water accumulation detection on the input image, and acquiring target group data from the input image when a water accumulation area exists within the road section displayed in the input image; Performing behavioral analysis on the target group data using a swarm intelligence model to obtain a predicted trajectory of the target group; and determining an actual trajectory of the target group based on the target group data; Calculating a difference between the actual trajectory and the predicted trajectory, and performing multiple iterative calculations based on the difference and a preset traffic rate variation pattern to determine an actual traffic rate in the flooded area; and determining information about the degree of waterlogging in the flooded area based on the actual traffic rate; When it is determined that a flooded road area exists in the input image based on the water level information of the flooded area, behavior recognition is performed on the target object, and when the recognition result meets a preset condition, an alarm prompt is issued to the target object; The recognition result is reported to the server.

8. A method for treating waterlogged areas based on swarm intelligence, characterized in that: include: Receive input image from client; Performing water accumulation detection on the input image, and acquiring target group data from the input image when a water accumulation area exists within the road section displayed in the input image; Performing behavioral analysis on the target group data using a swarm intelligence model to obtain a predicted trajectory of the target group; and determining an actual trajectory of the target group based on the target group data; Calculating a difference between the actual trajectory and the predicted trajectory, and performing multiple iterative calculations based on the difference and a preset traffic rate variation pattern to determine an actual traffic rate in the flooded area; and determining information about the degree of waterlogging in the flooded area based on the actual traffic rate; When it is determined based on the water level information of the waterlogged area that there is a flooded road area in the input image, performing behavior recognition on the target object; When the recognition result meets the preset conditions, the recognition result is returned to the client, so that the recognition result is displayed on the graphical user interface of the client and an alarm prompt is issued to the target object through the client.

9. A waterlogged area treatment device based on swarm intelligence, characterized in that: include: a detection module, configured to perform water accumulation detection on an input image to determine whether a water accumulation area exists within a road section displayed in the input image; an acquisition module, configured to acquire target group data from the input image when the waterlogged area exists in the input image; a determination module, configured to perform behavioral analysis on the target group data using a swarm intelligence model to obtain a predicted trajectory of the target group; and determine an actual trajectory of the target group based on the target group data; Calculating a difference between the actual trajectory and the predicted trajectory, and performing multiple iterative calculations based on the difference and a preset traffic rate variation rule to determine an actual traffic rate in the flooded area; Determining the degree of waterlogging in the waterlogged area according to the actual traffic rate; The processing module is used to perform behavior recognition on a target object when it is determined based on the water accumulation degree information that there is a flooded road area in the input image, and to issue an alarm prompt to the target object when the recognition result meets a preset condition.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the waterlogged area processing method based on swarm intelligence according to any one of claims 1 to 8.

11. An electronic device, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Performing water accumulation detection on the input image to determine whether there is a water accumulation area within the road section displayed in the input image; When the water accumulation area exists in the input image, acquiring target group data from the input image; Performing behavioral analysis on the target group data using a swarm intelligence model to obtain a predicted trajectory of the target group; determining an actual trajectory of a target group based on the target group data; Calculating a difference between the actual trajectory and the predicted trajectory, and performing multiple iterative calculations based on the difference and a preset traffic rate variation rule to determine an actual traffic rate in the flooded area; Determining the degree of waterlogging in the waterlogged area according to the actual traffic rate; When it is determined based on the water accumulation degree information that there is a flooded road area in the input image, behavior recognition is performed on the target object, and when the recognition result meets the preset conditions, an alarm prompt is issued to the target object.

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