An urban intelligent dog management platform based on the Internet of Things

Abstract

The present invention discloses an urban intelligent dog management platform based on the Internet of Things, including an urban intelligent dog management method based on the Internet of Things. The urban intelligent dog management method mainly includes the following steps: judging whether a pet dog may be in danger of safety based on road conditions; if the system determines that the current pet dog may be in danger of safety, real-time monitoring of the target pet dog is performed to identify whether a leash is connected between the dog and the owner; analyzing the degree of coordination between the pet dog and the pet dog owner when crossing the road based on the identification result; and transmitting prompt information. The present invention improves the monitoring accuracy of the dog management platform by identifying whether a leash is connected between the dog and the owner and analyzing the degree of coordination between the pet dog and the pet dog owner when crossing the road, greatly reducing the possibility of safety problems when the pet dog owner takes the dog across the road. The present invention has the characteristics of high road monitoring safety and strong monitoring target recognition ability.

Description

Technical Field

[0001] The present invention relates to the technical field of road monitoring, and in particular to an urban intelligent dog-raising management platform based on the Internet of Things. Background Art

[0002] When people travel with their dogs, they are generally more active outdoors, which can pose safety issues. This has led to the emergence of various dog-specific safety monitoring platforms. This is especially true when dog owners are walking along two-way urban roads. Dogs are smaller targets and move faster, making them more vulnerable to safety issues than people. Therefore, it is necessary to design a dog management platform that can effectively pull dogs away from dangerous areas in a timely manner if their owners fail to pay close attention to them while crossing the road. Therefore, it is essential to design an IoT-based urban intelligent dog management platform that provides high road monitoring security and strong target recognition capabilities. Summary of the Invention

[0003] The purpose of the present invention is to provide an urban intelligent dog management platform based on the Internet of Things to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: an urban intelligent dog management platform based on the Internet of Things, including an urban intelligent dog management method based on the Internet of Things, wherein the urban intelligent dog management method mainly comprises the following steps:

[0005] Step S1: Connecting the intelligent dog management system to the urban road monitoring terminal, and shooting images from both ends of the road through the surveillance cameras on the urban road;

[0006] Step S2: Based on the current road condition monitoring, determine whether the current pet dog may be in danger. If so, transmit the information to the processing terminal. The processing terminal monitors the target pet dog and dog owner passing through the road in real time and identifies whether the dog and owner are connected to a leash.

[0007] Step S3: Analyze the degree of coordination between the pet dog and its owner when crossing the road based on the obtained recognition results;

[0008] Step S4: The system determines whether the pet dog owner needs to remind the pet dog through the analysis result obtained in step S3. If necessary, the reminder information is transmitted to the mobile terminal carried by the pet dog owner.

[0009] According to the above technical solution, step S2 includes:

[0010] Step S21: The road information registration module of the roads stored in the urban road database records the intersection location, road speed limit and road width information, and the cameras set on both sides of the road capture the image of the area in the center of the road;

[0011] Step S22: Obtain the lateral length value of the current road, the speed of the vehicle closest to the current pet dog, and the distance value from the nearest vehicle to the pet dog, and calculate the time t seconds for the vehicle to reach the area where the pet dog is located by the ratio of the distance value from the nearest vehicle to the pet dog to the closest speed. When the time value for the vehicle to reach the pet dog's position is lower than the safety time value, it is determined that the current pet dog may be in danger of safety, and the information that the pet dog may be in danger of safety is transmitted to the management platform. The management platform transmits the information to the processing terminal, retrieves the camera of the current monitoring area, captures the image information of the monitoring target passing through the road and analyzes the image information, adjusts the shooting angle of the camera so that the pet dog and the pet dog owner enter the shooting picture, and locates the specific positions of the pet dog and the pet dog owner in the shooting picture respectively, wherein, if the pet dog has not crossed half of the lateral length value of the road, controls the shooting angle to the left of the pet dog's crossing direction, and if the pet dog has crossed half of the lateral length value of the road, adjusts the shooting angle of the camera to control the shooting angle to the right of the pet dog's crossing direction;

[0012] Step S23: A judgment node is set in the management platform, and feature recognition analysis is performed on the interval area between the pet dog and the pet dog owner in the shooting picture obtained in step S22 to identify whether a traction chain is connected between the dog and the owner. If so, the system transmits the information that the pet dog owner is carrying the pet dog without a traction chain to the management platform, records the judgment node as the first node and stores it in the management platform; if not, the system transmits the information that the pet dog owner is carrying the pet dog without a traction chain to the management platform, records the judgment node as the second node, and also stores it in the management platform.

[0013] According to the above technical solution, step S3 includes:

[0014] Step S31: Take the real-time position of the pet dog owner on the road as the coordinate origin, the visual direction of the pet dog owner as the positive half axis of the Y axis, rotate the Y axis 90 degrees through the coordinate origin in the road plane to the X axis, and establish a real-time moving spatial rectangular coordinate system perpendicular to the X and Y axes through the coordinate origin as the Z axis. The real-time coordinates of the pet dog owner's right hand on the road are (0, 0, z0). The camera set on the road is used to capture the image information on the road, and the position coordinates of the pet dog are obtained as (x0, y0, z1). The distance between the pet dog and the pet dog owner's right hand is

[0015] Step S32: When the dog starts to cross the road, the dog's movement speed is monitored. The timer set by the road monitoring terminal is set to t seconds, and the actual distance the dog travels on the road within t seconds is obtained as L3 meters. The above step S32 is repeated. The effective crossing distance L4 of the pet dog owner within t seconds when crossing the road is detected. The node currently monitored and stored in the management platform is detected. If the obtained judgment node is the first node, step S33 is executed; if the obtained judgment node is the second node, step S34 is executed.

[0016] Step S33: When the system identifies that the judgment node is the first node, this step is executed to monitor whether the pet dog is likely to be in danger on the road;

[0017] Step S34: When the system identifies that the judgment node is the second node, this step is executed to monitor whether there is a possibility that the pet dog is in danger on the road.

[0018] According to the above technical solution, in step S33, when the monitoring system detects that there is a leash between the pet dog and the pet dog owner, the method of monitoring the coordination degree between the pet dog and the pet dog owner when crossing the road specifically includes:

[0019] Step S331: Use a high-definition camera to capture an image of the traction chain used by the pet dog owner, analyze the image features, obtain the surface smoothness, surface material color, and surface material structure density of the traction chain, and search the database for traction chain types with the same features based on the identified information to determine the type of traction chain. In the database of traction chain types of this type, retrieve the traction chain toughness K, the traction chain length L1, the actual distance between the traction chain and the fully stretched length L2, and the actual total length of the traction chain that is not fully stretched L. 实 = L2-L1, when the pet dog passes through the road, the pet dog owner can use the traction chain to pull the pet dog to achieve the maximum traction distance L max =KL 实 ;

[0020] Step S332: Analyze the tortuosity of the traction chain used by the pet dog owner to pull the pet dog in the coordinate system, extract the corresponding coordinates of the pet dog and the pet dog owner's hand holding the dog chain in the spatial rectangular coordinate system, based on the construction of the traction chain curve formed between the pet dog coordinates and the pet dog owner coordinates, retrieve the traction chain length L stored in the database, obtain the traction chain function y=y(x) between the pet dog and the pet dog owner in the horizontal rectangular coordinate system formed by the current pet dog owner using the traction chain to pull the pet dog, and detect y=y(x). The derivative at is V, and V is recorded as the average tortuosity of the traction chain;

[0021] Step S333: The degree of coordination between the pet dog and its owner when crossing the road Among them L 00 is the longitudinal walking distance difference coordination reference value, and η is the lateral coordination level.

[0022] According to the above technical solution, in step S34, when the monitoring system detects that there is no leash between the pet dog and the pet dog owner, the method of monitoring the coordination degree between the pet dog and the pet dog owner when crossing the road specifically includes:

[0023] Step S341: using dynamic image feature recognition technology, detecting the dynamic trajectory of the pet dog's limbs contacting the ground when crossing the road, detecting the contact time difference t0 of the two pairs of combined limbs of the pet dog's left forelimb and left hind limb, and right forelimb and right hind limb, and obtaining the time difference values ​​t1 and t2 of the corresponding combined limbs of the current pet dog breed in the two different moving states of running and walking by retrieving the database; when t0>t2, it is determined that the current pet dog is in a running state, and when the pet dog is in the running state, the travel coefficient of the pet dog crossing the road is W1; when t0<t1, it is determined that the current pet dog is in a walking state, and when the pet dog is in the walking state, the travel coefficient of the pet dog crossing the road is W2; when t1<t0<t2, it is determined that the current pet dog is in a combined state of walking and running, and when the pet dog is in a combined state of walking and running, the travel coefficient of the pet dog crossing the road is W3;

[0024] Step S342: The surveillance camera adjusts the shooting angle to monitor the direction of the pet dog's movement, and detects the angle between the direction of the pet dog's movement and the Y axis in the spatial rectangular coordinate system constructed in step S31 in the coordinate system. The angle between the pet dog and the Y axis is obtained as S, and the degree of coordination between the pet dog and the pet dog owner when crossing the road is obtained. Where W is the travel coefficient, which is determined according to the travel coefficient value of the road the pet dog travels on obtained in step S341, and L 01 It is the collaborative reference value of the longitudinal walking distance difference.

[0025] According to the above technical solution, in step S4, the detection method for determining whether the pet dog owner needs to remind the pet dog is specifically:

[0026] The dangers that pet dogs may encounter when walking on the road Where δ is the unit conversion coefficient, μ is the unit conversion level, and the final E value is controlled to be in the range of [0, 100%]. If the obtained value of the possibility of the pet encountering danger exceeds the danger limit value E0, it is determined that the pet dog owner needs to provide a reminder.

[0027] According to the above technical solution, the dog management platform includes a data acquisition module, a camera unit, a traction identification module and a prompt module. The data acquisition module is used to collect road section information on the currently monitored pet dog and the pet dog owner; the camera unit is used to capture the picture information on the currently monitored road section; the traction identification module is used to detect the pet dog owner's ability to restrict the pet dog when the pet dog and the pet dog pass through the road; the prompt module is used to transmit the information that the pet dog owner needs to promptly remind the pet dog to the mobile terminal carried by the pet dog owner.

[0028] According to the above technical solution, the data acquisition module includes a road information acquisition module and a pet breed database collection module. The road information acquisition module is used to obtain the information of traveling vehicles on the monitored target road; the pet breed database collection module is used to collect database information of pet dog breeds and corresponding pet dog habits.

[0029] According to the above technical solution, the traction identification module includes a road coordinate system construction module, a non-traction relationship analysis module and a traction relationship analysis module. The road coordinate system construction module is used to construct a movable spatial rectangular coordinate system with the owner as the origin on the monitoring road to monitor the real-time distance between the pet dog and the pet dog owner; the non-traction relationship analysis module is used to analyze the travel coordination between the pet dog and the pet dog owner when the pet dog and the pet dog owner are not carrying a front traction chain; the traction relationship analysis module is used to analyze the travel coordination between the pet dog and the pet dog owner when the pet dog and the pet dog owner are carrying a traction chain.

[0030] According to the above technical solution, the traction relationship analysis module includes a traction chain tortuosity detection submodule and a maximum traction distance detection submodule. The traction chain tortuosity detection submodule is used to detect the tortuosity information of the traction chain between the pet dog and the pet dog owner; the maximum traction distance detection submodule is used to detect the maximum traction distance between the pet dog and the pet dog owner when they are traveling together.

[0031] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention identifies whether a leash is connected between the dog and the owner, and analyzes the coordination between the pet dog and the pet dog owner when crossing the road based on the identification result, establishes a movable spatial rectangular coordinate system with the pet dog owner as the center, and analyzes the coordination between the pet dog and the pet dog owner according to the crossing distance between the pet dog owner and the pet dog, effectively identifies whether the pet dog can be timely grasped by the pet dog owner when crossing the road, effectively analyzes whether the pet dog will encounter danger when crossing the road, improves the monitoring accuracy of the dog management platform, improves the intelligence level of the city, greatly reduces the possibility of safety problems when the pet dog owner brings the dog across the road, and improves the smoothness and stability of road operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0033] Figure 1 This is a flow chart of the method for operating the urban intelligent dog management platform provided by the present invention;

[0034] Figure 2 This is a schematic diagram of the modular composition of the urban intelligent dog management platform provided by the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] See also Figure 1 The present invention provides a technical solution: an urban intelligent dog management platform based on the Internet of Things, comprising:

[0037] An urban intelligent dog management method based on the Internet of Things, the urban intelligent dog management method mainly includes the following steps:

[0038] Step S1: Connecting the intelligent dog management system to the urban road monitoring terminal, and shooting images from both ends of the road through the surveillance cameras on the urban road;

[0039] Step S2: Based on the current road condition monitoring, determine whether the current pet dog may be in danger. If so, transmit the information to the processing terminal. The processing terminal monitors the target pet dog and dog owner passing through the road in real time and identifies whether the dog and owner are connected to a leash.

[0040] Step S3: Analyze the degree of coordination between the pet dog and its owner when crossing the road based on the obtained recognition results;

[0041] Step S4: The system determines whether the pet dog owner needs to remind the pet dog through the analysis results obtained in step S3. If necessary, the reminder information is transmitted to the mobile terminal carried by the pet dog owner.

[0042] The present invention identifies whether a leash is connected between the dog and the owner, and analyzes the coordination between the pet dog and the owner when crossing the road based on the identification result. A movable spatial rectangular coordinate system is established with the pet dog owner as the center, and the coordination between the pet dog and the owner is analyzed according to the crossing distance between the pet dog owner and the pet dog. This effectively identifies whether the pet dog can be timely grasped by the owner when crossing the road, and effectively analyzes whether the pet dog will encounter danger when crossing the road. This improves the monitoring accuracy of the dog management platform, improves the intelligence level of the city, greatly reduces the possibility of safety problems when the pet dog owner brings the dog across the road, and improves the smoothness and stability of road operation.

[0043] In a preferred embodiment, step S2 includes:

[0044] Step S21: The road information registration module of the roads stored in the urban road database records the intersection location, road speed limit and road width information, and the cameras set on both sides of the road capture the image of the area in the center of the road;

[0045] Step S22: Obtain the lateral length value of the current road, the speed of the vehicle closest to the current pet dog, and the distance value from the nearest vehicle to the pet dog, and calculate the time t seconds for the vehicle to reach the area where the pet dog is located by the ratio of the distance value from the nearest vehicle to the pet dog to the closest speed. When the time value for the vehicle to reach the pet dog's position is lower than the safety time value, it is determined that the current pet dog may be in danger of safety, and the information that the pet dog may be in danger of safety is transmitted to the management platform. The management platform transmits the information to the processing terminal, retrieves the camera of the current monitoring area, captures the image information of the monitoring target passing through the road and analyzes the image information, adjusts the shooting angle of the camera so that the pet dog and the pet dog owner enter the shooting picture, and locates the specific positions of the pet dog and the pet dog owner in the shooting picture respectively, wherein, if the pet dog has not crossed half of the lateral length value of the road, controls the shooting angle to the left of the pet dog's crossing direction, and if the pet dog has crossed half of the lateral length value of the road, adjusts the shooting angle of the camera to control the shooting angle to the right of the pet dog's crossing direction;

[0046] Step S23: A judgment node is set in the management platform, and feature recognition analysis is performed on the interval area between the pet dog and the pet dog owner in the shooting picture obtained in step S22 to identify whether a traction chain is connected between the dog and the owner. If so, the system transmits the information that the pet dog owner is carrying the pet dog without a traction chain to the management platform, records the judgment node as the first node and stores it in the management platform; if not, the system transmits the information that the pet dog owner is carrying the pet dog without a traction chain to the management platform, records the judgment node as the second node, and also stores it in the management platform.

[0047] In a preferred embodiment, step S3 includes:

[0048] Step S31: Take the real-time position of the pet dog owner on the road as the coordinate origin, the visual direction of the pet dog owner as the positive half axis of the Y axis, rotate the Y axis 90 degrees through the coordinate origin in the road plane to the X axis, and establish a real-time moving spatial rectangular coordinate system perpendicular to the X and Y axes through the coordinate origin as the Z axis. The real-time coordinates of the pet dog owner's right hand on the road are (0, 0, z0). The camera set on the road is used to capture the image information on the road, and the position coordinates of the pet dog are obtained as (x0, y0, z1). The distance between the pet dog and the pet dog owner's right hand is

[0049] Step S32: When the dog begins to cross the road, the dog's movement speed is monitored. The timer set by the road monitoring terminal is set to t seconds, and the actual distance the dog travels on the road within t seconds is obtained as L3 meters. The above step S32 is repeated. The effective crossing distance L4 of the pet dog owner within t seconds while crossing the road is detected. The node currently monitored and stored in the management platform is detected. If the obtained judgment node is the first node, step S33 is executed; if the obtained judgment node is the second node, step S34 is executed.

[0050] Step S33: When the system identifies that the judgment node is the first node, this step is executed to monitor whether the pet dog is likely to be in danger on the road;

[0051] Step S34: When the system identifies that the judgment node is the second node, this step is executed to monitor whether there is a possibility that the pet dog is in danger on the road.

[0052] Through this technical solution, the problem of the inability to effectively and accurately monitor the relative relationship between pet dogs and their owners when they pass through the road together is solved. By establishing a movable spatial rectangular coordinate system with the pet dog owner as the center and analyzing the degree of coordination between the pet dog and the pet dog owner through the travel distance between the pet dog owner and the pet dog, it is possible to effectively identify whether the pet dog can be timely understood by the pet dog owner when crossing the road, and effectively analyze whether the pet dog will encounter danger when crossing the road, thereby improving the monitoring accuracy of the dog management platform.

[0053] In step S33 of this embodiment, when the monitoring system detects that there is a leash between the pet dog and the pet dog owner, the method for monitoring the coordination between the pet dog and the pet dog owner when crossing the road specifically includes:

[0054] Step S331: Use a high-definition camera to capture an image of the traction chain used by the pet dog owner, analyze the image features, obtain the surface smoothness, surface material color, and surface material structure density of the traction chain, and search the database for traction chain types with the same features based on the identified information to determine the type of traction chain. In the database of traction chain types of this type, retrieve the traction chain toughness K, the traction chain length L1, the actual distance between the traction chain and the fully stretched length L2, and the actual total length of the traction chain that is not fully stretched L. 实 = L2-L1, when the pet dog passes through the road, the pet dog owner can use the traction chain to pull the pet dog to achieve the maximum traction distance L max =KL 实 ;

[0055] Step S332: Analyze the tortuosity of the traction chain used by the pet dog owner to pull the pet dog in the coordinate system, extract the corresponding coordinates of the pet dog and the pet dog owner's hand holding the dog chain in the spatial rectangular coordinate system, based on the construction of the traction chain curve formed between the pet dog coordinates and the pet dog owner coordinates, retrieve the traction chain length L stored in the database, obtain the traction chain function y=y(x) between the pet dog and the pet dog owner in the horizontal rectangular coordinate system formed by the current pet dog owner using the traction chain to pull the pet dog, and detect y=y(x). The derivative at is V, and V is recorded as the average tortuosity of the traction chain;

[0056] Step S333: The degree of coordination between the pet dog and its owner when crossing the road Among them L 00 is the longitudinal walking distance difference coordination reference value, η is the lateral coordination level, so that The results are similar to those of L max The ratio of values ​​is in the range [0, 2].

[0057] The degree of attention paid by the current pet dog owner to the dog is judged by the tortuosity of the traction chain. When it is detected that the pet dog owner's attention to the dog is too low and the current dog is in danger, a prompt message is sent to the pet dog owner through the management platform, so that the pet dog owner can notice the danger of the pet dog as soon as possible and pull the pet dog out of the dangerous area in time.

[0058] Depending on the current distance between the dog and the person, the pet dog owner's restraint force on the dog is differentiated into two levels. When the dog's current position is dangerous, the pet dog owner's restraint force on the dog is as small as possible, because the adjustable distance range is large, and the pet dog owner can quickly pull the dog out of the dangerous area; conversely, when the dog's current position is safe, the pet dog owner's restraint force on the dog is as large as possible, because within the current safe distance, the adjustable distance range is small, and the dog will not run around.

[0059] The higher the coordination value between the pet dog and the pet dog owner when crossing the road, the easier it is for the pet dog owner to pull the pet dog with the leash when the system detects that the pet dog is in danger, and the less likely the pet dog is to be in danger on the road; conversely, the lower the coordination value between the pet dog and the pet dog owner when crossing the road, the easier it is for the pet dog owner to pull the pet dog with the leash when the system detects that the pet dog is in danger, and the more likely the pet dog is to be in danger on the road.

[0060] This technical solution solves the problem of difficulty in effectively distinguishing between dog owners and their dogs when they are crossing the road, as different traction conditions may lead to different possible safety hazards for the dogs. By analyzing the traction chain between the dog owner and the dog as they cross the road, image feature analysis technology is used to identify the surface smoothness, surface material color, and surface material structure density of the traction chain to obtain the flexibility of the traction chain. Based on the distance information of the traction chain between the dog owner and the dog in the constructed coordinate system, the dog owner's ability to pull the dog away from the dangerous area in a timely manner is analyzed. This improves the intelligence level of the city and greatly reduces the possibility of safety problems for dogs crossing the road on urban roads.

[0061] In step S34 of this embodiment, when the monitoring system detects that there is no leash between the pet dog and the pet dog owner, the method for monitoring the coordination between the pet dog and the pet dog owner when crossing the road specifically includes:

[0062] Step S341: using dynamic image feature recognition technology, detecting the dynamic trajectory of the pet dog's limbs contacting the ground when crossing the road, detecting the contact time difference t0 of the two pairs of combined limbs of the pet dog's left forelimb and left hind limb, and right forelimb and right hind limb, and obtaining the time difference values ​​t1 and t2 of the corresponding combined limbs of the current pet dog breed in the two different moving states of running and walking by retrieving the database; when t0>t2, it is determined that the current pet dog is in a running state, and when the pet dog is in the running state, the travel coefficient of the pet dog crossing the road is W1; when t0<t1, it is determined that the current pet dog is in a walking state, and when the pet dog is in the walking state, the travel coefficient of the pet dog crossing the road is W2; when t1<t0<t2, it is determined that the current pet dog is in a combined state of walking and running, and when the pet dog is in a combined state of walking and running, the travel coefficient of the pet dog crossing the road is W3;

[0063] Step S342: The surveillance camera adjusts the shooting angle to monitor the direction of the pet dog's movement. The angle between the pet dog's movement direction and the Y axis in the spatial rectangular coordinate system constructed in step S31 is detected in the coordinate system. The angle between the pet dog and the Y axis is obtained as S. The degree of coordination between the pet dog and the pet dog owner when crossing the road is Where W is the travel coefficient, which is determined according to the travel coefficient value of the road the pet dog travels on obtained in step S341, and L 01 It is the collaborative reference value of the longitudinal walking distance difference.

[0064] Through this technical solution, the problem of difficulty in analyzing the various travel states of a pet dog in a short period of time when there is no leash between the pet dog owner and the pet dog when the pet dog is crossing the road is solved. The current travel state of the pet dog is detected by the time difference when two combinations of the pet dog's four limbs touch the ground during the pet dog's crossing the road, and a risk coefficient value is given for the pet dog crossing the road in different travel states. Based on the travel angle between the pet dog and the pet dog owner during the pet dog's crossing the road, the coordination information of the pet dog and the pet dog owner when crossing the road is detected, thereby avoiding the possibility of excessive distance changes when the pet dog is crossing the road without a leash, and improving the monitoring range of the dog management platform.

[0065] In a preferred embodiment, in step S4, the detection method for determining whether the pet dog owner needs to remind the pet dog is specifically:

[0066] The dangers that pet dogs may encounter when walking on the road Where δ is the unit conversion coefficient, μ is the unit conversion level, and the final E value is controlled to be in the range of [0, 100%]. If the obtained value of the possibility of the pet encountering danger exceeds the danger limit value E0, it is determined that the pet dog owner needs to provide a reminder.

[0067] In two different walking situations with their owners, the two different calculation methods for the dog under leash ultimately achieve the same goal of monitoring whether the dog can be restrained by the owner when danger approaches after the dog leaves the owner's control. Therefore, the same calculation method can be used to calculate the dog's potential for danger. When the dog is not restrained by a leash, it will still be retrieved by the owner's call. The dog is still subject to some restraint by the owner. Therefore, the obtained parameters can be used to analyze the degree of restraint on the dog in the two situations with and without a leash, and then the parameters can be used to analyze the degree of danger to the dog under monitoring.

[0068] The dog management platform includes a data acquisition module, a camera unit, a traction recognition module and a prompt module. The data acquisition module is used to collect information about the road section on which the currently monitored pet dog and the pet dog owner pass; the camera unit is used to capture image information on the currently monitored road section; the traction recognition module is used to detect the pet dog owner's ability to restrict the pet dog when the pet dog and the pet dog pass through the road; the prompt module is used to transmit information that the pet dog owner needs to promptly remind the pet dog to the mobile terminal carried by the pet dog owner.

[0069] The data acquisition module includes a road information acquisition module and a pet breed database collection module. The road information acquisition module is used to obtain information about vehicles traveling on the monitored target road; the pet breed database collection module is used to collect database information about pet dog breeds and corresponding pet dog habits.

[0070] The traction identification module includes a road coordinate system construction module, a non-traction relationship analysis module and a traction relationship analysis module. The road coordinate system construction module is used to construct a movable spatial rectangular coordinate system with the owner as the origin on the monitoring road to monitor the real-time distance between the pet dog and the pet dog owner; the non-traction relationship analysis module is used to analyze the travel coordination between the pet dog and the pet dog owner when the pet dog and the pet dog owner are not carrying a front traction chain; the traction relationship analysis module is used to analyze the travel coordination between the pet dog and the pet dog owner when the pet dog and the pet dog owner are carrying a traction chain.

[0071] The traction relationship analysis module includes a traction chain tortuosity detection submodule and a maximum traction distance detection submodule. The traction chain tortuosity detection submodule is used to detect the tortuosity information of the traction chain between the pet dog and the pet dog owner; the maximum traction distance detection submodule is used to detect the maximum traction distance between the pet dog and the pet dog owner when they are traveling together.

[0072] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0073] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An urban intelligent dog management method based on the Internet of Things, characterized by: The following steps are involved: Step S1: Connecting the intelligent dog management system to the urban road monitoring terminal, and shooting images from both ends of the road through the surveillance cameras on the urban road; Step S2: Based on the current road condition monitoring, determine whether the current pet dog may be in danger. If so, transmit the information to the processing terminal. The processing terminal monitors the target pet dog and dog owner passing through the road in real time and identifies whether the dog and owner are connected to a leash. Step S3: Analyze the degree of coordination between the pet dog and its owner when crossing the road based on the obtained recognition results; Step S4: The system determines whether the pet dog owner needs to remind the pet dog based on the analysis results obtained in step S3. If necessary, the system transmits the reminder information to the mobile terminal carried by the pet dog owner; The step S2 comprises: Step S21: The road information registration module of the roads stored in the urban road database records the intersection location, road speed limit and road width information, and the cameras set on both sides of the road capture the image of the area in the center of the road; Step S22: Obtain the lateral length value of the current road, the speed of the vehicle closest to the current pet dog, and the distance value from the nearest vehicle to the pet dog, and calculate the time t seconds for the vehicle to reach the area where the pet dog is located by the ratio of the distance value from the nearest vehicle to the pet dog to the closest speed. When the time value for the vehicle to reach the pet dog's position is lower than the safety time value, it is determined that the current pet dog may be in danger of safety, and the information that the pet dog may be in danger of safety is transmitted to the management platform. The management platform transmits the information to the processing terminal, retrieves the camera of the current monitoring area, captures the image information of the monitoring target passing through the road and analyzes the image information, adjusts the shooting angle of the camera so that the pet dog and the pet dog owner enter the shooting picture, and locates the specific positions of the pet dog and the pet dog owner in the shooting picture respectively, wherein, if the pet dog has not crossed half of the lateral length value of the road, controls the shooting angle to the left of the pet dog's crossing direction, and if the pet dog has crossed half of the lateral length value of the road, adjusts the shooting angle of the camera to control the shooting angle to the right of the pet dog's crossing direction; Step S23: A judgment node is set in the management platform to perform feature recognition analysis on the interval area between the pet dog and the pet dog owner in the captured image obtained in step S22 to identify whether the dog and the owner are connected to a leash. If so, the system transmits information that the pet dog owner is carrying a leash to the management platform, records the judgment node as a first node, and stores it in the management platform; if not, the system transmits information that the pet dog owner is carrying a leash to the management platform, records the judgment node as a second node, and also stores it in the management platform; The step S3 comprises: Step S31: With the real-time position of the pet dog owner on the road as the coordinate origin, the pet dog owner's visual direction as the positive half axis of the Y axis, the Y axis is rotated 90 degrees through the coordinate origin in the road plane to become the X axis, and the coordinate origin is perpendicular to the X and Y axes as the Z axes to establish a real-time moving spatial rectangular coordinate system. The real-time coordinates of the pet dog owner's right hand on the road are (0, 0, z0). The camera set on the road is used to capture the image information on the road, and the position coordinates of the pet dog are obtained as (x0, y0, z1). The distance between the pet dog and the pet dog owner's right hand is ; Step S32: When the dog starts to cross the road, the dog's movement speed is monitored. The timer set by the road monitoring terminal is set to t seconds, and the actual distance the dog travels on the road within t seconds is obtained as L3 meters. The above step S32 is repeated. The effective crossing distance L4 of the pet dog owner within t seconds when crossing the road is detected. The node currently monitored and stored in the management platform is detected. If the obtained judgment node is the first node, step S33 is executed; if the obtained judgment node is the second node, step S34 is executed. Step S33: When the system identifies that the judgment node is the first node, this step is executed to monitor whether the pet dog is likely to be in danger on the road; Step S34: When the system identifies that the judgment node is the second node, this step is executed to monitor whether the pet dog is likely to be in danger on the road; In step S33, when the monitoring system detects that there is a leash between the pet dog and the pet dog owner, the method for monitoring the coordination between the pet dog and the pet dog owner when crossing the road specifically includes: Step S331: Use a high-definition camera to capture an image of the traction chain used by the pet dog owner, and analyze the image features to obtain the surface smoothness, surface material color, and surface material structure density of the traction chain. Based on the identified information, the type of traction chain with the same features is retrieved from the database to determine the type of traction chain. The toughness of the traction chain is K, the length of the traction chain is L1, the actual distance between the traction chain and the fully stretched length is L2, and the total length of the traction chain that is not fully stretched is retrieved from the database of the type of traction chain. When a pet dog passes through a road, the maximum traction distance that the pet dog owner can achieve when using a traction chain to pull the pet dog ; Step S332: Analyze the tortuosity of the traction chain used by the pet dog owner to pull the pet dog in the coordinate system, extract the corresponding coordinates of the pet dog and the pet dog owner's hand holding the dog chain in the spatial rectangular coordinate system, construct a traction chain curve formed between the pet dog coordinates and the pet dog owner coordinates, retrieve the traction chain length L stored in the database, and obtain the traction chain constituting function between the pet dog and the dog owner in the horizontal rectangular coordinate system formed by the current pet dog owner using the traction chain to pull the pet dog: , detection of The derivative at is V, and V is recorded as the average tortuosity of the traction chain; Step S333: The degree of coordination between the pet dog and its owner when crossing the road , where L 00 is the longitudinal walking distance difference coordination reference value, and η is the lateral coordination level.

2. The method for urban intelligent dog breeding management based on the Internet of Things according to claim 1, characterized in that: In step S34, when the monitoring system detects that there is no leash between the pet dog and the pet dog owner, the method for monitoring the coordination between the pet dog and the pet dog owner when crossing the road specifically includes: Step S341: using dynamic image feature recognition technology, detecting the dynamic trajectory of the pet dog's limbs contacting the ground when crossing the road, detecting the contact time difference t0 of the two pairs of combined limbs of the pet dog's left forelimb and left hind limb, and right forelimb and right hind limb, and obtaining the time difference values ​​t1 and t2 of the corresponding combined limbs of the current pet dog breed in the two different moving states of running and walking by retrieving the database; when t0>t2, it is determined that the current pet dog is in a running state, and when the pet dog is in the running state, the travel coefficient of the pet dog crossing the road is W1; when t0<t1, it is determined that the current pet dog is in a walking state, and when the pet dog is in the walking state, the travel coefficient of the pet dog crossing the road is W2; when t1<t0<t2, it is determined that the current pet dog is in a combined state of walking and running, and when the pet dog is in a combined state of walking and running, the travel coefficient of the pet dog crossing the road is W3; Step S342: The surveillance camera adjusts the shooting angle to monitor the direction of the pet dog's movement, and detects the angle between the direction of the pet dog's movement and the Y axis in the spatial rectangular coordinate system constructed in step S31 in the coordinate system. The angle between the pet dog and the Y axis is obtained as S, and the degree of coordination between the pet dog and the pet dog owner when crossing the road is obtained. , where W is the travel coefficient, which is determined based on the travel coefficient value of the road the pet dog travels on obtained in step S341, and L 01 It is the collaborative reference value of the longitudinal walking distance difference.

3. An urban intelligent dog management platform based on the Internet of Things, which adopts the method according to claim 1, characterized in that: The dog management platform includes a data acquisition module, a camera unit, a traction recognition module, and a prompt module. The data acquisition module is used to collect information about the road section on which the currently monitored pet dog and the pet dog owner pass; the camera unit is used to capture image information on the currently monitored road section; the traction recognition module is used to detect the ability of the pet dog owner to restrain the pet dog when the pet dog and the pet dog pass through the road; and the prompt module is used to transmit information that the pet dog owner needs to prompt the pet dog in a timely manner to the mobile terminal carried by the pet dog owner; The data acquisition module includes a road information acquisition module and a pet breed database collection module. The road information acquisition module is used to obtain information about vehicles traveling on the monitored target road; the pet breed database collection module is used to collect database information about pet dog breeds and corresponding pet dog habits; The traction identification module includes a road coordinate system construction module, a non-traction relationship analysis module and a traction relationship analysis module. The road coordinate system construction module is used to construct a movable spatial rectangular coordinate system with the owner as the origin on the monitoring road to monitor the real-time distance between the pet dog and the pet dog owner; the non-traction relationship analysis module is used to analyze the travel coordination between the pet dog and the pet dog owner when the pet dog and the pet dog owner are not carrying a front traction chain; the traction relationship analysis module is used to analyze the travel coordination between the pet dog and the pet dog owner when the pet dog and the pet dog owner are carrying a traction chain.

4. The urban intelligent dog management platform based on the Internet of Things according to claim 3 is characterized by: The traction relationship analysis module includes a traction chain tortuosity detection submodule and a maximum traction distance detection submodule. The traction chain tortuosity detection submodule is used to detect the tortuosity information of the traction chain between the pet dog and the pet dog owner; the maximum traction distance detection submodule is used to detect the maximum traction distance between the pet dog and the pet dog owner when they are traveling together.

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