Urban intelligent dog raising management platform based on Internet of Things

Abstract

The invention discloses an urban intelligent dog-raising management platform based on the Internet of Things, and comprises an urban intelligent dog-raising management method based on the Internet of Things, and the urban intelligent dog-raising management method mainly comprises the following steps: judging whether a pet dog is in a potential safety hazard or not according to a road condition; if the system judges that the current pet dog may have the safety risk, monitoring the target pet dog in real time, and identifying whether a drag chain is connected between the dog and the owner; according to the identification result, analyzing the cooperation degree between the pet dog and the passing of the pet dog owner when the pet dog passes through the road; and transmitting the prompt information. According to the invention, whether the dog and the owner are connected with the drag chain or not is identified, and the cooperation degree of passing of the pet dog and the pet dog owner is analyzed, so that the monitoring accuracy of the dog raising management platform is improved, and the possibility of safety problems occurring when the pet dog owner carries the dog to pass the road is greatly reduced. The method has the characteristics of high road monitoring safety and high monitored target recognition capability.

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, since dogs are generally more active in outdoor activities, safety issues may arise, so various safety monitoring platforms dedicated to dog owners have been born. Especially when pet dog owners take their dogs through some two-way roads in cities, dogs are smaller targets and move faster, making them more likely to have safety issues than people. Therefore, it is necessary to design a dog management platform that can effectively pull the pet dog in time when the pet dog owner does not pay real-time attention to the pet dog crossing the road, and let the pet dog leave the dangerous area when the pet dog may be in danger. Therefore, it is very necessary to design an urban intelligent dog management platform based on the Internet of Things with high road monitoring safety and strong monitoring 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, the urban intelligent dog management method mainly comprising the following steps:

[0005] Step S1: Connecting the intelligent dog management system to the urban road monitoring terminal, and taking pictures at 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, and the processing terminal monitors the target pet dog and the dog owner passing through the road in real time to identify whether the dog and the owner are connected to a leash;

[0007] Step S3: analyzing the coordination between the pet dog and the pet dog owner when crossing the road according to the obtained recognition result;

[0008] Step S4: The system determines whether the pet dog owner needs to give a reminder to the pet dog based on 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 road 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 take images of the area in the center of the road;

[0011] Step S22: obtaining the lateral length value of the current road, the driving speed of the vehicle closest to the current pet dog, and the distance value from the nearest vehicle to the pet dog, and calculating the time for the vehicle to reach the area where the pet dog is located as t seconds by the ratio of the distance value from the nearest vehicle to the pet dog to the closest driving 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 in 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 to make 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 passed through half of the lateral length value of the road, controls the shooting angle to the left of the pet dog's traveling direction, and if the pet dog has passed through 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 traveling 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 captured image 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 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 pet dog owner's visual direction as the positive half axis of the Y axis, rotate the Y axis 90° through the coordinate origin in the road plane as the X axis, and establish a real-time moving spatial rectangular coordinate system through the coordinate origin perpendicular to the X and Y axes 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 captures the picture 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, and 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, and the effective travel distance L4 of the pet dog owner within t seconds when crossing the road is detected. The node currently stored in the management platform is monitored. When the judgment node obtained is the first node, step S33 is executed; when the judgment node obtained 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 there is a possibility of danger for the pet dog 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 of danger for the pet dog on the road.

[0018] According to the above technical solution, in step S33, when the monitoring system detects that there is a traction chain 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, and obtain the surface smoothness, surface material color, and surface material structure density of the traction chain through image feature analysis, and retrieve the type of traction chain with the same features in the database based on the identified information, determine the type of traction chain, and retrieve the toughness of the traction chain K, the length of the traction chain 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, and 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 Where 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 for monitoring the coordination between the pet dog and the pet dog owner when crossing the road specifically includes:

[0023] Step S341: using dynamic image feature recognition technology, the dynamic trajectory of the pet dog's limbs contacting the ground is detected in the process of crossing the road, and the ground contact time difference t0 of the two pairs of combined limbs of the left forelimb and left hind limb, right forelimb and right hind limb of the pet dog is detected, and the time difference values ​​of the combined limbs of the corresponding parts of the current pet dog breed in the two different moving states of running and walking are obtained by calling the database, and the time difference values ​​are t1 and t2 respectively. When t0>t2, it is judged that the current pet dog is in a running state. 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 judged that the current pet dog is in a walking state. 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 judged that the current pet dog is in a combined state of walking and running. 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, and obtains the angle between the pet dog and the Y axis as S, then the coordination degree 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 that the pet dog travels on obtained in step S341, and L 01 It is the reference value of 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 give a reminder to the pet dog is specifically:

[0026] The possibility of danger that pet dogs may encounter when walking on the road Wherein 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 give 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 ability of the pet dog owner to restrict the pet dog when the pet dog owner and the pet dog pass through the road; the prompt module is used to transmit the information that the pet dog owner needs to prompt the pet dog in time 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 moving together.

[0031] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention identifies whether a traction chain 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 through the walking 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 carries 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 It is a flow chart of the operation method of the urban intelligent dog management platform provided by the present invention;

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

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

[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 taking pictures at 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, and the processing terminal monitors the target pet dog and the dog owner passing through the road in real time to identify whether the dog and the owner are connected to a leash;

[0040] Step S3: analyzing the coordination between the pet dog and the pet dog owner when crossing the road according to the obtained recognition result;

[0041] Step S4: The system determines whether the pet dog owner needs to give a reminder to the pet dog based on 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 traction chain 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 walking 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, and effectively analyzes whether the pet dog will encounter danger when crossing the road, thereby improving the monitoring accuracy of the dog management platform, improving the intelligence level of the city, greatly reducing the possibility of safety problems when the pet dog owner carries the dog across the road, and improving the smoothness and stability of road operation.

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

[0044] Step S21: The road information registration module of the road 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 take images of the area in the center of the road;

[0045] Step S22: obtaining the lateral length value of the current road, the driving speed of the vehicle closest to the current pet dog, and the distance value from the nearest vehicle to the pet dog, and calculating the time for the vehicle to reach the area where the pet dog is located as t seconds by the ratio of the distance value from the nearest vehicle to the pet dog to the closest driving 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 in 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 to make 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 passed through half of the lateral length value of the road, controls the shooting angle to the left of the pet dog's traveling direction, and if the pet dog has passed through 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 traveling 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 captured image 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 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 pet dog owner's visual direction as the positive half axis of the Y axis, rotate the Y axis 90° through the coordinate origin in the road plane as the X axis, and establish a real-time moving spatial rectangular coordinate system through the coordinate origin perpendicular to the X and Y axes 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 captures the picture 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 starts to cross the road, the dog's movement speed is monitored, and 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, and the effective travel distance L4 of the pet dog owner within t seconds when crossing the road is detected. The node currently stored in the management platform is monitored. When the judgment node obtained is the first node, step S33 is executed; when the judgment node obtained 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 there is a possibility of danger for the pet dog 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 of danger for the pet dog on the road.

[0052] Through this technical solution, the problem that the relative relationship between pet dogs and pet dog owners when they pass through the road together cannot be effectively and accurately monitored 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 promptly grasped 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 degree 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, and obtain the surface smoothness, surface material color, and surface material structure density of the traction chain through image feature analysis, and retrieve the type of traction chain with the same features in the database based on the identified information, determine the type of traction chain, and retrieve the toughness of the traction chain K, the length of the traction chain 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, and 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 Where 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 the current pet dog owner pays to the dog is judged by the tortuosity of the traction chain. When it is detected that the pet dog owner pays too little attention to the dog and the 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 away from the dangerous area in time.

[0058] According to the current distance between the dog and the person, the restraint force of the pet dog owner on the dog is differentiated into two levels. When the current position of the dog is relatively dangerous, the smaller the restraint force of the pet dog owner on the dog, the better, 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 current position of the dog is relatively safe, the greater the restraint force of the pet dog owner on the dog, the better, 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 that it is difficult to effectively distinguish between different possible safety hazards that may occur to pet dogs during the process of their owners and pet dogs passing through the road due to different traction conditions. By analyzing the traction chain between the pet dog owner and the pet dog during the process of the pet dog owner and the pet dog passing through the road, the image feature analysis technology is used to identify the surface smoothness, surface material color and surface material structure tightness information of the traction chain, the flexibility of the traction chain is obtained, and based on the distance information of the traction chain between the pet dog owner and the pet dog in the constructed coordinate system, the initiative of the pet dog owner to pull the pet dog away from the dangerous area in time when the pet dog encounters danger is analyzed, thereby improving the intelligence level of the city and greatly reducing the possibility of safety problems for pet 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, the dynamic trajectory of the pet dog's limbs contacting the ground is detected in the process of crossing the road, and the ground contact time difference t0 of the two pairs of combined limbs of the left forelimb and left hind limb, right forelimb and right hind limb of the pet dog is detected, and the time difference values ​​of the combined limbs of the corresponding parts of the current pet dog breed in the two different moving states of running and walking are obtained by calling the database, and the time difference values ​​are t1 and t2 respectively. When t0>t2, it is judged that the current pet dog is in a running state. 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 judged that the current pet dog is in a walking state. 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 judged that the current pet dog is in a combined state of walking and running. 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, 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, and obtains the angle between the pet dog and the Y axis as S, then the coordination degree 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 that the pet dog travels on obtained in step S341, and L 01 It is the reference value of longitudinal walking distance difference.

[0064] Through this technical solution, the problem of difficulty in analyzing the various travel states of the pet dog in a short period of time when the pet dog is crossing the road without a leash between the pet dog owner and the pet dog 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 when the pet dog is crossing the road, and a danger coefficient value is given when the pet dog crosses the road in different travel states. Based on the travel angle between the pet dog and the pet dog owner when the pet dog crosses the road, the coordination information of the pet dog and the pet dog owner when crossing the road is detected, so as to avoid the possibility of excessive distance changes when the pet dog crosses the road without a leash, and improve 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 give a reminder to the pet dog is specifically:

[0066] The possibility of danger that pet dogs may encounter when walking on the road Wherein 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 give a reminder.

[0067] In two different walking states of pet dogs with pet dog owners, although the two different calculation methods for pet dogs in the two walking states are ultimately used to monitor whether the pet dog can be restrained by the pet dog owner when danger comes after the pet dog leaves the control range of the pet dog owner, the same calculation method can be used to calculate the possibility of danger to the pet dog. When the pet dog is not restrained by the traction chain, it will still be retrieved by the pet dog owner's shouting, and the pet dog is still restricted by some pet dog owners. Therefore, the obtained parameters can be used to analyze the degree of restriction on the pet dog in the two cases of having a traction chain and not having one, and then the parameters can be used to analyze the degree of danger to the pet 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 sections on which the currently monitored pet dogs and pet dog owners pass; the camera unit is used to capture image information on the currently monitored road sections; the traction recognition module is used to detect the ability of the pet dog owner to restrict the pet dog when the pet dog owner 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 collection module includes a road information collection module and a pet breed database collection module. The road information collection module is used to obtain the information of vehicles traveling 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.

[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 do not carry 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 carry 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 moving together.

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

[0073] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is 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 can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An urban intelligent dog management platform based on the Internet of Things, characterized by: The invention comprises an urban intelligent dog management method based on the Internet of Things, and the urban intelligent dog management method mainly comprises the following steps: Step S1: Connecting the intelligent dog management system to the urban road monitoring terminal, and taking pictures at 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, and the processing terminal monitors the target pet dog and the dog owner passing through the road in real time to identify whether the dog and the owner are connected to a leash; Step S3: analyzing the coordination between the pet dog and the pet dog owner when crossing the road according to the obtained recognition result; Step S4: The system determines whether the pet dog owner needs to give a reminder to the pet dog based on the analysis result obtained in step S3. If necessary, the reminder information is transmitted to the mobile terminal carried by the pet dog owner.

2. The urban intelligent dog management platform based on the Internet of Things according to claim 1 is characterized by: The step S2 comprises: Step S21: The road information registration module of the road 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 take images of the area in the center of the road; Step S22: obtaining the lateral length value of the current road, the driving speed of the vehicle closest to the current pet dog, and the distance value from the nearest vehicle to the pet dog, and calculating the time for the vehicle to reach the area where the pet dog is located as t seconds by the ratio of the distance value from the nearest vehicle to the pet dog to the closest driving 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 in 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 to make 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 passed through half of the lateral length value of the road, controls the shooting angle to the left of the pet dog's traveling direction, and if the pet dog has passed through 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 traveling direction; 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 captured image 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 traction chain to the management platform, records the judgment node as the second node, and also stores it in the management platform.

3. The urban intelligent dog management platform based on the Internet of Things according to claim 2 is characterized by: The step S3 comprises: Step S31: Take 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, rotate the Y axis 90° through the coordinate origin in the road plane as the X axis, and establish a real-time moving spatial rectangular coordinate system through the coordinate origin perpendicular to the X and Y axes 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 captures the picture 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, and 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, and the effective travel distance L4 of the pet dog owner within t seconds when crossing the road is detected. The node currently stored in the management platform is monitored. When the judgment node obtained is the first node, step S33 is executed; when the judgment node obtained 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 there is a possibility of danger for the pet dog on the road; 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 of danger for the pet dog on the road.

4. The urban intelligent dog management platform based on the Internet of Things according to claim 3 is characterized by: In step S33, when the monitoring system detects that there is a traction chain between the pet dog and the pet dog owner, the method for monitoring the coordination degree 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 obtain the surface smoothness, surface material color, and surface material structure density of the traction chain through image feature analysis, and retrieve the type of traction chain with the same features in the database based on the identified information, determine the type of traction chain, and retrieve the toughness of the traction chain K, the length of the traction chain 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 ; 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, and 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; 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.

5. The urban intelligent dog management platform based on the Internet of Things according to claim 4 is characterized by: 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, the dynamic trajectory of the pet dog's limbs contacting the ground is detected in the process of crossing the road, and the ground contact time difference t0 of the two pairs of combined limbs of the left forelimb and left hind limb, right forelimb and right hind limb of the pet dog is detected, and the time difference values ​​of the combined limbs of the corresponding parts of the current pet dog breed in the two different moving states of running and walking are obtained by calling the database, and the time difference values ​​are t1 and t2 respectively. When t0>t2, it is judged that the current pet dog is in a running state. 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 judged that the current pet dog is in a walking state. 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 judged that the current pet dog is in a combined state of walking and running. 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, and obtains the angle between the pet dog and the Y axis as S, then the coordination degree 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 that the pet dog travels on obtained in step S341, and L 01 It is the coordinated reference value of the longitudinal walking distance difference.

6. The urban intelligent dog management platform based on the Internet of Things according to claim 5 is characterized by: In step S4, the detection method for determining whether the pet dog owner needs to remind the pet dog is specifically: The possibility of danger that pet dogs may encounter when walking on the road Wherein 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 give a reminder.

7. The urban intelligent dog management platform based on the Internet of Things according to claim 6 is characterized by: 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 road section information on the currently monitored pet dog and the pet dog owner; the camera unit is used to capture the image information on the currently monitored road section; the traction recognition module is used to detect the ability of the pet dog owner to restrict the pet dog when the pet dog owner and the pet dog pass through the road; the prompt module is used to transmit the information that the pet dog owner needs to prompt the pet dog in time to the mobile terminal carried by the pet dog owner.

8. The urban intelligent dog management platform based on the Internet of Things according to claim 7 is characterized by: 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.

9. The urban intelligent dog management platform based on the Internet of Things according to claim 8 is characterized by: 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 do not carry 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 carry a traction chain.

10. The urban intelligent dog management platform based on the Internet of Things according to claim 9 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 moving together.

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