Remote intelligent monitoring and analyzing system and method for dog training device

Abstract

The invention belongs to the technical field of information, and provides a remote intelligent monitoring and analyzing system and method for a dog training device. The method comprises the following steps: receiving dog training device information, electronic collar information, attribute information of a pet dog and a plurality of corresponding training target information input by a user, and carrying out associated storage on the information and a user account; receiving a starting signal of the dog training device and an electronic collar associated with the dog training device and the training target information selected this time, generating a group of first working parameters of the dog training device according to the attribute information of the pet dog and the training target information, and sending the first working parameters to the dog training device; and receiving motion monitoring information of the pet dog in the process of executing the first working parameter by the dog training device, correcting the first working parameter based on the motion monitoring information, and sending a second working parameter obtained after correction to the dog training device. According to the invention, online automatic optimization and adjustment of the working parameters of the dog training device can be realized, so that the dog training effect is improved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and more particularly, to a remote intelligent monitoring and analysis system and method for a dog trainer. Background Art

[0002] With the improvement of people's living standards and the increasing demand for pet companionship, the number of pet dogs raised globally is constantly increasing. Pet owners are paying more and more attention to pet training, and they hope to train pet dogs in a scientific and effective way to help them develop good behavior habits. A dog trainer can help pet owners convey instructions more accurately during the training process and improve the training effect, so it is favored by more and more pet owners.

[0003] A dog trainer is a remote control transmitter, and the accompanying device is an electronic collar worn on the pet dog, that is, a receiver. The dog trainer sends a signal driving instruction, such as a sound signal, a vibration signal, or an electric shock signal. After receiving the signal instruction, the electronic collar performs corresponding response actions to remind the behavior of the pet dog. However, the existing dog trainers have unified standards for setting sound signals, vibration signals, and electric shock signals, and basically cannot be modified. For example, different pet dogs need to receive reminder signals of the same intensity (such as electric shock signals), which is not conducive to the efficient and personalized training of pet dogs.

[0004] Therefore, how to conveniently adjust the working parameters of the dog trainer in a personalized manner is a technical problem that needs to be solved currently. Summary of the Invention

[0005] In view of the above technical problems, the present invention provides a remote intelligent monitoring and analysis method, system, electronic device, computer storage medium, and computer program product for a dog trainer.

[0006] The present invention discloses a remote intelligent monitoring and analysis method for a dog trainer, and the method includes the following steps: Receiving the dog trainer information, electronic collar information, attribute information of the pet dog, and corresponding several training target information input by the user, and associating and storing the above information with the user account; Receiving the start signal of the dog trainer and its associated electronic collar, and the selected training target information this time, generating a set of first working parameters of the dog trainer according to the attribute information of the pet dog and the training target information, and sending the first working parameters to the dog trainer; Receive the motion monitoring information of the pet dog during the execution of the first working parameters by the dog trainer, correct the first working parameters based on the motion monitoring information, and send the second working parameters obtained after correction to the dog trainer; wherein, both the first working parameters and the second working parameters include at least one of sound signal parameters, vibration signal parameters, and electric shock signal parameters.

[0007] The present invention also discloses a remote intelligent monitoring and analysis system for a dog trainer, the system includes at least one processor and a memory, and the computer code stored in the memory is called and executed by the processor to implement the following steps: Receive the dog trainer information, electronic collar information, attribute information of the pet dog input by the user, and corresponding several training target information, and associate and store the above information with the user account. Receive the start signal of the dog trainer and its associated electronic collar, and the selected training target information this time, generate a set of first working parameters for the dog trainer according to the attribute information of the pet dog and the training target information, and send the first working parameters to the dog trainer. Receive the motion monitoring information of the pet dog during the execution of the first working parameters by the dog trainer, correct the first working parameters based on the motion monitoring information, and send the second working parameters obtained after correction to the dog trainer; wherein, both the first working parameters and the second working parameters include at least one of sound signal parameters, vibration signal parameters, and electric shock signal parameters.

[0008] The present invention also discloses an electronic device, including: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, and the processor executes the computer program to implement the method described in any of the previous items.

[0009] The present invention also discloses a computer storage medium, the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in any of the previous items.

[0010] The present invention also discloses a computer program product, which contains computer code, and when the computer code is executed by the processor of the electronic device, it implements the method described in any of the previous items.

[0011] The beneficial effects of the present invention are at least as follows: Compared with the prior art, the present invention can conveniently and automatically adjust the working parameters of the dog trainer through a mobile phone APP, and this automatic adjustment is based on the attribute information of the pet dog and the training target information to generate personalized working parameters, solving the problem that the signal intensity of the existing dog trainer is unified and cannot adapt to individual differences. This personalized setting can effectively improve the training effect while reducing unnecessary stress or harm to the pet dog. At the same time, the present invention can also real-time monitor the motion state and behavioral response of the pet dog through an electronic collar, and dynamically correct the working parameters of the dog trainer to ensure the accuracy and effectiveness of the training signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0013] Figure 1 is a schematic flow chart of a method for remotely and intelligently monitoring and analyzing a dog trainer according to an embodiment of the present invention; Figure 2 is a schematic structural diagram of a system for remotely and intelligently monitoring and analyzing a dog trainer according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] The following specific embodiments illustrate the implementation manners of the present application. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of them. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by the present application.

[0015] In addition, the technical features involved in different implementation manners of the present application described below can be combined with each other as long as they do not conflict with each other.

[0016] As Figure 1 shown, in view of the above technical problems, an embodiment of the present invention discloses a method for remotely and intelligently monitoring and analyzing a dog trainer, and the method includes the following steps: S100, receiving the dog trainer information, electronic collar information, attribute information of the pet dog, and corresponding several training target information input by the user, and associating and storing the above information with the user account.

[0017] The solution of the present invention is executed in the cloud, and the cloud is equipped with a corresponding APP. Users can register an account on the APP and enter the information of the dog trainer, the electronic collar, the attribute information of the pet dog, and several corresponding training target information. Among them, the information of the dog trainer includes hardware information such as the model and serial number of the dog trainer; the information of the electronic collar includes hardware information such as the model and serial number of the electronic collar. The attribute information of the pet dog includes the breed, age, weight, and personality characteristics (such as activity level, sensitivity) of the pet dog. The training target information is the training target that the user hopes to achieve, such as reducing barking, improving obedience, correcting jumping behavior, etc.

[0018] After the user finishes entering the information, the APP associates this information with the user's account and stores it in the cloud or a local database for subsequent calling and management.

[0019] S200, receive the start signal of the dog trainer and its associated electronic collar, and the selected training target information this time. Generate a set of first working parameters for the dog trainer according to the attribute information of the pet dog and the training target information, and send the first working parameters to the dog trainer.

[0020] After the above-mentioned relevant information is entered, the user should also connect the dog trainer and its associated electronic collar to the mobile phone installed with the APP, such as a Bluetooth connection or a Wi-Fi connection. In this way, when the user needs to train the pet dog, manually start the dog trainer and its associated electronic collar, and the cloud can receive the start signals of the two and the training target information selected by the user.

[0021] The cloud generates a set of initial working parameters, that is, the first working parameters, according to the attribute information of the pet dog (such as breed, age, weight, personality characteristics) and the training target information (such as reducing barking). The working parameters include: sound signal parameters: the type, volume, frequency, etc. of the sound; vibration signal parameters: the intensity, duration, etc. of the vibration; electric shock signal parameters: the intensity, duration, etc. of the electric shock. Send the generated first working parameters to the dog trainer and the electronic collar through wireless communication (such as Bluetooth, Wi-Fi). In this way, when the user uses the dog trainer to train the pet dog, the first working parameters will be executed.

[0022] It is understandable that the dog trainer in the present invention can be a manual dog trainer or an automatic dog trainer. For the manual dog trainer, the user presses the function buttons on the manual dog trainer to send different sound signals, vibration signals, and electric shock signals to the electronic collar, and the working parameters sent by the cloud are used to adjust the intensity or type of these parameters. For example, the electric shock intensity corresponding to the initial first gear of the manual dog trainer is level I, and the duration is 1 s, while the working parameters sent by the cloud are: the electric shock intensity corresponding to the first gear is level II, and the duration is 0.5 s. That is, after being adjusted by the cloud, the actual working parameters corresponding to different gears have changed.

[0023] For the automatic dog trainer, it can simultaneously monitor the dog training instructions issued by the user, and analyze whether the pet dog responds to the dog training instructions issued by the user by means of video monitoring or based on the motion data transmitted by the connected electronic collar. If it is found that the pet dog does not respond or responds in a timely manner, a control instruction can be generated according to the aforementioned working parameters sent by the cloud. The control instruction is used for the electronic collar to perform corresponding actions to remind the pet dog. Similar to the manual dog trainer, the working parameters sent by the cloud are also used to adjust the initial working parameters in the automatic dog trainer, which will not be elaborated here.

[0024] S300, receive the motion monitoring information of the pet dog during the execution of the first working parameter by the dog trainer, correct the first working parameter based on the motion monitoring information, and send the corrected second working parameter to the dog trainer; wherein, both the first working parameter and the second working parameter include at least one of a sound signal parameter, a vibration signal parameter, and an electric shock signal parameter.

[0025] During the execution of the above first working parameter by the dog trainer, the electronic collar uses built-in sensors (such as acceleration sensors, gyroscopes) to monitor the motion monitoring information of the pet dog in real time, such as body posture, activity intensity, behavior frequency, etc. (Some of the motion monitoring information can also be monitored by the dog trainer). The motion monitoring information is sent to the dog trainer and / or the mobile phone App through wireless communication, and the dog trainer and / or the mobile phone App upload it to the cloud. The cloud analyzes the reaction of the pet dog to the first working parameter based on the motion monitoring information, and then corrects the aforementioned first working parameter to obtain the second working parameter. For example, if the pet dog reacts excessively to the current signal (such as accelerating and running after being stimulated), it indicates that the stimulation intensity is too high and the signal intensity needs to be reduced. If the pet dog reacts insufficiently to the current signal (such as the original motion state has not changed significantly), it indicates that the training effect has not been achieved and the signal intensity needs to be increased. The second working parameter is sent to the dog trainer and the electronic collar to adjust the training strategy in real time.

[0026] Compared with the prior art, the present invention can conveniently and automatically adjust the working parameters of the dog trainer through a mobile phone APP, and the automatic adjustment is based on the attribute information of the pet dog and the training target information to generate personalized working parameters, solving the problem that the signal intensity of the existing dog trainer is unified and cannot adapt to individual differences. This personalized setting can effectively improve the training effect and reduce unnecessary stress or harm to the pet dog. At the same time, the present invention can also real-time monitor the movement state and behavioral response of the pet dog through an electronic collar, and dynamically correct the working parameters of the dog trainer to ensure the accuracy and effectiveness of the training signal.

[0027] Optionally, generating a set of first working parameters of the dog trainer according to the attribute information of the pet dog and the training target information includes: Matching a set of third working parameters according to the attribute information of the pet dog and the training target information; the attribute information includes but is not limited to the breed, age, weight, and personality characteristics of the pet dog; Obtaining the training duration of the pet dog according to the user account, matching a first weakening coefficient according to the training duration, and using the first weakening coefficient to weaken the third working parameters to obtain the first working parameters.

[0028] In this embodiment, according to various attribute information of the pet dog, such as breed, age, weight, and personality characteristics (such as high activity or strong sensitivity), as well as the training target information expected by the user (such as reducing barking, improving obedience, etc.), a set of corresponding third working parameters are obtained through pre-set matching rules or algorithms. For example, for a relatively sensitive small dog, when training to reduce barking, the initially matched sound signal can be a soft prompt sound, with a moderate volume and a low frequency; the electric shock signal intensity can be relatively weak and the duration can be short. It can be understood that a control relationship is pre-established between the working parameters and the attribute information and the training target information, and the subsequent query of this control relationship can be used; or, a pre-trained model based on a machine learning algorithm can also be used to process the attribute information and the training target information of the pet dog, and then predict the corresponding working parameters.

[0029] At the same time, the third working parameters obtained above are actually the standard or general working parameters of the same type of pet dog under the same training target, which represents a general situation. However, some pet dogs may have received relevant training, and more personalized working parameters should be determined for them. Specifically: Query the training duration of the pet dog based on the user account. For pet dogs with certain training experience, relatively weakened stimulation signals can usually be used for training, which can not only achieve the training effect but also reduce unnecessary stimulation to the pet dog. According to the pre-set rules, a first weakening coefficient is obtained by matching the training duration. For example, if the training duration of the pet dog is long, a smaller first weakening coefficient, such as 0.8, can be matched; if the training duration is short, the first weakening coefficient is a value closer to 1. Then, this first weakening coefficient is used to weaken the third working parameter obtained previously. For example, the original set intensity of the electric shock signal in the third working parameter is 5 levels. After being processed by the first weakening coefficient of 0.8, the intensity of the electric shock signal in the first working parameter becomes 4 levels. By analogy, other parameters such as the sound signal parameter and the vibration signal parameter are also adjusted for weakening accordingly, and finally the first working parameter suitable for the current training stage of the pet dog is obtained. In this way, during the training process, for pet dogs that have received a certain amount of training, the stimulation signals emitted by the dog trainer will be relatively milder and more in line with their training needs.

[0030] It should be noted that the weakening of the sound signal parameter includes various implementation methods, including but not limited to the weakening of the volume and the weakening of the sound content. Among them, the weakening of the sound content is, for example, replacing a certain piece of music that can produce strong stimulation to the pet dog with a gentle piece of music that produces weak stimulation to the pet dog.

[0031] Optionally, the correcting of the first working parameter based on the motion monitoring information includes: Obtain the activation mode of the dog trainer, where the activation mode includes a manual dog training mode and an automatic dog training mode, and determine the correction period according to the activation mode; among them, the automatic dog training mode is for multiple pet dogs with the same attribute information and the same training target information; Among them, the correction period corresponding to the manual dog training mode is the first period, the correction period corresponding to the automatic dog training mode is the second period, and the first period is greater than the second period; After sending the first working parameter to the dog trainer and when the correction period is reached, correct the first working parameter based on the motion monitoring information.

[0032] In this embodiment, the cloud determines whether the currently activated dog trainer is in the manual dog training mode or the automatic dog training mode by communicating with the dog trainer or based on the type of the currently activated dog trainer (multiple dog trainers can be bound under the same user account, such as a manual dog trainer and an automatic dog trainer).

[0033] In the manual dog training mode, it completely relies on the user to manually operate the function buttons on the dog trainer to selectively send sound, vibration, or electric shock signals to the electronic collar worn on the pet dog, so as to guide the pet dog to perform the expected behavior. The automatic dog training mode is applicable to commercial dog training institutions. In this mode, an automatic dog trainer is placed in the training space. The automatic dog trainer monitors the compliance of multiple pet dogs of the same type (i.e., with the same attribute information and training target information) with the instructions of the dog trainer. Once it is found that a pet dog fails to respond in a timely manner or the response does not meet the expectations (subsequent motion posture information can also be directly collected by the automatic dog trainer), the dog trainer will automatically generate a control instruction according to the working parameters set in the cloud, drive the electronic collar to perform corresponding actions, and remind the pet dog to adjust its behavior, so as to achieve efficient dog training.

[0034] Since there are significant differences in the operation methods and application scenarios between the manual dog training and automatic dog training modes, the present invention is configured to set different correction cycles for correcting the first working parameter for different dog training modes. Specifically: In the manual dog training mode, the corresponding correction cycle is the first cycle. Because in this mode, the user manually operates the dog trainer, and the timing, frequency of each operation, and the degree of stimulation to the pet dog are greatly affected by the user's personal subjective factors. The behavioral response of the pet dog is also closely related to the user's operation rhythm. Therefore, it is not necessary to adjust the first working parameter too frequently based on the motion monitoring information of the pet dog. Based on this, the first cycle is set relatively long. For example, the working parameter is corrected weekly based on the accumulated motion monitoring information.

[0035] The correction cycle corresponding to the automatic dog training mode is the second cycle, and the second cycle is significantly shorter than the first cycle. This is because the automatic dog training mode needs to continuously and real-time monitor the behavioral dynamics of multiple pet dogs with similar characteristics and training targets and make timely responses. In order to ensure the accuracy and effectiveness of the training effect, it is necessary to frequently optimize and adjust the first working parameter according to the real-time motion monitoring information of the pet dog. For example, the working parameter is checked and corrected every half day based on the motion monitoring information of this half day. Such a setting can make the automatic dog training mode more suitable for the complex behavioral changes of multiple pet dogs, timely optimize the training strategy, and improve the overall training effect. By setting different correction cycles according to different activation modes in this way, the working parameters of the dog trainer can be dynamically managed more scientifically and reasonably, and the needs of different training scenarios and pet dog groups can be met to the greatest extent. Of course, at this time, the motion monitoring information includes the motion monitoring information of multiple pet dogs, and the subsequent evaluation results are also for multiple pet dogs.

[0036] Optionally, the correcting the first working parameter based on the motion monitoring information includes: Draw the movement trajectory of the pet dog based on the positioning information in the movement monitoring information, and divide the movement trajectory into a strong training scenario and a weak training scenario according to the scenario information where the movement trajectory is located; Obtain multiple groups of first movement posture information and first dog training instruction information belonging to the strong training scenario, and multiple groups of second movement posture information and second dog training instruction information belonging to the weak training scenario; the first movement posture information, the first dog training instruction information, the second movement posture information, and the second dog training instruction information are all included in the movement monitoring information; the first dog training instruction information in each group is adjacent in time to the first movement posture information, and the second dog training instruction information in each group is adjacent in time to the second movement posture information; Conduct compliance evaluations on the first dog training instruction information and the first movement posture information, and the second dog training instruction information and the second movement posture information, respectively obtain a first compliance evaluation value and a second compliance evaluation value, and perform fusion processing on the first compliance evaluation value and the second compliance evaluation value to obtain a compliance evaluation value; If the compliance evaluation value is higher than the threshold, generate a second weakening coefficient based on the difference between the compliance evaluation value and the threshold, and use the second weakening coefficient to correct the first working parameter to obtain the corrected second working parameter.

[0037] In this embodiment, during the execution of the first working parameter, the cloud continuously obtains the movement monitoring information of the pet dog, from which multiple paired groups of dog training instruction information (collected by the microphone on the dog trainer and attached to the movement monitoring information) and movement posture information can be extracted, that is, the actual movement posture of the pet dog after the user issues a dog training instruction. By comprehensively analyzing and evaluating it, the training effect of the pet dog can be obtained. If the training effect is good, the foregoing first working parameter is weakened to obtain the corrected second working parameter, which can reduce the training stimulation to the pet dog. Specifically: First, use the positioning information in the movement monitoring information to draw the movement trajectory of the pet dog. By analyzing the movement path of the pet dog at different positions and combining scenario information such as the surrounding environment, the movement trajectory is divided into a strong training scenario and a weak training scenario. The strong training scenario may refer to an environment where there are many external interferences and the training difficulty is relatively high, such as an area in the park with many people and other animals; the weak training scenario may be a place with fewer interferences and a relatively simple and familiar environment, such as one's own yard, the venue of a dog training institution, etc.

[0038] Next, the cloud respectively obtains specific information groups belonging to the strong training scenario and the weak training scenario from the motion monitoring information. For the strong training scenario, multiple groups of first motion posture information (such as the standing, running, jumping postures of a pet dog, etc.) and first dog training instruction information adjacent in time thereto (instructions issued by the user through the dog trainer, such as issuing the "sit" instruction) are obtained. Similarly, for the weak training scenario, multiple groups of second motion posture information and second dog training instruction information adjacent in time thereto are obtained.

[0039] For the multiple groups of first dog training instruction information and first motion posture information in the strong training scenario, and the second dog training instruction information and second motion posture information in the weak training scenario, the system will respectively conduct compliance evaluations. The compliance evaluation mainly judges whether the actual motion posture of the pet dog conforms to the instruction requirements after receiving the dog training instruction. For example, if the dog training instruction is "sit", and the pet dog indeed makes a sitting posture within the corresponding time, then the compliance is high. These information are analyzed and integrated through a specific algorithm to respectively obtain a first compliance evaluation value (corresponding to the strong training scenario) and a second compliance evaluation value (corresponding to the weak training scenario). Then, these two evaluation values are fused, and the fusion method can be methods such as weighted average. Finally, a comprehensive compliance evaluation value is obtained. This evaluation value reflects the overall compliance degree of the pet dog with the dog training instructions in different scenarios.

[0040] Then, the cloud compares the obtained compliance evaluation value with a preset threshold. If the compliance evaluation value is higher than the threshold, it indicates that the pet dog has a good compliance with the dog training instructions and a good training effect. The current first working parameters (such as sound signal intensity, electric shock intensity, etc.) are relatively strong or unnecessary for the pet dog, and such a large stimulus is not required to achieve the training effect. At this time, a second weakening coefficient is generated based on the difference between the compliance evaluation value and the threshold. The larger the difference, the stronger the current working parameters are relative to the actual requirements of the pet dog, and the smaller the weakening coefficient. Then, this second weakening coefficient is used to correct the first working parameters. For example, the intensity, duration and other parameters of the sound signal, vibration signal, and electric shock signal are correspondingly reduced and adjusted to obtain the corrected second working parameters. This can make the working parameters of the dog trainer more in line with the actual training needs of the pet dog, avoid over-stimulating the pet dog, and ensure the training effect at the same time.

[0041] Optionally, the fusion process of the first compliance evaluation value and the second compliance evaluation value to obtain the compliance evaluation value includes: Performing a weighted calculation on the first compliance evaluation value and the second compliance evaluation value to obtain the compliance evaluation value; wherein, the first weight of the first compliance evaluation value is higher than the second weight of the second compliance evaluation value.

[0042] In this embodiment, compared with the weak training scenario, the compliance evaluation conclusion of the pet dog in the strong training scenario is more valuable for reference. Therefore, in the present invention, the first weight of the first compliance evaluation value is set higher than the second weight of the second compliance evaluation value.

[0043] As Figure 2 shown, an intelligent remote monitoring and analysis system for a dog trainer according to an embodiment of the present invention further discloses a system including at least one processor and a memory. The computer code stored in the memory is called and executed by the processor to implement the following steps: Receive the dog trainer information, electronic collar information, attribute information of the pet dog, and corresponding several training target information input by the user, and associate and store the above information with the user account; Receive the start signal of the dog trainer and its associated electronic collar, and the selected training target information this time. Generate a set of first working parameters for the dog trainer according to the attribute information of the pet dog and the training target information, and send the first working parameters to the dog trainer; Receive the motion monitoring information of the pet dog during the execution of the first working parameters by the dog trainer, correct the first working parameters based on the motion monitoring information, and send the corrected second working parameters to the dog trainer; wherein, the motion monitoring information is monitored by the electronic collar; wherein, both the first working parameters and the second working parameters include at least one of sound signal parameters, vibration signal parameters, and electric shock signal parameters.

[0044] An embodiment of the present invention further discloses an electronic device, including: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, and the processor executes the computer program to implement the method as described in the foregoing embodiment.

[0045] An embodiment of the present invention further discloses a computer storage medium storing a computer program, and the computer program is executed by a processor to implement the method as described in the foregoing embodiment.

[0046] An embodiment of the present invention further discloses a computer program product containing computer code, and when the computer code is executed by a processor of an electronic device, it implements the method as described in the foregoing embodiment.

[0047] The above computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or apparatuses, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0048] It should be understood that various forms of the processes shown above may be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention may be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0049] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A remote intelligent monitoring and analysis method for a dog training device, characterized in that: The method comprises the following steps: Receive dog training device information, electronic collar information, pet dog attribute information, and corresponding training target information input by the user, and associate the above information with the user account for storage; receiving a start signal of a dog training device and an electronic collar associated with the dog training device, as well as the training target information selected this time, generating a set of first working parameters of the dog training device according to the attribute information of the pet dog and the training target information, and sending the first working parameters to the dog training device; Receive the movement monitoring information of the pet dog when the dog training device executes the first working parameter, correct the first working parameter based on the movement monitoring information, and send the corrected second working parameter to the dog training device; wherein the first working parameter and the second working parameter both include at least one of a sound signal parameter, a vibration signal parameter, and an electric shock signal parameter.

2. A remote intelligent monitoring and analysis method for dog training device according to claim 1, characterized in that: The step of generating a set of first working parameters of the dog training device according to the attribute information of the pet dog and the training target information includes: A set of third working parameters is obtained by matching the attribute information of the pet dog with the training target information; the attribute information includes but is not limited to the breed, age, weight, and personality characteristics of the pet dog; The training time of the pet dog is obtained according to the user account, a first weakening coefficient is obtained according to the training time, and the third working parameter is weakened using the first weakening coefficient to obtain the first working parameter.

3. The remote intelligent monitoring and analysis method for dog training device according to claim 1, characterized in that: The modifying the first operating parameter based on the motion monitoring information includes: Acquire the activation mode of the dog training device, the activation mode including a manual dog training mode and an automatic dog training mode, and determine the correction period according to the activation mode; wherein the automatic dog training mode is for multiple pet dogs with the same attribute information and the same training target information; The correction period corresponding to the manual dog training mode is a first period, the correction period corresponding to the automatic dog training mode is a second period, and the first period is greater than the second period; After the first operating parameter is sent to the dog training device and when the correction period is reached, the first operating parameter is corrected based on the motion monitoring information.

4. A remote intelligent monitoring and analysis method for dog training device according to claim 3, characterized in that: The modifying the first operating parameter based on the motion monitoring information includes: Drawing a motion trajectory of the pet dog based on the positioning information in the motion monitoring information, and dividing the motion trajectory into a strong training scene and a weak training scene according to the scene information in which the motion trajectory is located; Acquire multiple groups of first motion posture information and first dog training instruction information belonging to the strong training scene, and multiple groups of second motion posture information and second dog training instruction information belonging to the weak training scene; the first motion posture information, the first dog training instruction information and the second motion posture information and the second dog training instruction information are all included in the motion monitoring information; the first dog training instruction information in each group is adjacent to the first motion posture information in time, and the second dog training instruction information in each group is adjacent to the second motion posture information in time; Performing compliance evaluation on the first dog training instruction information and the first motion posture information, and on the second dog training instruction information and the second motion posture information, respectively obtaining a first compliance evaluation value and a second compliance evaluation value, and performing fusion processing on the first compliance evaluation value and the second compliance evaluation value to obtain a compliance evaluation value; If the compliance evaluation value is higher than a threshold, a second weakening coefficient is generated based on a difference between the compliance evaluation value and the threshold, and the first operating parameter is corrected using the second weakening coefficient to obtain the corrected second operating parameter.

5. A remote intelligent monitoring and analysis method for dog training device according to claim 4, characterized in that: The fusing the first compliance evaluation value and the second compliance evaluation value to obtain a compliance evaluation value includes: The first compliance evaluation value and the second compliance evaluation value are weightedly calculated to obtain the compliance evaluation value; wherein a first weight of the first compliance evaluation value is higher than a second weight of the second compliance evaluation value.

6. A remote intelligent monitoring and analysis system for dog training devices, the system comprising at least one processor and a memory, characterized in that: The computer code stored in the memory is called and executed by the processor to implement the following steps: Receive dog training device information, electronic collar information, pet dog attribute information, and corresponding training target information input by the user, and associate the above information with the user account for storage; receiving a start signal of a dog training device and an electronic collar associated with the dog training device, as well as the training target information selected this time, generating a set of first working parameters of the dog training device according to the attribute information of the pet dog and the training target information, and sending the first working parameters to the dog training device; Receive the movement monitoring information of the pet dog when the dog training device executes the first working parameter, correct the first working parameter based on the movement monitoring information, and send the corrected second working parameter to the dog training device; wherein the first working parameter and the second working parameter both include at least one of a sound signal parameter, a vibration signal parameter, and an electric shock signal parameter.

7. A dog training device remote intelligent monitoring and analysis system according to claim 6, characterized in that: The step of generating a set of first working parameters of the dog training device according to the attribute information of the pet dog and the training target information includes: A set of third working parameters is obtained by matching the attribute information of the pet dog with the training target information; the attribute information includes but is not limited to the breed, age, weight, and personality characteristics of the pet dog; The training time of the pet dog is obtained according to the user account, a first weakening coefficient is obtained according to the training time, and the third working parameter is weakened using the first weakening coefficient to obtain the first working parameter.

8. An electronic device comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 5.

9. A computer storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the method according to any one of claims 1 to 5.

10. A computer program product, characterized in that: The computer program product includes computer codes, and when the computer codes are executed by a processor of an electronic device, the method according to any one of claims 1 to 5 is implemented.

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