Remote Intelligent Monitoring and Analysis System and Method for Dog Training Devices

Abstract

This invention belongs to the field of information technology and provides a remote intelligent monitoring and analysis system and method for dog trainers. The method includes: receiving user-inputted information about the dog trainer, electronic collar, pet dog attributes, and several corresponding training target information; associating and storing this information with the user account; receiving the activation signal from the dog trainer and its associated electronic collar, as well as the selected training target information; generating a set of first operating parameters for the dog trainer based on the pet dog's attributes and training target information; and sending the first operating parameters to the dog trainer; receiving motion monitoring information of the pet dog during the execution of the first operating parameters; correcting the first operating parameters based on the motion monitoring information; and sending the corrected second operating parameters to the dog trainer. This invention enables online automatic optimization and adjustment of the dog trainer's operating parameters to improve training effectiveness.

Description

Technical Field

[0001] This invention relates to the field of information technology, and more specifically, to a remote intelligent monitoring and analysis system and method for dog trainers. Background Technology

[0002] With the improvement of people's living standards and the increasing demand for pet companionship, the number of pet dogs kept globally is constantly increasing. Pet owners are paying more and more attention to pet training, hoping to train their dogs in a scientific and effective way to develop good behavioral habits. Dog training devices can help pet owners convey commands more accurately during training, improving training effectiveness, and are therefore gaining popularity among pet owners.

[0003] Dog training devices are remote-controlled transmitters, paired with electronic collars worn by the dogs, which act as receivers. The training device sends a signal to drive a command, such as a sound, vibration, or electric shock. Upon receiving the signal, the electronic collar responds accordingly, reminding the dog of appropriate behavior. However, existing dog training devices use standardized settings for sound, vibration, and electric shock signals, which are largely unmodifiable. This results in different dogs receiving the same intensity of alerts (e.g., electric shocks), hindering personalized and efficient training.

[0004] Therefore, how to conveniently and individually adjust the working parameters of dog training devices is a technical problem that needs to be solved. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a remote intelligent monitoring and analysis method, system, electronic device, computer storage medium, and computer program product for dog training devices.

[0006] This invention discloses a remote intelligent monitoring and analysis method for dog training devices, the method comprising the following steps:

[0007] The system receives information from the user, including information about the dog trainer, electronic collar, pet dog attributes, and several corresponding training objectives. It then associates and stores this information with the user's account.

[0008] The system receives the activation signal from the dog trainer and its associated electronic collar, as well as the selected training target information. Based on the pet dog's attribute information and the training target information, it generates a set of first operating parameters for the dog trainer and sends the first operating parameters to the dog trainer.

[0009] The system receives motion monitoring information of the pet dog during the execution of the first working parameters by the dog trainer, corrects the first working parameters based on the motion monitoring information, and sends the corrected second working parameters 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.

[0010] This invention also discloses a remote intelligent monitoring and analysis system for dog trainers. The system includes at least one processor and a memory. The computer code stored in the memory is called and executed by the processor to achieve the following steps:

[0011] The system receives information from the user, including information about the dog trainer, electronic collar, pet dog attributes, and several corresponding training objectives. It then associates and stores this information with the user's account.

[0012] The system receives the activation signal from the dog trainer and its associated electronic collar, as well as the selected training target information. Based on the pet dog's attribute information and the training target information, it generates a set of first operating parameters for the dog trainer and sends the first operating parameters to the dog trainer.

[0013] The system receives motion monitoring information of the pet dog during the execution of the first working parameters by the dog trainer, corrects the first working parameters based on the motion monitoring information, and sends the corrected second working parameters 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.

[0014] The present invention also discloses 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, the processor executing the computer program to implement the method as described in any of the preceding methods.

[0015] The present invention also discloses a computer storage medium storing a computer program that is executed by a processor to implement the methods described in any of the preceding methods.

[0016] The present invention also discloses a computer program product containing computer code, which, when executed by a processor of an electronic device, implements the method described in any of the preceding methods.

[0017] The beneficial effects of this invention are at least as follows:

[0018] Compared to existing technologies, this invention allows for convenient automatic adjustment of the dog trainer's operating parameters via a mobile app. This automatic adjustment generates personalized operating parameters based on the dog's attribute information and training goals, solving the problem of existing dog trainers having uniform signal strength and failing to adapt to individual differences. This personalized setting effectively improves training results while reducing unnecessary stress or harm to the dog. Furthermore, this invention can monitor the dog's movement and behavioral responses in real time via an electronic collar, dynamically correcting the trainer's operating parameters to ensure the accuracy and effectiveness of the training signal. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a remote intelligent monitoring and analysis method for a dog trainer disclosed in an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of the structure of a remote intelligent monitoring and analysis system for a dog trainer disclosed in an embodiment of the present invention. Detailed Implementation

[0022] The following specific embodiments illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] Furthermore, the technical features involved in the different embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0024] like Figure 1 As shown, to address the aforementioned technical problems, this invention discloses a remote intelligent monitoring and analysis method for dog trainers, the method comprising the following steps:

[0025] S100 receives information from the user, including information about the dog trainer, electronic collar, pet dog attributes, and several corresponding training objectives, and stores this information in association with the user's account.

[0026] The solution of this invention is implemented in the cloud, with a corresponding APP. Users can register an account on the APP and enter information about the dog trainer, electronic collar, pet dog attributes, and several corresponding training goals. The dog trainer information includes hardware information such as the model and serial number of the dog trainer; the electronic collar information includes hardware information such as the model and serial number of the electronic collar. The pet dog attribute information includes the breed, age, weight, and personality traits (such as activity level and sensitivity). The training goals are the training objectives the user wishes to achieve, such as reducing barking, improving obedience, and correcting jumping behavior.

[0027] After the user enters the information, the app associates this information with the user's account and stores it in the cloud or local database for easy access and management later.

[0028] S200: Receive the start signal from the dog trainer and its associated electronic collar, as well as the selected training target information. Based on the pet dog's attribute information and the training target information, generate a set of first operating parameters for the dog trainer and send the first operating parameters to the dog trainer.

[0029] After completing the aforementioned information entry, the user should also connect the dog trainer and its associated electronic collar to the mobile phone on which the app is installed, such as via Bluetooth or Wi-Fi. This way, when the user needs to train their dog, they can manually activate the dog trainer and its associated electronic collar, and the cloud will receive the activation signals from both, as well as the training target information selected by the user.

[0030] The cloud platform generates a set of initial operating parameters, or first operating parameters, based on the dog's attributes (such as breed, age, weight, and personality traits) and training goals (such as reducing barking). These parameters include: sound signal parameters (type, volume, and frequency of the sound); vibration signal parameters (intensity and duration of vibration); and electric shock signal parameters (intensity and duration of the electric shock). The generated first operating parameters are then transmitted wirelessly (such as via Bluetooth or Wi-Fi) to the dog trainer and electronic collar. When the user trains their dog using the dog trainer, these first operating parameters are executed.

[0031] It is understood that the dog trainer in this invention can be either a manual or an automatic dog trainer. For a manual dog trainer, the user presses function buttons on the trainer to send different sound signals, vibration signals, and electric shock signals to the electronic collar. The operating parameters sent from the cloud are used to adjust the intensity or type of these parameters. For example, the initial first setting of the manual dog trainer corresponds to a shock intensity of level I and a duration of 1 second, while the operating parameters sent from the cloud are: the shock intensity corresponding to the first setting is level II and the duration is 0.5 seconds. That is, after adjustment by the cloud, the actual operating parameters corresponding to different settings change.

[0032] As for automatic dog trainers, they can simultaneously monitor the training commands issued by the user and analyze whether the dog has responded to the command through video monitoring or motion data transmitted via a connected electronic collar. If the dog does not respond or responds slowly, a control command can be generated according to the aforementioned cloud-sent parameters. This control command is used by the electronic collar to perform a corresponding action to remind the dog. Similar to manual dog trainers, the cloud-sent parameters are also used to adjust the initial operating parameters of the automatic dog trainer, which will not be elaborated further.

[0033] S300, receiving motion monitoring information of the pet dog during the execution of the first working parameters by the dog trainer, correcting the first working parameters based on the motion monitoring information, and sending the corrected second working parameters 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.

[0034] During the execution of the first working parameters by the dog trainer, the electronic collar monitors the dog's movement information in real time through built-in sensors (such as accelerometers and gyroscopes), including body posture, activity intensity, and behavioral frequency (some of this movement information can also be obtained from the dog trainer itself). This movement information is wirelessly transmitted to the dog trainer and / or a mobile app, which then uploads it to the cloud. The cloud analyzes the dog's response to the first working parameters based on the movement information and adjusts them accordingly to obtain the second working parameters. For example, if the dog overreacts to the current signal (e.g., accelerates running and jumping after being stimulated), it indicates the stimulus intensity is too high, and the signal intensity needs to be reduced. If the dog underreacts to the current signal (e.g., its original movement state does not change significantly), it indicates the training effect has not been achieved, and the signal intensity needs to be increased. The second working parameters are then sent to the dog trainer and the electronic collar to adjust the training strategy in real time.

[0035] Compared to existing technologies, this invention allows for convenient automatic adjustment of the dog trainer's operating parameters via a mobile app. This automatic adjustment generates personalized operating parameters based on the dog's attribute information and training goals, solving the problem of existing dog trainers having uniform signal strength and failing to adapt to individual differences. This personalized setting effectively improves training results while reducing unnecessary stress or harm to the dog. Furthermore, this invention can monitor the dog's movement and behavioral responses in real time via an electronic collar, dynamically correcting the trainer's operating parameters to ensure the accuracy and effectiveness of the training signal.

[0036] Optionally, the step of generating a first set of operating parameters for the dog trainer based on the attribute information of the pet dog and the training target information includes:

[0037] A third set of working parameters is derived by matching the pet dog's attribute information with the training target information; the attribute information includes, but is not limited to, the pet dog's breed, age, weight, and personality traits.

[0038] The training duration of the pet dog is obtained based on the user account. A first weakening coefficient is obtained by matching the training duration. The first weakening coefficient is used to weaken the third working parameter to obtain the first working parameter.

[0039] In this embodiment, based on various attributes of the pet dog, such as breed, age, weight, and personality traits (e.g., high activity level or high sensitivity), as well as the user's desired training goals (e.g., reducing barking, improving obedience), a set of corresponding third working parameters is obtained through pre-set matching rules or algorithms. For example, for a more sensitive small dog, when training to reduce barking, the initial matched sound signal could be a gentle cue tone with moderate volume and low frequency; the electric shock signal intensity could be relatively weak and short-lasting. It is understood that a pre-established correlation is made between the working parameters and the attribute and training goal information, and this correlation can be queried subsequently; alternatively, a pre-trained model based on machine learning algorithms can be used to process the pet dog's attribute and training goal information to predict the corresponding working parameters.

[0040] Meanwhile, the aforementioned third working parameter is actually based on standard or general working parameters for similar breeds of dogs under the same training objectives, representing a general situation. However, some dogs may have already received relevant training, and their working parameters should be determined more individually. Specifically:

[0041] The training duration of the pet dog can be retrieved based on the user account. For pet dogs with some training experience, relatively weakened stimulus signals can usually be used for training. This achieves the training effect while reducing unnecessary stimulation to the pet dog. A first weakening coefficient is obtained based on pre-set rules and the training duration. For example, if the pet dog's training duration 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 previously obtained third working parameter. For example, if the electric shock signal intensity in the third working parameter is originally set to level 5, after processing with the first weakening coefficient of 0.8, the electric shock signal intensity in the first working parameter becomes level 4. Similarly, other parameters such as sound signal parameters and vibration signal parameters are also adjusted accordingly to obtain the first working parameter suitable for the pet dog's current training stage. In this way, during training, the stimulus signals emitted by the dog trainer will be relatively gentle for pet dogs that have received some training, better meeting their training needs.

[0042] It should be noted that weakening sound signal parameters can be implemented in various ways, including but not limited to weakening volume and weakening sound content. For example, weakening sound content could involve replacing music that strongly stimulates a dog with gentle music that provides a weaker stimulus.

[0043] Optionally, the step of correcting the first operating parameter based on the motion monitoring information includes:

[0044] The activation mode of the dog trainer is obtained, including a manual dog training mode and an automatic dog training mode, and a correction cycle is determined 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;

[0045] The correction period corresponding to the manual dog training mode is the first cycle, and the correction period corresponding to the automatic dog training mode is the second cycle, with the first cycle being longer than the second cycle.

[0046] After the first operating parameter is sent to the dog trainer and the correction cycle is reached, the first operating parameter is corrected based on the motion monitoring information.

[0047] In this embodiment, the cloud determines whether the currently activated dog trainer is in manual or automatic 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 manual dog trainers and automatic dog trainers).

[0048] In manual dog training mode, users rely entirely on manually operating the function buttons on the training device to selectively send sound, vibration, or electric shock signals to the electronic collar worn by the dog, thereby guiding the dog to perform the desired behavior. Automatic dog training mode is suitable for commercial dog training facilities. In this mode, an automatic training device is placed in the training space. This device monitors the compliance of multiple dogs of the same type (i.e., those with identical attribute information and training goals) with the trainer's commands. If it detects that a dog does not respond promptly or its response does not meet expectations (subsequent movement posture information can also be directly collected by the automatic training device), the device will automatically generate control commands based on cloud-set parameters, driving the electronic collar to perform the corresponding action and reminding the dog to adjust its behavior, thus achieving highly efficient dog training.

[0049] Because manual and automatic dog training modes differ significantly in operation and application scenarios, this invention sets different correction cycles for adjusting the first working parameter for different training modes. Specifically:

[0050] In manual dog training mode, the corresponding adjustment cycle is the first cycle. Because in this mode, the user manually operates the dog trainer, and the timing, frequency, and intensity of stimulation for the dog are greatly influenced by the user's subjective factors, the dog's behavioral responses are also closely related to the user's operating rhythm. Therefore, it is unnecessary to adjust the first operating parameters too frequently based on the dog's movement monitoring information; for this reason, the first cycle is set relatively long. For example, the operating parameters are adjusted weekly based on accumulated movement monitoring information.

[0051] The automatic dog training mode corresponds to the second correction cycle, which is significantly shorter than the first. This is because the automatic dog training mode needs to continuously and in real-time monitor the behavioral dynamics of multiple dogs with similar characteristics and training goals, and react promptly. To ensure the accuracy and effectiveness of the training, the primary operating parameters must be frequently optimized and adjusted based on the dogs' real-time movement monitoring information. For example, the operating parameters are checked and corrected every half day based on the movement monitoring information of that half-day. This setting allows the automatic dog training mode to better adapt to the complex behavioral changes of multiple dogs, optimize training strategies in a timely manner, and improve the overall training effect. By setting differentiated correction cycles based on different activation modes, the operating parameters of the dog trainer can be dynamically managed more scientifically and rationally, maximizing the satisfaction of the needs of different training scenarios and dog groups. Of course, the movement monitoring information at this time includes the movement monitoring information of multiple dogs, and the subsequent evaluation results are also for multiple dogs.

[0052] Optionally, the step of correcting the first operating parameter based on the motion monitoring information includes:

[0053] The movement trajectory of the pet dog is drawn based on the positioning information in the movement monitoring information, and the movement trajectory is divided into strong training scenario and weak training scenario according to the scene information of the movement trajectory.

[0054] Acquire multiple sets of first motion posture information and first dog training command information belonging to the strong training scenario, and multiple sets of second motion posture information and second dog training command information belonging to the weak training scenario; the first motion posture information, first dog training command information, and second motion posture information and second dog training command information are all included in the motion monitoring information; the first dog training command information in each group is adjacent to the first motion posture information in time, and the second dog training command information in each group is adjacent to the second motion posture information in time;

[0055] 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 are evaluated for compliance, and a first compliance evaluation value and a second compliance evaluation value are obtained respectively. The first compliance evaluation value and the second compliance evaluation value are fused to obtain a compliance evaluation value.

[0056] If the compliance assessment value is higher than the threshold, a second weakening coefficient is generated based on the difference between the compliance assessment value and the threshold. The first working parameter is then corrected using the second weakening coefficient to obtain the corrected second working parameter.

[0057] In this embodiment, during the execution of the first working parameter, the cloud continuously acquires the dog's motion monitoring information. From this information, multiple sets of paired dog training commands (collected by the microphone on the dog trainer and added to the motion monitoring information) and motion posture information—that is, the dog's actual motion posture after the user issues a training command—can be extracted. A comprehensive analysis and evaluation of this information yields the training effect on the dog. If the training effect is good, the aforementioned first working parameter is weakened, resulting in a revised second working parameter. This reduces the training stimulation on the dog. Specifically:

[0058] First, location information from motion monitoring is used to map the dog's movement trajectory. By analyzing the dog's movement paths in different locations and combining this with scene information such as the surrounding environment, the movement trajectory is divided into strong training scenarios and weak training scenarios. Strong training scenarios may refer to environments where the dog is in areas with more external interference and greater training difficulty, such as areas in parks with many people and other animals; weak training scenarios may refer to places with less interference and relatively simple and familiar environments, such as one's own yard or the training grounds of a dog training institution.

[0059] Next, the cloud retrieves specific information groups belonging to strong and weak training scenarios from the motion monitoring information. For strong training scenarios, it retrieves multiple sets of first motion posture information (such as a pet dog's standing, running, and jumping postures) and the first dog training command information adjacent to them in time (commands issued by the user through the dog trainer, such as issuing the "sit" command). Similarly, for weak training scenarios, it retrieves multiple sets of second motion posture information and the second dog training command information adjacent to them in time.

[0060] For multiple sets of first dog training commands and first movement posture information in intensive training scenarios, and second dog training commands and second movement posture information in intensive training scenarios, the system performs compliance assessments separately. The compliance assessment primarily determines whether the dog's actual movement posture conforms to the command after receiving it. For example, if the command is "sit," and the dog actually sits down within the specified time, then the compliance is high. Specific algorithms analyze and integrate this information to obtain a first compliance assessment value (corresponding to intensive training scenarios) and a second compliance assessment value (corresponding to intensive training scenarios). These two assessment values ​​are then fused, using methods such as weighted averaging, to obtain a comprehensive compliance assessment value. This assessment value reflects the dog's overall compliance with training commands in different scenarios.

[0061] Then, the cloud-based system compares the compliance assessment value with a preset threshold. If the compliance assessment value is higher than the threshold, it indicates that the dog's compliance with training commands is good and the training effect is good. The current first operating parameter (such as sound signal intensity, electric shock intensity, etc.) is too strong for the dog or unnecessary; such strong stimulation can achieve the training effect without it. At this point, a second weakening coefficient is generated based on the difference between the compliance assessment value and the threshold. The larger the difference, the stronger the current operating parameter is relative to the dog's actual needs, and the smaller the weakening coefficient is. Then, this second weakening coefficient is used to correct the first operating parameter, for example, by reducing or adjusting the intensity and duration of parameters such as sound signal, vibration signal, and electric shock signal, thus obtaining the corrected second operating parameter. This makes the operating parameters of the dog trainer more closely match the dog's actual training needs, avoiding overstimulation while ensuring training effectiveness.

[0062] Optionally, the step of fusing the first compliance assessment value and the second compliance assessment value to obtain a compliance assessment value includes:

[0063] The first compliance assessment value and the second compliance assessment value are weighted and calculated to obtain the compliance assessment value; wherein, the first weight of the first compliance assessment value is higher than the second weight of the second compliance assessment value.

[0064] In this embodiment, the compliance assessment results of pet dogs in a strong training scenario are more valuable than those in a weak training scenario. Therefore, the present invention sets the first weight of the first compliance assessment value to be higher than the second weight of the second compliance assessment value.

[0065] like Figure 2 As shown in the figure, this invention also discloses a remote intelligent monitoring and analysis system for dog trainers. The system includes at least one processor and a memory. The computer code stored in the memory is called and executed by the processor to achieve the following steps:

[0066] The system receives information from the user, including information about the dog trainer, electronic collar, pet dog attributes, and several corresponding training objectives. It then associates and stores this information with the user's account.

[0067] The system receives the activation signal from the dog trainer and its associated electronic collar, as well as the selected training target information. Based on the pet dog's attribute information and the training target information, it generates a set of first operating parameters for the dog trainer and sends the first operating parameters to the dog trainer.

[0068] The system receives motion monitoring information of the pet dog during the execution of the first working parameters by the dog trainer, corrects the first working parameters based on the motion monitoring information, and sends 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.

[0069] This invention also discloses 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 described in the foregoing embodiments.

[0070] This invention also discloses a computer storage medium storing a computer program that is executed by a processor to implement the methods described in the foregoing embodiments.

[0071] This invention also discloses a computer program product containing computer code, which, when executed by a processor of an electronic device, implements the method described in the foregoing embodiments.

[0072] The aforementioned computer-readable storage media may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination thereof. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0073] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0074] The specific embodiments described above do not constitute a limitation on the scope of protection of this 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 principles of this invention should be included within the scope of protection of this invention.

Claims

1. A remote intelligent monitoring and analysis method for dog training devices, characterized in that: The method includes the following steps: The system receives information from the user, including information about the dog trainer, electronic collar, pet dog attributes, and several corresponding training objectives. It then associates and stores this information with the user's account. The system receives the activation signal from the dog trainer and its associated electronic collar, as well as the selected training target information. Based on the pet dog's attribute information and the training target information, it generates a set of first operating parameters for the dog trainer and sends the first operating parameters to the dog trainer. The system receives motion monitoring information of a pet dog during the execution of the first working parameters by the dog trainer, corrects the first working parameters based on the motion monitoring information, and sends the corrected second working parameters 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; The step of correcting the first working parameter based on the motion monitoring information includes: The activation mode of the dog trainer is obtained, including a manual dog training mode and an automatic dog training mode, and a correction cycle is determined 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 the first cycle, and the correction period corresponding to the automatic dog training mode is the second cycle, with the first cycle being longer than the second cycle. After sending the first working parameters to the dog trainer and reaching the correction cycle, the first working parameters are corrected based on the motion monitoring information; The step of correcting the first working parameter based on the motion monitoring information includes: The movement trajectory of the pet dog is drawn based on the positioning information in the movement monitoring information, and the movement trajectory is divided into strong training scenario and weak training scenario according to the scene information of the movement trajectory. Acquire multiple sets of first motion posture information and first dog training command information belonging to the strong training scenario, and multiple sets of second motion posture information and second dog training command information belonging to the weak training scenario; the first motion posture information, first dog training command information, and second motion posture information and second dog training command information are all included in the motion monitoring information; the first dog training command information in each group is adjacent to the first motion posture information in time, and the second dog training command information in each group is adjacent to the second motion posture information in time; 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 are evaluated for compliance, and a first compliance evaluation value and a second compliance evaluation value are obtained respectively. The first compliance evaluation value and the second compliance evaluation value are fused to obtain a compliance evaluation value. If the compliance assessment value is higher than the threshold, a second weakening coefficient is generated based on the difference between the compliance assessment value and the threshold. The first working parameter is then corrected using the second weakening coefficient to obtain the corrected second working parameter.

2. The remote intelligent monitoring and analysis method for a dog training device according to claim 1, characterized in that: The step of generating a set of first operating parameters for the dog trainer based on the attribute information of the pet dog and the training target information includes: A third set of working parameters is derived by matching the pet dog's attribute information with the training target information; the attribute information includes, but is not limited to, the pet dog's breed, age, weight, and personality traits. The training duration of the pet dog is obtained based on the user account. A first weakening coefficient is obtained by matching the training duration. The first weakening coefficient is used to weaken the third working parameter to obtain the first working parameter.

3. The remote intelligent monitoring and analysis method for a dog training device according to claim 1, characterized in that: The process of fusing the first compliance assessment value and the second compliance assessment value to obtain a compliance assessment value includes: The first compliance assessment value and the second compliance assessment value are weighted and calculated to obtain the compliance assessment value; wherein, the first weight of the first compliance assessment value is higher than the second weight of the second compliance assessment value.

4. A remote intelligent monitoring and analysis system for dog trainers, 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 perform the following steps: The system receives information from the user, including information about the dog trainer, electronic collar, pet dog attributes, and several corresponding training objectives. It then associates and stores this information with the user's account. The system receives the activation signal from the dog trainer and its associated electronic collar, as well as the selected training target information. Based on the pet dog's attribute information and the training target information, it generates a set of first operating parameters for the dog trainer and sends the first operating parameters to the dog trainer. The system receives motion monitoring information of a pet dog during the execution of the first working parameters by the dog trainer, corrects the first working parameters based on the motion monitoring information, and sends the corrected second working parameters 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; The step of correcting the first working parameter based on the motion monitoring information includes: The activation mode of the dog trainer is obtained, including a manual dog training mode and an automatic dog training mode, and a correction cycle is determined 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 the first cycle, and the correction period corresponding to the automatic dog training mode is the second cycle, with the first cycle being longer than the second cycle. After sending the first working parameters to the dog trainer and reaching the correction cycle, the first working parameters are corrected based on the motion monitoring information; The step of correcting the first working parameter based on the motion monitoring information includes: The movement trajectory of the pet dog is drawn based on the positioning information in the movement monitoring information, and the movement trajectory is divided into strong training scenario and weak training scenario according to the scene information of the movement trajectory. Acquire multiple sets of first motion posture information and first dog training command information belonging to the strong training scenario, and multiple sets of second motion posture information and second dog training command information belonging to the weak training scenario; the first motion posture information, first dog training command information, and second motion posture information and second dog training command information are all included in the motion monitoring information; the first dog training command information in each group is adjacent to the first motion posture information in time, and the second dog training command information in each group is adjacent to the second motion posture information in time; 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 are evaluated for compliance, and a first compliance evaluation value and a second compliance evaluation value are obtained respectively. The first compliance evaluation value and the second compliance evaluation value are fused to obtain a compliance evaluation value. If the compliance assessment value is higher than the threshold, a second weakening coefficient is generated based on the difference between the compliance assessment value and the threshold. The first working parameter is then corrected using the second weakening coefficient to obtain the corrected second working parameter.

5. The remote intelligent monitoring and analysis system for a dog trainer according to claim 4, characterized in that: The step of generating a set of first operating parameters for the dog trainer based on the attribute information of the pet dog and the training target information includes: A third set of working parameters is derived by matching the pet dog's attribute information with the training target information; the attribute information includes, but is not limited to, the pet dog's breed, age, weight, and personality traits. The training duration of the pet dog is obtained based on the user account. A first weakening coefficient is obtained by matching the training duration. The first weakening coefficient is used to weaken the third working parameter to obtain the first working parameter.

6. 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, characterized in that: the processor executes the computer program to implement the method as claimed in any one of claims 1-3.

7. A computer storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the method as described in any one of claims 1-3.

8. A computer program product, characterized in that: The computer program product includes computer code, which, when executed by a processor of an electronic device, implements the method as described in any one of claims 1-3.

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