Domestic robot AI algorithm model synchronization method, device and equipment and storage medium

Abstract

The invention discloses a household robot AI algorithm model synchronization method, device and equipment and a storage medium, which are used for solving the problem that upgrading of an existing household robot AI algorithm model depends on manual operation and a centralized management strategy and is difficult to meet large-scale, high-efficiency and automatic upgrading requirements. The method comprises the following steps: acquiring position information of the domestic robot; receiving equipment information, an operation state and current home environment information sent by the home robot; judging the equipment state of the household robot according to the position information; matching a plurality of target AI algorithm models in a preset AI algorithm library according to the equipment state, the equipment information, the running state and the current home environment information; and synchronizing the target AI algorithm model to the household robot.

Description

Technical Field

[0001] The present invention relates to the technical field of household robots, and in particular to a household robot AI algorithm model synchronization method, device, equipment and storage medium. Background Art

[0002] With the development of the Internet of Things (IoT) and artificial intelligence (AI), the smart home market is experiencing explosive growth. More and more smart devices and sensors are being introduced into the construction of smart homes, and a large number of AI algorithms are being deployed in household robots in smart homes, such as sweeping robots, humanoid robots, quadruped robots, etc. These robots are equipped with many AI algorithms and have the ability to process large amounts of sensory data (such as images, sounds, and sensor data) and make real-time decisions.

[0003] Home scenarios place different requirements on the data processing capabilities of different robots, i.e. different AI algorithms, chips and sensors. For example, sweeping robots need to process indoor environmental data in real time to plan cleaning paths. Through built-in sensors and cameras, robots can sense the surrounding environment and perform path planning, obstacle avoidance and other operations to ensure efficient cleaning. Humanoid robots require more powerful data processing capabilities and more complex algorithms to achieve natural interaction with family members and perform a variety of tasks. They need to process a variety of sensory data including speech recognition, natural language processing (NLP), image recognition and motion control, and provide functions such as home services, companionship, and security monitoring. Quadruped robots also need to process sensory data such as images and sounds, and can move flexibly in complex home environments, avoid obstacles, execute predetermined patrol routes, and ensure timely responses in emergencies, such as the instant alarm function when abnormal situations are discovered.

[0004] As modern home scenarios become increasingly diversified and complex, the AI ​​algorithms used by robots in smart homes need to be constantly updated and iterated. However, due to factors such as the rapid iteration of AI algorithms and the diversity of terminal chip models, the complexity and workload of targeted upgrades of robots have increased significantly.

[0005] Traditional methods of solving terminal upgrade problems mainly rely on manual operations and centralized management strategies, which cannot meet the needs of large-scale, efficient and automated upgrades. Summary of the invention

[0006] The present invention provides a household robot AI algorithm model synchronization method, device, equipment and storage medium, which are used to solve the problem that the existing household robot AI algorithm model upgrade relies on manual operation and centralized management strategy, which is difficult to meet the large-scale, high-efficiency and automated upgrade requirements.

[0007] The present invention provides a method for synchronizing an AI algorithm model of a household robot, which is applied to a synchronization management platform. The method comprises:

[0008] Get the location information of the home robot;

[0009] Receiving device information, operating status and current home environment information sent by the home robot;

[0010] Determining a device state of the household robot according to the position information;

[0011] According to the device state, the device information, the operating state and the current home environment information, matching several target AI algorithm models in a preset AI algorithm library;

[0012] Synchronize the target AI algorithm model to the household robot.

[0013] Optionally, the step of obtaining the position information of the household robot includes:

[0014] Obtaining the propagation time of a signal sent by the household robot to a preset receiving end;

[0015] Calculating a transmission distance of the signal according to the propagation time;

[0016] The position information of the household robot is determined according to the transmission distance.

[0017] Optionally, the step of determining the device state of the household robot according to the position information includes:

[0018] Determine, based on the position information, whether the household robot is in a preset charging position or whether the static time has reached a preset threshold;

[0019] If not, determining that the device state of the household robot is a non-updatable state;

[0020] If so, it is determined that the device state of the household robot is an updateable state.

[0021] Optionally, the device information includes carrying capacity and computing resources; the step of synchronizing the target AI algorithm model to the household robot includes:

[0022] Compressing the target AI algorithm model according to the carrying capacity and the computing resources to obtain a target compressed AI algorithm model;

[0023] The target compression model is processed into blocks to obtain cloud model blocks;

[0024] Calculating the cloud hash value of each of the cloud model blocks;

[0025] Matching the target AI algorithm model on the household robot to obtain a terminal AI algorithm model;

[0026] The terminal AI algorithm model is processed into blocks to obtain terminal model blocks;

[0027] Calculating the terminal hash value of each terminal model block;

[0028] Compare the cloud hash value and the terminal hash value to determine the difference cloud model blocks;

[0029] Transmit data corresponding to the difference cloud model blocks to the household robot.

[0030] Optionally, after the step of synchronizing the target AI algorithm model to the household robot, the method further includes:

[0031] Receive a self-test report of the household robot.

[0032] The present invention also provides a household robot AI algorithm model synchronization device, which is applied to a synchronization management platform, and the device includes:

[0033] A position information acquisition module is used to obtain the position information of the household robot;

[0034] A device information, operating status and current home environment information receiving module, used to receive the device information, operating status and current home environment information sent by the home robot;

[0035] A device status determination module, used to determine the device status of the household robot according to the position information;

[0036] A matching module, configured to match a plurality of target AI algorithm models in a preset AI algorithm library according to the device state, the device information, the operating state and the current home environment information;

[0037] A synchronization module is used to synchronize the target AI algorithm model to the household robot.

[0038] Optionally, the location information acquisition module includes:

[0039] The propagation time acquisition submodule is used to acquire the propagation time of the signal sent by the household robot to the preset receiving end;

[0040] A transmission distance calculation submodule, used to calculate the transmission distance of the signal according to the propagation time;

[0041] A position information determination submodule is used to determine the position information of the household robot according to the transmission distance.

[0042] Optionally, the device status judgment module includes:

[0043] A judgment submodule, used to judge whether the household robot is in a preset charging position or whether the static time reaches a preset threshold according to the position information;

[0044] a non-updatable state determination submodule, for determining, if not, that the device state of the household robot is a non-updatable state;

[0045] The updatable state determination submodule is used to determine, if yes, that the device state of the household robot is an updatable state.

[0046] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0047] The memory is used to store program code and transmit the program code to the processor;

[0048] The processor is used to execute the household robot AI algorithm model synchronization method as described in any one of the above items according to the instructions in the program code.

[0049] The present invention also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the household robot AI algorithm model synchronization method as described in any of the above items.

[0050] It can be seen from the above technical solutions that the present invention has the following advantages: the present invention obtains the location information of the household robot; receives the operating status and current home environment information sent by the household robot; determines the device status of the household robot according to the location information; matches several target AI algorithm models in the preset AI algorithm library according to the device status, operating status and current home environment information; and synchronizes the target AI algorithm model to the household robot. This achieves large-scale, efficient and automated AI algorithm model upgrades. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0052] Figure 1 A flowchart of the steps of a method for synchronizing an AI algorithm model of a household robot provided by an embodiment of the present invention;

[0053] Figure 2 A flowchart of a method for synchronizing an AI algorithm model of a household robot provided by another embodiment of the present invention;

[0054] Figure 3 An overall architecture diagram of an AI algorithm for a household robot provided by an embodiment of the present invention;

[0055] Figure 4 A structural block diagram of a household robot AI algorithm model synchronization device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The embodiments of the present invention provide a household robot AI algorithm model synchronization method, device, equipment and storage medium, which are used to solve the problem that the existing household robot AI algorithm model upgrade relies on manual operation and centralized management strategies, which is difficult to meet the large-scale, high-efficiency and automated upgrade requirements.

[0057] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] See also Figure 1 , Figure 1 A flowchart of the steps of a household robot AI algorithm model synchronization method provided by an embodiment of the present invention.

[0059] A household robot AI algorithm model synchronization method provided by the present invention is applied to a synchronization management platform and may specifically include the following steps:

[0060] Step 101, obtaining location information of the household robot;

[0061] In the embodiment of the present invention, the household robot may be a sweeping robot, a humanoid robot, a quadruped robot, etc. Such robots are equipped with many AI algorithms and have the ability to process a large amount of sensory data (such as images, sounds, and sensor data) and make real-time decisions.

[0062] In a specific implementation, to realize remote data update of a household robot, it is first necessary to obtain the location information of the household robot to determine whether the household robot is in an updateable state.

[0063] Step 102, receiving device information, operating status and current home environment information sent by the home robot;

[0064] In the specific implementation, the device information of the household robot determines the AI ​​algorithm model it can use. The device information can be processor information, memory and storage information, power supply and battery life, sensor type and performance, etc. The synchronization management platform provides the corresponding AI algorithm model based on the robot's device information.

[0065] The operating status information of the household robot (such as power, workload) and the current household environment information (such as light, noise level) can dynamically adjust the parameters of the AI ​​algorithm model or enable different sub-models to achieve the best performance and energy efficiency. Specifically, dynamic adjustment can be performed according to the actual situation of the selected household robot, and the embodiment of the present invention does not impose specific restrictions on this.

[0066] Step 103, determining the device status of the household robot according to the location information;

[0067] In an embodiment of the present invention, in order not to affect the normal operation of the household robot, the AI ​​algorithm model can be sent using an idle time sending strategy, so the device status of the household robot can be whether it is charging at the terminal charging station or in a long-term static state, etc.

[0068] Step 104, matching several target AI algorithm models in a preset AI algorithm library according to the device status, device information, operating status and current home environment information;

[0069] In an embodiment of the present invention, after obtaining the device status, device information, operating status and current home environment information, several target AI algorithm models can be matched in the preset AI algorithm library.

[0070] Step 105, synchronizing the target AI algorithm model to the household robot.

[0071] After determining the target AI algorithm model for updating the household robot, the target AI algorithm model can be synchronized to the household robot to implement the AI ​​algorithm model update of the household robot.

[0072] The present invention obtains the location information of the household robot; receives the operating status and current home environment information sent by the household robot; determines the device status of the household robot according to the location information; matches several target AI algorithm models in the preset AI algorithm library according to the device status, operating status and current home environment information; and synchronizes the target AI algorithm model to the household robot. This achieves large-scale, efficient and automated AI algorithm model upgrades.

[0073] See also Figure 2 , Figure 2 This is a flowchart of a method for synchronizing an AI algorithm model for a household robot provided by another embodiment of the present invention. Specifically, the following steps may be included:

[0074] Step 201, obtaining the propagation time of the signal sent by the household robot to the preset receiving end;

[0075] Step 202, calculating the transmission distance of the signal according to the propagation time;

[0076] Step 203, determining the location information of the household robot according to the transmission distance;

[0077] In an embodiment of the present invention, a ToA positioning algorithm based on ranging can be used to measure the propagation time of a signal emitted by a household robot to multiple preset receiving ends, thereby calculating the transmission distance of the signal to each receiving end based on the propagation time, and then determining the position of the signal based on the transmission distance, that is, determining the current position of the household robot.

[0078] Step 204, receiving device information, operating status and current home environment information sent by the home robot;

[0079] In the specific implementation, the device information of the household robot determines the AI ​​algorithm model it can use. The device information can be processor information, memory and storage information, power supply and battery life, sensor type and performance, etc. The synchronization management platform matches the corresponding AI algorithm model according to the device information of the robot.

[0080] The operating status information of the household robot (such as power, workload) and the current household environment information (such as light, noise level) can dynamically adjust the parameters of the AI ​​algorithm model or enable different sub-models to achieve the best performance and energy efficiency. Specifically, dynamic adjustment can be performed according to the actual situation of the selected household robot, and the embodiment of the present invention does not impose specific restrictions on this.

[0081] Step 205, determining the device status of the household robot according to the location information;

[0082] In an embodiment of the present invention, in order not to affect the normal operation of the household robot, the AI ​​algorithm model can be sent using an idle time sending strategy, so the device status of the household robot can be whether it is charging at the terminal charging station or in a long-term static state, etc.

[0083] In one example, the step of determining the device state of the household robot according to the location information includes:

[0084] S51, judging whether the household robot is in a preset charging position or whether the static time reaches a preset threshold according to the position information;

[0085] S52, if not, determining that the device state of the household robot is a non-updatable state;

[0086] S53: If yes, determine that the device status of the household robot is an updateable status.

[0087] In a specific implementation, the position information of the household robot can be used to determine whether the household robot is in a preset charging position or has been stationary for a long time. If so, it indicates that the current transfer of the AI ​​algorithm model to the household robot will not affect the normal operation of the household robot, and the household robot can be determined to be in an updateable state. Otherwise, the household robot is determined to be in a non-updateable state.

[0088] Step 206, matching several target AI algorithm models in a preset AI algorithm library according to the device status, device information, operating status and current home environment information;

[0089] In an embodiment of the present invention, an AI algorithm library may be pre-built on the synchronization management platform to store the latest AI algorithm models available to the household robot.

[0090] In one example, the AI ​​algorithm library can be classified by algorithm functional modules: a library containing multiple AI algorithm modules is established, each module is targeted at a specific task type, such as:

[0091] Image recognition: including object detection, scene understanding, face recognition and other functions;

[0092] Language recognition: handles tasks such as speech recognition and speech synthesis;

[0093] Sentiment analysis: Analyzes the emotional tendencies in text or speech, which is applicable to emotional understanding in human-computer interaction;

[0094] Natural Language Processing (NLP): supports language understanding, semantic parsing, dialogue generation, etc.

[0095] Path planning: used for robot navigation and obstacle avoidance in dynamic environments.

[0096] At the same time, a standardized interface is designed for each functional module to ensure seamless combination and integration between modules and support flexible configuration and deployment.

[0097] In an embodiment of the present invention, after obtaining the device status, device information, operating status and current home environment information, several target AI algorithm models can be matched in the preset AI algorithm library.

[0098] Step 207, synchronizing the target AI algorithm model to the household robot.

[0099] After determining the target AI algorithm model for updating the household robot, the target AI algorithm model can be synchronized to the household robot to implement the AI ​​algorithm model update of the household robot.

[0100] In one example, the device information includes carrying capacity and computing resources; the steps of synchronizing the target AI algorithm model to the household robot include:

[0101] S71, compressing the target AI algorithm model according to the carrying capacity and computing resources to obtain a target compressed AI algorithm model;

[0102] S72, dividing the target compression model into blocks to obtain cloud model blocks;

[0103] S73, calculating the cloud hash value of each cloud model block;

[0104] S74, matching the target AI algorithm model on the home robot to obtain the terminal AI algorithm model;

[0105] S75, performing block processing on the terminal AI algorithm model to obtain terminal model blocks;

[0106] S76, calculating the terminal hash value of each terminal model block;

[0107] S77, comparing the cloud hash value and the terminal hash value to determine the difference cloud model blocks;

[0108] S78, transmitting data corresponding to the difference cloud model blocks to the household robot.

[0109] In the implementation of the present invention, the volume of the target AI algorithm model can be appropriately reduced through a model compression algorithm based on the carrying capacity and computing resources of the household robot, so as to reduce the carrying burden of the household robot and ensure that the best performance can be achieved in a specific hardware environment. Among them, the model compression algorithm can include model quantization, model pruning, model distillation, etc.

[0110] Then, during transmission, a differential synchronization technology based on hash verification can be used. Specifically, the target compression model can be processed in blocks to obtain cloud model blocks. And the cloud hash value of each cloud model block is calculated; at the same time, the corresponding terminal AI algorithm model is obtained in the household robot, and then the terminal AI algorithm model is processed in blocks to obtain terminal model blocks, and the terminal model blocks are hashed to obtain terminal hash values. Finally, the cloud hash value and the terminal hash value are compared one by one to identify the difference between the two, so as to determine the difference cloud model block, and then the data corresponding to the difference cloud model block is transmitted to the household robot. Through differential synchronization technology, the amount of data that needs to be transmitted can be greatly reduced, especially when the network bandwidth is limited or the update content is small, it can significantly improve the efficiency of data transmission and reduce the occupation of network resources.

[0111] Furthermore, in order to ensure the integrity and efficiency of data transmission, the network connection status may be combined to determine whether to start the algorithm update operation, thereby avoiding large-scale data transmission in an unfavorable environment.

[0112] Furthermore, the synchronization management platform can also integrate incremental learning technology, that is, automatically optimize the transmission and update strategy according to the usage and feedback data of the terminal device (home robot). For example, by analyzing the usage frequency and resource usage of the terminal device, the high-usage devices are updated first, and the lightweight algorithm version that better meets the hardware conditions of the device is pushed.

[0113] Furthermore, after synchronization is completed, the home robot will immediately start the self-test program. The self-test program will perform a series of functional tests and performance tests on the new AI algorithm model to ensure that it can work properly and achieve the expected results, and send a self-test report to the synchronization management platform.

[0114] For ease of understanding, the embodiment of the present invention is described by taking a household robot as a sweeping robot as an example:

[0115] The synchronization management platform obtains the latest AI algorithm model in the cloud, and performs algorithm compression based on the hardware type of the sweeping robot, including algorithm quantization and cropping. Then, the spatial positioning technology based on ranging, such as the ToA positioning algorithm, is used to monitor the position of the sweeping robot in the home in real time. Combined with the position data and movement trajectory of the sweeping robot, the synchronization management platform can intelligently determine its working status: if the position of the sweeping robot is constantly changing, and the movement trajectory meets the characteristics of the cleaning operation (such as moving along the wall, going back and forth between rooms, etc.), the sweeping robot is judged to be in a working state; on the contrary, if the sweeping robot stays in the same position for a long time (such as at the charging station), and the position coordinates do not change significantly, the sweeping robot is judged to be in an idle state, and the synchronization management platform transmits the latest AI algorithm model to the sweeping robot. The specific transmission method can be a differential synchronization technology based on hash verification. After the synchronization is completed, the sweeping robot will immediately start the self-test program. The self-test program will perform a series of functional and performance tests on the new AI algorithm model to ensure that it can work normally and achieve the expected results. If any problems are found during the self-check process, the sweeping robot will send an error report to the synchronization management platform and automatically roll back to the last stable algorithm version.

[0116] The present invention obtains the location information of the household robot; receives the operating status and current home environment information sent by the household robot; determines the device status of the household robot according to the location information; matches several target AI algorithm models in the preset AI algorithm library according to the device status, operating status and current home environment information; and synchronizes the target AI algorithm model to the household robot. This achieves large-scale, efficient and automated AI algorithm model upgrades.

[0117] See also Figure 3 , Figure 3 An overall architecture diagram of an AI algorithm for a household robot provided in an embodiment of the present invention.

[0118] The synchronization management platform can select and adapt AI algorithms. Based on the collected device information (such as CPU, GPU, memory, etc.), it uses matching algorithms to automatically select suitable AI algorithm types (such as image recognition, language recognition, path planning, NLP, sentiment analysis, etc.) from the AI ​​algorithm library. Based on the robot's carrying capacity and computing resources, it uses model compression algorithms (such as model distillation, model quantization, model pruning, etc.) to appropriately reduce the model size to reduce the robot's carrying burden and ensure optimal performance in a specific hardware environment. Finally, the compressed AI algorithm model is transmitted to household robots (including quadruped robots, humanoid robots, sweeping robots, etc.) by sending algorithms during idle time.

[0119] See also Figure 4 , Figure 4 A structural block diagram of a household robot AI algorithm model synchronization device provided in an embodiment of the present invention.

[0120] An embodiment of the present invention provides a household robot AI algorithm model synchronization device, which is applied to a synchronization management platform. The device includes:

[0121] The position information acquisition module 401 is used to acquire the position information of the household robot;

[0122] The device information, operation status and current home environment information receiving module 402 is used to receive the device information, operation status and current home environment information sent by the home robot;

[0123] The device status determination module 403 is used to determine the device status of the household robot according to the position information;

[0124] A matching module 404 is used to match a plurality of target AI algorithm models in a preset AI algorithm library according to device status, device information, operating status and current home environment information;

[0125] The synchronization module 405 is used to synchronize the target AI algorithm model to the household robot.

[0126] In the embodiment of the present invention, the location information acquisition module 401 includes:

[0127] The propagation time acquisition submodule is used to acquire the propagation time of the signal sent by the household robot to the preset receiving end;

[0128] A transmission distance calculation submodule is used to calculate the transmission distance of the signal according to the propagation time;

[0129] The position information determination submodule is used to determine the position information of the household robot according to the transmission distance.

[0130] In the embodiment of the present invention, the device status determination module 403 includes:

[0131] A judgment submodule, used to judge whether the household robot is in a preset charging position or whether the static time reaches a preset threshold according to the position information;

[0132] The non-updatable state determination submodule is used to determine if the device state of the household robot is a non-updatable state;

[0133] The updatable state determination submodule is used to determine if the device state of the household robot is an updatable state.

[0134] In the embodiment of the present invention, the device information includes the carrying capacity and the computing resources; the synchronization module 405 includes:

[0135] The compression submodule is used to compress the target AI algorithm model according to the carrying capacity and computing resources to obtain the target compressed AI algorithm model;

[0136] The first block processing submodule is used to perform block processing on the target compression model to obtain cloud model blocks;

[0137] The cloud hash value calculation submodule is used to calculate the cloud hash value of each cloud model block;

[0138] The terminal AI algorithm model matching submodule is used to match the target AI algorithm model on the home robot to obtain the terminal AI algorithm model;

[0139] The second block processing submodule is used to perform block processing on the terminal AI algorithm model to obtain terminal model blocks;

[0140] The terminal hash value calculation submodule is used to calculate the terminal hash value of each terminal model block;

[0141] The comparison submodule is used to compare the cloud hash value and the terminal hash value to determine the difference in cloud model blocks;

[0142] The transmission submodule is used to transmit the data corresponding to the difference cloud model blocks to the household robot.

[0143] In an embodiment of the present invention, it also includes:

[0144] The self-test report receiving module is used to receive the self-test report of the household robot.

[0145] An embodiment of the present invention further provides an electronic device, the device comprising a processor and a memory:

[0146] The memory is used to store the program code and transmit the program code to the processor;

[0147] The processor is used to execute the household robot AI algorithm model synchronization method of an embodiment of the present invention according to the instructions in the program code.

[0148] An embodiment of the present invention also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the household robot AI algorithm model synchronization method of an embodiment of the present invention.

[0149] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0150] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0151] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0152] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0153] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0154] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0155] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0156] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0157] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for synchronizing a household robot AI algorithm model, characterized in that: Applied to a synchronization management platform, the method includes: Get the location information of the home robot; Receiving device information, operating status and current home environment information sent by the home robot; Determining a device state of the household robot according to the position information; According to the device state, the device information, the operating state and the current home environment information, matching several target AI algorithm models in a preset AI algorithm library; Synchronize the target AI algorithm model to the household robot.

2. The method according to claim 1, characterized in that The step of obtaining the position information of the household robot comprises: Obtaining the propagation time of a signal sent by the household robot to a preset receiving end; Calculating a transmission distance of the signal according to the propagation time; The position information of the household robot is determined according to the transmission distance.

3. The method according to claim 1, characterized in that The step of determining the device state of the household robot according to the position information comprises: Determining whether the household robot is in a preset charging position or whether the static time reaches a preset threshold according to the position information; If not, determining that the device state of the household robot is a non-updatable state; If so, it is determined that the device state of the household robot is an updateable state.

4. The method according to claim 1, characterized in that: The device information includes carrying capacity and computing resources; the step of synchronizing the target AI algorithm model to the household robot includes: Compressing the target AI algorithm model according to the carrying capacity and the computing resources to obtain a target compressed AI algorithm model; The target compression model is processed into blocks to obtain cloud model blocks; Calculating the cloud hash value of each of the cloud model blocks; Matching the target AI algorithm model on the household robot to obtain a terminal AI algorithm model; The terminal AI algorithm model is processed into blocks to obtain terminal model blocks; Calculating the terminal hash value of each terminal model block; Compare the cloud hash value and the terminal hash value to determine the difference cloud model blocks; Transmit data corresponding to the difference cloud model blocks to the household robot.

5. The method according to any one of claims 1 to 4, characterized in that: After the step of synchronizing the target AI algorithm model to the household robot, the method further includes: Receive a self-test report of the household robot.

6. A household robot AI algorithm model synchronization device, characterized in that: Applied to a synchronization management platform, the device comprises: A position information acquisition module is used to obtain the position information of the household robot; A device information, operating status and current home environment information receiving module, used to receive the device information, operating status and current home environment information sent by the home robot; A device status determination module, used for determining the device status of the household robot according to the position information; A matching module, configured to match a plurality of target AI algorithm models in a preset AI algorithm library according to the device state, the device information, the operating state and the current home environment information; A synchronization module is used to synchronize the target AI algorithm model to the household robot.

7. The device according to claim 6, characterized in that The location information acquisition module includes: The propagation time acquisition submodule is used to acquire the propagation time of the signal sent by the household robot to the preset receiving end; A transmission distance calculation submodule, used to calculate the transmission distance of the signal according to the propagation time; A position information determination submodule is used to determine the position information of the household robot according to the transmission distance.

8. The device according to claim 6, characterized in that The device status judgment module includes: A judgment submodule, used to judge whether the household robot is in a preset charging position or whether the static time reaches a preset threshold according to the position information; a non-updatable state determination submodule, for determining, if not, that the device state of the household robot is a non-updatable state; The updatable state determination submodule is used to determine, if yes, that the device state of the household robot is an updatable state.

9. An electronic device, characterized in that: The device comprises a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the household robot AI algorithm model synchronization method described in any one of claims 1-5 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store program code, and the program code is used to execute the household robot AI algorithm model synchronization method described in any one of claims 1-5.

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