User intelligent model construction method and system based on Internet of Things equipment platform, and electronic equipment

Abstract

The invention relates to a user intelligent model construction method and system based on an Internet of Things equipment platform and electronic equipment. The Internet of Things equipment platform authorizes a corresponding Internet of Things equipment operation authority to a user based on login request voice data initiated by the user, and marks each voice interaction data between the user and the Internet of Things equipment through a unique identifier; the construction method comprises the following steps: acquiring user data in an Internet of Things equipment platform, and dividing the user data based on a unique identifier of a user to obtain a data set corresponding to each user; the corresponding AI large model is trained through the data set to obtain the user intelligent model corresponding to each user, that is, registration and login are carried out through voiceprint features, input of personal information is avoided, account data is also prevented from being mistakenly input, on the basis, the user data are marked through the unique identifier, and the user intelligent model corresponding to each user is obtained. Therefore, isolation of the user data is realized, the accuracy of the training data, namely the data set, is improved, and the accuracy of model prediction is further improved.

Description

Technical Field

[0001] The present invention relates to the field of AI big models, and in particular to a user intelligent model construction method, system and electronic equipment based on an Internet of Things device platform. Background Art

[0002] With the rapid development of society, the number of IoT devices has grown rapidly, and more and more devices are connected to the network, forming a huge Internet ecosystem. At the same time, with the access of IoT devices, the amount of data generated by the devices has increased exponentially. The massive amount of data makes user intelligent analysis possible. User intelligent analysis refers to a technology that collects, processes and analyzes user data to understand user behavior, preferences, needs and patterns. At present, IoT device platforms generally use an account system that logs in with a password to manage user identity, permissions and data. After registering an account, the user associates the IoT device with the user through device binding. In addition, user intelligent analysis is also based on the data within the account system. There are many problems with this method:

[0003] It is a common security measure to conduct intelligent analysis of users based on account systems with account names and passwords, but this method has some disadvantages, such as passwords are not encrypted during transmission, passwords are improperly stored in the database, relying on usernames and passwords as an authentication mechanism lacks an additional security layer, and passwords are easily cracked or forgotten. In addition, if account data is entered or interpreted incorrectly (for example, user A logs into user B's account, resulting in user A's interaction data with the device being stored under user B's account), the results of intelligent analysis may be inaccurate, leading to wrong decisions.

[0004] Secondly, most of the voiceprint information in the prior art is only used to control home appliances, and a large amount of stored data is left idle and unused.

[0005] At the same time, most of the existing technologies use personal identity information for registration, so there will be problems with user privacy protection. The challenge mainly comes from the collection, processing and use of personal data by the user intelligent analysis method. Once personal data (personal information, such as name, birthday, ID number, etc.) is collected, there is a risk of being leaked, stolen or abused. Summary of the invention

[0006] In order to train respective AI big models for different users by using interaction data between users and IoT devices without using personal identity information for registration and without relying on username and password for login, the present invention proposes a user intelligent model construction method based on IoT device platform, wherein the IoT device platform includes multiple AI big models and multiple IoT devices;

[0007] The IoT device is used to receive the login request voice data initiated by the user and send the login information to the IoT device platform; the login information includes the IoT device ID and the login request voice data;

[0008] The IoT device platform pre-processes the login request voice data to obtain a first voiceprint feature, matches the user voiceprint feature stored in a preset user library through the first voiceprint feature, and if there is a matching user voiceprint feature, authorizes the user to operate the corresponding IoT device, and obtains the user's unique identifier at the same time, and marks each voice interaction data of the user through the unique identifier during the user's interaction with the IoT device; the IoT device operation permission includes: the user's permission to initiate a voice conversation with the IoT device; the user intelligent model construction method includes:

[0009] Obtain user data in the IoT device platform, and divide the user data based on the user unique identifier to obtain a data set corresponding to each user; the data set corresponds to the user unique identifier one by one; the user data includes text data corresponding to the voice data of the login request initiated by the user, and text data corresponding to the voice interaction data between the user and each IoT device;

[0010] The corresponding AI big model is trained through the data set to obtain the user intelligent model corresponding to each user; the data set corresponds to the AI ​​big model one-to-one; the user intelligent model provides personalized services based on the conversation initiated by the corresponding user.

[0011] Furthermore, the preset user library includes multiple user tables; the Internet of Things devices correspond to the user tables one by one; the user tables store user voiceprint features of registered users of the corresponding Internet of Things devices.

[0012] Furthermore, the first voiceprint feature is matched with the user voiceprint feature stored in the preset user library, and if there is a matching user voiceprint feature, the user is authorized to operate the corresponding IoT device, specifically:

[0013] Obtain the corresponding user table through the IoT device ID as the target user table;

[0014] The first voiceprint feature is matched with the user voiceprint feature in the target user table. If there is a matching user voiceprint feature, the user is authorized to operate the Internet of Things device.

[0015] Furthermore, the IoT device platform further includes: a voiceprint recognition module;

[0016] The IoT device platform pre-processes the login request voice data, specifically:

[0017] The voiceprint recognition module reads the login request voice data received by the IoT device platform in real time, and identifies the sound frequency and sound amplitude in the login request voice data to obtain the first voiceprint feature.

[0018] Furthermore, the IoT device includes:

[0019] A voice collection module is used to receive voice conversation data initiated by the user;

[0020] The communication module is used to send the voice interaction data between the user and the IoT device to the IoT device platform.

[0021] Furthermore, the IoT device platform also includes an ASR speech recognition module, which is used to convert voice interaction data and user-initiated login request voice data into text data.

[0022] The present invention also proposes a user intelligent model construction system based on an Internet of Things device platform, wherein the Internet of Things device platform includes multiple AI large models and multiple Internet of Things devices;

[0023] The IoT device is used to receive the login request voice data initiated by the user and send the login information to the IoT device platform; the login information includes the IoT device ID and the login request voice data;

[0024] The IoT device platform pre-processes the login request voice data to obtain a first voiceprint feature, matches the user voiceprint feature stored in a preset user library through the first voiceprint feature, and if there is a matching user voiceprint feature, authorizes the user to operate the corresponding IoT device, and obtains the user's unique identifier at the same time, and marks each voice interaction data of the user through the unique identifier during the user's interaction with the IoT device; the IoT device operation permission includes: the user's permission to initiate a voice conversation with the IoT device; the user intelligent model construction system includes:

[0025] A data set construction module is used to obtain user data in the IoT device platform and divide the user data based on the user unique identifier to obtain a data set corresponding to each user; the data set corresponds to the user unique identifier one by one; the user data includes text data corresponding to the login request voice data initiated by the user, and text data corresponding to the voice interaction data between the user and each IoT device;

[0026] The model training module is used to train the corresponding AI big model through the data set to obtain the user intelligent model corresponding to each user; the data set corresponds to the AI ​​big model one by one; the user intelligent model provides personalized services based on the conversation initiated by the corresponding user.

[0027] Furthermore, the preset user library includes multiple user tables; the Internet of Things devices correspond to the user tables one by one; the user tables store user voiceprint features of registered users of the corresponding Internet of Things devices.

[0028] Furthermore, the first voiceprint feature is matched with the user voiceprint feature stored in the preset user library, and if there is a matching user voiceprint feature, the user is authorized to operate the corresponding IoT device, specifically:

[0029] Obtain the corresponding user table through the IoT device ID as the target user table;

[0030] The first voiceprint feature is matched with the user voiceprint feature in the target user table. If there is a matching user voiceprint feature, the user is authorized to operate the Internet of Things device.

[0031] The present invention further proposes an electronic device, comprising: a processor, and a memory storing a program, wherein the program comprises instructions, and when the instructions are executed by the processor, the processor executes the method described above.

[0032] The beneficial effects of the embodiments of the present invention include:

[0033] In the present invention, the Internet of Things device platform authorizes the user's corresponding Internet of Things device operation authority based on the login request voice data initiated by the user, and marks each voice interaction data between the user and the Internet of Things device through a unique identifier; the construction method of the present invention obtains user data in the Internet of Things device platform, and divides the user data based on the user's unique identifier to obtain a data set corresponding to each user; the corresponding AI large model is trained through the data set to obtain a user intelligent model corresponding to each user, that is, the present invention registers and logs in through voiceprint features, avoids the entry of personal information, and also avoids the account data from being entered incorrectly. On this basis, the present invention marks user data through a unique identifier, realizes the isolation of user data, improves the accuracy of the training data, that is, the data set, and thus improves the accuracy of the model prediction.

[0034] The details of one or more embodiments of the invention are set forth in the following drawings and description so that other features, objects, and advantages of the invention are more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other embodiments can be obtained based on these drawings without creative work.

[0036] Figure 1 This is a flow chart of a method for building a user intelligent model based on an Internet of Things device platform according to an embodiment of the present invention;

[0037] Figure 2 This is a module diagram of a user intelligent model construction system based on an Internet of Things device platform according to an embodiment of the present invention;

[0038] Figure 3 Schematic diagram of the structure of the electronic device of this embodiment. DETAILED DESCRIPTION

[0039] Embodiments of the present embodiment will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present embodiment are shown in the accompanying drawings, it should be understood that the present embodiment can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein, which are instead provided for a more thorough and complete understanding of the present embodiment. It should be understood that the drawings and embodiments of the present embodiment are only for exemplary purposes and are not intended to limit the scope of protection of the present embodiment.

[0040] Embodiment 1

[0041] In order to better understand the embodiments of the present application, the technical terms involved in the embodiments of the present application are explained as follows:

[0042] AI big models: refers to machine learning models with a large number of parameters. These models are usually trained with large data sets and can perform well on a variety of tasks. The characteristics of AI big models include but are not limited to:

[0043] Massive parameter size: Models contain millions to billions of parameters, enabling them to capture complex data distributions and patterns.

[0044] Pre-training and fine-tuning: These models are often first pre-trained on large-scale unlabeled datasets to learn common language structures or visual features. After that, they are fine-tuned for specific tasks to adapt to specific downstream task requirements.

[0045] Multimodal capabilities: Some large AI models are not limited to a single modality (such as text) but can also process cross-modal data such as images, audio, and video.

[0046] Strong generalization ability: Because the models are trained on rich and diverse data, they can show strong generalization ability when faced with new data.

[0047] ASR speech recognition technology: It is a technology that converts speech signals into text. It is achieved through components such as front-end processing, feature extraction, acoustic model, language model and decoder. This technology has been widely used in many fields, providing people with a more natural and efficient way of interaction. With the development of deep learning technology, ASR technology is also constantly improving its accuracy and robustness, becoming more practical and popular. The core task of ASR technology is to recognize and transcribe words and phrases in speech.

[0048] In order to solve the problems in the background technology, without using personal identity information for registration and without relying on username and password for login, it is still possible to train the respective AI models for different users by using the interaction data between users and IoT devices. Figure 1 As shown, the present invention proposes a method for constructing a user intelligent model based on an Internet of Things device platform, wherein the Internet of Things device platform includes multiple AI large models and multiple Internet of Things devices;

[0049] The IoT device is used to receive the login request voice data initiated by the user and send the login information to the IoT device platform; the login information includes the IoT device ID and the login request voice data;

[0050] The IoT device platform pre-processes the login request voice data to obtain a first voiceprint feature, matches the user voiceprint feature stored in a preset user library through the first voiceprint feature, and if there is a matching user voiceprint feature, authorizes the user to operate the corresponding IoT device, and obtains the unique identifier of the user at the same time, and marks each voice interaction data of the user through the unique identifier during the interaction between the user and the IoT device; the IoT device operation permission includes: the user's permission to initiate a voice conversation with the IoT device;

[0051] Voiceprint feature matching is an important part of voiceprint recognition technology. Its purpose is to determine whether two voiceprint features belong to the same speaker by comparing their similarity. Voiceprint feature matching usually includes the following steps:

[0052] 1. Feature extraction: Extract acoustic features, i.e., the first voiceprint features in this embodiment, such as Mel-frequency cepstral coefficients (MFCCs), linear prediction coefficients (LPCs), sound frequency, sound amplitude, etc., from the login request voice data.

[0053] 2. Feature comparison: Use distance measurement or similarity measurement to compare the voiceprint features to be identified with the user voiceprint features in the preset user library.

[0054] 3. Threshold setting: Set the threshold based on the comparison result to determine whether there is a match.

[0055] 4. Decision: If the similarity is higher than the set threshold, the two voiceprint features are considered to match; otherwise, they are considered to not match.

[0056] In this embodiment, by comparing multiple voiceprint features, the matching values ​​of multiple voiceprint features (such as the distance between two feature vectors) are used to comprehensively determine whether the first voiceprint feature matches the stored user voiceprint feature. Specifically, in order to comprehensively determine whether the first voiceprint feature matches the stored user voiceprint feature, a weighted value method can be used. This means that each feature has a corresponding weight in the judgment process, and these weights reflect the importance of different features. The steps of comprehensive judgment are as follows:

[0057] Calculate the distance or similarity of each feature: For each extracted voiceprint feature, calculate the distance or similarity between it and the stored user voiceprint feature.

[0058] Determine feature weights: Assign a weight value to each voiceprint feature. These weights can be determined empirically. For example, if MFCCs are found to be particularly effective in distinguishing different speakers, higher weights can be assigned to MFCCs.

[0059] Weighted average: Calculate the weighted average distance or similarity based on the weight value of each feature. This can be achieved by multiplying the distance or similarity of each feature by its corresponding weight, then summing them and finally dividing by the sum of the weights.

[0060] Set a threshold: Set a threshold based on the weighted average distance or similarity. If the weighted average distance is lower than the threshold, the first voiceprint feature is considered to match the stored user voiceprint feature; otherwise, it is considered not to match. The specific examples are as follows:

[0061] Suppose there are two features: MFCCs and LPCs. Assign a weight of 0.7 to MFCCs and a weight of 0.3 to LPCs. Suppose the calculated distance of MFCCs is 0.2 and the distance of LPCs is 0.4. We can calculate it as follows:

[0062] Calculate the weighted distance:

[0063] Weighted distance of MFCCs: 0.2×0.7=0.14;

[0064] Weighted distance of LPCs: 0.4 × 0.3 = 0.12;

[0065] Compute the weighted average distance:

[0066] Weighted average distance:

[0067] Setting the threshold:

[0068] Assuming that the threshold is 0.3, since the weighted average distance (0.26) is lower than the threshold (0.3), it is determined that the first voiceprint feature matches the stored user voiceprint feature.

[0069] In this way, the matching values ​​of multiple voiceprint features can be effectively used to comprehensively determine whether the first voiceprint feature matches the stored user voiceprint feature. This method improves the accuracy and robustness of voiceprint recognition.

[0070] The user intelligence model construction method comprises:

[0071] The IoT device platform also includes: a voiceprint recognition module;

[0072] The IoT device platform pre-processes the login request voice data, specifically:

[0073] The voiceprint recognition module reads the login request voice data received by the IoT device platform in real time, and identifies the sound frequency and sound amplitude in the login request voice data to obtain the first voiceprint feature.

[0074] The preset user library includes multiple user tables; the IoT devices correspond to the user tables one by one; the user tables store the user voiceprint features of the registered users of the corresponding IoT devices.

[0075] In this embodiment, the registration information of the registered user of the IoT device is the voiceprint feature, that is, the user registers by providing voice information. Voiceprint is a biometric feature, which is extracted when the speaker speaks and can be used as a unique identifier of the speaker and a voice feature that can be distinguished from other people. The short-time spectrum, sound source, temporal dynamics, rhythm, and linguistic characteristics of each person when speaking are different. Therefore, voiceprints have uniqueness and uniqueness similar to fingerprints. The personality characteristics contained in each person's speaking process, such as pronunciation habits, are almost unique. Even if they are imitated, the most essential characteristics of the speaker cannot be completely changed.

[0076] In addition, voiceprint passwords can change dynamically, so there is no need to worry about forgotten, lost or stolen passwords, because it is difficult for attackers to forge them. By logging in with voiceprints, account data can be prevented from being entered or interpreted incorrectly. At the same time, voiceprint technology does not require contact with the body, but only needs to collect the user's voice information, which means that the user does not need to provide sensitive information, so it is more privacy-friendly. It is difficult to match voice data with personal identity information, which helps reduce the risk of personal information being used, impersonated, tampered with or forged at will. Users do not need to remember complex passwords or carry physical credentials, nor do they need to worry about forgetting their passwords. Users only need to speak a specific text or natural language, and the platform can recognize their identity.

[0077] The first voiceprint feature is matched with the user voiceprint feature stored in the preset user library. If there is a matching user voiceprint feature, the user is authorized to operate the corresponding IoT device, specifically:

[0078] Obtain the corresponding user table through the IoT device ID as the target user table;

[0079] The first voiceprint feature is matched with the user voiceprint feature in the target user table. If there is a matching user voiceprint feature, the user is authorized to operate the Internet of Things device.

[0080] Obtain user data in the IoT device platform, and divide the user data based on the user unique identifier to obtain a data set corresponding to each user; the data set corresponds to the user unique identifier one by one; the user data includes text data corresponding to the voice data of the login request initiated by the user, and text data corresponding to the voice interaction data between the user and each IoT device;

[0081] The IoT devices include:

[0082] A voice collection module is used to receive voice conversation data initiated by the user;

[0083] The communication module is used to send the voice interaction data between the user and the IoT device to the IoT device platform.

[0084] The IoT device platform also includes an ASR speech recognition module, which is used to convert voice interaction data and login request voice data initiated by the user into text data.

[0085] The corresponding AI big model is trained through the data set to obtain the user intelligent model corresponding to each user; the data set corresponds to the AI ​​big model one-to-one; the user intelligent model provides personalized services based on the conversation initiated by the corresponding user (the AI ​​big model is trained with its special data set for the user, so that the model can provide personalized content recommendations based on the user's preferences and behavior patterns, predict the user's future behavior, make corresponding preparations and services in advance, and also generate more accurate reply content based on the conversation initiated by the user).

[0086] The AI ​​big model in the technical solution of the present invention refers to an advanced artificial intelligence model that can process a large amount of data and learn user characteristics from it. Such models usually have complex architectures and a large number of parameters, enabling them to learn deep patterns and relationships from massive amounts of data. In this scenario, the AI ​​big model will be used to process text data converted by ASR and learn from it the user's unique behavior patterns and preferences. The AI ​​big model in this embodiment is: a deep neural network (DNN) or a recurrent neural network (RNN) or a long short-term memory network (LSTM) or a Transformer model, etc.

[0087] In the present invention, the Internet of Things device platform authorizes the user's corresponding Internet of Things device operation authority based on the login request voice data initiated by the user, and marks each voice interaction data between the user and the Internet of Things device through a unique identifier; the construction method of the present invention obtains user data in the Internet of Things device platform, and divides the user data based on the user's unique identifier to obtain a data set corresponding to each user; the corresponding AI large model is trained through the data set to obtain a user intelligent model (or user intelligent analysis model) corresponding to each user, that is, the present invention registers and logs in through voiceprint features, avoids the entry of personal information, and also avoids the account data from being entered incorrectly. On this basis, the present invention marks user data through a unique identifier, realizes the isolation of user data, improves the accuracy of the training data, i.e., the data set, and thereby improves the accuracy of the model prediction.

[0088] An embodiment of the present invention further provides an electronic device, comprising: at least one processor; and a memory in communication with the at least one processor. The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the electronic device executes the method of the embodiment of the present invention.

[0089] An embodiment of the present invention further provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute the method of the embodiment of the present invention.

[0090] An embodiment of the present invention further provides a computer program product, including a computer program, wherein the computer program, when executed by a processor of a computer, is used to enable the computer to execute the method of the embodiment of the present invention.

[0091] refer to Figure 3 , a block diagram of an electronic device that can be used as a server or client of an embodiment of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0092] like Figure 3As shown, the electronic device includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In RAM 403, various programs and data required for the operation of the electronic device can also be stored. The computing unit 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0093] Multiple components in the electronic device are connected to the I / O interface 405, including: an input unit 406, an output unit 407, a storage unit 408, and a communication unit 409. The input unit 406 can be any type of device that can input information to the electronic device, and the input unit 406 can receive input digital or character information, and generate key signal input related to user settings and / or function control of the electronic device. The output unit 407 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 408 can include but is not limited to a disk, an optical disk. The communication unit 409 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0094] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a CPU, a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the methods and processes described above. For example, in some embodiments, the method embodiments of the present invention may be implemented as a computer program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on an electronic device via ROM 402 and / or communication unit 409. In some embodiments, the computing unit 401 may be configured to perform the above-described method in any other appropriate manner (e.g., by means of firmware).

[0095] The computer programs for implementing the methods of the embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of an embodiment of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0097] Embodiment 2

[0098] like Figure 2 As shown, the present invention also proposes a method for constructing a user intelligent model based on an Internet of Things device platform, wherein the Internet of Things device platform includes multiple AI large models and multiple Internet of Things devices;

[0099] The IoT device is used to receive the login request voice data initiated by the user and send the login information to the IoT device platform; the login information includes the IoT device ID and the login request voice data;

[0100] The IoT device platform pre-processes the login request voice data to obtain a first voiceprint feature, matches the user voiceprint feature stored in a preset user library through the first voiceprint feature, and if there is a matching user voiceprint feature, authorizes the user to operate the corresponding IoT device, and obtains the unique identifier of the user at the same time, and marks each voice interaction data of the user through the unique identifier during the interaction between the user and the IoT device; the IoT device operation permission includes: the user's permission to initiate a voice conversation with the IoT device;

[0101] The IoT device platform also includes: a voiceprint recognition module;

[0102] The IoT device platform pre-processes the login request voice data, specifically:

[0103] The voiceprint recognition module reads the login request voice data received by the IoT device platform in real time, and identifies the sound frequency and sound amplitude in the login request voice data to obtain the first voiceprint feature.

[0104] The preset user library includes multiple user tables; the IoT devices correspond to the user tables one by one; the user tables store the user voiceprint features of the registered users of the corresponding IoT devices.

[0105] The first voiceprint feature is matched with the user voiceprint feature stored in the preset user library. If there is a matching user voiceprint feature, the user is authorized to operate the corresponding IoT device, specifically:

[0106] Obtain the corresponding user table through the IoT device ID as the target user table;

[0107] The first voiceprint feature is matched with the user voiceprint feature in the target user table. If there is a matching user voiceprint feature, the user is authorized to operate the Internet of Things device.

[0108] The user intelligence model construction method comprises:

[0109] Obtain user data in the IoT device platform, and divide the user data based on the user unique identifier to obtain a data set corresponding to each user; the data set corresponds to the user unique identifier one by one; the user data includes text data corresponding to the voice data of the login request initiated by the user, and text data corresponding to the voice interaction data between the user and each IoT device;

[0110] The IoT devices include:

[0111] A voice collection module is used to receive voice conversation data initiated by the user;

[0112] The communication module is used to send the voice interaction data between the user and the IoT device to the IoT device platform.

[0113] The corresponding AI big model is trained through the data set to obtain the user intelligent model corresponding to each user; the data set corresponds to the AI ​​big model one-to-one; the user intelligent model provides personalized services based on the conversation initiated by the corresponding user.

[0114] It should be noted that the term "including" and its variations used in the embodiments of the present invention are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "multiple" mentioned in the embodiments of the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless the context clearly indicates otherwise, it should be understood as "one or more".

[0115] The term "embodiment" in this specification refers to specific features, structures or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments refer to each other. In particular, for the device, equipment, and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment.

[0116] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of patent protection. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the attached claims.

Claims

1. A method for constructing a user intelligent model based on an Internet of Things device platform, characterized in that: The IoT device platform includes multiple AI large models and multiple IoT devices; The IoT device is used to receive the login request voice data initiated by the user and send the login information to the IoT device platform; the login information includes the IoT device ID and the login request voice data; The IoT device platform pre-processes the login request voice data to obtain a first voiceprint feature, matches the user voiceprint feature stored in a preset user library through the first voiceprint feature, and if there is a matching user voiceprint feature, authorizes the user to operate the corresponding IoT device, and obtains the user's unique identifier at the same time, and marks each voice interaction data of the user with the unique identifier during the process of the user interacting with the IoT device; The IoT device operation permission includes: the permission of the user to initiate a voice conversation with the IoT device; the user intelligent model construction method includes: Obtain user data in the IoT device platform, and divide the user data based on the user unique identifier to obtain a data set corresponding to each user; the data set corresponds to the user unique identifier one by one; the user data includes text data corresponding to the voice data of the login request initiated by the user, and text data corresponding to the voice interaction data between the user and each IoT device; The corresponding AI big model is trained through the data set to obtain the user intelligent model corresponding to each user; the data set corresponds to the AI ​​big model one-to-one; the user intelligent model provides personalized services based on the conversation initiated by the corresponding user.

2. A method for constructing a user intelligent model based on an Internet of Things device platform according to claim 1, characterized in that: The preset user library includes multiple user tables; the IoT devices correspond to the user tables one by one; the user tables store the user voiceprint features of the registered users of the corresponding IoT devices.

3. A method for constructing a user intelligent model based on an Internet of Things device platform according to claim 2, characterized in that: The first voiceprint feature is matched with the user voiceprint feature stored in the preset user library. If there is a matching user voiceprint feature, the user is authorized to operate the corresponding IoT device, specifically: Obtain the corresponding user table through the IoT device ID as the target user table; The first voiceprint feature is matched with the user voiceprint feature in the target user table. If there is a matching user voiceprint feature, the user is authorized to operate the Internet of Things device.

4. A method for constructing a user intelligent model based on an Internet of Things device platform according to claim 3, characterized in that: The IoT device platform also includes: a voiceprint recognition module; The IoT device platform pre-processes the login request voice data, specifically: The voiceprint recognition module reads the login request voice data received by the IoT device platform in real time, and identifies the sound frequency and sound amplitude in the login request voice data to obtain the first voiceprint feature.

5. A method for constructing a user intelligent model based on an Internet of Things device platform according to claim 4, characterized in that: The IoT devices include: A voice collection module is used to receive voice conversation data initiated by the user; The communication module is used to send the voice interaction data between the user and the IoT device to the IoT device platform.

6. A method for constructing a user intelligence model based on an Internet of Things device platform according to claim 5, characterized in that: The IoT device platform also includes an ASR speech recognition module, which is used to convert voice interaction data and login request voice data initiated by the user into text data.

7. A user intelligent model construction system based on an Internet of Things device platform, characterized in that: The IoT device platform includes multiple AI large models and multiple IoT devices; The IoT device is used to receive the login request voice data initiated by the user and send the login information to the IoT device platform; the login information includes the IoT device ID and the login request voice data; The IoT device platform pre-processes the login request voice data to obtain a first voiceprint feature, matches the user voiceprint feature stored in a preset user library through the first voiceprint feature, and if there is a matching user voiceprint feature, authorizes the user to operate the corresponding IoT device, and obtains the user's unique identifier at the same time, and marks each voice interaction data of the user with the unique identifier during the process of the user interacting with the IoT device; The IoT device operation authority includes: the authority for the user to initiate a voice conversation with the IoT device; the user intelligent model construction system includes: A data set construction module is used to obtain user data in the IoT device platform and divide the user data based on the user unique identifier to obtain a data set corresponding to each user; the data set corresponds to the user unique identifier one by one; the user data includes text data corresponding to the login request voice data initiated by the user, and text data corresponding to the voice interaction data between the user and each IoT device; The model training module is used to train the corresponding AI big model through the data set to obtain the user intelligent model corresponding to each user; the data set corresponds to the AI ​​big model one by one; the user intelligent model provides personalized services based on the conversation initiated by the corresponding user.

8. A user intelligent model construction system based on an Internet of Things device platform according to claim 7, characterized in that: The preset user library includes multiple user tables; the IoT devices correspond to the user tables one by one; the user tables store the user voiceprint features of the registered users of the corresponding IoT devices.

9. A user intelligent model construction system based on an Internet of Things device platform according to claim 8, characterized in that: The first voiceprint feature is matched with the user voiceprint feature stored in the preset user library. If there is a matching user voiceprint feature, the user is authorized to operate the corresponding IoT device, specifically: Obtain the corresponding user table through the IoT device ID as the target user table; The first voiceprint feature is matched with the user voiceprint feature in the target user table. If there is a matching user voiceprint feature, the user is authorized to operate the Internet of Things device.

10. An electronic device comprising: A processor and a memory storing a program, wherein the program comprises instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 6.

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