Method and system for controlling link between Bluetooth keyboard and multiple devices

Abstract

The invention relates to the technical field of Bluetooth keyboard control, and discloses a link control method and system for a Bluetooth keyboard and multiple devices, and the method comprises the steps: obtaining the current user behavior data of each device; inputting the current user behavior data into a pre-trained user use habit model for prediction to obtain prediction equipment which is most likely to be operated by the current user; searching from a connection device list according to the prediction device to obtain a link instruction of the corresponding device; wherein the connection device list is a database storing link instructions of each device; and in response to a selection link operation triggered by a user, realizing link between the corresponding equipment and the Bluetooth keyboard according to the link instruction. According to the method, the problem that when the Bluetooth keyboard is connected with a plurality of devices, the target device can be controlled only through a plurality of steps, and the user operation is troublesome can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of Bluetooth keyboard control, and in particular to a method and system for controlling the connection between a Bluetooth keyboard and multiple devices. Background Art

[0002] At present, Bluetooth is mainly used in the field of smart home. Bluetooth is a wireless transmission technology. Compared with infrared technology, Bluetooth can transmit data without alignment. It supports short-distance communication between devices (transmission distance is less than 10 meters). With the help of signal amplifier, the communication distance can even reach about 100 meters. Bluetooth technology is very suitable for low-power digital devices to share data with each other, such as mobile phones and PDAs. Bluetooth technology operates in the 2.4GHz band and can transmit up to 1Mbps of data in one second. Compared with infrared technology, the heat generated by Bluetooth power consumption is very small, and there is no need to consider the angle measurement problem of infrared transmission path.

[0003] In one prior art, when a Bluetooth keyboard is connected to multiple devices, the user needs to operate a computer, open a browser, enter a URL, log in to the network version control software, and then control the set-top box and finally control the target device.

[0004] In the prior art, when a Bluetooth keyboard is connected to multiple devices, multiple steps are often required to achieve control of the target device, which causes the user to have trouble operating the device. Summary of the invention

[0005] The present invention provides a method and system for controlling the connection between a Bluetooth keyboard and multiple devices, so as to solve the problem that when the Bluetooth keyboard is connected to multiple devices, multiple steps are required to realize the control of the target device, which is troublesome for the user to operate.

[0006] In a first aspect, in order to solve the above technical problems, the present invention provides a method for controlling the connection between a Bluetooth keyboard and multiple devices, comprising: Get the current user behavior data of each device; Inputting the current user behavior data into a pre-trained user usage habit model for prediction, and obtaining the predicted device that the current user is most likely to operate; Searching the connection device list according to the predicted device to obtain the connection instruction of the corresponding device; wherein the connection device list is a database storing the connection instructions of each device; In response to a link selection operation triggered by the user, the corresponding device is linked to the Bluetooth keyboard according to the link instruction.

[0007] In an optional implementation, the inputting the current user behavior data into a pre-trained user usage habit model for prediction to obtain the predicted device that the current user is most likely to operate includes: Extract and analyze the current user behavior data to obtain first user behavior data and second user behavior data; Calculating according to the first user behavior data and the second user behavior data to obtain an operation distance between devices; Perform a model building operation according to the first user behavior data, the second user behavior data and the operation distance between the devices to obtain a triangle model; Input the triangle model into the user usage habit model for updating, to obtain an updated final user usage habit model; Performing value initialization according to the end user's usage habit model to obtain model vertex values; When the model vertex value is greater than a preset model vertex threshold, the usage probability of each device is calculated in turn to obtain the usage probability value of each device, and the device corresponding to the highest usage probability value is extracted to obtain the predicted device.

[0008] In an optional implementation, performing value assignment initialization according to the current user usage habit model to obtain model vertex values ​​includes: The model vertex values ​​are obtained in the following way: Wherein: M is the model vertex value; m is the user operation experience value; δ1 is the first associated parameter; δ2 is the second associated parameter; P represents the frequency of turning on or off the device; Time represents the time regularity data of turning on or off the device.

[0009] In an optional implementation, in response to the user-triggered selection link operation, linking the corresponding device with the Bluetooth keyboard according to the link instruction includes: In response to a link selection operation triggered by a user, a predicted correct link state and a predicted incorrect link state are obtained; When the user selects the correct link state, the Bluetooth keyboard directly links with the predicted device according to the link instruction; When the user selects the erroneous link state, the actual link device selected by the user for linking is recorded, and analysis is performed based on the actual link device to obtain actual device state data; Updating the user usage habit model according to the actual device status data to obtain an updated end-user usage habit model; A prediction device is regenerated according to the end-user usage habit model, and the Bluetooth keyboard is linked to the prediction device.

[0010] In an optional implementation, the model building operation is performed according to the first user behavior data, the second user behavior data and the inter-device operation distance to obtain a triangle model, including: Constructing according to the first user behavior data and the second user behavior data to obtain a minimum two-tuple; Add the inter-device operation distance into the minimum binary group for initialization to obtain an initial binary group; A triangle model is obtained by training with a machine learning method according to the initial binary group.

[0011] In an optional implementation, the inputting the current user behavior data into a pre-trained user usage habit model for prediction to obtain the predicted device most likely to be operated by the current user further includes: Get the predicted success rate of each device; Comparing the predicted success rates of the devices, when the predicted success rates are higher than a preset success rate threshold, selecting the device corresponding to the highest predicted success rate as the predicted device, to obtain the predicted device; When the prediction success rate is lower than the preset success rate threshold, the device closest to the device is selected to construct a tuple to obtain a first tuple. The first tuple is added to the user usage habit model for correction to obtain a corrected final user usage habit model. The final user usage habit model is used for calculation to obtain a predicted device.

[0012] In an optional implementation, the training process of the user usage habit model includes: Obtain user historical behavior data from a user historical behavior database; Perform a model building operation based on the user's historical behavior data to obtain an initial usage habit model; The initial usage habit model is trained, and when the number of training times is greater than or equal to the preset maximum number of training times, the training is determined to be completed, and the user usage habit model is obtained.

[0013] In a second aspect, the present invention provides a link control system for a Bluetooth keyboard and multiple devices, comprising: A data acquisition module is used to obtain the current user behavior data of each device; A device acquisition module, used to input the current user behavior data into a pre-trained user usage habit model for prediction, and obtain the predicted device that the current user is most likely to operate; An instruction acquisition module, used to search from a connection device list according to the predicted device to obtain a connection instruction of the corresponding device; wherein the connection device list is a database storing connection instructions of each device; The instruction link module is used to respond to the selection link operation triggered by the user and realize the link between the corresponding device and the Bluetooth keyboard according to the link instruction.

[0014] In a third aspect, the present invention further provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for controlling the connection between a Bluetooth keyboard and multiple devices as described in any one of the above is implemented.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium, the computer-readable storage medium comprising a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned methods for controlling the connection between a Bluetooth keyboard and multiple devices.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention discloses a method for controlling the connection of a Bluetooth keyboard with multiple devices, including obtaining the current user behavior data of each connected device in real time, inputting the collected current user behavior data into a pre-trained user usage habit model, generating a prediction result about the device that the user is most likely to operate, and based on the predicted device, the controller will search in the connection device list. The connection device list is a database containing all known devices and their corresponding connection instructions. Through an efficient retrieval algorithm, the connection instruction of the predicted device can be quickly located. In response to the user-triggered selection link operation, the connection between the target device and the Bluetooth keyboard will be automatically realized according to the acquired link instruction. Compared with the prior art, when the Bluetooth keyboard is connected to multiple devices, the user needs to operate the computer, open the browser, enter the website, log in to the network version control software, and then control the set-top box, and finally control the target device. The present invention constructs a usage habit model through intelligent user historical behavior analysis, accurately predicts the device that the user wants to connect and automates the device connection operation, realizes efficient interaction between the Bluetooth keyboard and each device, and improves the user's operation convenience. Therefore, the present invention can solve the problem that when the Bluetooth keyboard is connected to multiple devices, it takes multiple steps to realize the control of the target device, and there is a problem that the user is troublesome to operate. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic flow chart of a method for controlling the connection between a Bluetooth keyboard and multiple devices provided by a first embodiment of the present invention; Figure 2is a schematic diagram of a flow chart of a prediction algorithm provided by an embodiment of the present invention; Figure 3 It is a schematic diagram of the connection structure of the controller provided by the present invention; Figure 4 It is a schematic diagram of the structure of a connection control system between a Bluetooth keyboard and multiple devices provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] At present, Bluetooth is mainly used in the field of smart home. Bluetooth is a wireless transmission technology. Compared with infrared technology, Bluetooth can transmit data without alignment. It supports short-distance communication between devices (transmission distance is less than 10 meters). With the help of signal amplifier, the communication distance can even reach about 100 meters. Bluetooth technology is very suitable for low-power digital devices to share data with each other, such as mobile phones and PDAs. Bluetooth technology operates in the 2.4GHz band and can transmit up to 1Mbps of data in one second. Compared with infrared technology, the heat generated by Bluetooth power consumption is very small, and there is no need to consider the angle measurement problem of infrared transmission path.

[0020] In one prior art, when a Bluetooth keyboard is connected to multiple devices, the user needs to operate a computer, open a browser, enter a URL, log in to the network version control software, and then control the set-top box and finally control the target device.

[0021] In the prior art, when a Bluetooth keyboard is connected to multiple devices, multiple steps are often required to achieve control of the target device, which causes the user to have trouble operating the device.

[0022] To solve the above problems, refer to Figure 1 The first embodiment of the present invention provides a method for controlling the connection between a Bluetooth keyboard and multiple devices, comprising the following steps: S11, obtaining current user behavior data of each device; S12, inputting the current user behavior data into a pre-trained user usage habit model for prediction, and obtaining a predicted device that the current user is most likely to operate; S13, searching from a connection device list according to the predicted device to obtain a connection instruction of a corresponding device; wherein the connection device list is a database storing connection instructions of each device; S14, in response to a link selection operation triggered by the user, linking the corresponding device with the Bluetooth keyboard according to the link instruction.

[0023] Compared with the prior art, when a Bluetooth keyboard is connected to multiple devices, the user needs to operate the computer, open the browser, enter the URL, log in to the network version control software, and then control the set-top box, and finally control the target device. The present invention constructs a usage habit model through intelligent user historical behavior analysis, accurately predicts the devices that the user wants to link and automates the device linking operation, realizes efficient interaction between the Bluetooth keyboard and each device, and improves the user's operational convenience. Therefore, the present invention can solve the problem that when the Bluetooth keyboard is connected to multiple devices, multiple steps are required to achieve control of the target device, which is troublesome for the user to operate.

[0024] In step S11, the current user behavior data of each device is obtained.

[0025] It should be noted that the current user behavior data of each device includes the operation record of the user using the device, the operation frequency of the user using the device, and the time regularity data of the user using the device. The operation record of the user using the device refers to the data generated when the user performs various link operations on the device. The record of the user using the device operation can be realized through sensors and storage. By analyzing the user's operation record, the user's use path on the device can be drawn, and the functional modules most frequently accessed by the user can be identified, as well as the obstacles that the user may encounter in the process. The operation frequency of the user using the device refers to the number of times the user performs a specific operation within a certain period of time. The number of user operations can be automatically counted by the controller. For example, in the process of connecting a Bluetooth keyboard with multiple devices, the user's operation frequency data can be automatically collected at a set time interval (such as every hour or every day). The number of times the user links between various devices can be recorded, and the changing trend of user behavior can be monitored. In some cases, the status of the device itself (such as online time, standby time, etc.) can also reflect the user's operation frequency. By collecting the data of these state changes, the user's activity level can be inferred. By analyzing the operation frequency, the device can be divided into high-active use devices, medium-active use devices, and low-active user use devices. By distinguishing each device, the device that the user needs to link can be more accurately predicted. The time pattern data of users using devices refers to the specific time period during which users use devices in a day or a week. Every time a user uses a Bluetooth keyboard to connect a device, an exact timestamp is recorded. This time can be accurate to milliseconds to ensure the accuracy of the data. By analyzing the user's login time and duration, the peak usage period of users using Bluetooth keyboards to connect devices can be identified. For example, some users may be most active between 7 and 9 p.m., while others may use devices during their morning commute. By distinguishing the peak usage periods of each device, it is possible to more accurately predict the devices that users need to connect.

[0026] In step S12, the current user behavior data is input into a pre-trained user usage habit model for prediction, so as to obtain the predicted device that the current user is most likely to operate.

[0027] Reference Figure 2 In one implementation, the method includes: S121, extracting and analyzing the current user behavior data to obtain first user behavior data and second user behavior data; S122, performing calculation according to the first user behavior data and the second user behavior data to obtain an operation distance between devices; S123, performing a model building operation according to the first user behavior data, the second user behavior data and the operation distance between the devices to obtain a triangle model; S124, updating the user usage habit model according to the triangle model input to obtain an updated final user usage habit model; S125, performing value assignment initialization according to the end user's usage habit model to obtain a model vertex value; S126, when the model vertex value is greater than a preset model vertex threshold, the usage probability of each device is calculated in turn to obtain the usage probability value of each device, and the device corresponding to the highest usage probability value is extracted to obtain the predicted device.

[0028] In step S123, the model building operation is performed according to the first user behavior data, the second user behavior data and the operation distance between the devices to obtain a triangular model, including: building according to the first user behavior data and the second user behavior data to obtain a minimum binary group; adding the operation distance between the devices to the minimum binary group for initialization to obtain an initial binary group; training according to the initial binary group through a machine learning method to obtain a triangular model.

[0029] In step S126, the assignment initialization is performed according to the current user usage habit model to obtain the model vertex value, including: obtaining the model vertex value by the following method: Wherein: M is the model vertex value; m is the user operation experience value; δ1 is the first associated parameter; δ2 is the second associated parameter; P represents the frequency of turning on or off the device; Time represents the time regularity data of turning on or off the device.

[0030] The model vertex value is a comprehensive indicator that reflects the user's usage habits. It can describe the interaction intensity between the user and a specific device and is used for subsequent probability calculations to predict the device that the user is most likely to choose. By optimizing the model vertex value, the understanding of user behavior can also be improved, so that the recommendation controller can more accurately predict user needs and realize the connection between the Bluetooth keyboard and various devices. The user operation experience value represents the experience value accumulated by the user in the past interaction with the device, which can be quantified as the user's familiarity with the device, the frequency of use, and the number of effective operations. The user operation experience value can be obtained by analyzing the user's historical behavior data. For example, the total time of using the Bluetooth keyboard to link the device, the number of successful operations, and other data are used to comprehensively calculate an experience value. If the user often uses the Bluetooth keyboard to link a device, the value of (m) will be higher, indicating that the user is familiar with the device and has a strong willingness to use it.

[0031] It is worth noting that the first associated parameter is a weight coefficient, which indicates the partial correlation of the time regularity change of the user turning on / off the device to the mathematical model value, and the partial correlation of the operation frequency change of the user turning on / off the device to the mathematical model value. It is used to adjust the influence of the device turning on or off time regularity data (Time) on the model vertex value (M), and determines the importance of the time factor in the calculation of the model vertex value. If the first associated parameter is large, it means that the time regularity (such as the user's habit of using the device in a specific time period) is considered to be a key factor; conversely, if it is small, it means that the influence of the time factor is weak. This parameter can also be dynamically adjusted through a data-driven method to ensure that the model output is more in line with actual user behavior.

[0032] It is worth noting that the second associated parameter is another weight coefficient, which indicates the partial correlation of the change in the user's operating frequency of turning on / off the device on the mathematical model value. It is used to adjust the degree of influence of the frequency of turning on or off the device (P) on the model vertex value (M). The size of this parameter directly affects the contribution of frequency to the final result. If the second associated parameter is large, it means that the impact of the device's usage frequency on user habits is highly valued; conversely, if it is small, it means that the impact of frequency on user habits is relatively minor. This parameter can be automatically optimized through the machine learning training process to adapt it to the behavior patterns of different types of users. The frequency of turning on or off a device indicates the frequency of a user turning on or off a specific device within a certain period of time. This value can be obtained by counting the total number of times the user has turned on and off the device in the past period of time. A higher (P) value means that the user frequently uses the device, indicating a higher preference or dependence; while a lower (P) value indicates that the user may rarely touch the device.

[0033] It is worth noting that the time pattern data for turning on or off devices represents the time distribution of users turning on or off devices in a day or a week. By analyzing user history records, we can extract the peak times of users using Bluetooth keyboard-linked devices in various time periods, such as morning, noon, and evening. Time series analysis methods can be used to capture these patterns. Some devices may only be frequently used at specific times. For example, home entertainment devices are more frequently used at night, while work-related devices may be more frequently used during the day on weekdays.

[0034] Extract and analyze key user behavior data from current user behavior data, such as the user's active time period, commonly used device types, and device usage frequency, and divide user behavior data into the first category and the second category. The first category can be the main device data, and the second category can be the auxiliary or occasionally used device data. At the same time, it is also necessary to clean the user behavior data, that is, remove invalid or duplicate records to ensure the accuracy of the data. After obtaining the first user behavior data and the second user behavior data, select a suitable mathematical model (such as Euclidean distance, Manhattan distance, etc.) to calculate the relative position between devices. According to the frequency of device use and the user's interaction intensity, different weights are assigned to the distance between different devices, which can make the calculation results more accurate.

[0035] Specifically, a triangle model is constructed with two devices as vertices and the operating distance between the two devices as the long side. The vertex value of the triangle model is 1. Check whether the model conforms to the actual usage and make adjustments if necessary to improve the accuracy of the model. The data of the triangle model is used as input and imported into the user usage habit model. Machine learning algorithms (such as decision trees, neural networks, etc.) are applied to retrain the usage habit model to adapt to new user behavior data, and an updated end-user usage habit model is obtained, which can more accurately predict the device that the user wants to use the Bluetooth keyboard to link. After the model vertex value is calculated by the preset formula for the end-user usage habit model, check whether the model vertex value is greater than the preset threshold. When the model vertex value is less than the preset threshold, the device is defined as an inactive device. For each device, its usage probability is calculated based on the data provided by the model. Statistical methods (such as Bayesian inference) are usually used for prediction. The usage probability values ​​of all devices are sorted, and the device with the highest probability value is extracted as the device that the user is most likely to use next.

[0036] In another implementation, the method further includes obtaining the predicted success rate of each device; comparing the predicted success rates of each device, and when the predicted success rate is higher than a preset success rate threshold, selecting the device corresponding to the highest predicted success rate as the predicted device to obtain the predicted device; when the predicted success rate is lower than the preset success rate threshold, selecting the device closest to construct a tuple to obtain a first tuple, adding the first tuple to the user usage habit model for correction to obtain a corrected final user usage habit model, and performing calculations based on the final user usage habit model to obtain a predicted device.

[0037] In specific implementations, the predicted success rate refers to the probability of a device being successfully selected and used by a user, obtained by analyzing historical data. The final value is usually expressed as a percentage. For example, if device A is successfully used by users in 80% of cases, its predicted success rate is 80%. The predicted success rates of all devices are sorted into a list or array, and these success rates are sorted. The preset success rate threshold is a standard automatically set by the controller to evaluate whether the predicted success rate of the device is high enough. When the predicted success rate is higher than this threshold, it can be considered that the device performs well in the current environment and is suitable for recommending to users to use a Bluetooth keyboard for connection. When it is lower than this threshold, it means that the device is not ideal.

[0038] It should be noted that the training process of the user usage habit model includes: obtaining user historical behavior data from a user historical behavior database; constructing a model based on the user historical behavior data to obtain an initial usage habit model; training the initial usage habit model, and when the number of training times is greater than or equal to the preset maximum number of training times, determining that the training is completed, and obtaining the user usage habit model.

[0039] The user's historical behavior data comes from the monitoring data of the past five years. The user's historical behavior data is used to train the user's usage habit model, so that the model can learn the complex relationship and change rules between the current user's behavior data. During the training process, the model will try to find the best mapping relationship between input data and output. The trained user usage habit model can be used to predict the best device and help users quickly use the Bluetooth keyboard to connect with the device.

[0040] In step S13, a connection instruction of a corresponding device is obtained by searching the connection device list according to the predicted device; wherein the connection device list is a database storing the connection instructions of each device.

[0041] The connected device list is a database or data structure, which usually adopts efficient data structures such as hash tables or tree structures, so that the query time complexity is reduced to O(1) or O(log n). It is used to store relevant information of all connectable devices, including the link instructions of each device and its functional description, including: device ID, which is a unique identifier used to distinguish different devices, usually a string or integer string; device type, which refers to the category to which the device belongs, such as smart light bulbs, thermostats, surveillance cameras, etc.; link instructions, which are specific instructions for controlling devices, such as turning on, off, adjusting brightness, adjusting temperature, etc.; network protocol, which is the communication protocol used by the specified device, such as Wi-Fi, Bluetooth, Zigbee, etc., in order to correctly implement the connection. The controller sends a query request to the connected device list and searches based on the unique identifier of the predicted device (such as the device ID). The entry in the database will be matched with the ID of the predicted device to find the corresponding record. After a successful query, this operation can be recorded in the log system for future analysis and monitoring. When a record matching the predicted device is found, the controller will extract the link instructions therein. After obtaining the link instructions of the device, the controller will execute these instructions to establish a connection with the predicted device, which usually involves the following aspects: Sending instructions, which is used to send link instructions to the target device through an appropriate network protocol (such as HTTP request, MQTT message, etc.); Status feedback, which is used to monitor the response of the device and confirm whether the device successfully executes the instruction, for example, the device responds with information such as "enabled" or "operation failed". If the corresponding device ID is not found, the system should return an error message or notify the controller that the device does not exist to avoid unnecessary subsequent operations.

[0042] In step S14, in response to the link selection operation triggered by the user, the corresponding device is linked to the Bluetooth keyboard according to the link instruction.

[0043] In one implementation, in response to a link selection operation triggered by a user, a predicted correct link state and a predicted incorrect link state are obtained; when the user selects the correct link state, the Bluetooth keyboard is directly linked to the predicted device according to the link instruction; when the user selects the incorrect link state, the actual link device actually selected by the user for linking is recorded, and analysis is performed based on the actual link device to obtain actual device state data; the user usage habit model is updated based on the actual device state data to obtain an updated final user usage habit model; A prediction device is regenerated according to the end-user usage habit model, and the Bluetooth keyboard is linked to the prediction device.

[0044] It should be noted that the link selection operation is a selection made by the user through the Bluetooth keyboard interface, which is intended to connect a specific device. For example, the user may perform this operation by clicking a button, etc. The Bluetooth keyboard interface will display a list of connectable devices, including the predicted device and its status. The user can select "correct link status" or "wrong link status" to indicate whether the predicted device selected by the controller meets the user's expectations. The correct link status refers to the device status in which the user believes that the device selected by the controller to link is correct and can be connected. When the user selects "correct link status", the controller directly connects the Bluetooth keyboard to the predicted device according to the link instruction obtained in advance. Among them, the connection process of the Bluetooth keyboard includes: sending a connection request, which means that the Bluetooth keyboard sends a Bluetooth connection request to the predicted device; confirming the connection, which means that the predicted device returns a connection status confirmation after receiving the request; establishing a connection: Once the confirmation is successful, the Bluetooth keyboard establishes a stable communication channel with the device to facilitate subsequent data transmission and control.

[0045] It should be noted that the error link state means that the user believes that the device selected by the controller to link is not the device he wants to connect. If the user selects the "error link state", the controller needs to record the device actually selected by the user, that is, the device the user is trying to connect to, and collect the status data of the device, which includes: the current working status of the device (on / off), the response time and performance indicators of the device, and the frequency and time period of the user's attempt to connect. Based on the status data of the actual selected device, the controller will update the existing user usage habit model. In the update process, machine learning algorithms (such as decision trees, cluster analysis, etc.) will be used to analyze the collected data, extract the user's behavior characteristics, and input them into the user usage habit model for update according to the user's behavior characteristics to obtain an updated final user usage habit model. According to the updated user usage habit model, the controller will regenerate a set of predicted devices. This process involves statistical analysis of the user's past behavior to identify the correlation between devices. As the new predicted device is generated and confirmed, the controller will try to link the Bluetooth keyboard with the new predicted device again.

[0046] In order to facilitate the understanding of the present invention, some preferred embodiments of the present invention are further described below.

[0047] The following describes the working process of the present invention using a common scenario as an example. Figure 3 , is a schematic diagram of the controller connection structure provided by the present invention.

[0048] In the office of a technology company, a user is making a project report. He needs to frequently switch between different devices, including laptops, tablets, and projectors. The user is accustomed to using a Bluetooth keyboard for text input and hopes to be able to quickly switch between connected objects to improve work efficiency. When the user starts working, the controller monitors the devices he has recently connected and the frequency of use in real time through the Bluetooth adapter. For example, in the past few days, the user mainly used a laptop and a projector, and occasionally used a tablet to look up information. A user habit model based on machine learning is running in the background of the controller. The model has been trained with historical user behavior data and can identify user usage patterns. For example, when a user opens a document, the most likely device to be connected is a laptop. When preparing to make a report, the projector is usually connected. When the user picks up the Bluetooth keyboard and presses the connect button, the controller obtains the current user behavior data in real time and inputs it into the user usage habit model. After model analysis, a prediction result is quickly generated, judging that the target device that the user is most likely to want to connect to is a projector. Based on the prediction result, the controller searches for the link instruction of the projector in the list of connected devices. This list is a database containing all known devices and their corresponding link instructions. The controller uses an efficient retrieval algorithm to accurately and quickly locate the link information of the projector. Once the link command for the projector is found, the controller automatically performs the connection operation without the user having to manually select the device to connect. At this point, the projector immediately establishes a connection with the Bluetooth keyboard, and the user can start the report without stopping to perform tedious device switching. During the report, if the user needs to find information on the tablet, simply press the switch key on the Bluetooth keyboard. The controller will once again update the habit model in real time based on the user's usage behavior and predict the device that may be connected next. Since the user has been frequently switching between tablets during the meeting, the controller eventually sets the tablet as a high-priority device. The traditional method requires users to manually select and connect devices, and this process greatly shortens the connection time through automation, allowing users to focus on the content of the report. With the support of the predictive model, users can switch between devices in a smoother way, and no longer delay work due to device connection problems. Users no longer feel that the operation is complicated, especially in a high-pressure work environment, reducing the anxiety caused by device switching. Users have developed a sense of trust in this new technology, believing that the controller can understand their needs and respond quickly, further enhancing their dependence on smart office equipment.

[0049] In summary, the present invention discloses a method for controlling the connection of a Bluetooth keyboard with multiple devices, including obtaining the current user behavior data of each connected device in real time, inputting the collected current user behavior data into a pre-trained user usage habit model, generating a prediction result about the device that the user is most likely to operate, and based on the predicted device, the controller will search in the list of connected devices. The list of connected devices is a database containing all known devices and their corresponding link instructions. Through an efficient retrieval algorithm, the link instruction of the predicted device can be quickly located. In response to the user-triggered selection link operation, the target device and the Bluetooth keyboard will be automatically connected according to the acquired link instruction. The present invention constructs a usage habit model through intelligent user historical behavior analysis, accurately predicts the device that the user wants to link, and automates the device link operation, so as to achieve efficient interaction between the Bluetooth keyboard and each device and improve the user's operating convenience. Therefore, the present invention can solve the problem that when the Bluetooth keyboard is connected to multiple devices, multiple steps are required to achieve control of the target device, which is troublesome for the user to operate.

[0050] Reference Figure 4 The second embodiment of the present invention provides a link control system for a Bluetooth keyboard and multiple devices, including: A data acquisition module is used to obtain the current user behavior data of each device; A device acquisition module, used to input the current user behavior data into a pre-trained user usage habit model for prediction, and obtain the predicted device that the current user is most likely to operate; An instruction acquisition module, used to search from a connection device list according to the predicted device to obtain a connection instruction of the corresponding device; wherein the connection device list is a database storing connection instructions of each device; The instruction link module is used to respond to the selection link operation triggered by the user and realize the link between the corresponding device and the Bluetooth keyboard according to the link instruction.

[0051] It should be noted that a connection control system of a Bluetooth keyboard and multiple devices provided in an embodiment of the present invention is used to execute all process steps of a connection control method of a Bluetooth keyboard and multiple devices in the above embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be elaborated on.

[0052] The embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an algorithm program. When the processor executes the computer program, the steps in the above-mentioned method for controlling the connection between the Bluetooth keyboard and multiple devices are implemented, such as Figure 1Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned system embodiments are realized, such as the instruction linking module.

[0053] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0054] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0055] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0056] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store the application required for the operation controller and at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0057] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or system capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0058] It should be noted that the system embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the system embodiment provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0059] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling the connection between a Bluetooth keyboard and multiple devices, characterized in that: Executed by the controller, including: Get the current user behavior data of each device; Inputting the current user behavior data into a pre-trained user usage habit model for prediction, and obtaining the predicted device that the current user is most likely to operate; Searching the connection device list according to the predicted device to obtain the connection instruction of the corresponding device; wherein the connection device list is a database storing the connection instructions of each device; In response to a link selection operation triggered by the user, the corresponding device is linked to the Bluetooth keyboard according to the link instruction.

2. The method for controlling the connection between a Bluetooth keyboard and multiple devices according to claim 1, characterized in that: The step of inputting the current user behavior data into a pre-trained user usage habit model for prediction to obtain the predicted device that the current user is most likely to operate includes: Extract and analyze the current user behavior data to obtain first user behavior data and second user behavior data; Calculating according to the first user behavior data and the second user behavior data to obtain an operation distance between devices; Perform a model building operation according to the first user behavior data, the second user behavior data and the operation distance between the devices to obtain a triangle model; Input the triangle model into the user usage habit model for updating, to obtain an updated final user usage habit model; Performing value initialization according to the end user's usage habit model to obtain model vertex values; When the model vertex value is greater than a preset model vertex threshold, the usage probability of each device is calculated in turn to obtain the usage probability value of each device, and the device corresponding to the highest usage probability value is extracted to obtain the predicted device.

3. The method for controlling the connection between a Bluetooth keyboard and multiple devices according to claim 2, characterized in that: The assigning and initializing according to the current user usage habit model to obtain the model vertex value includes: The model vertex values ​​are obtained in the following way: Wherein: M is the model vertex value; m is the user operation experience value; δ1 is the first associated parameter; δ2 is the second associated parameter; P represents the frequency of turning on or off the device; Time represents the time regularity data of turning on or off the device.

4. The method for controlling the connection between a Bluetooth keyboard and multiple devices according to claim 1, characterized in that: The step of responding to the user-triggered selection link operation and linking the corresponding device with the Bluetooth keyboard according to the link instruction includes: In response to a link selection operation triggered by a user, a predicted correct link state and a predicted incorrect link state are obtained; When the user selects the correct link state, the Bluetooth keyboard directly links with the predicted device according to the link instruction; When the user selects the erroneous link state, the actual link device selected by the user for linking is recorded, and analysis is performed based on the actual link device to obtain actual device state data; Updating the user usage habit model according to the actual device status data to obtain an updated end-user usage habit model; A prediction device is regenerated according to the end-user usage habit model, and the Bluetooth keyboard is linked to the prediction device.

5. The method for controlling the connection between a Bluetooth keyboard and multiple devices according to claim 2, characterized in that: The step of constructing a model according to the first user behavior data, the second user behavior data and the operation distance between the devices to obtain a triangle model includes: Constructing according to the first user behavior data and the second user behavior data to obtain a minimum two-tuple; Add the inter-device operation distance into the minimum binary group for initialization to obtain an initial binary group; A triangle model is obtained by training with a machine learning method according to the initial binary group.

6. The method for controlling the connection between a Bluetooth keyboard and multiple devices according to claim 1, characterized in that: The inputting the current user behavior data into a pre-trained user usage habit model for prediction to obtain the prediction device most likely to be operated by the current user also includes: Get the predicted success rate of each device; Comparing the predicted success rates of the devices, when the predicted success rates are higher than a preset success rate threshold, selecting the device corresponding to the highest predicted success rate as the predicted device, to obtain the predicted device; When the prediction success rate is lower than a preset success rate threshold, the nearest device is selected to construct a tuple to obtain a first tuple, and the first tuple is added to the user usage habit model for correction to obtain a corrected final user usage habit model; Calculation is performed according to the end-user usage habit model to obtain a predicted device.

7. The method for controlling the connection between a Bluetooth keyboard and multiple devices according to claim 1, characterized in that: The training process of the user usage habit model includes: Obtain user historical behavior data from a user historical behavior database; Perform a model building operation based on the user's historical behavior data to obtain an initial usage habit model; The initial usage habit model is trained, and when the number of training times is greater than or equal to the preset maximum number of training times, the training is determined to be completed, and the user usage habit model is obtained.

8. A link control system for a Bluetooth keyboard and multiple devices, characterized in that: include: A data acquisition module is used to obtain the current user behavior data of each device; A device acquisition module, used to input the current user behavior data into a pre-trained user usage habit model for prediction, and obtain the predicted device that the current user is most likely to operate; An instruction acquisition module, used to search from a connection device list according to the predicted device to obtain a connection instruction of the corresponding device; wherein the connection device list is a database storing connection instructions of each device; The instruction link module is used to respond to the selection link operation triggered by the user and realize the link between the corresponding device and the Bluetooth keyboard according to the link instruction.

9. An electronic device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the link control method between the Bluetooth keyboard and multiple devices as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for controlling the connection between a Bluetooth keyboard and multiple devices as described in any one of claims 1 to 7.

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