Multi-device automatic switching method and system of intelligent mouse

Abstract

The invention provides a multi-device automatic switching method and system for an intelligent mouse, and the method comprises the steps: collecting the connection signal strength of a plurality of external computing devices through a built-in wireless signal receiver of the intelligent mouse, and constructing a dynamically updated signal strength matrix; calculating the optimal probability of switching the mouse to each device by combining the device feature vector, the spatial position relationship, the use scene similarity and the historical use frequency; constructing a switching cost function based on a dynamic programming algorithm, and solving an optimal equipment switching path; through a switching pre-connection mechanism, a self-adaptive learning algorithm, a switching rollback mechanism and a mistaken touch prevention mechanism, efficient, stable and low-delay automatic switching of the intelligent mouse is realized. According to the method, the connection strategy can be dynamically adjusted according to the real-time scene, the mouse parameter configuration is optimized, cross-device switching is more intelligent and seamlessly connected, and the user operation experience is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of computer peripheral devices, and in particular relates to a multi-device automatic switching method and system for an intelligent mouse. Background Art

[0002] With the increasing popularity of computers and smart devices, users often need to interact across multiple devices, such as desktops, laptops, and tablets. However, traditional mouse devices are primarily designed for single-device use and are unable to meet the needs of modern users for efficient cross-device collaboration in a multi-device environment. Existing multi-device mouse switching solutions have the following major problems:

[0003] (1) Most of them use manual switching, such as button switching, software interface switching, etc. Users need to actively select the target device, which is cumbersome and affects the smoothness of work. Although some wireless mice support Bluetooth / wireless pairing of multiple devices, users still need to switch through physical buttons or manual configuration, lacking an intelligent switching experience;

[0004] (2) Some mice can use software to assist in switching, but manual settings are still required, and they cannot automatically adapt to the device's usage scenario. Device switching does not fully consider factors such as signal strength, device priority, spatial location, and frequency of use, resulting in unintelligent switching decisions. Switching between devices is usually delayed, making it difficult to achieve seamless connection.

[0005] (3) The lack of an intelligent pre-connection mechanism requires re-establishing the wireless connection when switching devices, which adds additional delays. In addition, when an abnormality occurs, the mouse lacks a switch rollback mechanism, which can easily cause the mouse to become unresponsive or fail to switch, affecting normal use. Because the switch is too sensitive or unstable, it may be triggered by mistake, affecting the user's focus on the target device.

[0006] Therefore, we need to develop a multi-device automatic switching method and system for an intelligent mouse, which can improve the intelligence level of multi-device switching of the intelligent mouse, reduce switching delay, and enhance user experience. Summary of the Invention

[0007] The purpose of the present invention is to provide a multi-device automatic switching method and system for an intelligent mouse, so as to solve the problems mentioned in the above background technology that the existing mouse multi-device switching solution is too dependent on manual operation, the switching process lacks intelligence and adaptability, and lacks optimization mechanism, which affects the user experience.

[0008] To achieve the above objectives, the present invention provides a method for automatically switching multiple devices of an intelligent mouse, the method being as follows:

[0009] Step S1: collecting the connection signal strength between the intelligent mouse and each external computing device through the multi-channel wireless signal receiver built into the intelligent mouse, and constructing a dynamically updated signal strength matrix based on the connection signal strength;

[0010] Step S2: Obtain the feature vector of the external computing device and calculate the spatial position relationship between the intelligent mouse and the external computing device;

[0011] Step S3: Calculate the similarity of the usage scenarios of the intelligent mouse and the external computing device in the current scenario, and the historical usage frequency, and combine the signal strength matrix, eigenvector, and spatial position relationship to obtain the switching probability of the intelligent mouse switching to each external computing device in the current scenario. The calculation formula is:

[0012]

[0013] in, Indicates that the intelligent mouse is currently connected to External computing devices The connection signal strength between Indicates that the intelligent mouse and External computing devices The spatial relationship between Represents the usage scenario similarity between the 𝑖th and 𝑗th external computing devices 、 Respectively represent the intelligent mouse , The historical usage frequency of external computing devices, n is the total number of external computing devices, is the weight coefficient, satisfying ; The value range is [0, 1]. The larger the value, the smart mouse switches to the The greater the possibility of an external computing device;

[0014] Based on the switching probability, obtaining a switching probability vector of the intelligent mouse switching to each external computing device in the current usage scenario;

[0015] Step S4: Based on the switching probability vector and in combination with multiple cost factors of the external computing device switching, a switching cost function of the external computing device is constructed, and a dynamic programming algorithm is used to solve the optimal switching path;

[0016] Step S5: According to the optimal switching path, the switching pre-connection mechanism is controlled by a step function, and the adaptive learning algorithm, the switching rollback mechanism and the switching anti-false triggering mechanism are called to finally achieve the optimal switching of the intelligent mouse between external computing devices.

[0017] Based on the above scheme, + Signal strength matrix within 1 sampling period Perform dynamic updates as follows:

[0018] Assume that in the past In the sampling period, the historical record of the signal strength matrix is , then + Signal strength matrix within 1 sampling period The update formula is: ,in, is the time decay factor, satisfying , , that is, from the current time The more recent the historical signal strength matrix record, the greater the weight it has in the dynamic update.

[0019] Based on the above solution, the intelligent mouse is connected to the external computing device through a wired or wireless method, and a device information request is sent. After the external computing device receives the device information request, it sets its own device type to , operating system type and the user's priority for the device Packaged as a feature vector, The feature vector of an external computing device is expressed as: ;

[0020] Spatial position relationship: the spatial distance between the intelligent mouse and the external computing device and spatial perspective To characterize: ,in is the three-dimensional space coordinate of the intelligent mouse, is the three-dimensional space coordinate of the external computing device, is the current environmental attenuation factor;

[0021] Based on spatial location relationship , we can further get the spatial position relationship vector .

[0022] Based on the above scheme, based on the feature vector, the intelligent mouse is calculated in the current usage scenario and the and The similarity of usage scenarios of external computing devices is calculated using the weighted Euclidean distance method. The calculation formula is: ,in, is a positive real number, representing the weight coefficient of the three characteristic attributes of device type, operating system type and user priority, satisfying ;

[0023] Calculate the intelligent mouse The historical usage frequency of external computing devices is calculated as follows: ,in, Indicates that in the past T sampling periods, the intelligent mouse and the The cumulative number of interactive operations between external computing devices, is the number of sampling cycles, The value range is [0, 1]. The larger the value, the closer the intelligent mouse is to the first The more frequent the interaction with external computing devices.

[0024] Based on the above scheme, based on the switching probability vector and the intelligent mouse and The spatial position relationship vector of an external computing device is then combined with the pre-set signal strength cost weight , spatial location cost weight and scene similarity cost weight , construct the switching cost function of the external computing device ;

[0025] The switching cost function Indicates that the intelligent mouse is Switch the external computing device to The comprehensive switching cost generated by the external computing device is calculated as follows: ,in, Indicates that the intelligent mouse is in external computing devices and The spatial position relationship between the external computing devices is calculated according to the first Quantity and the jth component The calculation shows that: ; The connection signal strength of the intelligent mouse when it switches from the i-th external computing device to the j-th external computing device can be obtained from the first signal strength matrix at the current moment. Rank Column elements are given; Indicates that the intelligent mouse is in external computing devices and The similarity of usage scenarios between external computing devices.

[0026] Based on the above scheme, construct an n×n switching cost matrix:

[0027]

[0028] Among them, the matrix elements Indicates that the intelligent mouse is Switch the external computing device to The comprehensive switching cost of external computing devices, Indicates that the intelligent mouse remains in The cost on the external computing device is 0;

[0029] The system takes the switching cost matrix as an input parameter. Based on the switching cost matrix, the system uses a dynamic programming algorithm to solve the optimal switching path from the currently connected device to the target device according to the environmental attenuation factor, the maximum allowable switching delay set by the user, and the device configuration synchronization time.

[0030] Based on the above scheme, An external computing device is considered as a directed weighted graph Node, node and nodes The weight of the directed edge between them is the comprehensive switching cost , that is, converting the optimal switching path problem into a directed weighted graph Find the minimum cost path from the starting point to the end point;

[0031] Through the dynamic programming algorithm, the directed weighted graph is recursively calculated from the bottom up The minimum cost of each node in , and finally get from the starting point To the end The minimum cost And the corresponding minimum cost path is the optimal switching path.

[0032] Based on the above scheme, based on the optimal switching path, according to the node sequence in the path, from the starting point First, build a virtual connection between the intelligent mouse and the external computing device step by step, specifically:

[0033] Set the current node to , the successor node is , each edge in the optimal switching path , through the step function Controlling the intelligent mouse and external computing devices The pre-connection between the step function as follows:

[0034]

[0035] in, Indicates that the intelligent mouse is connected to an external computing device Switching to an external computing device The switching probability, ∈(0, 1) is the preset switching probability threshold;

[0036] when hour, Output is 1, start the switching pre-connection mechanism, establish the intelligent mouse and external computing device Otherwise, the pre-connection mechanism will not be activated and no pre-connection will be performed.

[0037] Based on the above scheme, the switching anti-false triggering mechanism is to track and analyze the operating behavior of the intelligent mouse, identify the continuous and stable mouse action pattern, mark it as "focus mode", and avoid external computing device switching in focus mode through dynamic threshold control.

[0038] In another aspect, the present invention provides a multi-device automatic switching system for an intelligent mouse, comprising:

[0039] A multi-channel wireless signal receiver is wirelessly connected to multiple wireless communication units built into the intelligent mouse, and is used to collect the connection signal strength between the intelligent mouse and multiple external computing devices in real time, and dynamically build and maintain a signal strength matrix;

[0040] The device identification module is used to extract the characteristic information of the external computing device, generate the characteristic vector of the external computing device, and calculate the spatial position relationship between the intelligent mouse and each external computing device, providing necessary reference information for subsequent external computing device switching decisions;

[0041] The scenario analysis module is responsible for calculating the similarity and historical usage frequency of the intelligent mouse and different external computing devices. It also dynamically calculates the probability of the intelligent mouse switching to each external computing device based on the real-time connection signal strength and spatial position relationship.

[0042] A switching decision module is used to construct a switching cost function for the external computing device based on the switching probability vector and multiple cost factors of the external computing device switching, and to solve the optimal switching path using a dynamic programming algorithm;

[0043] The switching execution module is used to execute the connection state conversion between the intelligent mouse and the external computing device according to the optimal device switching solution, that is, the optimal switching path, and control the opening and closing of the switching pre-connection mechanism through a step function.

[0044] The present invention has the following advantages and effects compared to the prior art:

[0045] (1) Based on the signal strength matrix, device feature vector, and spatial position relationship, the switching probability between the intelligent mouse and the external computing device is calculated, and combined with the historical usage frequency, the target switching device is automatically selected to avoid manual switching. The target connection device of the mouse can be intelligently identified and automatically switched, reducing manual operations and improving the smoothness of interaction.

[0046] (2) Based on the dynamic programming algorithm, the optimal switching path is calculated. Combined with the scene similarity analysis, user needs are intelligently predicted and the switching strategy is dynamically adjusted to make the switching of mouse connection devices more intelligent and efficient. The optimal connection device can be adaptively selected according to the actual usage scenario, thus reducing unnecessary switching and improving the user experience.

[0047] (3) Introduce switching pre-connection mechanism, switching rollback mechanism, and anti-false triggering mechanism to reduce switching delay and ensure switching stability under abnormal conditions, avoid unintentional switching, and through adaptive learning algorithm, dynamically adjust the mouse parameters with the connected device, making the switching of mouse-connected devices smoother and more seamless. Even if the target device is abnormal, it can automatically roll back to reduce false switching. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0049] Figure 1 This is a flow chart of a multi-device automatic switching method for an intelligent mouse provided by an embodiment of the present invention;

[0050] Figure 2 This is a structural diagram of a multi-device automatic switching system for an intelligent mouse provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to more clearly illustrate the purpose, technical solutions and advantages of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The example implementation methods can be implemented in various forms and should not be understood as being limited to the examples described herein. On the contrary, these implementation methods are provided to make the present invention more comprehensive and complete, and to fully convey the concepts of the example implementation methods to those skilled in the art.

[0052] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present invention. However, it will be appreciated by those skilled in the art that the technical solutions of the present invention can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present invention.

[0053] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0054] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0055] The present invention will be described in detail below with reference to specific embodiments:

[0056] Example 1:

[0057] As attached Figure 1 As shown, embodiment 1 of the present invention provides a method for automatically switching multiple devices of an intelligent mouse, and the specific steps of the method are as follows:

[0058] Step S1: collecting the connection signal strength between the intelligent mouse and each external computing device through the multi-channel wireless signal receiver built into the intelligent mouse, and constructing a dynamically updated signal strength matrix based on the connection signal strength;

[0059] Specifically, the multi-channel wireless signal receiver is connected to the built-in There is a one-to-one correspondence between wireless communication units, where . No. Wireless communication unit With the External computing devices Establish a wireless connection between them and periodically send wireless detection signals; take over Extract the signal strength indicator parameter from the returned wireless response signal , as the signal strength sampling value. In a sampling period, the multi-channel wireless signal receiver Wireless communication unit A set of signal strength sampling values ​​are collected at , and according to Reference signal strength of wireless response signal , calculate the intelligent mouse and the External computing devices The connection signal strength between , the calculation formula is: , where The larger the value, the and The higher the wireless connection quality, the better. Conversely, the lower the connection quality, the worse.

[0060] In obtaining Connection signal strength After that, the multi-channel wireless signal receiver selects the n largest connection signal strengths to construct the Within a sampling period dimensional signal strength matrix :

[0061]

[0062] Among them, the matrix elements Indicates in In the sampling period, from the The visual angle of the external computing device is measured by the intelligent mouse and the External computing devices The connection signal strength between is a non-negative real number and satisfies .

[0063] Furthermore, as the intelligent mouse and external computing device Dynamic changes in relative positions between There will also be corresponding changes, in order to truly reflect the The real-time connection quality between + Signal strength matrix within 1 sampling period Perform dynamic updates as follows:

[0064] Assume that in the past In the sampling period, the historical record of the signal strength matrix is , then + Signal strength matrix within 1 sampling period The update formula is:

[0065]

[0066] in, is the time decay factor, satisfying , , that is, from the current time The more recent the historical signal strength matrix record, the greater the weight it has in the dynamic update.

[0067] Step S2: Obtain the feature vector of the external computing device and calculate the spatial position relationship between the intelligent mouse and the external computing device;

[0068] Specifically, the intelligent mouse is connected to an external computing device by wired or wireless means. Establish a data communication connection and send a device information request. External computing device After receiving the device information request, the device type , operating system type and the user's priority for the device Equivalent characteristic parameters are encapsulated as characteristic vectors Among them, the device type Can be an enumeration value, for example Indicates desktop computers, Indicates laptop computers, Indicates tablet computer, etc.; operating system type It can also be an enumeration value, for example Indicates Windows operating system, Indicates the Linux operating system, Indicates Android operating system, etc.; user priority It is a real number in the interval [0, 1]. The larger the value, the more importance the user attaches to the device.

[0069] Further, the External computing devices The eigenvector of Expressed as: , in obtaining External computing devices The eigenvector of After that, further calculate the intelligent mouse and various external computing devices The spatial relationship between them.

[0070] For example, the smart mouse obtains its own three-dimensional spatial coordinates in real time through global positioning systems such as GPS, Beidou, indoor positioning systems such as Wi-Fi positioning, Bluetooth positioning, ultra-wideband positioning or inertial navigation systems. At the same time, external computing devices It also has a similar built-in spatial positioning unit, which can obtain its own three-dimensional spatial coordinates in real time. .

[0071] Specifically, the spatial position relationship By using the intelligent mouse with external computing devices The spatial distance between and spatial perspective To characterize: ,in, The dimension of is a unit of length (e.g. meter), The dimension of is an angular unit (e.g., radian). Preferably, based on the spatial position relationship , we can further get the spatial position relationship vector .

[0072] In summary, the intelligent mouse obtained in step S2 is External computing devices The spatial relationship between It can be expressed as: ,in is the current environmental attenuation factor; through the above steps, we can get the intelligent mouse and External computing devices The eigenvector of and spatial position relationships .

[0073] Step S3: Calculate the usage scenario similarity and historical usage frequency of the smart mouse in the current scenario and the external computing device, and combine the signal strength matrix, eigenvector, and spatial position relationship to obtain the switching probability of the smart mouse switching to each external computing device.

[0074] Specifically, based on the external computing device feature vector obtained in step 2 , calculate the intelligent mouse in the current usage scenario and the and Similarity of usage scenarios of external computing devices , usage scenario similarity The weighted Euclidean distance method is used for calculation, and the calculation formula is:

[0075]

[0076] in, Respectively represent and The device type of the external computing device, Respectively represent and The operating system type of the external computing device, Respectively represent the user's and Priority of external computing devices, is a positive real number, representing the weight coefficient of the three characteristic attributes of device type, operating system type and user priority, satisfying The specific value can be adjusted according to actual application requirements.

[0077] It should be noted that due to the device type , operating system type and user priority The value ranges of are different, so they need to be normalized before calculating the scene similarity, and their value ranges are uniformly mapped to the interval [0, 1]. and operating system type , using the Min-Max normalization method, the normalized value and The calculation formula is: , ,in, and Respectively represent the minimum and maximum values ​​of the device type, and Respectively represents the minimum and maximum values ​​of the operating system type. User priority It is a real number in the interval [0, 1], so no normalization is required.

[0078] Furthermore, the normalized 、 and Substitute into the scene similarity calculation formula to get the usage scene similarity The value range is (0, 1]. The larger the value, the more similar the usage scenarios of the two external computing devices are.

[0079] Further, calculate the intelligent mouse Historical usage frequency of external computing devices , the calculation formula is: ,in, Indicates that in the past T sampling periods, the intelligent mouse and the External computing devices The cumulative number of interactive operations between is the number of sampling cycles, The value range is [0, 1]. The larger the value, the closer the intelligent mouse is to the first External computing devices The more frequent the interactions.

[0080] Furthermore, combined with the signal strength matrix , spatial position relationship , usage scenario similarity and historical usage frequency , calculate the current scene intelligent mouse switch to the The switching probability of an external computing device , the calculation formula is:

[0081]

[0082] in, Indicates that the intelligent mouse is currently connected to External computing devices The connection signal strength between Indicates that the intelligent mouse and External computing devices The spatial relationship between Represents the usage scenario similarity between the 𝑖th and 𝑗th external computing devices 、 Respectively represent the intelligent mouse , The historical usage frequency of external computing devices, n is the total number of external computing devices, is the weight coefficient, satisfying Switching probability The value range is [0, 1]. The larger the value, the smart mouse switches to the Through the above calculation process, we can get the switching probability vector of the intelligent mouse switching to each external computing device in the current usage scenario: .

[0083] Step S4: Based on the switching probability vector and in combination with multiple cost factors of the external computing device switching, a switching cost function of the external computing device is constructed, and a dynamic programming algorithm is used to solve the optimal switching path.

[0084] Specifically, based on the switching probability vector And intelligent mouse and The spatial position relationship vector of the external computing device , and obtain the current environmental attenuation factor , and then combined with the pre-set signal strength cost weight , spatial location cost weight and scene similarity cost weight , construct the switching cost function of the external computing device .

[0085] Specifically, the switching cost function Indicates that the intelligent mouse is Switch the external computing device to The comprehensive switching cost generated by an external computing device is calculated as follows:

[0086]

[0087] in, Indicates that the intelligent mouse is in external computing devices and The spatial position relationship between the external computing devices is calculated according to the spatial position relationship vector L. Quantity and the jth component The calculation shows that: ; Indicates the connection signal strength when the intelligent mouse switches from the i-th external computing device to the j-th external computing device, which can be obtained from the signal strength matrix at the current moment No. Rank Column elements are given; Indicates that the intelligent mouse is in external computing devices and The similarity of usage scenarios between external computing devices.

[0088] It should be noted that when switching the cost function In the calculation formula, the spatial position relationship Proportional to the cost, connection signal strength Similarity to usage scenarios is inversely proportional to the cost, i.e. The bigger, The smaller, The smaller the switching cost, In addition, the environmental attenuation factor is introduced This is to consider the impact of environmental factors on the cost of device switching. The value range of is (0, 1]. The smaller the value, the greater the impact of the environment on signal transmission attenuation, and the higher the switching cost.

[0089] Furthermore, considering n external computing devices, we can obtain an n×n switching cost matrix :

[0090]

[0091] Among them, the matrix elements Indicates that the intelligent mouse is Switch the external computing device to The comprehensive switching cost of external computing devices, Indicates that the intelligent mouse remains in The cost on the external computing device is 0.

[0092] Furthermore, the system will switch the cost matrix As input parameters, based on the switching cost matrix , according to the environmental attenuation factor , the maximum allowable switching delay set by the user And device configuration synchronization time ,A dynamic programming algorithm is used to solve the optimal switching path from the ,currently connected device to the target device.

[0093] Specifically, An external computing device is considered as a directed weighted graph Node, node and nodes The weight of the directed edge between them is the comprehensive switching cost , that is, converting the optimal switching path problem into a directed weighted graph Find the minimum cost path from the starting point (currently connected device) to the end point (target device). is a directed weighted graph The node set of , the end point is , then the state transfer equation is: ,in, Indicates starting point To Node The minimum cost, represents the inherent delay of device switching, is a decision variable, when the node With node When configuration synchronization is required The value is 1, otherwise it is 0. The physical meaning of the state transfer equation is: arriving at the node The minimum cost depends on All predecessor nodes Departure via the border arrive The cost required , while also meeting the requirements of arrive Time cost Does not exceed the maximum allowed switching delay ; The boundary conditions of the state transfer equation are: , that is, from the starting point Arrival at the starting point The cost is 0.

[0094] Furthermore, the directed weighted graph is recursively calculated from the bottom up through the dynamic programming algorithm. The minimum cost of each node in , and finally get from the starting point To the end The minimum cost And the corresponding minimum cost path is the optimal switching path:

[0095]

[0096] in are the numbers of the intermediate nodes that the optimal path passes through, is the number of intermediate nodes.

[0097] It should be noted that the time complexity of the dynamic programming algorithm is , where n is the number of external computing devices. When n is large, the computational overhead of the algorithm will increase dramatically. Therefore, in practical applications, the algorithm can be optimized according to the specific scenario, such as using approximate algorithms, pruning strategies, etc., to reduce the computational complexity while ensuring the switching effect. In addition, due to the environmental attenuation factor It will affect the calculation results of the switching cost matrix C. The optimal switching path is updated in time when changes occur. At the same time, when the number of external computing devices changes (increases or decreases), the dimension of the switching cost matrix C needs to be adjusted accordingly, and the dynamic programming algorithm needs to be re-executed to obtain a new optimal switching path.

[0098] Furthermore, the optimal switching path obtained is Output, by the optimal switching path Control the intelligent mouse to perform actual connection switching operations with external computing devices.

[0099] Step S5: Switching based on the optimal path , the switching pre-connection mechanism is controlled by a step function, and the adaptive learning algorithm, switching rollback mechanism and switching anti-false trigger mechanism are called to finally achieve the optimal switching of the intelligent mouse between external computing devices.

[0100] Specifically, based on the optimal switching path obtained in the above steps , according to the node sequence in the path, from the starting point First, build a virtual connection between the intelligent mouse and the external computing device step by step, specifically:

[0101] set up The current node is , the successor node is ,and Indicates the external computing device that the intelligent mouse is currently connected to. Indicates that the intelligent mouse Switch to The connection status of the device is 0 or 1, 0 means not connected, 1 means connected. .

[0102] Furthermore, for Each edge in , through the step function Controlling the intelligent mouse and external computing devices The pre-connection between the step function as follows:

[0103]

[0104] in, Indicates that the intelligent mouse is connected to an external computing device Switching to an external computing device The switching probability, ∈(0, 1) is the preset switching probability threshold, which can be adjusted according to specific application requirements. hour, Output is 1, start the switching pre-connection mechanism, establish the intelligent mouse and external computing device Pre-connect between Set to 1; otherwise, the pre-connection mechanism will not be activated and no pre-connection will be performed. Keep it at 0.

[0105] Furthermore, while establishing the pre-connection, the adaptive learning algorithm is called to Interaction data, learn and generate targeted mouse parameter configuration sets ,in Represents intelligent mouse and external computing device The kth parameter of the interaction, such as mouse sensitivity, double-click speed, pointer speed, etc. In this embodiment, based on the adaptive learning algorithm, the intelligent mouse is updated on the external computing device through continuous trial and error and environmental feedback. The optimal parameter strategy on , the update formula is:

[0106]

[0107] in, For intelligent mouse in external computing device Take parameter configuration Expected connection quality, For the device Take parameter configuration Instant rewards received, is the learning rate, is the discount factor, which enables the mouse to gradually find the most suitable parameter configuration strategy for the external computing device through reinforcement learning; Indicates The next device node after that, For all possible parameter configurations;

[0108] It should be noted that the parameter configuration is iteratively optimized by updating the formula. In this process, Indicates that the intelligent mouse is on an external computing device The expected return of using parameter configuration Θ is, is an immediate reward, and The maximum possible benefit in the future is taken into account. As the algorithm continues to iterate, the Q value will gradually converge to the optimal value. Control the step size of each update, discount factor Determines the importance of future rewards. When the Q value is stable, the system will select the parameter configuration that maximizes the Q value for each external computing device, that is, =argmax , this is the optimal parameter strategy. Through this adaptive learning method, the smart mouse can automatically adjust its parameter configuration according to the user's usage habits.

[0109] Furthermore, through repeated iterative updates, the adaptive learning algorithm eventually converges to obtain the optimal parameter strategy , so that the intelligent mouse can be used on external computing devices Whenever the intelligent mouse switches to a new external computing device, When , the corresponding optimal parameter strategy is loaded , to achieve a seamless user experience across devices.

[0110] Furthermore, when the intelligent mouse follows the optimal switching path Finish from the starting point To the end After the migration, all nodes in the path have been actually connected by the intelligent mouse. , indicating that the switch is complete. During the switch execution process, the working status of the external computing device must be monitored in real time. If an external computing device is found to have an abnormal condition, such as device offline, device crash, port damage, etc., the switch rollback mechanism will be activated: Indicates that the device When an exception occurs, the intelligent mouse falls back to the last external device before the exception occurred. , achieving smooth rollback in fault conditions, avoiding overall performance bottlenecks caused by single-point device problems, and at the same time, abnormal information will be fed back to the path optimization unit in a timely manner to trigger the optimal switching path Replan and remove faulty device nodes.

[0111] In order to prevent the intelligent mouse from frequently and unnecessarily switching external computing devices and affecting the normal operation of the user, a switching error prevention mechanism is built in. The switching error prevention mechanism tracks and analyzes the operation behavior of the intelligent mouse, identifies a continuous and stable mouse action pattern, marks it as "focus mode", and uses dynamic threshold control to avoid external computing device switching in focus mode; specifically, the stable duration of the mouse operation behavior is set to , then the determination condition of the focus mode is:

[0112]

[0113] in, is the minimum duration threshold of the focus mode. When the intelligent mouse enters the focus mode, it locks the connection with the current external computing device. Output is 0 to prevent device switching from occurring; otherwise the intelligent mouse will remain in the free switching state. The value of can be preset or learned based on the user's historical behavior data.

[0114] Furthermore, when the focus mode ends, that is, the stable duration of the mouse operation behavior Fall to After the following, the switch anti-false trigger mechanism will automatically release the inhibition of device switching, restore the free switching function of the intelligent mouse, and continue to follow the optimal switching path Complete the optimal switching of the remaining external computing devices.

[0115] Through the above steps, the entire process of optimizing the switching of the smart mouse is controlled and managed. While improving the switching efficiency and success rate, it also takes into account the switching experience and device exception handling, maximizes the advantages of multi-device collaboration, and provides users with a seamless, smooth, and intimate cross-device mouse experience.

[0116] In this embodiment, the intelligent mouse collects the connection signal strength with the external computing device through a wireless signal receiver, and constructs a dynamically updated signal strength matrix to accurately reflect the real-time connection quality; obtains the characteristic vector of the external device, and calculates the spatial position relationship between the mouse and each device to assist in switching decisions; by calculating the similarity of the usage scenarios and historical usage frequency between the mouse and the device, combined with the signal strength and spatial position relationship, the switching probability of the intelligent mouse switching to different devices is obtained. In the switching decision stage, a dynamic programming algorithm is used to construct a switching cost function, which comprehensively considers factors such as signal quality, spatial position, and usage scenario similarity to optimize the switching path and reduce unnecessary frequent switching; finally, by switching the pre-connection mechanism, adaptive learning algorithm, switching rollback mechanism and anti-false touch mechanism, the mouse switching process is ensured to be stable and smooth, avoid false triggering of switching, and improve the seamlessness and accuracy of cross-device operations. This embodiment fully verifies the effectiveness of the method and realizes efficient, low-latency, and multi-device intelligent switching of the intelligent mouse.

[0117] Example 2:

[0118] As attached Figure 2 As shown, embodiment 2 of the present invention provides a multi-device automatic switching system for an intelligent mouse, including five components: a multi-channel wireless signal receiver 10, a device identification module 20, a scene analysis module 30, a switching decision module 40, and a switching execution module 50. Specifically, the system includes the following:

[0119] The multi-channel wireless signal receiver 10 is wirelessly connected to multiple wireless communication units built into the intelligent mouse to collect the connection signal strength between the intelligent mouse and multiple external computing devices in real time, and dynamically build and maintain the signal strength matrix ;

[0120] The device identification module 20 is used to extract feature information of external computing devices, generate feature vectors of external computing devices, and calculate the spatial position relationship between the intelligent mouse and each external computing device, providing necessary reference information for subsequent external computing device switching decisions;

[0121] The scenario analysis module 30 is responsible for calculating the similarity and historical usage frequency of the intelligent mouse and different external computing devices, and dynamically calculating the probability of the intelligent mouse switching to each external computing device based on the real-time connection signal strength and spatial position relationship;

[0122] A switching decision module 40 is configured to construct a switching cost function for the external computing device based on the switching probability vector and multiple cost factors of the external computing device switching, and to use a dynamic programming algorithm to solve the optimal switching path;

[0123] The switching decision module 40 is further divided into a switching cost calculation unit 41 and a path optimization unit 42. The switching cost calculation unit 41 comprehensively considers multiple cost factors of the intelligent mouse in the device switching process and constructs a switching cost function ; The path optimization unit 42 uses each external computing device as a node and the comprehensive switching cost as the edge weight to construct a directed weighted graph of the device switching cost, and uses a dynamic programming algorithm to solve the minimum cost path on the directed graph as the optimal switching path, that is, the optimal device switching solution.

[0124] The switching execution module 50 executes the connection state transition between the intelligent mouse and the external computing device based on the optimal device switching solution and controls the activation and deactivation of the pre-connection switching mechanism via a step function. Furthermore, the switching execution module 50 also incorporates an adaptive learning algorithm, a switch rollback mechanism, and a switch accidental touch prevention mechanism to generate personalized mouse configuration solutions for external computing devices, address switching anomalies, and prevent unnecessary device switching triggers.

[0125] The intelligent switching method of this embodiment can significantly improve the working efficiency and switching experience of the intelligent mouse in a multi-device environment, and realize seamless connection between the intelligent mouse and the external computing device. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can be modified and varied in various ways. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A method for automatically switching multiple devices of an intelligent mouse, characterized in that: include: Step S1: collecting the connection signal strength between the intelligent mouse and each external computing device through the multi-channel wireless signal receiver built into the intelligent mouse, and constructing a dynamically updated signal strength matrix based on the connection signal strength; Step S2: Obtain the feature vector of the external computing device and calculate the spatial position relationship between the intelligent mouse and the external computing device; Step S3: Calculate the similarity of the usage scenarios of the intelligent mouse and the external computing device in the current scenario, and the historical usage frequency, and combine the signal strength matrix, eigenvector, and spatial position relationship to obtain the switching probability of the intelligent mouse switching to each external computing device in the current scenario. The calculation formula is: ,in, Indicates that the intelligent mouse is currently connected to External computing devices The connection signal strength between Indicates that the intelligent mouse and External computing devices The spatial relationship between Represents the usage scenario similarity between the 𝑖th and 𝑗th external computing devices 、 Respectively represent the intelligent mouse , The historical usage frequency of external computing devices, n is the total number of external computing devices, is the weight coefficient, satisfying ; The value range is [0, 1]. The larger the value, the smart mouse switches to the The greater the possibility of an external computing device; Based on the switching probability, obtaining a switching probability vector of the intelligent mouse switching to each external computing device in the current usage scenario; Step S4: Based on the switching probability vector and in combination with multiple cost factors of the external computing device switching, a switching cost function of the external computing device is constructed, and a dynamic programming algorithm is used to solve the optimal switching path; Step S5: According to the optimal switching path, the switching pre-connection mechanism is controlled by a step function, and the adaptive learning algorithm, the switching rollback mechanism and the switching anti-false triggering mechanism are called to finally achieve the optimal switching of the intelligent mouse between external computing devices.

2. The method for automatically switching multiple devices of an intelligent mouse according to claim 1, characterized in that: For the first + Signal strength matrix within 1 sampling period Perform dynamic updates as follows: Assume that in the past In the sampling period, the historical record of the signal strength matrix is , then + Signal strength matrix within 1 sampling period The update formula is: ,in, is the time decay factor, satisfying , , that is, from the current time The more recent the historical signal strength matrix record, the greater the weight it has in the dynamic update.

3. The method for automatically switching multiple devices of an intelligent mouse according to claim 1, wherein: The intelligent mouse is connected to an external computing device through a wired or wireless method, and a device information request is sent. After receiving the device information request, the external computing device sets its own device type to the device type. , operating system type and the user's priority for the device Packaged as a feature vector, The feature vector of an external computing device is expressed as: ; Spatial position relationship: the spatial distance between the intelligent mouse and the external computing device and spatial perspective To characterize: ,in is the three-dimensional space coordinate of the intelligent mouse, is the three-dimensional space coordinate of the external computing device, is the current environmental attenuation factor; Based on spatial location relationship , we can further get the spatial position relationship vector .

4. The method for automatically switching multiple devices of an intelligent mouse according to claim 3, wherein: Based on the feature vector, calculate the intelligent mouse in the current usage scenario and the and The similarity of usage scenarios of external computing devices is calculated using the weighted Euclidean distance method. The calculation formula is: ,in, is a positive real number, representing the weight coefficient of the three characteristic attributes of device type, operating system type and user priority, satisfying ; Calculate the intelligent mouse The historical usage frequency of external computing devices is calculated as follows: ,in, Indicates that in the past T sampling periods, the intelligent mouse and the The cumulative number of interactive operations between external computing devices, is the number of sampling cycles, The value range is [0, 1]. The larger the value, the closer the intelligent mouse is to the first The more frequent the interaction with external computing devices.

5. The method for automatically switching multiple devices of an intelligent mouse according to claim 1, wherein: Based on the switching probability vector and intelligent mouse and The spatial position relationship vector of an external computing device is then combined with the pre-set signal strength cost weight , spatial location cost weight and scene similarity cost weight , construct the switching cost function of the external computing device ; The switching cost function Indicates that the intelligent mouse is Switch the external computing device to The comprehensive switching cost generated by the external computing device is calculated as follows: ,in, Indicates that the intelligent mouse is in external computing devices and The spatial position relationship between the external computing devices is calculated according to the first Quantity and the jth component The calculation shows that: ; The connection signal strength of the intelligent mouse when it switches from the i-th external computing device to the j-th external computing device can be obtained from the first signal strength matrix at the current moment. Rank Column elements are given; Indicates that the intelligent mouse is in external computing devices and The similarity of usage scenarios between external computing devices.

6. The method for automatically switching multiple devices of an intelligent mouse according to claim 1, characterized in that: Construct an n×n switching cost matrix: , where the matrix elements Indicates that the intelligent mouse is Switch the external computing device to The comprehensive switching cost of external computing devices, Indicates that the intelligent mouse remains in The cost on the external computing device is 0; The system takes the switching cost matrix as an input parameter. Based on the switching cost matrix, the system uses a dynamic programming algorithm to solve the optimal switching path from the currently connected device to the target device according to the environmental attenuation factor, the maximum allowable switching delay set by the user, and the device configuration synchronization time.

7. The method for automatically switching multiple devices of an intelligent mouse according to claim 6, characterized in that: Will An external computing device is considered as a directed weighted graph Node, node and nodes The weight of the directed edge between them is the comprehensive switching cost , that is, converting the optimal switching path problem into a directed weighted graph Find the minimum cost path from the starting point to the end point; Through the dynamic programming algorithm, the directed weighted graph is recursively calculated from the bottom up The minimum cost of each node in , and finally get from the starting point To the end The minimum cost And the corresponding minimum cost path is the optimal switching path.

8. The method for automatically switching multiple devices of an intelligent mouse according to claim 7, characterized in that: Based on the optimal switching path, according to the node sequence in the path, from the starting point First, build a virtual connection between the intelligent mouse and the external computing device step by step, specifically: Set the current node to , the successor node is , each edge in the optimal switching path , through the step function Controlling the intelligent mouse and external computing devices The pre-connection between the step function as follows: ,in, Indicates that the intelligent mouse is connected to an external computing device Switching to an external computing device The switching probability, ∈(0, 1) is the preset switching probability threshold; when hour, Output is 1, start the switching pre-connection mechanism, establish the intelligent mouse and external computing device Otherwise, the pre-connection mechanism will not be activated and no pre-connection will be performed.

9. The method for automatically switching multiple devices of an intelligent mouse according to claim 8, characterized in that: The switching anti-false triggering mechanism tracks and analyzes the operation behavior of the intelligent mouse, identifies a continuous and stable mouse action pattern, marks it as "focus mode", and uses dynamic threshold control to avoid external computing device switching in focus mode.

10. An intelligent mouse multi-device automatic switching system, used to implement the intelligent mouse multi-device automatic switching method according to any one of claims 1 to 9, characterized in that: include: A multi-channel wireless signal receiver is wirelessly connected to multiple wireless communication units built into the intelligent mouse, and is used to collect the connection signal strength between the intelligent mouse and multiple external computing devices in real time, and dynamically build and maintain a signal strength matrix; The device identification module is used to extract the characteristic information of the external computing device, generate the characteristic vector of the external computing device, and calculate the spatial position relationship between the intelligent mouse and each external computing device, providing necessary reference information for subsequent external computing device switching decisions; The scenario analysis module is responsible for calculating the similarity and historical usage frequency of the intelligent mouse and different external computing devices. It also dynamically calculates the probability of the intelligent mouse switching to each external computing device based on the real-time connection signal strength and spatial position relationship. A switching decision module is used to construct a switching cost function for the external computing device based on the switching probability vector and multiple cost factors of the external computing device switching, and to solve the optimal switching path using a dynamic programming algorithm; The switching execution module is used to execute the connection state conversion between the intelligent mouse and the external computing device according to the optimal device switching solution, that is, the optimal switching path, and control the opening and closing of the switching pre-connection mechanism through a step function.

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