A mobile device control method and device under a variable autonomy level and an electronic device

Abstract

The present disclosure relates to a mobile device control method and device with variable autonomy level, and an electronic device, and belongs to the technical field of device control. The method comprises: obtaining running information of the mobile device within a first time window of performing a preset task; predicting whether there is an uncertain event after the first time window of performing the task according to the running information; wherein the uncertain event is an event that does not conform to the set information of the task; in the case of predicting an uncertain event, evaluating the current state of the mobile device according to the running information to obtain an evaluation result; and updating the operation mode of the mobile device performing the preset task according to the evaluation result.

Description

A method, apparatus and electronic device for controlling mobile devices under varying levels of autonomy Technical Field

[0001] This disclosure relates to the field of device control technology, and more specifically, to a method, apparatus, and electronic device for controlling mobile devices under variable autonomy levels. Background Technology

[0002] With the development of intelligent equipment, intelligent devices are gradually replacing human labor. During the execution of tasks, users can switch between different operating modes of some intelligent devices, allowing the equipment to complete tasks autonomously or in machine-assisted mode.

[0003] Currently, equipment may encounter unexpected situations during actual operation, and it is difficult to control the equipment in a timely and effective manner solely through human judgment by the user. Summary of the Invention

[0004] One objective of this disclosure is to provide a new technical solution for a mobile device control method, apparatus, and electronic device under variable autonomy levels.

[0005] According to a first aspect of this disclosure, a method for controlling a mobile device under a variable autonomy level is provided, the method comprising:

[0006] When the mobile device performs a preset task, obtain the running information of the mobile device within the first time window of the task execution;

[0007] Based on the operational information, it is predicted whether there will be any uncertain events after the mobile device executes the task within the first time window; wherein, the uncertain events are events that do not conform to the setting information of the task.

[0008] In the event of an uncertain event, the current state of the mobile device is evaluated based on the operational information to obtain an evaluation result;

[0009] Based on the evaluation results, the operation mode of the mobile device performing the preset task is updated;

[0010] The different degrees of human intervention correspond to the execution of the preset tasks by the mobile device in different operating modes.

[0011] Optionally, the operation information includes single-point motion information of the mobile device at at least one sampling moment within the first time window;

[0012] The step of predicting whether there are uncertain events after the first time window in which the mobile device executes the task, based on the operational information, includes:

[0013] Based on the single-point motion information, detect whether there is an uncertain event at the first moment when the mobile device performs the action according to the preset task;

[0014] The first moment is after the first time window.

[0015] Optionally, the task setting information includes information about the scope of actions to perform the task and information about the target object of the task, and the uncertain events include a first uncertain event and a second uncertain event;

[0016] Wherein, the first uncertain event is an event in which the movement range of the mobile device exceeds the movement range;

[0017] The second uncertain event is an event in which the identified actual object does not match the target object.

[0018] Optionally, the evaluation result is an assessment of the degree of human intervention in the mobile device performing the task.

[0019] Optionally, updating the operation mode for the mobile device to perform the preset task includes:

[0020] According to a set of multiple operation modes, the operation mode of the mobile device performing the preset task is updated to the first operation mode among the multiple operation modes; wherein, the multiple operation modes include at least two of the following: manual operation mode, machine-assisted mode, human-machine negotiation mode, manual assistance mode and device autonomous mode, and different operation modes among the multiple operation modes correspond to different degrees of human intervention, and the first operation mode is the operation mode that matches the degree of human intervention reflected by the evaluation result.

[0021] Optionally, the step of evaluating the current state of the mobile device based on the operational information to obtain an evaluation result includes:

[0022] Based on the mobile device's operating information, determine at least one current state of the mobile device in at least one matter, and determine the attribute array corresponding to the at least one current state; wherein, the at least one current state corresponds one-to-one with the at least one matter;

[0023] The attribute values ​​corresponding to the first degree of human intervention in each attribute array are input into a preset evaluation model to obtain the first priority corresponding to the first degree of human intervention; wherein, the attribute array includes at least one attribute value, the at least one attribute value corresponds to different degrees of human intervention, and the degree of human intervention includes the first degree of human intervention;

[0024] The second priority with the highest priority among all the first priority levels is selected as the evaluation result.

[0025] Optionally, updating the current operating mode of the mobile device based on the evaluation result includes:

[0026] Based on a preset mapping relationship, a first operation mode corresponding to the first degree of human interference is determined; wherein, the mapping relationship is used to represent an operation mode corresponding to at least one degree of human interference, and the at least one degree of human interference includes the first degree of human interference;

[0027] Update the operating mode of the mobile device for performing the preset task to the first operating mode.

[0028] Optionally, the at least one current state includes at least one of the following: the current user state for user matters, the current environment state for external environment matters, and the current task state for task execution matters.

[0029] According to a second aspect of this disclosure, an apparatus for controlling a mobile device at a variable autonomy level is also provided, the apparatus comprising:

[0030] An information acquisition module is used to acquire the operating information of the mobile device when the mobile device performs a preset task; an action detection module is used to detect, based on the operating information, whether there are any uncertain events within a set time period during which the mobile device performs actions according to the preset task; a result acquisition module is used to evaluate the current state of the mobile device based on the operating information of the mobile device when an uncertain event is detected, and obtain an evaluation result; a mode update module is used to update the current operation mode of the mobile device based on the evaluation result; wherein, the mobile device performs the preset task in different operation modes, corresponding to different degrees of human intervention.

[0031] According to a third aspect of this disclosure, an electronic device is also provided, including a memory and a processor, the memory being used to store a computer program; the processor being used to execute the computer program to implement the method according to a first aspect of this disclosure.

[0032] According to a fourth aspect of this disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the method described according to a first aspect of this disclosure.

[0033] One beneficial effect of this disclosure is that, by obtaining the mobile device's operational information, it is possible to predict whether the mobile device will encounter uncertain events. If uncertain events are predicted, the mobile device's operational information is used to evaluate it, and the evaluation results are then used to update the mobile device's operating mode. Updating the mobile device's operating mode according to the method described in this application can predict unforeseen circumstances the mobile device will encounter, allowing for timely updates to the operating mode. This enables users or the mobile device itself to adjust accordingly, thereby improving the efficiency of the mobile device in performing tasks.

[0034] Other features and advantages of the embodiments of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0035] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the present disclosure and, together with their description, serve to explain the principles of the embodiments of the present disclosure.

[0036] Figure 1 is a schematic diagram of the composition structure of a mobile device control system with variable autonomy level that can be applied according to an embodiment of the mobile device control method with variable autonomy level.

[0037] Figure 2 is a flowchart illustrating a mobile device control method under variable autonomy level according to another embodiment;

[0038] Figure 3 is a schematic diagram of a mobile device performing a task according to another embodiment;

[0039] Figure 4 is a schematic diagram illustrating the correspondence between the degree of human intervention and the attribute array according to another embodiment;

[0040] Figure 5 is a block schematic diagram of an electronic device according to another embodiment;

[0041] Figure 6 is a schematic diagram of the hardware structure of an electronic device according to another embodiment. Detailed Implementation

[0042] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0043] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0044] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0045] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0046] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0047] <System Implementation Example>

[0048] Figure 1 is a schematic diagram of the composition of a mobile device control system with variable autonomy level that can be applied to a mobile device control method according to one embodiment. As shown in Figure 1, the system includes a server 100 and at least one mobile device 200, and the system can be applied to scenarios involving the control of the mobile device 200.

[0049] Server 100 can interact with each mobile device 200 wirelessly. Server 100 can also be configured with corresponding detection devices such as radar, and the detection devices are used to detect other objects near each mobile device 200.

[0050] Mobile device 200 can be a drone or a vehicle; no specific limitation is made here. Mobile device 200 can perform tasks output by server 100. During operation, sensors on mobile device 200 can feed back its movement information to server 100. Server 100 can use this movement information to determine if there are any operational anomalies. Specifically, if the sensors detect an obstacle in the mobile device 200's path while it is moving according to the task, it is considered an operational anomaly. Upon determining an anomaly, server 100 issues control commands, allowing for human intervention in mobile device 200.

[0051] In the embodiments of this disclosure, the memory of server 100 is used to store a computer program for controlling the processor of server 100 to operate in order to implement a variable autonomy-level mobile device control method according to any embodiment. Those skilled in the art can design the computer program based on the scheme of the embodiments of this disclosure. How the computer program controls the processor to operate is well known in the art and will not be described in detail here.

[0052] <Method Implementation>

[0053] Figure 2 is a flowchart illustrating a mobile device control method under variable autonomy levels according to one embodiment. This embodiment uses the aforementioned server as the execution entity.

[0054] As shown in Figure 2, a mobile device control method under variable autonomy level in this embodiment may include the following steps S210 to S240:

[0055] Step S210: When the mobile device performs a preset task, obtain the running information of the mobile device within the first time window of task execution.

[0056] The server can pre-set different tasks for different mobile devices. For example, a mobile device is a vehicle, and the vehicle performs a patrol task. The task content can include the coordinates of the starting point, the coordinates of the passing points, the number of patrols, and the patrol start time.

[0057] Specifically, after the server issues a preset task, the mobile device receives and executes the preset task. The server can obtain the mobile device's operational information within the first time window of task execution through the configured detection devices and sensors configured on the mobile device. Continuing with the vehicle patrol example, the first time window can be the time period corresponding to the movement of the vehicle from one transit point coordinate to another during the patrol process, and the operational information can be the operational information of the mobile device collected by the detection devices and the mobile device within the aforementioned time period.

[0058] Step S220: Based on the operation information, predict whether there are any uncertain events after the first time window of the mobile device executing the task; wherein, the uncertain events are events that do not conform to the setting information of the task.

[0059] The server pre-stores uncertain events, which can be specific events manifested by operational anomalies in the aforementioned mobile devices; that is, uncertain events are events that do not conform to the task's preset information. As shown in Figure 1, the presence of obstacles during the mobile device's movement along its path can be considered an uncertain event.

[0060] Specifically, during the execution of a task by a mobile device, the operational information acquired by the aforementioned detection devices and sensors can predict whether uncertain events will occur after the first time window of the task execution. For example, continuing with the vehicle patrol example, if the sensors on the vehicle detect an obstacle at a certain distance and report it to the server, and the server determines that the vehicle on the driving path passes through the obstacle at a certain time interval, it can be considered that an uncertain event occurred during that time interval after the first time window.

[0061] In one embodiment, the operational information includes single-point motion information of the mobile device at at least one sampling moment within a first time window. Step 220 may specifically include: based on the single-point motion information, detecting whether an uncertain event exists at the first moment when the mobile device performs an action according to a preset task; wherein the first moment is after the first time window.

[0062] Specifically, the detection devices configured on the server and the sensors configured on the mobile device can monitor the mobile device performing a task at preset time intervals. That is, the server can receive single-point motion information from the detection devices or sensors at least at one sampling moment within a first time window. Upon receiving this single-point motion information, the server can detect whether there are any uncertain events at a future moment when the mobile device is performing a preset task. This future moment can be the first moment. In other words, by collecting the operational information, it can determine whether a task yet to be started by the mobile device is affected, thereby effectively avoiding uncertain times when the mobile device is performing a task.

[0063] In one embodiment, the task setting information includes information about the range of motion for performing the task and information about the target object targeted by the task. Uncertain events include a first uncertain event and a second uncertain event. The first uncertain event is when the mobile device's movement exceeds its range of motion; the second uncertain event is when the identified actual object does not match the target object.

[0064] Specifically, each mobile device can perform different tasks, such as autonomous driving, patrolling, mechanical operations, and target following. For different mobile devices and different tasks, information about the range of motion and the target object of the task can be set for each mobile device. Correspondingly, uncertain events can include first uncertain events and second uncertain events, which will be elaborated on below.

[0065] In this scenario, taking autonomous driving as an example, the first uncertain event can be determined by pre-setting waypoints and destinations for the autonomous driving task. Corresponding detection devices can be deployed at these points and destinations to detect the activity of mobile devices within their detection range, as shown in Figure 3. The detection devices can acquire information about the movement of mobile devices within their detection range. The server receives the mobile device's operational information within the first time window of the task execution. Based on the detection information sent by the waypoint detection devices and the aforementioned movement information, the server determines the mobile device's movement path within the detection range. The server then uses the preset waypoint range and the vicinity of any point not yet reached to determine whether the mobile device will reach a waypoint. If it is predicted that the mobile device will not reach a waypoint, meaning the movement amplitude of the mobile device exceeds the preset action range, the first uncertain event occurs.

[0066] Similarly, the aforementioned operational information may include the actual coordinates of the mobile device, and the task may include the coordinates of various target points. Based on the actual coordinates, the server determines the first coordinate position of the previous target point and the second coordinate position of the next target point in the task, as well as the desired path for the mobile device. Based on the actual coordinates, the first coordinate position, and the second coordinate position, the server determines the current path and the deviation value between the current path and the desired path. If the deviation value exceeds a preset deviation threshold, it is considered that an uncertain event has occurred after the first time window of task execution for the mobile device. Specifically, the current position of the mobile device... The previous target point is The next target point is Therefore, the expected path is Accordingly, deviation value

[0067] The second uncertainty event, taking target tracking as an example, involves a mobile device equipped with sensors capable of detecting the distance between the target object and the mobile device. The server can pre-set a task to track the target object within a predetermined distance, ensuring that the motion information relayed by the mobile device to the server via its sensors includes the distance between the target object and the mobile device. Based on the trend of the distance between the target object and the mobile device over time, the server determines the probability that the mobile device will lose track of the target object. If this probability exceeds a preset value, the server considers a second uncertainty event to have occurred after the first time window of the task execution.

[0068] In addition, corresponding sensors can be set on mobile devices so that the server can obtain the movement information of obstacles on the mobile deployment path. The presence of obstacles on the mobile path is the second uncertain event.

[0069] Step S230: In the event of an uncertain event, the current state of the mobile device is evaluated based on the operating information to obtain an evaluation result.

[0070] Specifically, when an uncertain event is predicted, the server evaluates the current state of the mobile device based on operational information and obtains an evaluation result. The current state of the mobile device can be related to the environment in which the mobile device is located, the task being performed, and the current state of the user.

[0071] Step S240: Based on the evaluation results, update the operation mode of the mobile device for performing preset tasks, wherein different degrees of human intervention correspond to different operation modes of the mobile device performing preset tasks.

[0072] Specifically, based on the evaluation results, the server updates the operating mode for the mobile device to perform preset tasks. This update can involve unlocking or locking configured operating functions, such as enabling manual functions in the operating mode. Alternatively, it can involve switching to a different operating mode. Different operating modes correspond to different levels of human intervention when the mobile device performs preset tasks. The level of human intervention reflects the mobile device's autonomy level; higher levels of human intervention indicate lower autonomy.

[0073] In one embodiment, the evaluation result is an assessment of the degree of human intervention in the performance of tasks by the mobile device.

[0074] Specifically, the degree of human intervention refers to the extent to which a mobile device requires manual intervention, and this degree can be reflected by the ratio between manual and automatic control of the mobile device. In other words, when a mobile device has a large number of operating modes or operating modes with a large number of operating functions, the degree of human intervention reflects the corresponding evaluation results, and the updated operating modes can be more adapted to the current mobile devices.

[0075] In one embodiment, updating the operation mode of the mobile device performing the preset task in step S240 may include the following: updating the operation mode of the mobile device performing the preset task to a first operation mode among the multiple operation modes according to the multiple preset operation modes; wherein, the multiple operation modes include at least two of the following: manual operation mode, machine-assisted mode, human-machine negotiation mode, manual assistance mode and device autonomous mode, and different operation modes among the multiple operation modes correspond to different degrees of human intervention, and the first operation mode is the operation mode that matches the degree of human intervention reflected by the evaluation result.

[0076] The mobile device can be pre-configured with multiple operating modes, including two or more of the following: manual operation mode, machine-assisted mode, human-machine negotiation mode, manual-assisted mode, and device autonomous mode. For example, when the mobile device is a drone, the drone can be configured with manual operation mode, machine-assisted mode, and device autonomous mode. In manual operation mode, the user can directly control and drive the drone's movements; in machine-assisted mode, the user can operate and drive the drone according to a route provided by the drone itself; in device autonomous mode, the drone moves according to its own path. Since different operating modes correspond to different degrees of human intervention, the corresponding operating mode can be determined through evaluation results.

[0077] Specifically, after receiving the evaluation results, the server selects a matching first operating mode from multiple operating modes based on the degree of human intervention indicated by the evaluation results, and then updates the operating mode of the mobile device performing the preset task to the first operating mode among the multiple operating modes. In other words, the corresponding operating mode is determined according to different degrees of human intervention, so that the updated operating mode has a high degree of adaptability to mobile devices in the presence of uncertain events.

[0078] In one embodiment, step S230 may include the following: determining at least one current state of the mobile device on at least one matter based on the mobile device's operating information, and determining an attribute array corresponding to the at least one current state of the mobile device; wherein, the at least one current state of the mobile device corresponds one-to-one with at least one matter of the mobile device; inputting the attribute values ​​corresponding to the first degree of human intervention in each attribute array into a preset evaluation model to obtain the first priority corresponding to the first degree of human intervention; wherein, the mobile device attribute array includes at least one attribute value, the at least one attribute value of the mobile device corresponds to different degrees of human intervention, and the degree of human intervention of the mobile device includes the first degree of human intervention; selecting the second priority with the highest priority from each first priority and using it as the evaluation result.

[0079] In this system, at least one current state for at least one item corresponds to a set of attribute arrays, and different attribute values ​​in each set of attribute arrays correspond to different degrees of human intervention. The server also has a pre-configured evaluation model, which is used to calculate the second priority among the various degrees of human intervention, based on the attribute values ​​corresponding to the same degree of human intervention, thereby determining the appropriate operating mode for the mobile device.

[0080] Specifically, the server determines at least one current state of the mobile device in at least one matter based on the mobile device's operational information, and determines the attribute array corresponding to the at least one current state. The attribute values ​​corresponding to the degree of human intervention in each attribute array are input into a preset evaluation model to obtain the first priority corresponding to the degree of human intervention. The second priority, which has the highest priority among the first priorities, is selected as the evaluation result.

[0081] Accordingly, taking the three types of items—environmental items of the mobile device's environment, task items of the task being performed, and user items of user attributes—as examples, environmental items can correspond to dynamic and static environmental states, tasks can correspond to task difficulty and task achievement states, and user items can correspond to user preference states. As shown in Figure 4, each current state can correspond to an attribute array, which can contain 5 attribute values, each corresponding to an autonomy level of 1-5, and each autonomy level corresponds to a degree of human intervention. The attribute values ​​in the attribute array can be represented as M. ij , i = 1, 2, 3, 4, 5; j = 1, 2, 3, 4, 5. Each attribute array can contain five attribute values ​​corresponding to different degrees of human intervention. Correspondingly, the attribute array corresponding to the dynamic environment state can also be {m 11 m 12 m 13 m 14 m 15}

[0082] The attribute value m mentioned above ij It can be obtained through a preset calculation model, and the specific expression can be: Where, p j f is the probability of user intervention at different preset levels of human intervention. k These are the preset values ​​corresponding to different current states, such as the dynamic environment state, static environment state, task difficulty state, and task achievement state mentioned above. The value corresponds to the preset user preference state, where i represents the corresponding degree of human intervention and j represents the corresponding current state.

[0083] After obtaining the attribute values ​​in each attribute array, the evaluation model calculates the attribute value corresponding to the degree of human intervention, and obtains the first priority corresponding to the degree of human intervention.

[0084] Specifically, determining the first priority in the evaluation model can include determining the priority of each attribute value for the first degree of human intervention and the weight of the current state.

[0085] This allows us to obtain the attribute value priority of each attribute value in the first person's degree of intervention, specifically expressed as attribute value priority P. k (l i l j )=m ik -m jk , where l i and l j These represent different degrees of human intervention. In P... k When = 0, it means l i and l j There is no difference; when P k When it approaches 1, l i than l j High priority; when P k When it equals 1, l i Strictly superior to l j When P k When it is negative, it means l i Better than l j .

[0086] Specifically, corresponding weights can be assigned to the current state. For example, dynamic environment state, static environment state, task difficulty state, and task achievement state correspond to W1-W5 respectively. The corresponding variable b is obtained by using the weight corresponding to the current state. ij =w i / w j Thus, with b ij Construct matrix B. According to the predefined formula (B-nI)w = 0, we can obtain Bw = λw, and thus the vector W = {w1, w2, ..., w...}. n} T Thus, the weight values ​​of each attribute corresponding to the degree of interference by the same person can be represented as w. n .

[0087] In general, regarding the degree of human intervention... j In terms of the degree of human intervention, l i First priority This yields the first priority level corresponding to the degree of intervention for each first person. The second priority level, which has the highest priority among all first priorities, is then selected as the evaluation result.

[0088] Similarly, the second priority can also be determined by the average score obtained through a comprehensive weighted method. Specifically, the attribute value can be set as follows: Where i represents the degree of human intervention, j represents the current state, and k represents the sequence number of different mobile devices, thus obtaining the result from... The matrix U formed k , Accordingly, all first priorities for each mobile device can be represented as

[0089] Subsequently, arithmetic weighted averages and geometric weighted averages were calculated for the attribute values ​​with different degrees of human interference. Specifically, the arithmetic weighted average was calculated... (i = 1, 2, ..., m; j = 1, 2, ..., n; k = 1, 2, ..., s), geometrically weighted average (i = 1, 2, ..., m; j = 1, 2, ..., n; k = 1, 2, ..., s). Here, ω represents the preset weight values ​​corresponding to different degrees of human interference. This ensures that the mixed average of different degrees of human interference... Overall average score Evaluation coefficient Then we get the first priority order {e1, e2, e3, e4, e5} arranged from largest to smallest, and e1 is the second priority.

[0090] In summary, the second priority obtained through the two methods described above has a high degree of matching with the current state of the mobile device, and thus the updated operating mode has a high degree of adaptability.

[0091] In one embodiment, the at least one current state includes at least one of the following: the current user state for user matters, the current environment state for external environment matters, and the current task state for task execution matters.

[0092] Specifically, at least one current state may include the current user state in terms of user matters, the current environment state in terms of external environment matters, and the current task state in terms of task execution matters. By comprehensively evaluating the current state of the mobile device through user matters and / or external environment matters, the accuracy of the evaluation can be effectively improved.

[0093] In one embodiment, step S240 may include the following: determining a first operation mode corresponding to a first degree of human interference according to a preset mapping relationship; wherein the mapping relationship is used to represent an operation mode corresponding to at least one degree of human interference, and the at least one degree of human interference includes the first degree of human interference; updating the operation mode of the mobile device performing the preset task to the first operation mode.

[0094] The server pre-sets a mapping relationship, which is used to represent the operation mode corresponding to at least one degree of human interference. The at least one degree of human interference includes the first degree of human interference.

[0095] Specifically, after obtaining the evaluation result, the server determines the degree of first human interference reflected in the evaluation result. Based on this degree of first human interference, the server determines the corresponding first operating mode. The operating mode for the mobile device to perform the preset task is then updated to the first operating mode. In other words, the first operating mode obtained through the mapping relationship has higher accuracy.

[0096] <Equipment Example 1>

[0097] Figure 5 is a schematic block diagram of an electronic device according to one embodiment. As shown in Figure 5, the electronic device 500 may include an information acquisition module 501, used to acquire the operating information of the mobile device when the mobile device performs a preset task; an action detection module 502, used to detect whether there is an uncertain event within a set time period during which the mobile device performs an action according to the preset task, based on the operating information; a result obtaining module 503, used to evaluate the current state of the mobile device based on the operating information of the mobile device when an uncertain event is detected, and obtain an evaluation result; and a mode update module 504, used to update the current operating mode of the mobile device according to the evaluation result.

[0098] Optionally, the motion detection module 502 is further configured to detect, based on single-point motion information, whether there is an uncertain event at the first moment when the mobile device performs an action according to a preset task; wherein the first moment is after the first time window.

[0099] Optionally, the mode update module 504 is further configured to update the operation mode of the mobile device performing the preset task to the first operation mode among the multiple operation modes according to the multiple preset operation modes; wherein, the multiple operation modes include at least two of the following: manual operation mode, machine-assisted mode, human-machine negotiation mode, manual assistance mode and device autonomous mode, and different operation modes among the multiple operation modes correspond to different degrees of human intervention, and the first operation mode is the operation mode that matches the degree of human intervention reflected in the evaluation result.

[0100] Optionally, the result obtaining module 503 is further configured to determine, based on the operating information of the mobile device, at least one current state of the mobile device in at least one matter, and determine the attribute array corresponding to the at least one current state; wherein, the at least one current state corresponds one-to-one with the at least one matter; input the attribute values ​​corresponding to the first degree of human intervention in each attribute array into a preset evaluation model to obtain the first priority corresponding to the first degree of human intervention; wherein, the attribute array includes at least one attribute value, the at least one attribute value corresponds to different degrees of human intervention, and the degree of human intervention includes the first degree of human intervention; select the second priority with the highest priority from each first priority and use it as the evaluation result.

[0101] Optionally, the mode update module 504 is further configured to determine the first operation mode corresponding to the first degree of human interference according to a preset mapping relationship; wherein the mapping relationship is used to represent the operation mode corresponding to at least one degree of human interference, and the at least one degree of human interference includes the first degree of human interference; and update the operation mode of the mobile device performing the preset task to the first operation mode.

[0102] The electronic device 500 can be the server 100 in Figure 1.

[0103] <Equipment Example 2>

[0104] Figure 6 is a schematic diagram of the hardware structure of an electronic device according to another embodiment.

[0105] As shown in FIG6, the electronic device 600 includes a processor 610 and a memory 620. The memory 620 is used to store an executable computer program, and the processor 610 is used to execute the method as described in any of the above method embodiments under the control of the computer program.

[0106] The electronic device 600 can be the server 100 in Figure 1.

[0107] Each module of the above electronic device 500 can be implemented by the processor 610 in this embodiment executing the computer program stored in the memory 610, or it can be implemented by other structures, which are not limited here.

[0108] This invention can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the invention.

[0109] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0110] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0111] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0112] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0113] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0114] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. It will be known to those skilled in the art that implementation in hardware, implementation in software, and implementation using a combination of software and hardware are equivalent.

[0116] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A method for controlling mobile devices under variable autonomy levels, characterized in that, The method includes: when the mobile device executes a preset task, acquiring the mobile device's operating information within a first time window of executing the task; predicting, based on the operating information, whether an uncertain event will occur after the first time window of the task execution; wherein the uncertain event is an event that does not conform to the task's set information; if an uncertain event is predicted, evaluating the current state of the mobile device based on the operating information to obtain an evaluation result; updating the operation mode of the mobile device executing the preset task based on the evaluation result; wherein the mobile device executing the preset task in different operation modes corresponds to different degrees of human intervention; the current state of the mobile device is updated based on the operating information. The evaluation process involves: determining at least one current state of the mobile device for at least one matter based on its operational information, and determining an attribute array corresponding to the at least one current state; wherein the at least one current state corresponds one-to-one with the at least one matter; inputting the attribute values ​​corresponding to the first degree of human intervention in each attribute array into a preset evaluation model to obtain a first priority corresponding to the first degree of human intervention; wherein the attribute array includes at least one attribute value, the at least one attribute value corresponds to different degrees of human intervention, and the degree of human intervention includes the first degree of human intervention; selecting the second priority with the highest priority from each first priority and using it as the evaluation result.

2. The method according to claim 1, characterized in that, The operational information includes single-point motion information of the mobile device at at least one sampling moment within the first time window; predicting whether there is an uncertain event after the mobile device executes the task within the first time window based on the operational information includes: detecting whether there is an uncertain event at the first moment when the mobile device performs the action according to the preset task based on the single-point motion information; wherein, the first moment is after the first time window.

3. The method according to claim 2, characterized in that, The task setting information includes information about the range of actions to perform the task and information about the target object targeted by the task. The uncertain events include a first uncertain event and a second uncertain event. The first uncertain event is an event in which the movement range of the mobile device exceeds the range of actions. The second uncertain event is an event in which the identified actual object does not match the target object.

4. The method according to claim 1, characterized in that, The evaluation result is an assessment of the degree of human intervention in the mobile device performing the task.

5. The method according to claim 4, characterized in that, The step of updating the operation mode of the mobile device performing the preset task includes: updating the operation mode of the mobile device performing the preset task to a first operation mode among the multiple operation modes according to multiple preset operation modes; wherein, the multiple operation modes include at least two of the following: manual operation mode, machine-assisted mode, human-machine negotiation mode, manual assistance mode, and device autonomous mode, and different operation modes among the multiple operation modes correspond to different degrees of human intervention, and the first operation mode is the operation mode that matches the degree of human intervention reflected by the evaluation result.

6. The method according to claim 5, characterized in that, The step of updating the current operating mode of the mobile device based on the evaluation result includes: determining a first operating mode corresponding to the first degree of human interference according to a preset mapping relationship; wherein the mapping relationship is used to represent an operating mode corresponding to at least one degree of human interference, and the at least one degree of human interference includes the first degree of human interference; and updating the operating mode of the mobile device performing the preset task to the first operating mode.

7. The method according to claim 5, characterized in that, The at least one current state includes at least one of the following: the current user state for user matters, the current environment state for external environment matters, and the current task state for task execution matters.

8. A device for controlling mobile devices under variable autonomy levels, characterized in that, The device includes: an information acquisition module, used to acquire the operating information of the mobile device when the mobile device performs a preset task; an action detection module, used to detect whether there are uncertain events within a set time period during which the mobile device performs actions according to the preset task, based on the operating information; a result obtaining module, used to evaluate the current state of the mobile device based on the operating information of the mobile device when an uncertain event is detected, and obtain an evaluation result; and a mode update module, used to update the current operating mode of the mobile device according to the evaluation result; wherein, the mobile device performs the preset task in different operating modes corresponding to different degrees of human intervention; the mode update module, It is also used to determine, based on the mobile device's operating information, at least one current state of the mobile device in at least one matter, and to determine the attribute array corresponding to the at least one current state; wherein, the at least one current state corresponds one-to-one with the at least one matter; input the attribute values ​​corresponding to the first degree of human intervention in each attribute array into a preset evaluation model to obtain the first priority corresponding to the first degree of human intervention; wherein, the attribute array includes at least one attribute value, the at least one attribute value corresponds to different degrees of human intervention, and the degree of human intervention includes the first degree of human intervention; select the second priority with the highest priority from each first priority and use it as the evaluation result.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the steps of the mobile device control method under variable autonomy as described in any one of claims 1-7.

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