A robot cloud interaction method, device, equipment and medium

Abstract

The application discloses a robot cloud interaction method and device, equipment and medium, and relates to the field of robot application. The method comprises the following steps: when receiving a robot triggering a preset condition for a target identifier, scoring information sent by the target identifier according to a scoring decision is judged to determine whether the scoring information is consistent with historical scoring information recorded locally in the cloud; if the scoring information is not consistent with the historical scoring information recorded locally, a cloud score of the target identifier is calculated according to a preset consensus calculation method; and the cloud score of the target identifier is updated to a cloud score board, so that all robots connected to the cloud update a robot local board based on score information in the cloud score board. The application can share the single knowledge of a single robot to other robots by means of the cloud, form a group consensus, and improve the group intelligence.

Description

Technical Field

[0001] This invention relates to the field of robot applications, and in particular to a robot cloud interaction method, device, equipment, and medium. Background Technology

[0002] Robots may encounter abnormal situations during operation, such as obstructed passage, charging stations not being powered, or inability to interact with target devices. When faced with these abnormalities, robots typically have mechanisms or strategies to mitigate the disruptive effects. However, environmental factors are variable, and the duration of these abnormalities is uncertain; some may be brief (e.g., pedestrian obstruction), while others may persist. This raises the question of whether memory is necessary and for how long that memory should be retained.

[0003] Currently, most strategies, when faced with unknown or undefined environmental changes, assume they are temporary and hope to succeed through continuous trial and error. However, this leads to the phenomenon we observe in real-world scenarios where robots repeatedly fall and get up in the same place. Furthermore, most strategies remain at the individual robot level; even if one robot learns from its mistakes, another similar robot will likely repeat the same errors.

[0004] Therefore, how to provide a method for forming consensus among robots and enhance the collective intelligence of robots is a problem to be solved in this field. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a robot cloud interaction method, device, equipment, and medium, which enables the sharing of individual robot knowledge with other robots via the cloud, forming a collective consensus and improving collective intelligence. The specific solution is as follows:

[0006] In a first aspect, this application discloses a robot cloud interaction method, applied in the cloud, comprising:

[0007] When a robot triggers a preset condition for a target identifier and scores the target identifier based on a scoring decision, it is determined whether the scoring information is consistent with the historical scoring information recorded locally in the cloud.

[0008] If the scoring information is inconsistent with the historical scoring information recorded locally, the cloud score of the target identifier is calculated according to the preset consensus calculation method;

[0009] The cloud score of the target identifier is updated to the cloud score dashboard so that all robots connected to the cloud can update their local dashboards based on the score information in the cloud score dashboard.

[0010] Optionally, when the robot triggers preset conditions for the target identifier and scores the target identifier based on a scoring decision, and then sends the scoring information, determining whether the scoring information is consistent with locally recorded historical scoring information includes:

[0011] When a robot triggers a preset condition for a target identifier and then scores the target identifier based on the robot's local scoring decision and / or cloud scoring decision, the robot determines whether the scoring information is consistent with the historical scoring information recorded locally in the cloud.

[0012] Optionally, when the robot triggers preset conditions for the target identifier and scores the target identifier based on a scoring decision, and then sends the scoring information, determining whether the scoring information is consistent with historical scoring information recorded locally in the cloud includes:

[0013] When a robot triggers a preset condition for a target identifier and scores the target identifier based on a scoring decision, the robot sends a scoring information containing the robot number, the target identifier, and the score of the target identifier. Then, based on the robot number and the target identifier in the scoring information, it is determined whether the score of the target identifier in the scoring information is consistent with the score of the target identifier in the historical scoring information recorded locally in the cloud.

[0014] Optionally, calculating the cloud score of the target identifier according to a preset consensus calculation method includes:

[0015] The cloud score of the target identifier is calculated according to a preset half-life weighted calculation method.

[0016] Optionally, calculating the cloud score of the target identifier according to a preset half-life weighted calculation method includes:

[0017] Based on the preset half-life parameter corresponding to the target identifier, and using the historical scoring information sent by each robot connected to the cloud, which is recorded locally in the cloud, the score weight of each robot corresponding to the target identifier is determined.

[0018] The cloud score of the target identifier is determined based on the historical scoring information sent by each robot and the corresponding score weight of each robot.

[0019] Optionally, updating the cloud score of the target identifier to the cloud score dashboard, so that all robots connected to the cloud update their local dashboards based on the score information in the cloud score dashboard, includes:

[0020] The cloud score of the target identifier is updated to the cloud score dashboard, and the score dashboard update information is sent to all robots connected to the cloud, so that after all robots receive the score dashboard update information, the cloud score of the target identifier in the cloud score dashboard is updated to the robot's local dashboard.

[0021] Alternatively, the cloud score of the target identifier can be updated to the cloud score dashboard, so that all robots connected to the cloud can obtain the cloud score dashboard from the cloud at preset intervals and update the cloud score of the target identifier in the cloud score dashboard to the robot's local dashboard.

[0022] Secondly, this application discloses a robot cloud interaction method, applied to robots, including:

[0023] When a preset condition for a target identifier is triggered, a score is assigned to the target identifier based on the scoring decision, and the scoring information is sent to the cloud. When the cloud determines that the scoring information is inconsistent with the historical scoring information recorded locally in the cloud, it calculates the cloud score of the target identifier according to the preset consensus calculation method and updates the cloud score of the target identifier to the cloud score dashboard.

[0024] The robot's local dashboard is updated based on the score information in the cloud-based score dashboard.

[0025] Thirdly, this application discloses a robot cloud interaction device, including:

[0026] The scoring information receiving module is used to determine whether the scoring information is consistent with the historical scoring information recorded locally in the cloud after receiving the scoring information sent by the robot after triggering the preset conditions for the target identifier and scoring the target identifier according to the scoring decision.

[0027] The consensus calculation module is used to calculate the cloud score of the target identifier according to a preset consensus calculation method if the scoring information is inconsistent with the historical scoring information recorded locally.

[0028] The Kanban update module is used to update the cloud score of the target identifier to the cloud score Kanban, so that all robots connected to the cloud can update their local Kanban based on the score information in the cloud score Kanban.

[0029] Fourthly, this application discloses an electronic device, comprising:

[0030] Memory, used to store computer programs;

[0031] A processor is used to execute the computer program to implement the aforementioned robot cloud interaction method.

[0032] Fifthly, this application discloses a computer storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the steps of the aforementioned disclosed robot cloud interaction method.

[0033] This application proposes a method whereby a robot scores a target identifier based on a pre-defined condition triggered by the robot, and then sends the scoring information sent after scoring the target identifier according to a scoring decision. The method determines whether the scoring information is consistent with historical scoring information recorded locally in the cloud. If the scoring information is inconsistent with the historical scoring information recorded locally, the cloud score of the target identifier is calculated according to a pre-defined consensus calculation method. The cloud score of the target identifier is then updated to the cloud score dashboard, so that all robots connected to the cloud can update their local dashboards based on the score information in the cloud score dashboard. This application proposes a method where a robot scores locally, and a collective consensus is formed through the cloud to influence subsequent decisions. Considering that robots may spread incorrect perceptions due to their own reasons, the robot consensus mechanism proposed in this invention can promptly prevent or limit the spread of incorrect perceptions by individual robots. This method is universal and applicable, but not limited to, scenarios such as regional accessibility evaluation and abnormal device identification. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0035] Figure 1 A flowchart of a robot cloud interaction method provided in this application;

[0036] Figure 2 A scoring decision flowchart is provided for this application;

[0037] Figure 3 A schematic diagram of a combined scoring decision-making process provided in this application;

[0038] Figure 4 A schematic diagram of a cloud-based half-life weighted calculation method provided in this application;

[0039] Figure 5 This application provides a schematic diagram of a robot interacting with the cloud.

[0040] Figure 6 A flowchart of a robot cloud interaction method provided in this application;

[0041] Figure 7 This application provides a schematic diagram of the structure of a robot cloud interaction device;

[0042] Figure 8 This application provides a structural diagram of an electronic device. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] In existing technologies, robots often exhibit the phenomenon of repeatedly falling and getting up in the same place. Furthermore, most strategies remain at the individual robot level; even if one robot learns from its mistakes, other similar robots may repeat the same errors, resulting in poor collective intelligence. This application addresses this by leveraging the cloud to share the individual knowledge of a single robot with other robots, forming a collective consensus and improving collective intelligence.

[0045] This invention discloses a robot cloud interaction method, applied in the cloud, see [link to relevant documentation]. Figure 1 The method includes:

[0046] Step S11: When the robot triggers the preset conditions for the target identifier and scores the target identifier according to the scoring decision, it is determined whether the scoring information is consistent with the historical scoring information recorded locally in the cloud.

[0047] In this embodiment, the target identifier is an identifier used to distinguish scoring objects. This type of identifier is global, meaning the same identifier represents the same object for both the cloud and other robots; examples include coordinates and device numbers. The preset conditions for the target identifier can be conditions such as movement being hindered or the detection of a specified object.

[0048] In this embodiment, the step of determining whether the scoring information is consistent with the historical scoring information recorded locally can include: when the robot triggers the preset conditions for the target identifier and scores the target identifier according to the robot's local scoring decision and / or cloud scoring decision, the step of determining whether the scoring information is consistent with the historical scoring information recorded locally in the cloud can include: when the robot triggers the preset conditions for the target identifier and scores the target identifier according to the robot's local scoring decision and / or cloud scoring decision, the step of determining whether the scoring information is consistent with the historical scoring information recorded locally in the cloud.

[0049] In this embodiment, after the robot triggers preset conditions for the target identifier, it scores the target identifier based on a scoring decision. This scoring decision can be a local robot scoring decision and / or a cloud-based scoring decision. In a specific implementation, such as... Figure 2 The diagram illustrates a scoring decision flowchart provided by the present invention. The robot first determines whether a cloud score exists for the target identifier and judges the first time difference between the current time and the update time of the cloud score. If the cloud score does not exist or the first time difference is greater than a preset first effective time difference threshold T1, the robot scores according to the local scoring decision. If the cloud score exists and the first time difference is not greater than the preset first effective time difference threshold T1, the robot judges whether a local score exists for the target identifier and judges the second time difference between the current time and the update time of the local score. If the local score does not exist or the second time difference is greater than a preset second effective time difference threshold T2, the robot scores according to the cloud scoring decision. If the local score exists and the second time difference is not greater than the preset second effective time difference threshold T2, the robot combines the cloud scoring decision and the local scoring decision for scoring.

[0050] It should be noted that when a single robot executes according to local decision-making, it continues execution based on local scoring (if the target does not exist, local scoring is performed first); when a single robot executes according to cloud-based decision-making, it makes judgments directly based on consensus knowledge recorded in the cloud without needing to attempt it itself; wherein, the consensus knowledge is the decision-making method recorded in the cloud corresponding to the target identifier; when a single robot scores by combining cloud-based scoring decisions and local scoring decisions, the specific execution method can be adjusted according to the actual scenario, preferably by weighting by half-life and making subsequent decisions based on the average score. In specific implementations, such as Figure 3 This diagram illustrates the scoring process when combining cloud-based and local scoring decisions as proposed in this invention. The diagram shows the local scores s1, s2…, local update times t1, t2…, cloud scores c1, c2…, and cloud score update times d1, d2… for the target identifier, based on the local score dashboard, and calculates the current score for that identifier using the current time t, half-life parameters h1, h2…, local weight, and cloud weight. Specifically, the local score weight can be calculated first using the current time t and the half-life parameter h corresponding to the target identifier. and cloud score weight Then, the weighted average of the local and cloud scores is calculated to obtain the current score for that identifier.

[0051] In this embodiment, when a robot triggers a preset condition for a target identifier and scores the target identifier according to a scoring decision, and then sends the scoring information, determining whether the scoring information is consistent with the historical scoring information recorded locally in the cloud can include: when a robot triggers a preset condition for a target identifier and scores the target identifier according to a scoring decision, and then sends the scoring information containing the robot number, the target identifier, and the score of the target identifier, then based on the robot number and the target identifier in the scoring information, determining whether the score of the target identifier in the scoring information is consistent with the score of the target identifier in the historical scoring information recorded locally in the cloud.

[0052] It is understood that in this embodiment, the scoring information includes the robot number, the target identifier, and the score of the target identifier. When the cloud receives the scoring information, it will use (target identifier, robot number) to determine whether the score of the target identifier in the scoring information is consistent with the historical score of the robot for the target identifier recorded locally in the cloud.

[0053] Step S12: If the scoring information is inconsistent with the historical scoring information recorded locally, the cloud score of the target identifier is calculated according to the preset consensus calculation method.

[0054] In this step, if the cloud detects that the scoring information is inconsistent with the historical scoring information recorded locally, the cloud score of the target identifier is calculated according to a preset consensus calculation method. Specifically, if the cloud detects that the score of the target identifier in the scoring information is inconsistent with the historical score of the robot for the target identifier recorded locally in the cloud, the cloud score of the target identifier is calculated according to a preset consensus calculation method.

[0055] In this embodiment, calculating the cloud score of the target identifier according to a preset consensus calculation method may include: calculating the cloud score of the target identifier according to a preset half-life weighted calculation method. It is understood that the preset consensus calculation method in this embodiment may be a half-life weighted calculation method.

[0056] In this embodiment, calculating the cloud score of the target identifier according to the preset half-life weighted calculation method may include: determining the score weight of each robot corresponding to the target identifier based on the preset half-life parameter corresponding to the target identifier and using the historical scoring information sent by each robot connected to the cloud that is locally recorded in the cloud; and determining the cloud score of the target identifier based on the historical scoring information sent by each robot and the corresponding score weight of each robot.

[0057] In this embodiment, as Figure 4This is a schematic diagram of a cloud-based half-life weighted calculation method proposed in this invention. In the diagram, the cloud classifies the collected scores by identifier. For each target identifier from 1 to m, robot numbers 1 to n and the robot's scores s1 to s2 for that target identifier are recorded. n and update time t1~t n (i.e., the time when the robot sends the corresponding score), according to t = max(t1, ..., t n The weight of each score corresponding to the target identifier is calculated by using h as the half-life. (更新时间-t) / h Where t is the time of the last score update, and then the weighted average score corresponding to each target identifier is calculated as its cloud score. Here, h is a settable parameter, representing the half-life. In a specific implementation, if h is 1 hour and (update time - t) is -1 hour, then the weight is calculated to be 0.5.

[0058] Step S13: Update the cloud score of the target identifier to the cloud score dashboard so that all robots connected to the cloud can update their local dashboards based on the score information in the cloud score dashboard; here, connection refers to the robot establishing communication with the cloud, that is, the robot communicating and connecting with the cloud. The cloud can also be a central cluster decision-making device.

[0059] In this embodiment, after the cloud calculates the cloud score for each identifier, the score will be updated to the cloud score dashboard, which is a dashboard used to record the target identifier and its corresponding score.

[0060] Figure 5 This invention provides a schematic diagram of a robot-cloud interaction process. Each robot scores a target and records the update time of the score. The score for the target is uploaded to the cloud. The cloud determines whether the received score differs from the historical score. If a difference exists, consensus calculation is triggered, and the score for the target is updated on the cloud score dashboard. Each robot can then obtain the latest score data from the cloud score dashboard. If the latest score differs from the previous score, the score data in the cloud score dashboard is updated to the robot's local dashboard. This process realizes the interaction of robots sending score information to the cloud and the cloud updating the score information to the robots' local dashboards.

[0061] In this embodiment, updating the cloud score of the target identifier to the cloud score dashboard so that all robots connected to the cloud can update their local dashboards based on the score information in the cloud score dashboard can include: updating the cloud score of the target identifier to the cloud score dashboard and sending score dashboard update information to all robots connected to the cloud, so that all robots, upon receiving the score dashboard update information, update the cloud score of the target identifier in the cloud score dashboard to their local dashboards; or, updating the cloud score of the target identifier to the cloud score dashboard so that all robots connected to the cloud can retrieve the cloud score dashboard from the cloud at preset intervals and update the cloud score of the target identifier in the cloud score dashboard to their local dashboards.

[0062] When the cloud updates the cloud score of the target identifier to the cloud score dashboard, the robot can update its local score dashboard through a two-way link. That is, the cloud actively sends update information to all robots after the score dashboard is updated, or the robot periodically obtains the latest score dashboard from the cloud.

[0063] Understandably, after obtaining the latest cloud-based score dashboard, the robot will update the data in the cloud-based score dashboard to the robot's local dashboard.

[0064] It is worth noting that anomalies encountered by a single robot may originate from itself rather than from the outside. For example, if a laser is blocked, it may mistakenly perceive an obstacle in front of it. Such erroneous cognitive transmission should be stopped or limited in a timely manner.

[0065] In this embodiment, when the cloud receives the scoring information sent after the robot triggers preset conditions for the target identifier and scores the target identifier according to the scoring decision, it determines whether the scoring information is consistent with the historical scoring information recorded locally in the cloud. If the scoring information is inconsistent with the historical scoring information recorded locally, the cloud score of the target identifier is calculated according to the preset consensus calculation method. The cloud score of the target identifier is updated to the cloud score dashboard so that all robots connected to the cloud can update their local dashboards based on the score information in the cloud score dashboard. This embodiment proposes a method in which individual robots score locally, form a group consensus with the help of the cloud, and influence subsequent decisions. Considering that robots may spread incorrect perceptions due to their own reasons, the consensus mechanism formed by robots using half-life weighting as the consensus score calculation method proposed in this invention can promptly prevent or limit the spread of incorrect perceptions by individual robots. This method has universality and is applicable to, but not limited to, scenarios such as regional accessibility evaluation and abnormal device identification.

[0066] Figure 6This is a flowchart illustrating a robot cloud interaction method provided in an embodiment of this application, applied to a robot. See also... Figure 6 As shown, the method includes:

[0067] Step S21: When a preset condition for a target identifier is triggered, a score is assigned to the target identifier based on the scoring decision, and the scoring information is sent to the cloud. When the cloud determines that the scoring information is inconsistent with the historical scoring information recorded locally in the cloud, it calculates the cloud score of the target identifier according to the preset consensus calculation method and updates the cloud score of the target identifier to the cloud score dashboard.

[0068] Step S22: Update the robot's local dashboard based on the score information in the cloud-based score dashboard.

[0069] This embodiment applies to robots. When a preset condition for a target identifier is triggered, the robot scores the target identifier based on a scoring decision and sends the scoring information to the cloud. The cloud then determines if the scoring information is inconsistent with historical scoring information recorded locally. If so, it calculates the target identifier's cloud score according to a preset consensus calculation method and updates the cloud score dashboard. The robot's local dashboard is then updated based on the score information in the cloud score dashboard. Thus, this invention proposes a method where a single robot scores locally, forming a collective consensus through the cloud and influencing subsequent decisions. The robot consensus mechanism proposed in this invention can promptly prevent or limit the spread of erroneous perceptions by individual robots. This method is universally applicable and suitable for scenarios such as regional accessibility evaluation and abnormal equipment identification.

[0070] See Figure 7 As shown in the figure, this application discloses a robot cloud interaction device, which is applied in the cloud and may specifically include:

[0071] The scoring information receiving module 11 is used to determine whether the scoring information is consistent with the historical scoring information recorded locally in the cloud after receiving the scoring information sent by the robot after scoring the target identifier according to the scoring decision after triggering the preset conditions for the target identifier.

[0072] The consensus calculation module 12 is used to calculate the cloud score of the target identifier according to a preset consensus calculation method if the scoring information is inconsistent with the historical scoring information recorded locally.

[0073] The Kanban update module 13 is used to update the cloud score of the target identifier to the cloud score Kanban, so that all robots connected to the cloud can update their local Kanban based on the score information in the cloud score Kanban.

[0074] In this invention, when the cloud receives a scoring message from a robot that has triggered preset conditions for a target identifier, and scores the target identifier based on a scoring decision, it determines whether the scoring message is consistent with historical scoring information recorded locally in the cloud. If the scoring message is inconsistent with the historical scoring information recorded locally, the cloud score of the target identifier is calculated according to a preset consensus calculation method. The cloud score of the target identifier is then updated to the cloud score dashboard, so that all robots connected to the cloud can update their local dashboards based on the score information in the cloud score dashboard. This invention proposes a method where individual robots score locally, forming a collective consensus through the cloud and influencing subsequent decisions. Considering the possibility that robots may spread incorrect perceptions due to their own reasons, the robot consensus mechanism proposed in this invention can promptly prevent or limit the spread of incorrect perceptions by individual robots. This method is universally applicable and suitable for scenarios such as regional accessibility evaluation and abnormal device identification.

[0075] Furthermore, embodiments of this application also disclose an electronic device, Figure 8 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of use of this application.

[0076] Figure 8 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a display screen 24, an input / output interface 25, a communication interface 26, and a communication bus 27. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the robot cloud interaction method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0077] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 26 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0078] Furthermore, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk, or optical disk, etc. The resources stored on it can include an operating system 221, computer programs 222, and virtual machine data 223, etc. The virtual machine data 223 can include various types of data. The storage method can be temporary storage or permanent storage.

[0079] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including computer programs capable of performing the robot cloud interaction method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0080] Furthermore, this application also discloses a computer-readable storage medium, which includes random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, magnetic disks, optical disks, or any other form of storage medium known in the art. When the computer program is executed by a processor, it implements the aforementioned disclosed robot cloud interaction method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0081] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatuses disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section. Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0082] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0083] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0084] The robot cloud interaction method, apparatus, device, and storage medium provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A robot cloud interaction method, characterized in that, Applied to the cloud, including: When a robot triggers a preset condition for a target identifier and scores the target identifier based on a scoring decision, it is determined whether the scoring information is consistent with the historical scoring information recorded locally in the cloud. If the scoring information is inconsistent with the historical scoring information recorded locally in the cloud, the cloud score of the target identifier is calculated according to the preset consensus calculation method; Update the cloud score of the target identifier to the cloud score dashboard so that all robots connected to the cloud can update their local dashboards based on the score information in the cloud score dashboard. The process for determining the scoring information is as follows: The robot determines whether a cloud score exists for the target identifier and determines a first time difference between the current time and the update time of the cloud score; if the cloud score does not exist or the first time difference is greater than a preset first effective time difference threshold, the robot scores according to its local scoring decision; if the cloud score exists and the first time difference is not greater than the preset first effective time difference threshold, the robot determines whether a local score exists for the target identifier and determines a second time difference between the current time and the update time of the local score; if the local score does not exist or the second time difference is greater than a preset second effective time difference threshold, the robot scores according to its cloud scoring decision; if the local score exists and the second time difference is not greater than the preset second effective time difference threshold, the robot combines the cloud scoring decision and the robot's local scoring decision to score. The process of combining cloud-based scoring decision and robot local scoring decision is as follows: The robot's score weight corresponding to the local score of the target identifier is calculated using the current time and the half-life parameter corresponding to the target identifier. Weighting of cloud scores Then, a weighted average is calculated between the robot's score weight and the cloud score weight to obtain the target identifier's score; where, This is the update time for local scores. t For the current time, This refers to the update time of the scores in the cloud. h The half-life parameter is used to identify the target.

2. The robot cloud interaction method according to claim 1, characterized in that, When the robot triggers preset conditions for the target identifier and scores the target identifier based on a scoring decision, and then sends the scoring information, the determination of whether the scoring information is consistent with the historical scoring information recorded locally in the cloud includes: When a robot triggers a preset condition for a target identifier and scores the target identifier based on a scoring decision, the robot sends a scoring information containing the robot number, the target identifier, and the score of the target identifier. Then, based on the robot number and the target identifier in the scoring information, it is determined whether the score of the target identifier in the scoring information is consistent with the score of the target identifier in the historical scoring information recorded locally in the cloud.

3. The robot cloud interaction method according to claim 1, characterized in that, The calculation of the cloud score of the target identifier according to the preset consensus calculation method includes: The cloud score of the target identifier is calculated according to a preset half-life weighted calculation method.

4. The robot cloud interaction method according to claim 3, characterized in that, The calculation of the cloud score of the target identifier according to the preset half-life weighted calculation method includes: Based on the preset half-life parameter corresponding to the target identifier, and using the historical scoring information sent by each robot connected to the cloud, which is recorded locally in the cloud, the score weight of each robot corresponding to the target identifier is determined. The cloud score of the target identifier is determined based on the historical scoring information sent by each robot and the corresponding score weight of each robot.

5. The robot cloud interaction method according to claim 1, characterized in that, The step of updating the cloud score of the target identifier to the cloud score dashboard, so that all robots connected to the cloud can update their local dashboards based on the score information in the cloud score dashboard, includes: The cloud score of the target identifier is updated to the cloud score dashboard, and the score dashboard update information is sent to all robots connected to the cloud, so that after all robots receive the score dashboard update information, the cloud score of the target identifier in the cloud score dashboard is updated to the robot's local dashboard. Alternatively, the cloud score of the target identifier can be updated to the cloud score dashboard, so that all robots connected to the cloud can obtain the cloud score dashboard from the cloud at preset intervals and update the cloud score of the target identifier in the cloud score dashboard to the robot's local dashboard.

6. A robot cloud interaction method, characterized in that, Applications in robots, including: When a preset condition for a target identifier is triggered, a score is assigned to the target identifier based on the scoring decision, and the scoring information is sent to the cloud. When the cloud determines that the scoring information is inconsistent with the historical scoring information recorded locally in the cloud, it calculates the cloud score of the target identifier according to the preset consensus calculation method and updates the cloud score of the target identifier to the cloud score dashboard. The robot's local dashboard is updated based on the score information in the cloud-based score dashboard. The process for determining the scoring information is as follows: First, determine if a cloud score exists for the target identifier, and determine a first time difference between the current time and the update time of the cloud score. If the cloud score does not exist or the first time difference is greater than a preset first effective time difference threshold, then score according to the robot's local scoring decision. If the cloud score exists and the first time difference is not greater than the preset first effective time difference threshold, then determine if a local score exists for the target identifier, and determine a second time difference between the current time and the update time of the robot's local score. If the local score does not exist or the second time difference is greater than a preset second effective time difference threshold, then score according to the cloud scoring decision. If the local score exists and the second time difference is not greater than the preset second effective time difference threshold, then combine the cloud scoring decision and the robot's local scoring decision for scoring. The process of combining cloud-based scoring decision and robot local scoring decision is as follows: The robot's score weight corresponding to the local score of the target identifier is calculated using the current time and the half-life parameter corresponding to the target identifier. Weighting of cloud scores Then, a weighted average is calculated between the robot's score weight and the cloud score weight to obtain the target identifier's score; where, This is the update time for local scores. t For the current time, This refers to the update time of the scores in the cloud. h The half-life parameter is used to identify the target.

7. A robot cloud interaction device, characterized in that, Applied to the cloud, including: The scoring information receiving module is used to determine whether the scoring information is consistent with the historical scoring information recorded locally in the cloud after receiving the scoring information sent by the robot after triggering the preset conditions for the target identifier and scoring the target identifier according to the scoring decision. The consensus calculation module is used to calculate the cloud score of the target identifier according to a preset consensus calculation method if the scoring information is inconsistent with the historical scoring information recorded locally in the cloud. The Kanban update module is used to update the cloud score of the target identifier to the cloud score Kanban, so that all robots connected to the cloud can update their local Kanban based on the score information in the cloud score Kanban. The process for determining the scoring information is as follows: The robot determines whether a cloud score exists for the target identifier and determines a first time difference between the current time and the update time of the cloud score; if the cloud score does not exist or the first time difference is greater than a preset first effective time difference threshold, the robot scores according to its local scoring decision; if the cloud score exists and the first time difference is not greater than the preset first effective time difference threshold, the robot determines whether a local score exists for the target identifier and determines a second time difference between the current time and the update time of the local score; if the local score does not exist or the second time difference is greater than a preset second effective time difference threshold, the robot scores according to its cloud scoring decision; if the local score exists and the second time difference is not greater than the preset second effective time difference threshold, the robot combines the cloud scoring decision and the robot's local scoring decision to score. The process of combining cloud-based scoring decision and robot local scoring decision is as follows: The robot's score weight corresponding to the local score of the target identifier is calculated using the current time and the half-life parameter corresponding to the target identifier. Weighting of cloud scores Then, a weighted average is calculated between the robot's score weight and the cloud score weight to obtain the target identifier's score; where, This is the update time for local scores. t For the current time, This refers to the update time of the scores in the cloud. h The half-life parameter is used to identify the target.

8. An electronic device, characterized in that, It includes a processor and a memory; wherein, when the processor executes a computer program stored in the memory, it implements the robot cloud interaction method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Used to store computer programs; wherein, when the computer programs are executed by a processor, they implement the robot cloud interaction method as described in any one of claims 1 to 6.

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