Control methods, devices, and readable storage media for robotic arms

Abstract

This application discloses a control method, device, and readable storage medium for a robotic arm, relating to the field of data processing technology, for increasing the flexibility of production lines and improving production efficiency. The method includes: acquiring multiple target parameters; the multiple target parameters include environmental parameters of a first region, network parameters of a second robotic arm located in the first region, vibration amplitude, and stress magnitude; the first region is any one of the multiple regions; determining the failure rate of the second robotic arm located in the first region based on a preset fault algorithm and the multiple target parameters, thus obtaining the failure rates of the second robotic arms located in the multiple regions; determining the target deployment number of the first robotic arms in the first region based on the failure rates of the second robotic arms in the multiple regions, thus obtaining the target deployment number of the first robotic arms in each region, and deploying the multiple first robotic arms to the multiple regions according to the target deployment numbers.

Description

Technical Field

[0001] This application relates to the field of robotics, and more particularly to a control method, apparatus, and readable storage medium for a robotic arm. Background Technology

[0002] With the increasing demand for intelligent manufacturing, the number of smart factories is growing, and smart factories typically deploy multiple production lines. A production line, also known as an assembly line, is a production method in industry where each production unit focuses on handling only one specific segment of the work to improve efficiency and output.

[0003] Currently, the assembly line operates by having multiple robotic arms work sequentially. After the first robotic arm completes its task, the second robotic arm continues the work based on the first. Therefore, if any robotic arm malfunctions, subsequent tasks cannot proceed, thus affecting the entire workflow. Summary of the Invention

[0004] This application provides a control method, apparatus, and readable storage medium for a robotic arm, which increases the flexibility of production lines and improves production efficiency.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] Firstly, a method for controlling a robotic arm is provided, comprising: an application to a server, the server controlling multiple robotic arms in a production line, the production line including multiple regions, the multiple robotic arms including multiple first robotic arms, and a second robotic arm located in each region; the first robotic arms are standby robotic arms in a non-operating state, and the second robotic arms are robotic arms in an operating state; the method includes: acquiring multiple target parameters; the multiple target parameters include environmental parameters of the first region, network parameters of the second robotic arm located in the first region, vibration amplitude, and stress magnitude; the first region is any one of the multiple regions; determining the failure rate of the second robotic arm located in the first region according to a preset fault algorithm and the multiple target parameters, thereby obtaining the failure rate of the second robotic arms located in the multiple regions; determining the target deployment number of the first robotic arms in the first region according to the failure rate of the second robotic arms in the multiple regions, thereby obtaining the target deployment number of the first robotic arms in each region, and deploying the multiple first robotic arms to the multiple regions according to the target deployment number; the target deployment number of the first robotic arms in a region is positively correlated with the failure rate of the second robotic arms in that region.

[0007] Optionally, the target deployment quantity of the first robotic arm in the first region is determined based on the failure rates of the second robotic arms in multiple regions. This includes: determining the pre-deployment quantity of the first robotic arms in the first region based on the failure rates of the second robotic arms in the first region and a first mapping relationship, thus obtaining the pre-deployment quantity of the first robotic arms in each region; the first mapping relationship includes different failure rates and corresponding pre-deployment quantities; if the sum of the pre-deployment quantities of the first robotic arms in each region is the same as the total number of the multiple first robotic arms, the pre-deployment quantity of the first robotic arms in the first region is determined as the target deployment quantity of the first robotic arms in the first region; if the sum of the pre-deployment quantities of the first robotic arms in each region is different from the total number of the multiple first robotic arms, the target deployment quantity of the first robotic arms in the first region is determined based on the target difference and the pre-deployment quantity of the first robotic arms in the first region; the target difference is the difference between the sum of the pre-deployment quantities of the first robotic arms in each region and the total number of the multiple first robotic arms.

[0008] Optionally, the target deployment number of the first robotic arm in the first region is determined based on the target difference and the pre-deployed number of the first robotic arm in the first region, including: if the ranking of the pre-deployed number of the first robotic arm in the first region is greater than the target difference, the pre-deployed number of the first robotic arm in the first region is determined as the target deployment number of the first robotic arm in the first region; if the ranking of the pre-deployed number of the first robotic arm in the first region is less than or equal to the target difference, the pre-deployed number of the first robotic arm in the first region is adjusted, and the adjusted pre-deployed number of the first robotic arm in the first region is determined as the target deployment number of the first robotic arm in the first region.

[0009] Optionally, based on a preset fault algorithm and multiple target parameters, the failure rate of the second robotic arm located in the first region is determined, including: determining the failure probability scores of multiple target parameters; wherein, the failure probability scores of multiple target parameters include environmental parameter scores, network parameter scores, vibration amplitude scores, and stress scores; the environmental parameter scores are related to the temperature, humidity, and dust level of the first region, the network parameter scores are related to the network bandwidth, network latency, and network jitter of the second robotic arm in the first region, the vibration amplitude scores are related to the magnitude of the vibration amplitude of the second robotic arm located in the first region, and the stress scores are related to the magnitude of the stress of the second robotic arm located in the first region; the failure probability scores of multiple target parameters are weighted to obtain the failure rate of the second robotic arm located in the first region.

[0010] Optionally, the method further includes: deploying a target function algorithm for a first robotic arm in a first region; the target function algorithm is a function algorithm deployed in a first or second robotic arm, the first and second robotic arms being located in the first region.

[0011] Based on the technical solution provided in this application, the failure rate of the second robotic arm located in the first region is determined according to the fault algorithm and multiple target parameters, thus obtaining the failure rates of the second robotic arms located in multiple regions. Since the multiple target parameters include environmental parameters of the first region, the second robotic arm located in the first region, network parameters, vibration amplitude, and stress magnitude, these multiple target parameters can reflect the operating conditions and status of the second robotic arm in the first region in real time, thereby more accurately determining the failure rate of the second robotic arm in the first region and obtaining the failure rates of the second robotic arms located in multiple regions. Furthermore, based on the failure rates of the second robotic arms in multiple regions, the target deployment number of the first robotic arms in the first region is determined, obtaining the target deployment number of the first robotic arms in each region, and multiple first robotic arms are deployed to multiple regions according to the target deployment number. Since the target deployment number of the first robotic arms in a region is positively correlated with the failure rate of the second robotic arms in that region, more first robotic arms can be deployed in regions with higher failure rates to replace malfunctioning second robotic arms in a timely manner. This avoids the technical problem that a failure of a robotic arm in one region of the production line can affect the entire work process, thus improving production efficiency.

[0012] Secondly, a control device for a robotic arm is provided. The device controls multiple robotic arms in an assembly line, the assembly line comprising multiple regions, and the multiple robotic arms including multiple first robotic arms and second robotic arms located in each region. The first robotic arms are standby robotic arms in a non-operating state, and the second robotic arms are robotic arms in an operating state. The device includes: an acquisition unit and a determination unit. The acquisition unit is used to acquire multiple target parameters. The multiple target parameters include environmental parameters of the first region, network parameters of the second robotic arm located in the first region, vibration amplitude, and stress magnitude. The first region is any one of the multiple regions. The determination unit is used to determine the failure rate of the second robotic arm located in the first region based on a preset fault algorithm and the multiple target parameters, thus obtaining the failure rate of the second robotic arms located in the multiple regions. The determination unit is also used to determine the target deployment number of first robotic arms in the first region based on the failure rate of the second robotic arms in the multiple regions, thus obtaining the target deployment number of first robotic arms in each region, and deploying the multiple first robotic arms to the multiple regions according to the target deployment number. The target deployment number of first robotic arms in a region is positively correlated with the failure rate of the second robotic arms in that region.

[0013] Optionally, the determining unit is specifically used for: determining the pre-deployment quantity of the first robotic arm in the first region based on the failure rate of the second robotic arm in the first region and the first mapping relationship, thereby obtaining the pre-deployment quantity of the first robotic arm in each region; the first mapping relationship includes different failure rates and corresponding pre-deployment quantities; when the sum of the pre-deployment quantities of the first robotic arms in each region is the same as the total number of multiple first robotic arms, determining the pre-deployment quantity of the first robotic arms in the first region as the target deployment quantity of the first robotic arms in the first region; when the sum of the pre-deployment quantities of the first robotic arms in each region is different from the total number of multiple first robotic arms, determining the target deployment quantity of the first robotic arms in the first region based on the target difference and the pre-deployment quantity of the first robotic arms in the first region; the target difference is the difference between the sum of the pre-deployment quantities of the first robotic arms in each region and the total number of multiple first robotic arms.

[0014] Optionally, the determining unit is further configured to: determine the pre-deployment quantity of the first robotic arm in the first region as the target deployment quantity of the first robotic arm in the first region when the ranking of the pre-deployment quantity of the first robotic arm in the first region is greater than the target difference; and adjust the pre-deployment quantity of the first robotic arm in the first region when the ranking of the pre-deployment quantity of the first robotic arm in the first region is less than or equal to the target difference, and determine the adjusted pre-deployment quantity of the first robotic arm in the first region as the target deployment quantity of the first robotic arm in the first region.

[0015] Optionally, the determining unit is further configured to: determine the failure probability scores of multiple target parameters; wherein, the failure probability scores of the multiple target parameters include environmental parameter scores, network parameter scores, vibration amplitude scores, and stress scores; the environmental parameter scores are related to the temperature, humidity, and dust levels of the first region; the network parameter scores are related to the network bandwidth, network latency, and network jitter of the second robotic arm in the first region; the vibration amplitude scores are related to the magnitude of the vibration amplitude of the second robotic arm located in the first region; and the stress scores are related to the magnitude of the stress of the second robotic arm located in the first region; and the failure probability scores of the multiple target parameters are weighted to obtain the failure rate of the second robotic arm located in the first region.

[0016] Optionally, the device further includes: a deployment unit; the deployment unit is used to deploy a target function algorithm for a first robotic arm in the first region; the target function algorithm is a function algorithm deployed in a second robotic arm located in the first region.

[0017] Thirdly, a control device for a robotic arm is provided. This control device can realize the functions performed by the control device of the robotic arm in the above-mentioned aspects or possible designs. The functions can be implemented by hardware. For example, in one possible design, the control device of the robotic arm may include a processor and a communication interface. The processor can be used to support the control device of the robotic arm in realizing the functions involved in the first aspect or any possible design of the first aspect.

[0018] In another possible design, the control device of the robotic arm may also include a memory for storing necessary computer execution instructions and data. When the control device of the robotic arm is running, the processor executes the computer execution instructions stored in the memory to cause the control device of the robotic arm to perform the first aspect or any of the possible robotic arm control methods described above.

[0019] Fourthly, a computer-readable storage medium is provided, which may be a readable non-volatile storage medium storing computer instructions or programs that, when executed on a computer, enable the computer to perform the control method of the robotic arm described in the first aspect or any of the possible methods described in the first aspect.

[0020] Fifthly, a computer program product containing instructions is provided, which, when run on a computer, enables the computer to execute the control method of the robotic arm described in the first aspect or any possible design of the above aspects.

[0021] In a sixth aspect, an electronic device is provided, comprising one or more processors and one or more memories. The one or more memories are coupled to the one or more processors, and the one or more memories are used to store computer program code, including computer instructions, which, when executed by the one or more processors, cause the electronic device to perform a control method for a robotic arm as described in the first aspect or any possible design of the first aspect.

[0022] In a seventh aspect, a chip system is provided, including a processor and a communication interface, which can be used to implement the functions performed by the control device of the robotic arm in the first aspect or any possible design of the first aspect. In one possible design, the chip system further includes a memory for storing program instructions and / or data. The chip system may be composed of chips or may include chips and other discrete devices, without limitation. Attached Figure Description

[0023] Figure 1 A schematic diagram of an assembly line system provided in an embodiment of this application;

[0024] Figure 2 A schematic diagram of a control system for a robotic arm provided in an embodiment of this application;

[0025] Figure 3 This is a schematic diagram of the structure of a control device for a robotic arm provided in an embodiment of this application;

[0026] Figure 4 A flowchart illustrating a control method for a robotic arm provided in an embodiment of this application;

[0027] Figure 5 A flowchart illustrating another control method for a robotic arm provided in an embodiment of this application;

[0028] Figure 6 A flowchart illustrating another control method for a robotic arm provided in an embodiment of this application;

[0029] Figure 7 A flowchart illustrating another control method for a robotic arm provided in an embodiment of this application;

[0030] Figure 8 A flowchart illustrating another control method for a robotic arm provided in an embodiment of this application;

[0031] Figure 9 This is a schematic diagram of the structure of another control device for a robotic arm provided in an embodiment of this application. Detailed Implementation

[0032] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0033] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0034] It should also be understood that the term "comprising" indicates the presence of the described feature, whole, step, operation, element and / or component, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements and / or components.

[0035] The Industrial Internet is a new economic ecosystem, key infrastructure, and novel application model that deeply integrates next-generation information technology with the industrial economy. Through the comprehensive interconnection of people, machines, and things, it achieves full connectivity across all factors, the entire industrial chain, and the entire value chain, driving the formation of a completely new production, manufacturing, and service system. Calculations show that in 2018 and 2019, the added value of my country's Industrial Internet industry reached 1.42 trillion yuan and 2.13 trillion yuan respectively, representing year-on-year real growth of 55.7% and 47.3%, accounting for 1.5% and 2.2% of GDP, and contributing 6.7% and 9.9% to economic growth, respectively.

[0036] One typical application scenario of the Industrial Internet is the smart factory. A smart factory utilizes various modern technologies to automate factory operations, management, and production, aiming to strengthen and standardize enterprise management, reduce errors, plug loopholes, improve efficiency, ensure safe production, provide decision-making support, enhance external connections, and expand into international markets. To some extent, smart factories achieve coordinated cooperation between humans and machines. With the increasing demand for intelligent manufacturing, the number of smart factories is growing, and they typically deploy multiple production lines. A production line, also known as an assembly line, is a production method in industry where each production unit focuses on handling only a specific segment of the work to improve efficiency and output.

[0037] In one example, such as Figure 1 As shown, Figure 1 Each dashed line area represents a region, and each dashed line area includes one robotic arm. The assembly line works as follows: multiple robotic arms work sequentially, meaning that after the first robotic arm completes its work, the second robotic arm continues the work based on the first. Therefore, if any robotic arm malfunctions, subsequent work will be impossible, thus affecting the entire workflow.

[0038] In view of this, embodiments of this application provide a control method for a robotic arm, comprising: acquiring multiple target parameters; the multiple target parameters include environmental parameters of a first region, network parameters of a second robotic arm located in the first region, vibration amplitude, and stress magnitude; the first region is any one of the multiple regions; determining the failure rate of the second robotic arm located in the first region according to a preset fault algorithm and the multiple target parameters, thereby obtaining the failure rate of the second robotic arm located in the multiple regions; determining the target deployment number of the first robotic arm in the first region according to the failure rate of the second robotic arm in the multiple regions, thereby obtaining the target deployment number of the first robotic arm in each region, and deploying the multiple first robotic arms to the multiple regions according to the target deployment number.

[0039] The methods provided in the embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0040] It should be noted that the network system described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network systems and the emergence of other network systems, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0041] Figure 2 The diagram shown is a schematic representation of a control system for a robotic arm according to an embodiment of this application. Figure 2 As shown, the control system of the robotic arm may include a control device 11 (hereinafter referred to as the control device) and a robotic arm 12. The control device 11 is connected to the robotic arm 12. The control device 11 and the robotic arm 12 can be connected wirelessly.

[0042] In the embodiments of this application, the control device 11 can be any electronic device with data processing capabilities. For example, the control device 11 can be a computer, a server, etc. The server can be a single server or a server cluster composed of multiple servers. In some embodiments, the server cluster can also be a distributed cluster. The embodiments of this application do not limit the specific technology, quantity, or form of the control device 11.

[0043] In the embodiments of this application, the robotic arm 12 can be an industrial robot or other device capable of performing relevant process functions. The embodiments of this application do not limit the specific technology, quantity, or form of the robotic arm 12. For example, multiple robotic arms may include multiple first robotic arms and second robotic arms located in each region.

[0044] The control device 11 is used to determine the failure rate of the second robotic arms in multiple areas, determine the target deployment number of the first robotic arms in each area based on the failure rate of the second robotic arms in multiple areas, and deploy multiple first robotic arms to multiple areas according to the target deployment number.

[0045] The second robotic arm in the robotic arm 12 is used to perform the work corresponding to the second area, and the first robotic arm in the robotic arm 12 is used to move to the target area and perform the work corresponding to the target area according to the instructions issued by the control device 11.

[0046] In different application scenarios, the robotic arm 12 and the control device 11 can be independent devices or integrated into the same device. This embodiment of the invention does not impose specific limitations on this.

[0047] It should be noted that, Figure 2This is just an example framework diagram. Figure 2 The names of the various devices included are unrestricted, and except for Figure 2 In addition to the functional nodes shown, other nodes may also be included, but this application embodiment does not limit this.

[0048] It should be noted that, Figure 2 This is just an example framework diagram. Figure 2 The names of the modules included are unrestricted, and except for Figure 1 In addition to the functional modules shown, other modules may also be included, but this application embodiment does not limit this.

[0049] In practical implementation, Figure 2 Each device in the process can be adopted Figure 3 The shown composition structure, or including Figure 3 The components shown. Figure 3 This is a schematic diagram illustrating the composition of a control device 200 provided in an embodiment of this application. The control device 200 can be a server, or it can be a chip or system-on-a-chip within the server. Figure 2 As shown, the control device 200 includes a processor 201, a communication interface 202, and a communication line 203.

[0050] Furthermore, the control device 200 may also include a memory 204. The processor 201, the memory 204, and the communication interface 202 can be connected via a communication line 203.

[0051] The processor 201 can be a CPU, a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The processor 201 can also be other devices with processing capabilities, such as circuits, devices, or software modules, without limitation.

[0052] Communication interface 202 is used to communicate with other devices or other communication networks. Communication interface 202 can be a module, circuit, communication interface, or any device capable of enabling communication.

[0053] Communication line 203 is used to transmit information between the components included in control device 200.

[0054] Memory 204 is used to store instructions. These instructions can be computer programs.

[0055] The memory 204 can be a read-only memory (ROM) or other type of static storage device that can store static information and / or instructions; it can also be a random access memory (RAM) or other type of dynamic storage device that can store information and / or instructions; it can also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, etc., without limitation.

[0056] It should be noted that the memory 204 can exist independently of the processor 201 or can be integrated with the processor 201. The memory 204 can be used to store instructions, program code, or some data, etc. The memory 204 can be located inside or outside the control device 200, without limitation. The processor 201 is used to execute the instructions stored in the memory 204 to implement the robotic arm control method provided in the following embodiments of this application.

[0057] In one example, processor 201 may include one or more CPUs, for example, Figure 3 CPU0 and CPU1 in the CPU.

[0058] As an optional implementation, the control device 200 includes multiple processors, for example, besides Figure 3 In addition to processor 201, it may also include processor 205.

[0059] It should be pointed out that, Figure 3 The composition shown does not constitute a basis for this. Figure 2 The limitations of each device in the process, except Figure 3 In addition to the components shown, Figure 2 The various devices in the can include ratio Figure 3 More or fewer components, or combinations of certain components, or different arrangements of components.

[0060] In this embodiment of the application, the chip system may be composed of chips or may include chips and other discrete devices.

[0061] Furthermore, the actions, terms, etc., involved in the various embodiments of this application can be referenced interchangeably without limitation. The message names or parameter names in the messages exchanged between the various devices in the embodiments of this application are merely examples, and other names may be used in specific implementations without limitation.

[0062] To facilitate a clear description of the technical solutions in the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0063] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0064] In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0065] The following is combined with Figure 2 The control system of the robotic arm shown is described, and the control method of the robotic arm provided in the embodiments of this application is described.

[0066] Figure 3 This application provides a method for controlling a robotic arm, applicable to a server or a control device. The control device can be... Figure 2 The control device 11 can also be a component in the control device 11, such as a chip.

[0067] The server controls multiple robotic arms in the production line, which includes multiple areas. Each robotic arm comprises multiple first robotic arms and a second robotic arm located in each area. For example, ... Figure 4As shown, the multiple regions of the production line may include M1, M2, M3...Mm, each region may include a second robotic arm, and may also include one or more first robotic arms.

[0068] It should be noted that the functional algorithms for deploying robotic arms differ in each area, while the functional algorithms for robotic arms deployed within the same area are the same. The functional algorithms for robotic arm deployment can be modified. The second robotic arm can represent a robotic arm that is currently in operation. The first robotic arm can represent a standby robotic arm that is not currently in operation.

[0069] For example, in a scenario with multiple zones including M1, M2, and M3, the functional algorithm deployed in the second robotic arm of zone M1 can be used to move a target item (e.g., a parcel box) to a target location (such as a conveyor belt). The functional algorithm deployed in the second robotic arm of zone M2 can be used to package the target item. The functional algorithm deployed in the second robotic arm of zone M3 can be used to classify the target item (e.g., if the target item has a QR code containing its receiving address on its surface, the functional algorithm can scan the QR code to obtain the receiving address and classify the target item according to the receiving address).

[0070] This application uses an example of its application in a control device 11 for illustration. Figure 4 As shown, the method includes the following steps S301-S304:

[0071] S301, The control device acquires multiple target parameters.

[0072] Among them, multiple target parameters include environmental parameters of the first region, network parameters of the second robotic arm located in the first region, vibration amplitude, and stress magnitude. The first region can be any one of the multiple regions.

[0073] Environmental and network parameters can be set as needed. For example, environmental parameters may include the temperature, humidity, and dust levels in the first area. Network parameters may include the network bandwidth, latency, and jitter of the second robotic arm in the first area.

[0074] In one possible implementation, the first area can be equipped with multiple sensors, such as a temperature sensor, a humidity sensor, a dust level sensor, a vibration amplitude sensor, and a stress sensor. The control device is communicatively connected to these sensors and can send a first subscription message to each sensor, requesting multiple environmental parameters. Upon receiving the first subscription message, the sensors can send multiple environmental parameters, vibration amplitude, and stress magnitude to the control device at a preset frequency. Correspondingly, the control device receives the multiple environmental parameters, vibration amplitude, and stress magnitude sent by the sensors.

[0075] As one possible implementation, the control device can obtain the network parameters of the second robotic arm located in the first area by detecting the network signaling data of the second robotic arm in the first area.

[0076] It should be noted that multiple sensors can be located inside the second robotic arm in the first area, or they can be placed in the area surrounding the second robotic arm. The preset frequency can be set as needed. For example, it can be once per second, once every 10 seconds, once per minute, etc.

[0077] S302. The control device determines the failure rate of the second robotic arm located in the first region based on the preset fault algorithm and multiple target parameters, and obtains the failure rate of the second robotic arm located in multiple regions.

[0078] As one possible implementation, the control device can use a preset fault algorithm to process multiple target parameters, determine the failure rate of the second robotic arm located in the first region, and obtain the failure rate of the second robotic arm located in multiple regions.

[0079] It should be noted that the specific details of determining the failure rate of the second robotic arm located in the first region in this possible implementation will be explained in a later section, and will not be repeated here.

[0080] It should be noted that, since multiple target parameters change in real time, the failure rate of the second robotic arm located in multiple areas also changes dynamically.

[0081] S303. The control device determines the target deployment number of the first robotic arm in the first region based on the failure rate of the second robotic arm in multiple regions, and obtains the target deployment number of the first robotic arm in each region.

[0082] The number of first robotic arms deployed in a region is positively correlated with the failure rate of second robotic arms in that region.

[0083] As one possible implementation, the control device can determine the pre-deployment number of first robotic arms in each region based on the failure rate of the second robotic arms in multiple regions, and determine the target deployment number of first robotic arms in the first region based on the relationship between the pre-deployment number and the total number of first robotic arms, thus obtaining the target deployment number of first robotic arms in each region.

[0084] For example, when the pre-deployed number and the total number of first robotic arms are the same, the control device can determine the target deployment number of the first robotic arms in the first region as the pre-deployed number of the first robotic arms in the first region, and then obtain the target deployment number of the first robotic arms in each region.

[0085] For example, when the pre-deployed number and the total number of first robotic arms are not the same, the control device can determine whether it is necessary to adjust the pre-deployed number of first robotic arms in the first area. If it is necessary to adjust the pre-deployed number of first robotic arms in the first area, the control device can increase or decrease the pre-deployed number of first robotic arms in the first area, determine the target deployment number of first robotic arms in the first area as the adjusted pre-deployed number of first robotic arms in the first area, and then obtain the target deployment number of first robotic arms in each area.

[0086] It should be noted that the specific details of determining the target deployment number of the first robotic arm in the first region in this possible implementation will be explained in a later section, and will not be repeated here.

[0087] S304. The control device deploys multiple first robotic arms to multiple areas according to the target deployment quantity.

[0088] As one possible implementation, the control device can send the corresponding area coordinates to each first robotic arm. Accordingly, each first robotic arm receives its corresponding area coordinates and can move to the corresponding area based on the received coordinates.

[0089] In some embodiments, each area may also be equipped with a distance sensor. After the multiple first robotic arms move to the corresponding area, the distance sensor in each area can sense the number of first robotic arms in that area and send a first signal to the control device. The first signal includes an area identifier and the number of first robotic arms in that area identifier. The control device can determine whether the multiple first robotic arms have been successfully deployed to the multiple areas based on the first signal.

[0090] Based on the technical solution provided in this application, according to the fault algorithm and multiple target parameters, determine the failure rate of the second robotic arm located in the first area, and obtain the failure rates of the second robotic arms located in multiple areas. Since the multiple target parameters include the environmental parameters of the first area, the network parameters of the second robotic arm located in the first area, the vibration amplitude, and the stress magnitude, thus, the multiple target parameters can reflect the operating conditions and states of the second robotic arm in the first area in real time, and further, the failure rate of the second robotic arm in the first area can be determined more accurately, and the failure rates of the second robotic arms located in multiple areas can be obtained. Further, according to the failure rates of the second robotic arms in multiple areas, determine the target deployment quantity of the first robotic arm in the first area, obtain the target deployment quantities of the first robotic arms in each area, and deploy multiple first robotic arms to multiple areas according to the target deployment quantities; since the target deployment quantity of the first robotic arm in an area is positively correlated with the failure rate of the second robotic arm in the area, in this way, more first robotic arms can be deployed in the area with a higher failure rate to timely replace the abnormal second robotic arm, avoiding the technical problem that the robotic arm in an area of the production line fails and affects the entire work process, and improving the production efficiency.

[0091] A possible embodiment is as Figure 5 shown. In order to determine the target deployment quantity of the first robotic arm in the first area, S303 in the control method of this application may further specifically include the following S401 - S403.

[0092] S401. The control device determines the preliminary deployment quantity of the first robotic arm in the first area according to the failure rate of the second robotic arm in the first area and the first mapping relationship, and obtains the preliminary deployment quantities of the first robotic arms in each area.

[0093] Among them, the first mapping relationship includes different failure rates and corresponding preliminary deployment quantities. For example, the first mapping relationship may be as shown in Table 1 below.

[0094] Table 1 First Mapping Relationship Table

[0095] Failure rate P Pre-deployment quantity N [0，P1] N1 [P1, P2] N2 … [Pi-1, Pi] Ni … [Pn-1, 1] Nn

[0096] Among them, 0 < P1 < P2 < … < pi < … Pn - 1 < 1, 1 < N1 < N2 < … < Ni < … Nn. The specific values of P1, P2, pi, Pn - 1, N1, N2, Ni, and Nn are set according to needs and are not limited.

[0097] S402. When the sum of the preliminary deployment quantities of the first robotic arms in each area is the same as the total quantity of multiple first robotic arms, the control device determines the preliminary deployment quantity of the first robotic arm in the first area as the target deployment quantity of the first robotic arm in the first area.

[0098] S403. When the sum of the pre-deployed number of first robotic arms in each area is different from the total number of multiple first robotic arms, the control device determines the target deployment number of first robotic arms in the first area based on the target difference and the pre-deployed number of first robotic arms in the first area.

[0099] The target difference is the difference between the sum of the pre-deployed number of first robotic arms in each region and the total number of multiple first robotic arms.

[0100] As one possible implementation, the control device can determine the pre-deployment number of the first robotic arm in the first area as the target deployment number of the first robotic arm in the first area if the pre-deployment number of the first robotic arm in the first area is less than the target difference.

[0101] If the pre-deployment number of the first robotic arm in the first area is greater than or equal to the target difference, the control device can adjust the pre-deployment number of the first robotic arm in the first area and determine the adjusted pre-deployment number of the first robotic arm in the first area as the target deployment number of the first robotic arm in the first area.

[0102] It should be noted that the specific details of determining the target deployment number of the first robotic arm in the first region in this possible implementation will be explained in a later section, and will not be repeated here.

[0103] One possible implementation, such as Figure 6 As shown, in order to determine the target number of the first robotic arm deployed in the first region, S403 in the control method of this application may further include the following S501-S503.

[0104] S501, The control device determines whether the ranking of the pre-deployed number of the first robotic arm in the first area is greater than the target difference.

[0105] As one possible implementation, the control device includes a comparator that compares the pre-deployment quantity ranking with the target difference. The control device can then determine, based on the comparator, whether the pre-deployment quantity ranking of the first robotic arm in the first area is greater than the target difference.

[0106] S502. If the pre-deployment number of the first robotic arm in the first area ranks higher than the target difference, the control device determines the pre-deployment number of the first robotic arm in the first area as the target deployment number of the first robotic arm in the first area.

[0107] In one example, multiple regions may include region 1, region 2, and region 3. The number of pre-deployed first robotic arms in each region may be region 1 (1), region 2 (2), and region 3 (3), respectively.

[0108] If the total number of first robotic arms is 5, the control device can determine that the target difference is 1+2+3-5=1. When the first region is region 1, the ranking of the number of first robotic arms to be deployed in the first region is 3. The control device can determine that the ranking of the number of first robotic arms to be deployed in the first region is greater than the target difference. The target number of first robotic arms to be deployed in the first region is the number of first robotic arms to be deployed in the first region (1).

[0109] S503. If the pre-deployment number of the first robotic arm in the first area is less than or equal to the target difference, the control device adjusts the pre-deployment number of the first robotic arm in the first area and determines the adjusted pre-deployment number of the first robotic arm in the first area as the target deployment number of the first robotic arm in the first area.

[0110] As one possible implementation, the control device can reduce the number of pre-deployed first robotic arms in a first region if the sum of the pre-deployed number of first robotic arms in each region is greater than the total number of multiple first robotic arms, and determine the reduced number of pre-deployed first robotic arms in the first region as the target number of first robotic arms deployed in the first region.

[0111] In one example, multiple regions may include region 1, region 2, and region 3. The number of first robotic arms pre-deployed in each region may be 3 in region 1, 2 in region 2, and 1 in region 3.

[0112] If the total number of first robotic arms is 5, the control device can determine the target difference as 1+2+3-5=1. When the first region is region 1, the pre-deployment number of the first robotic arms in the first region ranks as 1. The control device can determine that the pre-deployment number of the first robotic arms in the first region is equal to the target difference. The control device can determine that the pre-deployment number of the first robotic arms in the first region is reduced by a preset step size (e.g., 1), and determine that the target deployment number of the first robotic arms in the first region is the reduced pre-deployment number of the first robotic arms in the first region (e.g., when the budget step size is 1, the reduced pre-deployment number of the first robotic arms in the first region can be 2).

[0113] As another possible implementation, if the sum of the pre-deployed number of first robotic arms in each region is less than the total number of multiple first robotic arms, the control device may increase the pre-deployed number of first robotic arms in the first region by a preset step size, and determine the increased pre-deployed number of first robotic arms in the first region as the target deployment number of first robotic arms in the first region.

[0114] In one example, multiple regions may include region 1, region 2, and region 3. The number of pre-deployed first robotic arms in each region may be region 1 (3), region 2 (2), and region 3 (1).

[0115] If the total number of the multiple first robotic arms is 8, then the control device can determine the target difference to be 8-

[0116] (1+2+3=1)=2.

[0117] When the first region is region 1, the pre-deployment number of the first robotic arm in the first region is ranked as 1. The pre-deployment number of the first robotic arm in the first region is greater than the target difference. The control device can increase the pre-deployment number of the first robotic arm in the first region by a preset step size (e.g., 1). The target deployment number of the first robotic arm in the first region is determined to be the pre-deployment number of the first robotic arm in the first region (region 1) after the increase (e.g., when the budget step size is 1, the pre-deployment number of the first robotic arm in the first region (region 1) after the increase can be 4).

[0118] When the first region is region 2, the pre-deployment number of the first robotic arm in the first region is ranked as 2. The pre-deployment number of the first robotic arm in the first region is equal to the target difference. The control device can increase the pre-deployment number of the first robotic arm in the first region (region 2) by a preset step size (e.g., it can be 1), and determine the target deployment number of the first robotic arm in the first region (region 2) as the increased pre-deployment number of the first robotic arm in the first region (region 2) (e.g., when the budget step size is 1, the increased pre-deployment number of the first robotic arm in the first region (region 2) can be 3).

[0119] One possible implementation, such as Figure 7 As shown, in order to determine the failure rate of the second robotic arm located in the first region, the control method of this application may further include the following S601-S602 in step S302.

[0120] S601, The control device determines the fault probability score of multiple target parameters.

[0121] The failure probability scores for multiple target parameters include environmental parameter scores, network parameter scores, vibration amplitude scores, and stress scores. Environmental parameter scores are related to the temperature, humidity, and dust levels in the first area. Network parameter scores are related to the network bandwidth, network latency, and network jitter of the second robotic arm in the first area. Vibration amplitude scores are related to the magnitude of the vibration amplitude of the second robotic arm located in the first area, and stress scores are related to the magnitude of the stress on the second robotic arm located in the first area.

[0122] The process of determining the failure probability score for multiple target parameters is explained below.

[0123] 1. Environmental parameter score.

[0124] The control device can determine the environmental parameter score of the first area based on the temperature, humidity, and dust level of the first area. For example, the control device can determine the environmental parameter score of the first area according to the following formula.

[0125] S1=a1*c1+a2*c2+a3*c3 Formula 1

[0126] Where S1 represents the environmental parameter score of the first region. c1 represents the temperature score of the first region. c2 represents the humidity score of the first region. c3 represents the dust score of the first region. a1 represents the weight corresponding to the temperature score. a2 represents the weight corresponding to the humidity score. a3 represents the weight corresponding to the dust score.

[0127] It should be noted that the mapping relationships between temperature scores, humidity scores, and dust scores, as well as a1, a2, and a3, can all be set as needed. For example, a1 can be 0.3, a2 can be 0.3, and a3 can be 0.4.

[0128] For example, the relationship between temperature score and temperature can be shown in Table 2 below.

[0129] Table 2 Relationship between Temperature Score and Temperature

[0130] Temperature score Temperature (degrees Celsius) 0 ≤20 0.1 (20,50] 0.5 (50，80] 1 ＞80

[0131] It should be noted that the data in Table 2 is merely exemplary. In the embodiments of this application, the relationship between temperature score and temperature may also include other settings, which are not limited.

[0132] For example, the relationship between humidity score and humidity can be shown in Table 3 below.

[0133] Table 3 Relationship between Humidity Score and Humidity

[0134] Humidity score humidity 0 ≤0.7 0.1 (0.7,0.8] 0.5 (0.8，0.9] 1 ＞0.9

[0135] It should be noted that the data in Table 3 is merely exemplary. In the embodiments of this application, the relationship between humidity score and humidity may also include other settings, which are not limited.

[0136] For example, the relationship between dust score and dust level can be shown in Table 4 below.

[0137] Table 4. Relationship between dust intensity score and dust intensity

[0138] Dust level score Dust concentration (mg / m³) 0 ≤10 0.1 (10,50] 0.5 (50，100] 1 ＞100

[0139] It should be noted that the data in Table 4 is merely exemplary. In the embodiments of this application, the relationship between dust score and dust level can also include other settings, which are not limited.

[0140] 2. Network parameter score.

[0141] The control device can determine the network parameter score of the first area based on the network bandwidth score, network latency score, and network jitter score of the second robotic arm in the first area. For example, the control device can determine the environmental parameter score of the first area according to the following formula 2.

[0142] S2 = a4*d1 + a5*d2 + a6*d3 (Formula 2)

[0143] Where S2 represents the network parameter score of the first region. c1 represents the network bandwidth score of the first region. c2 represents the network latency score of the first region. c3 represents the network jitter score of the first region. a4 represents the weight corresponding to the network bandwidth score. a5 represents the weight corresponding to the network latency score. a6 represents the weight corresponding to the dust level score.

[0144] It should be noted that the mapping relationships between network bandwidth score and network bandwidth, network latency score and network latency, network jitter score and humidity, as well as a4, a5, and a6, can all be set as needed, and will not be elaborated further.

[0145] 3. Vibration amplitude score.

[0146] The control device can determine a vibration amplitude score based on the magnitude of the vibration amplitude of the second robotic arm in the first region. For example, if the vibration amplitude of the second robotic arm in the first region is less than 1 mm, the control device can determine a vibration amplitude score of 0. If the vibration amplitude of the second robotic arm in the first region is greater than or equal to 1 mm and less than 5 mm, the vibration amplitude score is determined to be 0.5. If the vibration amplitude of the second robotic arm in the first region is greater than or equal to 5 mm, the vibration amplitude score is determined to be 1.

[0147] 4. Stress score.

[0148] The control device can determine the stress score based on the stress magnitude of the second robotic arm in the first region. For example, if the stress magnitude of the second robotic arm in the first region is less than 5 Pa, the control device can determine the stress score as 0. If the stress magnitude of the second robotic arm in the first region is greater than or equal to 5 Pa and less than 50 Pa, the stress score is determined to be 0.5. If the stress magnitude of the second robotic arm in the first region is greater than or equal to 50 Pa, the stress score is determined to be 1.

[0149] S602, The control device performs weighted processing on the failure probability scores of multiple target parameters to obtain the failure rate of the second robotic arm located in the first region.

[0150] As one possible approach, after determining the failure probability scores of multiple target parameters and the weight of each failure probability score, the control device can multiply each failure probability score by its corresponding weight and sum them up to obtain the failure rate of the second robotic arm located in the first region.

[0151] For example, the control device can determine the failure rate of the second robotic arm located in the first region according to the following formula three.

[0152] Formula 3: S5 = a7*S1 + a8*S2 + a9*S3 + a10*S4

[0153] Wherein, S5 represents the failure rate of the second robotic arm in the first region. S3 represents the vibration amplitude score of the second robotic arm in the first region. S4 represents the stress score of the second robotic arm in the first region. a7 represents the weight corresponding to the environmental parameter score. a8 represents the weight corresponding to the network parameter score. a9 represents the weight corresponding to the vibration amplitude score. a10 represents the weight corresponding to the stress score.

[0154] For example, a7, a8, a9, and a10 can be 0.25, etc., without restriction.

[0155] One possible implementation, such as Figure 8 As shown, in order to determine that the first robotic arm in the first region can execute the functional instructions of the first region, the control method of this application may further include the following S701.

[0156] S701, the control device deploys the target function algorithm for the first robotic arm in the first area.

[0157] The target function algorithm is the function algorithm deployed in the second robotic arm located in the first area.

[0158] As one possible approach, the control device can pre-store the target function algorithm for each region inside the first robotic arm, and activate the target function algorithm when the first robotic arm is deployed to the first region.

[0159] As another possible implementation, the control device can update the target function algorithm to the first robotic arm in the first area when the first robotic arm in the first area is deployed in the first area.

[0160] The various solutions in the above embodiments of this application can be combined without contradiction.

[0161] This application embodiment can divide the control device into functional modules or functional units according to the above method examples. For example, each function can be divided into a separate functional module or functional unit, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or in software functional modules or functional units. The module or unit division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0162] When dividing each function into modules according to its corresponding function. Figure 9 A schematic diagram of a control device is shown. This control device can be a server or a chip applied in a server. This control device can be used to perform the functions of the server involved in the above embodiments. This control device is used to control multiple robotic arms in an assembly line. The assembly line includes multiple regions, and the multiple robotic arms include multiple first robotic arms and second robotic arms located in each region. The first robotic arms are standby robotic arms in a non-operating state, and the second robotic arms are robotic arms in an operating state. The device includes: an acquisition unit 801 and a determination unit 802. The acquisition unit 801 is used to acquire multiple target parameters. The multiple target parameters include environmental parameters of the first region, network parameters of the second robotic arm located in the first region, vibration amplitude, and stress magnitude. The first region can be any one of the multiple regions. The determination unit 802 is used to determine the failure rate of the second robotic arm located in the first region according to a preset fault algorithm and multiple target parameters, thereby obtaining the failure rate of the second robotic arms located in multiple regions. The determination unit 802 is also used to determine the target deployment number of the first robotic arms in the first region based on the failure rate of the second robotic arms in multiple regions, thereby obtaining the target deployment number of the first robotic arms in each region, and deploying the multiple first robotic arms to multiple regions according to the target deployment number. The target deployment number of the first robotic arms in a region is positively correlated with the failure rate of the second robotic arms in that region.

[0163] In one possible design, the determining unit 802 is specifically used for: determining the pre-deployment quantity of the first robotic arms in the first region based on the failure rate of the second robotic arms in the first region and a first mapping relationship, thereby obtaining the pre-deployment quantity of the first robotic arms in each region; the first mapping relationship includes different failure rates and corresponding pre-deployment quantities; when the sum of the pre-deployment quantities of the first robotic arms in each region is the same as the total number of multiple first robotic arms, determining the pre-deployment quantity of the first robotic arms in the first region as the target deployment quantity of the first robotic arms in the first region; when the sum of the pre-deployment quantities of the first robotic arms in each region is different from the total number of multiple first robotic arms, determining the target deployment quantity of the first robotic arms in the first region based on the target difference and the pre-deployment quantity of the first robotic arms in the first region; the target difference is the difference between the sum of the pre-deployment quantities of the first robotic arms in each region and the total number of multiple first robotic arms.

[0164] In one possible design, the determining unit 802 is further configured to: determine the pre-deployment number of the first robotic arm in the first region as the target deployment number of the first robotic arm in the first region when the ranking of the pre-deployment number of the first robotic arm in the first region is greater than the target difference; and adjust the pre-deployment number of the first robotic arm in the first region when the ranking of the pre-deployment number of the first robotic arm in the first region is less than or equal to the target difference, and determine the adjusted pre-deployment number of the first robotic arm in the first region as the target deployment number of the first robotic arm in the first region.

[0165] In one possible design, the determining unit 802 is further used to: determine the failure probability scores of multiple target parameters; wherein, the failure probability scores of the multiple target parameters include environmental parameter scores, network parameter scores, vibration amplitude scores, and stress scores; the environmental parameter scores are related to the temperature, humidity, and dust level of the first region; the network parameter scores are related to the network bandwidth, network latency, and network jitter of the second robotic arm in the first region; the vibration amplitude scores are related to the magnitude of the vibration amplitude of the second robotic arm located in the first region; and the stress scores are related to the magnitude of the stress of the second robotic arm located in the first region; the failure probability scores of the multiple target parameters are weighted to obtain the failure rate of the second robotic arm located in the first region.

[0166] In one possible design, the device further includes: a deployment unit 803; the deployment unit 803 is used to deploy a target function algorithm for a first robotic arm in a first region; the target function algorithm is a function algorithm deployed in a second robotic arm located in the first region.

[0167] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be implemented by a computer program instructing related hardware. This program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the control device (including a data transmitter and / or a data receiver) in any of the foregoing embodiments, such as the hard disk or memory of the control device. The computer-readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device. Further, the computer-readable storage medium can include both the internal storage unit of the control device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the control device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0168] It should be noted that the terms "first" and "second," etc., in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0169] It should be understood that in this application, "at least one (item)" means one or more, "more than one" means two or more, "at least two (items)" means two or three or more, and "and / or" is used to describe the relationship between related objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0170] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be deployed by different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0172] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0173] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0174] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0175] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A control method for a robotic arm, characterized in that, An application is made to a server, which controls multiple robotic arms in a production line, the production line including multiple areas, the multiple robotic arms including multiple first robotic arms, and a second robotic arm located in each area; The first robotic arm is a standby robotic arm in a non-operating state, and the second robotic arm is a robotic arm in an operating state. The method includes: Multiple target parameters are acquired; the multiple target parameters include environmental parameters of a first region, network parameters of a second robotic arm located in the first region, vibration amplitude, and stress magnitude; the first region is any one of the multiple regions; Based on the preset fault algorithm and the multiple target parameters, the failure rate of the second robotic arm located in the first region is determined, and the failure rate of the second robotic arm located in the multiple regions is obtained. Based on the failure rate of the second robotic arms in the multiple regions, the target deployment number of the first robotic arms in the first region is determined, the target deployment number of the first robotic arms in each region is obtained, and the multiple first robotic arms are deployed to the multiple regions according to the target deployment number; the target deployment number of the first robotic arms in a region is positively correlated with the failure rate of the second robotic arms in that region.

2. The method according to claim 1, characterized in that, Determining the target deployment number of the first robotic arm in the first region based on the failure rate of the second robotic arm in the multiple regions includes: Based on the failure rate of the second robotic arm in the first region and the first mapping relationship, the pre-deployment quantity of the first robotic arm in the first region is determined, thus obtaining the pre-deployment quantity of the first robotic arm in each region; the first mapping relationship includes different failure rates and corresponding pre-deployment quantities. If the sum of the pre-deployed number of first robotic arms in each region is the same as the total number of the plurality of first robotic arms, the pre-deployed number of first robotic arms in the first region is determined as the target deployment number of first robotic arms in the first region. If the sum of the pre-deployed number of first robotic arms in each region is different from the total number of the plurality of first robotic arms, the target deployment number of first robotic arms in the first region is determined based on the target difference and the pre-deployed number of first robotic arms in the first region; the target difference is the difference between the sum of the pre-deployed number of first robotic arms in each region and the total number of the plurality of first robotic arms.

3. The method according to claim 2, characterized in that, Determining the target deployment number of the first robotic arms in the first area based on the target difference and the pre-deployed number of the first robotic arms in the first area includes: If the pre-deployment number of the first robotic arm in the first region ranks greater than the target difference, the pre-deployment number of the first robotic arm in the first region is determined as the target deployment number of the first robotic arm in the first region. If the pre-deployment number of the first robotic arm in the first region is less than or equal to the target difference, the pre-deployment number of the first robotic arm in the first region is adjusted, and the adjusted pre-deployment number of the first robotic arm in the first region is determined as the target deployment number of the first robotic arm in the first region.

4. The method according to claim 1, characterized in that, Determining the failure rate of the second robotic arm located in the first region based on a preset fault algorithm and the multiple target parameters includes: Determine the failure probability score of the multiple target parameters; The failure probability scores of the multiple target parameters include environmental parameter scores, network parameter scores, vibration amplitude scores, and stress scores. The environmental parameter scores are related to the temperature, humidity, and dust levels in the first area; the network parameter scores are related to the network bandwidth, network latency, and network jitter of the second robotic arm in the first area; the vibration amplitude scores are related to the magnitude of the vibration amplitude of the second robotic arm located in the first area; and the stress scores are related to the magnitude of the stress on the second robotic arm located in the first area. The failure probability scores of the multiple target parameters are weighted to obtain the failure rate of the second robotic arm located in the first region.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: A target function algorithm is deployed for the first robotic arm in the first region; the target function algorithm is a function algorithm deployed in the second robotic arm located in the first region.

6. A control device for a robotic arm, characterized in that, The device is used to control multiple robotic arms in an assembly line, the assembly line including multiple regions, the multiple robotic arms including multiple first robotic arms, and a second robotic arm located in each region; The first robotic arm is a standby robotic arm in a non-operating state, and the second robotic arm is a robotic arm in an operating state. The device includes: an acquisition unit and a determination unit. The acquisition unit is used to acquire multiple target parameters; the multiple target parameters include environmental parameters of a first region, network parameters of a second robotic arm located in the first region, vibration amplitude, and stress magnitude; the first region is any one of the multiple regions; The determining unit is used to determine the failure rate of the second robotic arm located in the first region according to a preset fault algorithm and the plurality of target parameters, and to obtain the failure rate of the second robotic arm located in the plurality of regions. The determining unit is further configured to determine the target deployment number of the first robotic arm in the first region based on the failure rate of the second robotic arm in the plurality of regions, obtain the target deployment number of the first robotic arm in each region, and deploy the plurality of first robotic arms to the plurality of regions according to the target deployment number; the target deployment number of the first robotic arm in a region is positively correlated with the failure rate of the second robotic arm in a region.

7. The apparatus according to claim 6, characterized in that, The determining unit is specifically used for: Based on the failure rate of the second robotic arm in the first region and the first mapping relationship, the pre-deployment quantity of the first robotic arm in the first region is determined, thus obtaining the pre-deployment quantity of the first robotic arm in each region; the first mapping relationship includes different failure rates and corresponding pre-deployment quantities. If the sum of the pre-deployed number of first robotic arms in each region is the same as the total number of the plurality of first robotic arms, the pre-deployed number of first robotic arms in the first region is determined as the target deployment number of first robotic arms in the first region. If the sum of the pre-deployed number of first robotic arms in each region is different from the total number of the plurality of first robotic arms, the target deployment number of first robotic arms in the first region is determined based on the target difference and the pre-deployed number of first robotic arms in the first region. The target difference is the difference between the sum of the pre-deployed number of first robotic arms in each region and the total number of the plurality of first robotic arms.

8. The apparatus according to claim 7, characterized in that, The determining unit is further configured to: If the pre-deployment number of the first robotic arm in the first region ranks greater than the target difference, the pre-deployment number of the first robotic arm in the first region is determined as the target deployment number of the first robotic arm in the first region. If the pre-deployment number of the first robotic arm in the first region is less than or equal to the target difference, the pre-deployment number of the first robotic arm in the first region is adjusted, and the adjusted pre-deployment number of the first robotic arm in the first region is determined as the target deployment number of the first robotic arm in the first region.

9. The apparatus according to claim 6, characterized in that, The determining unit is further configured to: Determine the failure probability score of the multiple target parameters; The failure probability scores of the multiple target parameters include environmental parameter scores, network parameter scores, vibration amplitude scores, and stress scores. The environmental parameter scores are related to the temperature, humidity, and dust levels in the first area; the network parameter scores are related to the network bandwidth, network latency, and network jitter of the second robotic arm in the first area; the vibration amplitude scores are related to the magnitude of the vibration amplitude of the second robotic arm located in the first area; and the stress scores are related to the magnitude of the stress on the second robotic arm located in the first area. The failure probability scores of the multiple target parameters are weighted to obtain the failure rate of the second robotic arm located in the first region.

10. The apparatus according to any one of claims 6-9, characterized in that, The device further includes: a deployment unit; The deployment unit is used to deploy a target function algorithm for a first robotic arm in the first region; the target function algorithm is a function algorithm deployed in a second robotic arm located in the first region.

11. A computer-readable storage medium, characterized in that, The readable storage medium stores instructions that, when executed, implement the method as described in any one of claims 1-5.

12. A control device for a robotic arm, characterized in that, include: A processor, a memory, and a communication interface; wherein the memory is used to store one or more programs, the one or more programs including computer-executable instructions, and when the control device of the robotic arm is running, the processor executes the computer-executable instructions stored in the memory to cause the control device of the robotic arm to perform the method of any one of claims 1-5.

Images