Method, device and electronic equipment for evaluating robot operation

Abstract

The application provides a robot operation evaluation method and device and electronic equipment, and relates to the technical field of robots, and the method comprises the following steps: obtaining operation-related data generated by the robot during operation, extracting feature data related to the performance of the robot from the operation-related data; calculating the score index of the robot according to the feature data; and calculating the quality and efficiency score of the robot operation based on the score index to evaluate the operation of the robot. The robot operation evaluation method and device and electronic equipment provided by the application can calculate the quality and efficiency score of the robot operation based on the score index to evaluate the operation of the robot. During the evaluation process, the feature data is extracted from the operation-related data of the robot, so that the quality and efficiency of the robot operation can be evaluated, and the evaluation process does not need to rely on manual observation and experience judgment, so that the evaluation process has a certain objectivity and accuracy.

Description

Technical Field

[0001] This invention relates to the technical field of robots, and in particular to an evaluation method, apparatus, and electronic device for robot operations. Background Technology

[0002] A live-line working robot (hereinafter referred to as the robot) is used to replace manual operation. It typically has one or more robotic arms. At the end of each arm, different grippers can be fixed via quick-connect devices to perform corresponding tasks. These grippers include wire cutters, connectors, and holding tools, and can perform tasks such as cutting leads, connecting leads, and replacing bird deterrents and surge arresters. Furthermore, during operation, the robot needs to acquire external information through depth cameras, LiDAR, etc. The controller processes this information to generate coordinates, thereby determining the location of wire breaks, connections, etc., so that the grippers carried by the robotic arms can perform the corresponding operations.

[0003] In the existing technology, many companies have produced live-line working robots. However, it is currently difficult to quantify the effectiveness of the robots and how to evaluate their work. Therefore, it is also difficult to achieve real-time monitoring and timely feedback of robot operations. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide an evaluation method, apparatus and electronic device for robot operations to alleviate the above-mentioned technical problems.

[0005] In a first aspect, embodiments of the present invention provide a method for evaluating robot operations. The method includes: acquiring operation-related data generated by the robot during operation; extracting feature data related to the robot's performance from the operation-related data; wherein the feature data includes task data and energy data completed by the robot during operation; the task data includes data on the robot completing tasks within a specified time, and data on errors occurring during the robot's operation; calculating a scoring index for the robot based on the feature data; the scoring index includes task completion rate, error rate, and energy consumption rate; the task completion rate is used to measure the proportion of predetermined task completed by the robot within a specified time; the error rate is used to measure the frequency of errors occurring by the robot during operation; the energy consumption rate is used to measure the ratio of energy consumed by the robot during operation to the energy required to complete a unit task; and calculating a quality and efficiency score for the robot's operation based on the scoring index to evaluate the robot's operation.

[0006] In conjunction with the first aspect, the present invention provides a first possible implementation of the first aspect, wherein the step of calculating the robot's scoring index based on the feature data includes: calculating the robot's task completion rate based on data of the robot completing tasks within a specified time; calculating the robot's error rate based on data of errors occurring during the operation; and calculating the robot's energy consumption rate during the operation based on the energy data.

[0007] In conjunction with the first possible implementation of the first aspect, this embodiment of the invention provides a second possible implementation of the first aspect, wherein the data on the robot completing tasks within a specified time includes: the number of tasks completed, the total number of tasks in the operation process, and the total number of operations performed by the robot during the operation process; the task completion rate is calculated using the following formula: n = (N / N0) × 100%, where N represents the number of tasks completed and N0 represents the total number of tasks; the data on errors occurring during the operation process includes the number of errors occurring during the operation process; the error rate is calculated using the following formula: F = (F / F0) × 100%, where F represents the number of errors occurring and F0 represents the total number of operations; the energy consumption rate of the robot during the operation process includes the total energy consumption of the robot during the operation process; the energy consumption rate is calculated using the following formula: w = (W / N) × 100%, where W represents the total energy consumption and N represents the number of tasks completed.

[0008] In conjunction with the first aspect, this embodiment of the invention provides a third possible implementation of the first aspect, wherein the step of calculating the quality and efficiency score of the robot operation based on the scoring indicators includes: obtaining a pre-configured weight parameter for each of the scoring indicators; and performing a weighted calculation on each of the scoring indicators based on the weight parameters to obtain the quality and efficiency score of the robot operation.

[0009] In conjunction with the first aspect, this embodiment of the invention provides a fourth possible implementation of the first aspect, wherein the above method further includes: extracting automated operation data of the robot during the operation process based on the operation-related data; wherein the automated operation data includes each operation step and the degree of automation corresponding to each operation step; calculating the robot's operation automation rate based on the automated operation data, and graphically presenting the operation automation rate.

[0010] In conjunction with the fourth possible implementation of the first aspect, this embodiment of the invention provides a fifth possible implementation of the first aspect, wherein the aforementioned degree of automation is used to characterize whether the actual operation of each of the work steps is an automatic process or a process requiring manual intervention; the step of calculating the robot's work automation rate based on the automated operation data includes: counting the total number of work steps, and the number of work steps whose actual operation is an automatic process and the number of work steps whose actual operation is a process requiring manual intervention; calculating the proportion of work steps whose actual operation is an automatic process to the total number of work steps; and determining the proportion as the robot's work automation rate.

[0011] In conjunction with the fourth possible implementation of the first aspect, this embodiment of the invention provides a sixth possible implementation of the first aspect, wherein the above method further includes: extracting the operation information of the robot operation from the operation-related data, wherein the operation information includes at least: basic operation information, operation conclusion data, operation process analysis data, operation deduction data, and operation record data; and generating an operation score report containing the quality and efficiency score and the operation automation rate based on the operation information.

[0012] Secondly, embodiments of the present invention also provide an evaluation device for robot operations. The device includes: an acquisition module, configured to acquire operation-related data generated by the robot during operation, and extract feature data related to the robot's performance from the operation-related data; wherein the feature data includes task data and energy data completed by the robot during operation; the task data includes data on the robot completing tasks within a specified time, and data on errors occurring during the robot's operation; a first calculation module, configured to calculate a scoring index for the robot based on the feature data; the scoring index includes a task completion rate, an error rate, and an energy consumption rate; the task completion rate is used to measure the proportion of predetermined operation tasks completed by the robot within a specified time; the error rate is used to measure the frequency of errors occurring by the robot during operation; the energy consumption rate is used to measure the ratio of energy consumed by the robot during operation to the energy required to complete a unit task; and a second calculation module, configured to calculate a quality and efficiency score for the robot's operation based on the scoring index, so as to evaluate the robot's operation.

[0013] Thirdly, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in the first aspect above.

[0014] Fourthly, embodiments of the present invention also provide a machine-readable storage medium storing machine-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the method described in the first aspect.

[0015] The embodiments of the present invention bring the following beneficial effects:

[0016] The robot operation evaluation method, apparatus, and electronic device provided in this invention can acquire operation-related data generated by the robot during operation, extract feature data related to robot performance from the operation-related data, calculate the robot's scoring index based on the feature data, and then calculate the quality and efficiency score of the robot operation based on the scoring index to evaluate the robot's operation. In the evaluation process, since the feature data used is extracted from the robot's operation-related data, the quality and efficiency of the robot operation can be evaluated. At the same time, the evaluation process does not rely on human observation and experience judgment, so it has a certain degree of objectivity and accuracy.

[0017] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a method for evaluating robot operations as provided in an embodiment of the present invention;

[0021] Figure 2 A flowchart of another robot task evaluation method provided in an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of the structure of an evaluation device for robot operations provided in an embodiment of the present invention;

[0023] Figure 4This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions 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, 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.

[0025] Currently, the evaluation of live-line working robots (hereinafter referred to as robots) in power distribution networks mainly relies on human observation and experience-based judgment, lacking objectivity and accuracy. Furthermore, there is a lack of unified standards, as different evaluators may use different evaluation criteria and indicators, leading to inconsistent evaluation results. In addition, existing evaluation methods often only provide qualitative results and cannot quantitatively evaluate the quality and efficiency of robot operations. Moreover, existing evaluation methods are usually conducted offline, making real-time monitoring and timely feedback of robot operations impossible.

[0026] Based on this, the robot operation evaluation method, apparatus and electronic device provided in the embodiments of the present invention can effectively alleviate the above-mentioned technical problems.

[0027] To facilitate understanding of this embodiment, a method for evaluating robot operations disclosed in this embodiment of the invention will first be described in detail.

[0028] In one possible implementation, embodiments of the present invention provide a method for evaluating robot operations, such as... Figure 1 The flowchart shown illustrates a method for evaluating robot operations, which includes the following steps:

[0029] Step S102: Obtain task-related data generated by the robot during the operation, and extract feature data related to robot performance from the task-related data;

[0030] In this embodiment of the invention, the robot refers to a live-line working robot for power distribution networks. Typically, in order to achieve automated operation, the robot is equipped with sensors such as depth cameras, lasers, and radars to acquire external information. Furthermore, these sensors can acquire external information in real time during the robot's operation.

[0031] Therefore, the process of acquiring the operation-related data generated by the robot during the operation in step S102 above is actually a real-time data collection process. That is, the operation-related data during the operation of the robot can be collected in real time through the data generated by the sensors on the robot itself, the robot's operation log files, etc. In addition, the operation-related data in this embodiment of the invention usually includes operation information, task completion time, task accuracy, energy consumption, as well as various indicators, system information, error information, etc. of the robot during operation. Then, feature data related to robot performance is extracted from the above operation-related data.

[0032] Furthermore, considering the influence of external environmental factors, the data collected by the robot during operation often includes some useless data. Therefore, during the data collection process, the collected data can be further preprocessed, such as data cleaning, sorting and standardization, to remove outliers, fill in missing values, etc., thereby ensuring the accuracy and consistency of the data.

[0033] The specific preprocessing steps can be set according to the actual usage, and the embodiments of the present invention do not impose any restrictions on this.

[0034] Furthermore, the feature data in this embodiment of the invention includes task data and energy data completed by the robot during operation; the task data includes data on the robot completing tasks within a specified time, and data on errors that occur during the robot's operation; and the process of extracting feature data related to robot performance in step S102 is actually a process of in-depth analysis of operation-related data. Specifically, statistical and machine learning algorithms can typically be used to conduct in-depth analysis of the preprocessed operation-related data to extract feature data related to robot performance. For example, methods such as Pearson correlation coefficient and Spearman rank correlation coefficient can be used to analyze the correlation between various feature data, thereby screening out features highly correlated with robot performance. In addition, the analysis of variance (ANOVA) statistical method can be used to test the impact of different factors (such as different operation tasks, environmental conditions, etc.) on robot performance, thereby identifying significant features.

[0035] The specific algorithm used in the feature data extraction process can be set according to the actual usage, and this embodiment of the invention does not impose any restrictions on it.

[0036] Step S104: Calculate the robot's scoring index based on the feature data;

[0037] Step S106: Calculate the quality and efficiency score of the robot's operation based on the scoring index to evaluate the robot's operation.

[0038] In practical use, the above-mentioned scoring indicators are defined based on the characteristics and quality requirements of the robot's operation. Therefore, these scoring indicators can also be called quality scoring indicators and efficiency scoring indicators. Furthermore, since the robot's operation process is actually a complex one, there are usually multiple scoring indicators in step S104, such as task completion rate, error rate, and energy consumption rate. The task completion rate measures the proportion of the robot that completes the predetermined task within a specified time; the error rate measures the frequency of errors that occur during the robot's operation; and the energy consumption rate measures the ratio of the energy consumed by the robot during the operation to the energy required to complete a unit of task.

[0039] Then, in step S106, these scoring indicators are further processed to obtain the final quality and efficiency score, which is used to evaluate the robot's operation process.

[0040] For example, the final quality and efficiency score for a robot's operation is a single score. If the score is greater than a preset threshold, it indicates that the robot's operation meets the quality and efficiency requirements. Otherwise, it does not meet the quality and efficiency requirements and the robot's operation needs further improvement.

[0041] Therefore, the robot operation evaluation method provided in this embodiment of the invention can acquire operation-related data generated by the robot during the operation process, extract feature data related to robot performance from the operation-related data, calculate the robot's scoring index based on the feature data, and then calculate the quality and efficiency score of the robot operation based on the scoring index to evaluate the robot's operation. In the evaluation process, since the feature data used is extracted from the robot's operation-related data, the quality and efficiency of the robot operation can be evaluated. At the same time, the evaluation process does not rely on human observation and experience judgment, so it has a certain degree of objectivity and accuracy.

[0042] In practical use, for the above-mentioned task completion rate, error rate and energy consumption rate, according to the actual operation needs and priorities, corresponding weights can usually be assigned to each scoring indicator. Therefore, when calculating the quality and efficiency score, it is also necessary to perform weighted calculation based on the weight of each scoring indicator.

[0043] For ease of understanding, in the above Figure 1 On this basis, Figure 2 A flowchart of another method for evaluating robotic tasks is also shown, further explaining the calculation process of scoring indicators and quality and efficiency scores.

[0044] like Figure 2 As shown, it includes the following steps:

[0045] Step S202: Obtain task-related data generated by the robot during the operation, and extract feature data related to robot performance from the task-related data;

[0046] The feature data includes task data and energy data completed by the robot during operation; the task data includes data on the robot completing tasks within a specified time, as well as data on errors that occur during the operation.

[0047] Step S204: Calculate the robot's task completion rate based on the data of the robot completing the task within the specified time;

[0048] Specifically, in this embodiment of the invention, the task completion rate is used to measure the proportion of a robot that completes a predetermined task within a specified time.

[0049] Step S206: Calculate the robot's error rate based on the data of errors that occurred during the robot's operation;

[0050] In this embodiment of the invention, the error rate is used to measure the frequency of errors that occur during the operation of the robot;

[0051] Step S208: Calculate the energy consumption rate of the robot during operation based on the energy data;

[0052] In this embodiment of the invention, the energy consumption rate is used to measure the ratio of energy consumed by the robot during the operation to the energy required to complete a unit task.

[0053] In specific implementation, the data on the robot's completion of tasks within a specified time in the above embodiments of the present invention includes: the number of tasks completed, the total number of tasks in the operation process, and the total number of operations performed by the robot in the operation process;

[0054] Therefore, the formula for calculating the task completion rate is as follows:

[0055] n = (N / N0) × 100%, where N represents the number of tasks completed and N0 represents the total number of tasks.

[0056] Furthermore, the data on errors that occurred during the operation of the robot includes the number of errors that occurred during the operation;

[0057] The formula for calculating the error rate is as follows:

[0058] F = (F / F0) × 100%, where F represents the number of errors that occurred and F0 represents the total number of operations;

[0059] Furthermore, the energy consumption rate of the robot during operation includes the total energy consumption of the robot during operation;

[0060] The formula for calculating the energy consumption rate mentioned above is as follows:

[0061] w = (W / N) × 100%, where W represents the total energy consumption and N represents the number of tasks completed.

[0062] Step S210: Calculate the quality and efficiency score of the robot's operation based on the scoring index to evaluate the robot's operation.

[0063] Specifically, when calculating the quality and efficiency score of robot operations based on scoring indicators, it is necessary to obtain the pre-configured weight parameters of each scoring indicator; then, based on the weight parameters, each scoring indicator is weighted and calculated to obtain the quality and efficiency score of robot operations.

[0064] In specific implementation, since the scoring indicators in this embodiment of the invention include task completion rate, error rate and energy consumption rate, in this step S210, it is necessary to obtain the weights of task completion rate, error rate and energy consumption rate respectively, and then perform weighted calculation on task completion rate, error rate and energy consumption rate to obtain quality and efficiency score.

[0065] In practical use, the above weights are allocated based on the actual needs and priorities of each scoring indicator in the work process to reflect its importance. At the same time, they can also be determined based on actual experience values. The specific weight allocation method can be set according to the actual usage situation, and this embodiment of the invention does not impose any restrictions on it.

[0066] Furthermore, the process of calculating the quality and efficiency score of the robot's work by weighting each scoring indicator based on weight parameters can be further enhanced by using statistical or machine learning methods to construct a scoring model, which can then be trained and optimized. For example, the scoring model can employ regression models, decision tree models, or neural network models. Moreover, for an established scoring model, the robot can continuously optimize its work steps, force control, and recognition system parameters based on the scoring indicators or quality and efficiency scores during continuous work. In other words, the scoring model can employ an iterative optimization loop, aiming to gradually improve the robot's work score and ultimately find the most suitable parameters for the robot's work. Simultaneously, the scoring model can be gradually improved based on the massive amounts of work-related data generated during the robot's work. The specific scoring model can be set according to actual usage, and this embodiment of the invention does not impose any limitations on this.

[0067] Furthermore, the robot operation evaluation method in this embodiment of the invention can further evaluate the degree of robot automation. Specifically, the automated operation data of the robot during the operation process can be extracted from the operation-related data. The automated operation data includes each operation step and the degree of automation corresponding to each operation step. Then, the robot's operation automation rate is calculated based on the automated operation data, and the operation automation rate is graphically presented.

[0068] In practical implementation, since the robot replaces human operation, its normal operation process is theoretically 100% automated. However, because the robot's operation process requires collecting and analyzing information, and then controlling the robotic arm to move according to its own control logic, the robotic arm often does not work as expected due to information limitations. Therefore, for some operation steps, human intervention is required. For example, when wiring, human intervention is needed to identify leads, strip wires, or thread wires. If there is too much human intervention, the robot's operation efficiency will be reduced. Therefore, when evaluating the quality and efficiency of the robot, in addition to quality and efficiency, the degree of automation, i.e., the operation automation rate in the embodiments of this invention, should also be considered.

[0069] Typically, the automation rate of a task can be calculated based on the degree of automation of each task step, that is, whether the actual operation of each task step is an automatic process or a process requiring manual intervention. Specifically, the degree of automation mentioned above in this embodiment of the invention is used to characterize whether the actual operation of each task step is an automatic process or a process requiring manual intervention. When calculating the robot's automation rate, the total number of task steps can be counted, along with the number of task steps whose actual operation is an automatic process and the number of task steps whose actual operation is a process requiring manual intervention. Then, the percentage of task steps whose actual operation is an automatic process is calculated out of the total number of task steps; this percentage is then determined as the robot's automation rate.

[0070] For example, a wiring operation includes 12 steps, of which 5 are completed with manual intervention and the rest are completed by an automated process. The automation rate of the operation can be expressed as the percentage of the number of steps completed by the automated process to the total number of steps. Usually, it is expressed as a percentage, which can be expressed as the automation rate of the operation in the embodiment of the present invention.

[0071] In practice, the analysis process of the automation rate of the task and the aforementioned quality and efficiency score can also be presented in the form of a task score report for visualization. For example, the task automation rate, score indicators and quality and efficiency score results can be displayed to users through visualization tools, and corresponding feedback and improvement suggestions can be provided to help users optimize the execution quality and efficiency of robot tasks.

[0072] Specifically, the robot's task information can be extracted from task-related data. This task information includes at least: basic task information, task conclusion data, task process analysis data, task deduction data, and task record data. Then, a task evaluation report containing quality and efficiency scores and task automation rate can be generated based on the task information.

[0073] For example, taking the connection of a diversion line as an example, the basic information of the operation may include the start time of the operation, the end time of the operation, the location of the operation, the operation number, the equipment used for the operation, as well as the line type, the operation type, the operation result (e.g., whether the operation was successful), and the aforementioned evaluation indicators and quality and efficiency scores, etc.

[0074] Furthermore, the aforementioned work conclusion data may include the actual work time, the theoretical work time, and the aforementioned work automation rate, such as the expected work automation rate being 100%, the actual work automation rate being A%, and the decrease from the expected rate being (100-A)%, etc. Simultaneously, the work automation rate can be visualized graphically, such as using a pie chart or bar chart to indicate the degree of automation of specific work steps, etc.

[0075] In addition, the work process analysis data can include each step in the work process, as well as the operation process of each step, such as the degree of automation of each step, whether there is human intervention, the target time, actual time, whether timeout, whether errors occur, and the level of errors, etc., all of which can be displayed to the user.

[0076] Furthermore, the system can also display task deduction data. If the robot makes an error in a particular task step, the system can record the execution details of that step, the type of error, a brief description of the error, as well as the error classification and severity level. Important points to note during the task or key records from the work site can be extracted from the task record data.

[0077] Based on the above, a task scoring report as described in this embodiment of the invention can be generated and displayed to the user through a visualization tool. Furthermore, based on this task scoring report, the robot's task process can be monitored in real time. For example, task-related data generated by the robot during the task can be acquired in real time, and scoring indicators and quality / efficiency scores can be updated promptly and displayed through a visualization interface. Simultaneously, according to set rules and thresholds, such as setting the number of tasks, operations, and errors in the task process, changes in scoring indicators can be monitored in real time to send warnings and reminders when abnormalities occur or exceed expected ranges. This provides corresponding feedback and improvement suggestions, helping users optimize the execution quality and efficiency of the robot's tasks.

[0078] Furthermore, based on the above embodiments, this invention also provides an evaluation device for robot operations, such as... Figure 3 The diagram shows a structural schematic of an evaluation device for robot operations. The device includes:

[0079] The acquisition module 30 is used to acquire task-related data generated by the robot during the operation, and extract feature data related to the robot's performance from the task-related data; wherein, the feature data includes task data and energy data completed by the robot during the operation; the task data includes data on the robot completing tasks within a specified time, and data on errors that occur during the operation.

[0080] The first calculation module 32 is used to calculate the robot's scoring indicators based on the feature data; the scoring indicators include task completion rate, error rate, and energy consumption rate; the task completion rate is used to measure the proportion of the robot that completes a predetermined task within a specified time; the error rate is used to measure the frequency of errors that occur during the operation; the energy consumption rate is used to measure the ratio of the energy consumed by the robot in completing the task to the energy required to complete a unit task;

[0081] The second calculation module 34 is used to calculate the quality and efficiency score of the robot's operation based on the scoring index, so as to evaluate the robot's operation.

[0082] The robot operation evaluation device provided in this embodiment of the invention has the same technical features as the robot operation evaluation method provided in the above embodiments, so it can also solve the same technical problems and achieve the same technical effects.

[0083] Furthermore, embodiments of the present invention also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above method.

[0084] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described method.

[0085] Furthermore, embodiments of the present invention also provide a schematic diagram of the structure of an electronic device, such as... Figure 4 The diagram shows the structure of the electronic device, which includes a processor 41 and a memory 40. The memory 40 stores computer-executable instructions that can be executed by the processor 41, and the processor 41 executes the computer-executable instructions to implement the above-described method.

[0086] exist Figure 4 In the illustrated embodiment, the electronic device further includes a bus 42 and a communication interface 43, wherein the processor 41, the communication interface 43, and the memory 40 are connected via the bus 42.

[0087] The memory 40 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 43 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 42 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 42 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0088] Processor 41 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 41 or by software instructions. Processor 41 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor 41 reads the information in the memory and uses its hardware to complete the aforementioned method.

[0089] The computer program product of the robot operation evaluation method, apparatus and electronic device provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0090] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0091] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0092] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0093] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0094] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of evaluating a robot job, characterized by, The method comprises: obtaining work-related data generated by a robot during work, and extracting feature data related to the performance of the robot from the work-related data; wherein the feature data comprises task data and energy data completed by the robot during work; the task data comprises data of the robot completing a task within a specified time, and data of errors occurring in the robot during work; the robot refers to a live-line distribution network work robot; the work-related data is data generated by the robot during work collected in real time; calculating a score index of the robot according to the feature data; the score index comprises a task completion rate, an error rate, and an energy consumption rate; the task completion rate is used to measure the proportion of the robot completing a predetermined work task within a specified time; the error rate is used to measure the frequency of errors occurring in the robot during work; and the energy consumption rate is used to measure the proportion of energy consumed by the robot in completing work and energy required for completing a unit task; calculating a quality and efficiency score of the work of the robot based on the score index, so as to evaluate the work of the robot; wherein for the work process of the robot, if the quality and efficiency score is greater than a preset score threshold, it indicates that the work process of the robot meets the quality and efficiency requirements, otherwise, it does not meet the quality and efficiency requirements; wherein the step of calculating the score index of the robot according to the feature data comprises: calculating the task completion rate of the robot according to the data of the robot completing a task within a specified time; calculating the error rate of the robot according to the data of errors occurring in the robot during work; and calculating the energy consumption rate of the robot during work according to the energy data; the data of the robot completing a task within a specified time comprises the number of completed tasks, the total number of tasks in the work process, and the total number of operations of the robot during work; the calculation formula of the task completion rate is as follows: n= (N / N0) x 100%, wherein N represents the number of completed tasks, and N0 represents the total number of tasks; the data of errors occurring in the robot during work comprises the number of errors occurring in the robot during work; the calculation formula of the error rate is as follows: F= (F / F0) x 100%, wherein F represents the number of errors, and F0 represents the total number of operations; the energy consumption rate of the robot during work comprises the total energy consumption of the robot during work; the calculation formula of the energy consumption rate is as follows: w= (W / N) x 100%, wherein W represents the total energy consumption, and N represents the number of completed tasks.

2. The method of claim 1, wherein, The step of calculating the quality and efficiency score of the work of the robot based on the score index comprises: obtaining a weight parameter of each score index pre-configured; weighting and calculating each score index based on the weight parameter to obtain the quality and efficiency score of the work of the robot.

3. The method of claim 1, wherein, The method further comprises: According to the job-related data, automatic operation data of the robot in the job process is extracted; wherein, the automatic operation data includes each job step, and an automation degree corresponding to each job step; Based on the automatic operation data, a job automation rate of the robot is calculated, and the job automation rate is graphically presented.

4. The method of claim 3, wherein, The automation degree is used to represent whether the actual operation of each job step is an automatic process or a manual intervention process; The step of calculating the job automation rate of the robot based on the automatic operation data includes: The total number of job steps, the number of job steps with automatic operation, and the number of job steps with manual intervention operation are counted; The proportion of job steps with automatic operation in the total number of job steps is calculated; The proportion is determined as the job automation rate of the robot.

5. The method of claim 3, wherein, The method further includes: From the job-related data, job information of the robot job is extracted, wherein the job information at least includes: job basic information, job conclusion data, job process analysis data, job deduction data, and job record data; According to the job information, a job score report containing the quality and efficiency score and the job automation rate is generated.

6. An evaluation device for a robot job, characterized in that The device includes: An acquisition module is configured to acquire job-related data generated by a robot in a job process, and extract feature data related to the performance of the robot from the job-related data; wherein, the feature data includes task data and energy data completed by the robot in the job process; the task data includes data of the robot completing a task within a specified time, and data of the robot occurring errors in the job process; the robot refers to a live-line distribution network operation robot; the job-related data is real-time collected data generated by the robot in the job process; A first calculation module is configured to calculate a score indicator of the robot based on the feature data; the score indicator includes a task completion rate, an error rate, and an energy consumption rate; the task completion rate is used to measure the proportion of the robot completing a predetermined job task within a specified time; the error rate is used to measure the frequency of errors occurring in the robot in the job process; the energy consumption rate is used to measure the proportion of energy consumed by the robot in completing the job process and the energy required for completing a unit task; A second calculation module is configured to calculate a quality and efficiency score of the robot job based on the score indicator, so as to evaluate the job of the robot; wherein, for the job process of the robot, if the quality and efficiency score is greater than a preset score threshold, it indicates that the job process of the robot meets the quality and efficiency requirements, otherwise, it does not meet the quality and efficiency requirements; Wherein, the step of calculating the score indicator of the robot based on the feature data includes: The task completion rate of the robot is calculated based on the data of the robot completing a task within a specified time; The error rate of the robot is calculated based on the data of the robot occurring errors in the job process; and, The energy consumption rate of the robot during the working process is calculated according to the energy data; The data of the robot completing tasks within a specified time includes the number of completed tasks, the total number of tasks in the working process, and the total number of operations of the robot during the working process; The calculation formula of the task completion rate is as follows: n = (N / N0) x 100%, wherein N represents the number of completed tasks, and N0 represents the total number of tasks; The data of the robot occurring errors during the working process includes the number of errors occurring in the robot during the working process; The calculation formula of the error rate is as follows: F = (F / F0) x 100%, wherein F represents the number of errors occurring, and F0 represents the total number of operations; The energy consumption rate of the robot during the working process includes the total energy consumption of the robot during the working process; The calculation formula of the energy consumption rate is as follows: w = (W / N) x 100%, wherein W represents the total energy consumption, and N represents the number of completed tasks.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the method in any one of claims 1-5.

8. A machine-readable storage medium, characterized in that, The machine readable storage medium stores machine executable instructions, and when the machine executable instructions are called and executed by the processor, the machine executable instructions cause the processor to realize the method in any one of claims 1-5.

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