Log processing method and device of robot, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a log processing method and device of a robot, electronic equipment and a storage medium, and belongs to the technical field of intelligent robots. The method comprises the steps of collecting log data of a robot in real time; carrying out filtering processing operation on the log data to obtain filtered log data, carrying out verification processing operation on the filtered log data, and if verification is passed, determining the filtered log data as candidate log data; performing fragmentation processing on the candidate log data according to the log data range; classifying the fragment log data according to robot models; obtaining error reporting information according to the log data of the various types of robots, and obtaining error log data of the various types of robots according to the error reporting information; and filtering error log data from the log data to obtain target log data. Through the provided scheme, the log data of the robot is collected in real time, the data lag problem is reduced, and the reliability and stability of the system are improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent robots, and in particular to a robot log processing method, device, electronic device and storage medium. Background Art

[0002] In the field of intelligent robots, with the rapid development of technology, the robot platform needs a powerful robot log processing method to monitor its operating status, help diagnose problems and optimize operations. The current log method has limited real-time data processing capabilities, resulting in data lag. Summary of the invention

[0003] In order to solve the above technical problems, the embodiments of the present application provide a robot log processing method, device, electronic device and storage medium.

[0004] In a first aspect, an embodiment of the present application provides a robot log processing method, the method comprising: collecting robot log data in real time, the log data comprising: user interaction log data and system performance log data;

[0005] Performing a filtering operation on the log data to obtain filtered log data, performing a verification operation on the filtered log data, and if the verification passes, determining the filtered log data as candidate log data;

[0006] Slice the candidate log data according to the log data range to obtain multiple sliced ​​log data;

[0007] Classify the shard log data according to the robot model to obtain log data of each type of robot;

[0008] Obtain error information according to log data of each type of robot, and obtain error log data of each type of robot according to the error information;

[0009] The error log data is filtered from the log data to obtain target log data.

[0010] In one implementation, the log data is divided into valid data and invalid data according to the validity of the data;

[0011] Cleaning up the invalid data in the log data to obtain cleaned up data;

[0012] Duplicate data in the cleaned data is cleaned to obtain the filtered log data.

[0013] In one implementation, the filtered log data is normalized to obtain normalized log data;

[0014] Adding geographic feature information to the normalized log data to obtain enhanced log data;

[0015] The storage capacity of the enhanced log data is verified, and if the storage capacity is less than a preset storage threshold, the verification is passed.

[0016] In one implementation, the candidate log data is divided into a plurality of independent log data according to the log data range;

[0017] An index tag is established for each independent log data according to the log data range corresponding to the independent log data and preset information, and each independent log data and the index tag are stored to obtain a plurality of the shard log data.

[0018] In one implementation, a preset log feature value of each of the shard log data is obtained;

[0019] The robot operation status is obtained according to the preset log feature value, and a real-time analysis report of the robot is obtained according to the robot operation status.

[0020] In one embodiment, log data to be determined within a preset time range is determined from the log data of each type of robot, and the error information is obtained from the log data to be determined;

[0021] Determine whether the error information is greater than or equal to a preset error threshold;

[0022] If the error information in the log data to be determined is greater than or equal to the preset error threshold, the log data to be determined is used as the error log data.

[0023] In one embodiment, multiple user roles and multiple user permissions are pre-set for the log data of the robot, and each user permission is used to authorize different management permissions for the log data of the robot;

[0024] A target user role is selected from the plurality of user roles and assigned to a user terminal, and corresponding target user usage permissions are assigned according to the target user role.

[0025] In a second aspect, an embodiment of the present application provides a log processing device for a robot, the log processing device for the robot comprising:

[0026] A collection module, used to collect the robot's log data in real time, the log data including: user interaction log data and system performance log data;

[0027] a processing module, configured to perform a filtering operation on the log data to obtain filtered log data, perform a verification operation on the filtered log data, and if the verification passes, determine the filtered log data as candidate log data;

[0028] A sharding module, used for sharding the candidate log data according to the log data range to obtain a plurality of sharded log data;

[0029] A classification module, used to classify the slice log data according to the robot model to obtain log data of each type of robot;

[0030] An acquisition module, used to acquire error information according to log data of each type of robot, and acquire error log data of each type of robot according to the error information;

[0031] The filtering module is used to filter the error log data from the log data to obtain target log data.

[0032] In a third aspect, an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and when the computer program is run by the processor, the robot log processing method provided in the first aspect is executed.

[0033] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which executes the robot log processing method provided in the first aspect when the computer program is run on a processor.

[0034] The robot log processing method, device, electronic device and storage medium provided in the present application include: real-time collection of robot log data, the log data including: user interaction log data and system performance log data; filtering the log data to obtain filtered log data, and verifying the filtered log data. If the verification passes, the filtered log data is determined as candidate log data; slicing the candidate log data according to the log data range to obtain multiple slicing log data; classifying each slicing log data according to the robot model to obtain log data of each type of robot; obtaining error information according to the log data of each type of robot, and obtaining error log data of each type of robot according to the error information; filtering the error log data from the log data to obtain target log data. The present application collects the log data of the robot in real time, filters and verifies the log data to obtain candidate log data, segments the candidate log data, establishes classification labels for the log data of each segment, and filters the error log data in the log data according to the error information, thereby improving the data quality and the processing efficiency of the log data, and further improving the efficiency of managing and querying the log data. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present application and should not be regarded as limiting the scope of protection of the present application. In each of the drawings, similar components are numbered similarly.

[0036] Figure 1 A schematic diagram of a process flow of a robot log processing method provided by an embodiment of the present application is shown;

[0037] Figure 2 Another schematic diagram of the process of the robot log processing method provided by the embodiment of the present application is shown;

[0038] Figure 3 A schematic diagram of the structure of a log processing device of a robot provided in an embodiment of the present application is shown;

[0039] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown.

[0040] Icon: 300 - log processing device of the robot, 301 - collection module, 302 - processing module, 303 - segmentation module, 304 - classification module, 305 - acquisition module, 306 - filtering module, 400 - electronic device, 401 - transceiver, 402 - processor, 403 - memory. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0042] The components of the embodiments of the present application generally described and shown in the drawings herein may be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0043] Hereinafter, the terms "including", "having" and their cognates, which may be used in various embodiments of the present application, are intended only to indicate specific features, numbers, steps, operations, elements, components, or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing items or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations of the foregoing items.

[0044] Furthermore, the terms “first”, “second”, “third”, etc. are merely used for distinguishing descriptions and are not to be understood as indicating or implying relative importance.

[0045] Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meanings as those generally understood by those skilled in the art to which the various embodiments of the present application belong. The terms (such as those defined in generally used dictionaries) will be interpreted as having the same meanings as the contextual meanings in the relevant technical field and will not be interpreted as having idealized meanings or overly formal meanings unless clearly defined in the various embodiments of the present application.

[0046] Example 1

[0047] An embodiment of the present application provides a log processing method for a robot.

[0048] See also Figure 1 , the robot's log processing methods include:

[0049] S101, collecting log data of the robot in real time, wherein the log data includes: user interaction log data and system performance log data.

[0050] It should be noted that the robot generates a large amount of log data during operation, including user interaction log data, where the user interaction log data includes: text data of the user-robot conversation, timestamp, user ID, session ID, user input, robot reply, conversation context and conversation status, etc. System performance logs include: performance data of each module of the robot system, such as response time, CPU usage, memory usage, etc., timestamp, module name, CPU usage, memory usage, response time, error information, etc.

[0051] Log collectors monitor and capture log data in real time and transmit them to the log system for processing. Log collectors can be deployed on each server node to ensure comprehensive log coverage.

[0052] S102, performing a filtering operation on the log data to obtain filtered log data, performing a verification operation on the filtered log data, and if the verification passes, determining the filtered log data as candidate log data.

[0053] In one implementation, the log data is divided into valid data and invalid data according to the validity of the data; the invalid data in the log data is cleaned to obtain cleaned data; and duplicate data in the cleaned data is cleaned to obtain the filtered log data.

[0054] It should be noted that valid information in the log data is pre-defined, such as time information, IP information, and application information, and all information other than the defined valid information is cleared as invalid information to ensure the accuracy and consistency of the data. Each log data has a corresponding unique ID number. If the ID number is repeated, the duplicate log data needs to be deleted one by one to ensure the uniqueness of the log data.

[0055] In one embodiment, the filtered log data is normalized to obtain normalized log data; geographic feature information is added to the normalized log data to obtain enhanced log data; the storage capacity of the enhanced log data is verified, and if the storage capacity is less than a preset storage threshold, the verification is passed.

[0056] It should be noted that all log data must be converted into a unified format and structure, such as converting the timestamp in the log data into a standard format to facilitate time range queries and subsequent processing, and enhancing the converted data, such as adding geographic feature information or other useful information to the log data header to improve the analytical value of the log data. Converting to a unified format and structure helps optimize data storage methods, reduce waste of storage space, and improve data retrieval efficiency.

[0057] It is further explained that the standardized storage capacity threshold and the standardized format are pre-defined, and the storage capacity and format of the log data are verified to ensure that they meet the quality standards and improve the quality of the log data.

[0058] S103, fragmenting the candidate log data according to the log data range to obtain a plurality of fragmented log data.

[0059] In this embodiment, the candidate log data is divided into different shards according to a certain range of data (such as user ID, timestamp, etc.). In other embodiments, the number of shards can be determined according to system capacity and performance requirements, and the shards can be allocated to different storage nodes to ensure load balancing and fault tolerance.

[0060] In one embodiment, if Figure 2 As shown, S201, divide the candidate log data into multiple independent log data according to the log data range; S202, establish an index tag for each independent log data according to the log data range and preset information corresponding to the independent log data, and store each independent log data and the index tag to obtain multiple shard log data.

[0061] It should be noted that the data set is divided into multiple parts through the sharding mechanism, and each part is stored on a different node. This method can significantly improve the scalability and concurrent processing capabilities of the system. Indexes are established according to preset information such as time, user information, and application keywords, and the corresponding location is located through the index. The query speed is accelerated by indexing the data. In the search server (Elasticsearch), each independent log data corresponds to a different index type to support efficient search and analysis.

[0062] In one embodiment, a preset log feature value of each of the sliced ​​log data is obtained; the robot operation status is obtained according to the preset log feature value, and a real-time analysis report of the robot is obtained according to the robot operation status.

[0063] For example, if the preset log feature value is a charging feature value, the charging status of the robot is obtained based on the charging feature value, and the obtained charging status is substituted into the data analysis platform (Kibana) to obtain the charging status analysis report of the robot. For another example, if the preset log feature value is a time feature value, the motion status of the robot at different times can be obtained based on the time feature value, and a real-time analysis report can be further obtained based on the motion status.

[0064] S104, classifying the slice log data according to the robot model to obtain log data of each type of robot.

[0065] In this embodiment, in addition to classifying data according to the robot model, the data can also be classified according to the level of the log data, for example, the log information is classified according to the degree of urgency, where the degree of urgency includes: emergency, alarm, critical, error, warning, notification, information and debugging. These levels represent the severity of the log and the degree of attention required from high to low. By classifying the log data of the robot, the data processing efficiency is improved, and the analysis of the log data is facilitated.

[0066] Suppose there is a web application whose log data is collected and stored in a search server (Elasticsearch). In the data analysis platform (Kibana), you can create a dashboard for the log data of robot model 1. The dashboard contains a time-ordered bar chart showing the number of log data of robot model 1 per hour. At the same time, you can also add a pie chart to show the most common error types or error messages. Through the analysis chart, you can further understand the time period with a large number of errors and the most common error types, so as to quickly locate the problem and take measures to solve it.

[0067] S105, obtaining error information according to the log data of each type of robot, and obtaining error log data of each type of robot according to the error information.

[0068] In one embodiment, log data to be judged within a preset time range is determined from the log data of the various types of robots, and the error information is obtained from the log data to be judged; it is determined whether the error information is greater than or equal to a preset error threshold; if the error information in the log data to be judged is greater than or equal to the preset error threshold, the log data to be judged is used as the error log data.

[0069] For example: by obtaining the log data to be judged within one minute, the system parses the log data to be judged within this minute to identify the error information contained therein. If the number of error messages in the log data to be judged is greater than or equal to 5 times, the system will mark this log data as error log data. Among them, the error information may appear in the form of a specific error code, exception description or warning message. It should be noted that the preset time range and preset error threshold can be set according to needs and are not limited here.

[0070] S106, filtering the error log data from the log data to obtain target log data.

[0071] It should be noted that the error log data is filtered out from the log data. By obtaining the error log data, the source and type of high-frequency errors in the system can be further identified to help quickly locate the problem.

[0072] In one embodiment, multiple user roles and multiple user permissions are pre-set for the robot's log data, and each user permission is used to authorize different management permissions for the robot's log data; a target user role is selected from the multiple user roles and assigned to a user terminal, and the corresponding target user permission is assigned according to the target user role.

[0073] It should be noted that different user roles and user permissions are defined. For example: Administrator: has all permissions, including user management, log viewing, system configuration, and cleanup task execution. Operator: can view logs and execute cleanup tasks, but cannot modify system configuration and user management. Viewer: can only view logs and cannot perform other operations.

[0074] Select a target user role from the user roles and assign it to the user end to ensure that the user end can only access functions within its permission range. Limit the access and operation of logs according to user roles. By providing user permission management and access control functions, the access security of log data is improved.

[0075] The robot log processing method provided in the present embodiment collects the robot's log data in real time, filters and verifies the log data to obtain candidate log data, segments the candidate log data, establishes classification labels for the log data of each segment, and filters the error log data in the log data according to the error information, thereby improving the data quality and the processing efficiency of the log data, and further improving the efficiency of managing and querying the log data.

[0076] Example 2

[0077] In addition, an embodiment of the present application provides a log processing device for a robot.

[0078] like Figure 3 As shown, the log processing device 300 of the robot includes:

[0079] The collection module 301 is used to collect the log data of the robot in real time, and the log data includes: user interaction log data and system performance log data;

[0080] The processing module 302 is used to perform a filtering operation on the log data to obtain filtered log data, perform a verification operation on the filtered log data, and if the verification passes, determine the filtered log data as candidate log data;

[0081] A sharding module 303 is used to perform sharding processing on the candidate log data according to the log data range to obtain a plurality of sharded log data;

[0082] A classification module 304 is used to classify the slice log data according to the robot model to obtain log data of each type of robot;

[0083] An acquisition module 305 is used to acquire error information according to log data of each type of robot, and acquire error log data of each type of robot according to the error information;

[0084] The filtering module 306 is used to filter the error log data from the log data to obtain target log data.

[0085] The robot log processing device 300 provided in this embodiment can implement the robot log processing method provided in Example 1, and will not be described again here to avoid repetition.

[0086] The robot log processing device provided in the present embodiment collects the robot's log data in real time, filters and verifies the log data to obtain candidate log data, segments the candidate log data, establishes classification labels for the log data of each segment, and filters the error log data in the log data according to the error information, thereby improving the data quality and the processing efficiency of the log data, and further improving the efficiency of managing and querying the log data.

[0087] Example 3

[0088] In addition, an embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program runs on the processor, the robot log processing method provided in Example 1 is executed.

[0089] For details, see Figure 4 The electronic device 400 includes: a transceiver 401, a bus interface and a processor 402, wherein the processor 402 is used to: collect the log data of the robot in real time, wherein the log data includes: user interaction log data and system performance log data; perform filtering processing operations on the log data to obtain filtered log data, perform verification processing operations on the filtered log data, and if the verification passes, determine the filtered log data as candidate log data; perform slicing processing on the candidate log data according to the log data range to obtain multiple slicing log data; classify each slicing log data according to the robot model to obtain log data of each type of robot; obtain error information according to the log data of each type of robot, and obtain error log data of each type of robot according to the error information; filter the error log data from the log data to obtain target log data.

[0090] In one embodiment, the processor 402 is also used to: divide the log data into valid data and invalid data according to the validity of the data; clean the invalid data in the log data to obtain cleaned data; clean the duplicate data in the cleaned data to obtain the filtered log data.

[0091] In one embodiment, the processor 402 is also used to: normalize the filtered log data to obtain normalized log data; add geographic feature information to the normalized log data to obtain enhanced log data; and verify the storage capacity of the enhanced log data. If the storage capacity is less than a preset storage threshold, the verification is successful.

[0092] In one embodiment, the processor 402 is also used to: divide the candidate log data into multiple independent log data according to the log data range; establish an index tag for each independent log data according to the log data range and preset information corresponding to the independent log data, and store each independent log data and the index tag to obtain multiple shard log data.

[0093] In one embodiment, the processor 402 is further used to: obtain a preset log feature value of each of the sliced ​​log data; obtain the robot operation status according to the preset log feature value, and obtain a real-time analysis report of the robot according to the robot operation status.

[0094] In one embodiment, the processor 402 is also used to: determine the log data to be judged within a preset time range from the log data of the various types of robots, and obtain the error information from the log data to be judged; determine whether the error information is greater than or equal to a preset error threshold; if the error information in the log data to be judged is greater than or equal to the preset error threshold, then use the log data to be judged as the error log data.

[0095] In one embodiment, the processor 402 is also used to: pre-set multiple user roles and multiple user usage permissions for the robot's log data, and each user usage permission is used to authorize different management permissions for the robot's log data; select a target user role from the multiple user roles and assign it to a user terminal, and assign corresponding target user usage permissions according to the target user role.

[0096] In the embodiment of the present application, the electronic device 400 further includes a memory 403. Figure 4In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 402 and memory represented by memory 403. The bus architecture may also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 401 may be a plurality of components, i.e., including a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium. The processor 402 is responsible for managing the bus architecture and general processing, and the memory 403 may store data used by the processor 402 when performing operations.

[0097] The electronic device 400 provided in the embodiment of the present application can execute the steps of the robot log processing method provided in the above method embodiment 1, and will not be described again here to avoid repetition.

[0098] The electronic device provided in this embodiment collects the log data of the robot in real time, filters and verifies the log data to obtain candidate log data, segments the candidate log data, establishes classification labels for the log data of each segment, and filters the error log data in the log data according to the error information, thereby improving the data quality and the processing efficiency of the log data, and further improving the efficiency of managing and querying the log data.

[0099] Example 4

[0100] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the robot log processing method provided in Example 1 is implemented.

[0101] In this embodiment, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0102] The computer-readable storage medium provided in this embodiment can implement the robot log processing method provided in Example 1, and will not be described again here to avoid repetition.

[0103] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or terminal including the element.

[0104] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0105] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A robot log processing method, characterized in that: The method comprises: Collecting robot log data in real time, the log data includes: user interaction log data and system performance log data; Performing a filtering operation on the log data to obtain filtered log data, performing a verification operation on the filtered log data, and if the verification passes, determining the filtered log data as candidate log data; Slice the candidate log data according to the log data range to obtain multiple sliced ​​log data; Classify the shard log data according to the robot model to obtain log data of each type of robot; Obtain error information according to log data of each type of robot, and obtain error log data of each type of robot according to the error information; The error log data is filtered from the log data to obtain target log data.

2. The robot log processing method according to claim 1, characterized in that: The filtering operation on the log data includes: Dividing the log data into valid data and invalid data according to the validity of the data; Cleaning up the invalid data in the log data to obtain cleaned up data; Duplicate data in the cleaned data is cleaned to obtain the filtered log data.

3. The robot log processing method according to claim 2, characterized in that: The verifying and processing operation on the filtered log data includes: Normalizing the filtered log data to obtain normalized log data; Adding geographic feature information to the normalized log data to obtain enhanced log data; The storage capacity of the enhanced log data is verified, and if the storage capacity is less than a preset storage threshold, the verification is passed.

4. The robot log processing method according to claim 1, characterized in that: The processing of slicing the candidate log data according to the log data range to obtain a plurality of slicing log data includes: Dividing the candidate log data into a plurality of independent log data according to the log data range; An index tag is established for each independent log data according to the log data range corresponding to the independent log data and preset information, and each independent log data and the index tag are stored to obtain a plurality of the shard log data.

5. The robot log processing method according to claim 1, characterized in that: The method further comprises: Obtaining a preset log feature value of each of the shard log data; The robot operation status is obtained according to the preset log feature value, and a real-time analysis report of the robot is obtained according to the robot operation status.

6. The robot log processing method according to claim 1, characterized in that: The error information is obtained according to the log data of each type of robot, including: Determine the log data to be judged within a preset time range from the log data of the robots of each type, and obtain the error information from the log data to be judged; The obtaining error log data of each type of robot according to the error information includes: Determine whether the error information is greater than or equal to a preset error threshold; If the error information in the log data to be determined is greater than or equal to the preset error threshold, the log data to be determined is used as the error log data.

7. The robot log processing method according to claim 1, characterized in that: The method further comprises: Pre-setting multiple user roles and multiple user permissions for the log data of the robot, each user permission is used to authorize different management permissions for the log data of the robot; A target user role is selected from the plurality of user roles and assigned to a user terminal, and corresponding target user usage permissions are assigned according to the target user role.

8. A robot log processing device, characterized in that: The device comprises: A collection module, used to collect the robot's log data in real time, the log data including: user interaction log data and system performance log data; a processing module, configured to perform a filtering operation on the log data to obtain filtered log data, perform a verification operation on the filtered log data, and if the verification passes, determine the filtered log data as candidate log data; A sharding module, used for sharding the candidate log data according to the log data range to obtain a plurality of sharded log data; A classification module, used to classify the slice log data according to the robot model to obtain log data of each type of robot; An acquisition module, used to acquire error information according to log data of each type of robot, and acquire error log data of each type of robot according to the error information; The filtering module is used to filter the error log data from the log data to obtain target log data.

9. An electronic device, characterized in that: The device comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the log processing method of the robot according to any one of claims 1 to 7 is executed.

10. A computer-readable storage medium, characterized in that: The computer program stores a computer program, which executes the log processing method of the robot according to any one of claims 1 to 7 when running on a processor.

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