Method, computing device and storage medium for parsing data

Abstract

Embodiments of the present invention relate to a method, computing device, and storage medium for parsing data. The method includes determining, at a master device, data to be parsed based on predetermined parameters or in response to user operations, the data to be parsed includes multiple data segments, and the multiple data segments come from at least one input source; for the determined data to be parsed, based on the computing resources of the master device and / or the computing resources of the leaf device, generating a parsing task for the data to be parsed; assigning the parsing task to the device participating in the parsing task, so that the data to be parsed is parsed via the device participating in the parsing task, and the device participating in the parsing task includes the master device and / or at least one leaf device. Thereby, automatic parsing of multi-protocol mixed data sources and multi-threaded parsing can be supported.

Description

Technical Field

[0001] Embodiments of the present invention generally relate to the field of data processing, and more particularly to a method, a computing device, and a storage medium for parsing data. Background Art

[0002] Traditional methods for parsing data, such as parsing data from a specific input source (such as CAN, etc.), can only parse data from a specific single protocol. When the data format of the access data source is a mixture of multiple protocols, it is impossible to automatically parse such data effectively, and the data cannot be parsed in a timely manner, which leads to the inability to smoothly proceed with data analysis.

[0003] In summary, the traditional method for parsing data has the following shortcomings: it does not support automatic parsing of multi-protocol mixed data sources, does not support custom parsing, and does not support automatic parsing of multi-threaded and multi-source data. Summary of the invention

[0004] In view of the above problems, the present invention provides a method, a computing device and a storage medium for parsing data, which can support automatic parsing of multi-protocol mixed data sources and support multi-threaded parsing.

[0005] According to a first aspect of the present invention, a method for parsing data is provided, comprising: at a master device, based on predetermined parameters or in response to a user's operation, determining data to be parsed, wherein the data to be parsed comprises a plurality of data segments, and the plurality of data segments come from at least one input source; for the determined data to be parsed, based on the computing resources of the master device and / or the computing resources of a leaf device, generating a parsing task regarding the data to be parsed; assigning the parsing task to devices participating in the parsing task so that the data to be parsed is parsed via the devices participating in the parsing task, wherein the devices participating in the parsing task include the master device and / or at least one leaf device.

[0006] According to a second aspect of the present invention, a computing device is provided, comprising: at least one processing unit; at least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, enabling the computing device to perform the steps of the method according to the first aspect.

[0007] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a machine, the method of the first aspect of the present invention is executed.

[0008] According to a fourth aspect of the present invention, there is further provided a computer program product, comprising a computer program, wherein when the computer program is executed by a machine, the method of the first aspect of the present invention is performed.

[0009] In some embodiments, assigning a parsing task to a device participating in the parsing task so that parsing the data to be parsed via the device participating in the parsing task includes: determining a parsing parameter table that matches the parsing task, the parsing parameter table at least including the data type, the amount of data, the parsing order of the data, and the parsing method of the data; and sending the parsing parameter table to the device participating in the parsing task so that each device participating in the parsing task parses the data to be parsed based on the parsing parameter table.

[0010] In some embodiments, the data type includes one or more of: bit, byte, integer, character, single-precision floating point, double-precision floating point, long integer, Boolean, array, and structure.

[0011] In some embodiments, the data parsing method includes: loop parsing, where the loop parsing indicates that based on the data type and data quantity in the parsing parameter table, the data to be parsed is parsed the number of times indicated by the loop number parameter in the order of parsing the data.

[0012] Judgment analysis, judgment analysis indicates to make a judgment based on the stage of the current cycle analysis to determine to enter the next state, the next state includes the following items: the next analysis stage; stop the current cycle analysis; and data analysis is completed.

[0013] In some embodiments, judging based on the stage of the current loop parsing to determine whether to enter the next state includes: judging the entry conditions of the loop parsing; during the loop parsing process, obtaining the calculation results of numerical values, characters, operators, formulas, encryption methods and / or decryption methods, and performing comparisons based on the obtained calculation results to determine whether to enter the next parsing stage, stop the current loop parsing, enter the next loop parsing and / or complete data parsing; based on the comparison results, performing an abnormality judgment on the current loop parsing.

[0014] In some embodiments, generating a parsing task regarding the data to be parsed based on the computing resources of the main device and / or the computing resources of the leaf devices includes: determining candidate devices regarding the parsing task based on the computing resources of the main device and / or the available computing resources of the leaf devices; determining the devices participating in the parsing task from the candidate devices based on the data type, data volume, parsing requirements and / or data path of the data to be parsed; and determining at least the data path, data volume and data identification of the data to be parsed that the devices participating in the parsing task need to parse, so as to generate a parsing task regarding the data to be parsed.

[0015] In some embodiments, determining the data to be parsed includes determining a data path of the data to be parsed, where the data path indicates a storage path for local data, an acquisition path for offline data, and / or a receiving path for real-time data.

[0016] In some embodiments, the data to be parsed includes files and / or folders. When the data to be parsed is a folder, parsing the data to be parsed includes: continuously parsing multiple files in the folder; and continuously playing the multiple parsed files of the data to be parsed.

[0017] In some embodiments, it also includes one or more of the following: at least based on the device type and the parsing target, the data to be parsed is pre-stored in the main device and / or at least one leaf device via predetermined parameters or in response to user operations; the data to be parsed is stored in a public storage device, and the main device and / or leaf devices can access and obtain the data to be parsed stored on the public storage device through the data path; at least based on the device type, the parsing task and the real-time data source, the receiving path of the real-time data and the storage location of the real-time data are determined.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other features, advantages and aspects of the embodiments of the present invention will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements.

[0020] Figure 1 A schematic diagram of a system for implementing a method for parsing data according to an embodiment of the present invention is shown.

[0021] Figure 2 A flow chart of a method for parsing data according to an embodiment of the present invention is shown.

[0022] Figure 3 A flow chart of a method for generating a parsing task according to an embodiment of the present invention is shown.

[0023] Figure 4 A flow chart of a method for allocating parsing tasks to devices participating in the parsing tasks according to an embodiment of the present invention is shown.

[0024] Figure 5 A flow chart of a method for loop parsing according to an embodiment of the present invention is shown.

[0025] Figure 6A flow chart of a method for storing data to be parsed according to an embodiment of the present invention is shown.

[0026] Figure 7 A block diagram of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0027] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0028] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0029] As described above, when parsing data from a specific input source (such as CAN, etc.), the traditional method for parsing data can only parse data from a specific single protocol. When the data format of the access data source is a mixed data of multiple protocols, it is impossible to effectively automatically parse such data.

[0030] In summary, the shortcomings of traditional methods for parsing data are: they do not support automatic parsing of multi-protocol mixed data sources, do not support custom parsing, and do not support automatic parsing of multi-threaded and multi-source data.

[0031] In order to at least partially solve one or more of the above problems and other potential problems, an exemplary embodiment of the present invention proposes a scheme for parsing data. In the scheme of the present invention, at the main device, based on predetermined parameters or in response to user operations, the data to be parsed is determined, the data to be parsed includes multiple data segments, and the multiple data segments come from at least one input source, so that multiple data sources and multi-protocol mixed data can be supported; for the determined data to be parsed, based on the computing resources of the main device and / or the computing resources of the leaf device, a parsing task about the data to be parsed is generated; the parsing task is assigned to the device participating in the parsing task, so that the data to be parsed is parsed via the device participating in the parsing task, and the device participating in the parsing task includes the main device and / or at least one leaf device; so that the parsing task can be flexibly allocated according to the computing resources in the system, greatly improving the parsing efficiency and supporting the parsing of multi-threaded multi-source data. Therefore, the present invention can support the automatic parsing of multi-protocol mixed data sources and support multi-threaded parsing of multi-source data, so that the multi-protocol source data can be parsed in time, thereby promoting the subsequent data analysis.

[0032] Figure 1 FIG. 1 is a schematic diagram of a system 100 for implementing a method for parsing data according to an embodiment of the present invention. Figure 1 As shown in , the system 100 includes a main device 110 and leaf devices (130-1 to 130-N), a network 140, and a storage device 150. The main device 110, the leaf devices (130-1 to 130-N), and the storage device 150 can exchange data through the network 140 (e.g., the Internet, a local area network, an Ethernet, an industrial Ethernet, an EPA network).

[0033] Any of the leaf devices (130-1 to 130-N) can, for example, receive data to be parsed from at least one input source in real time, store the data to be parsed through its own memory, and obtain the data to be parsed from the storage device 150. Any of the leaf devices (130-1 to 130-N) can have the ability to parse data in order to accept and complete the parsing task, and can have one or more processing units, including dedicated processing units such as GPU, FPGA and ASIC, and general processing units such as CPU. In addition, one or more virtual machines can also be running on each main device 110.

[0034] Regarding the main device 110, the main device 110 may have one or more processing units, including special processing units such as GPU, FPGA and ASIC, and general processing units such as CPU. In addition, one or more virtual machines may also be running on each main device 110. In some embodiments, the main device 110 includes, for example, a data determination module 112, a parsing task generation module 114, a parsing task allocation module 116, and a data playback module 118.

[0035] The data determination module 112 is used to determine the data to be parsed based on a predetermined parameter or in response to a user's operation, wherein the data to be parsed includes a plurality of data segments, and the plurality of data segments come from at least one input source.

[0036] Regarding the parsing task generation module 114, it is used to generate a parsing task for the determined data to be parsed based on the computing resources of the main device and / or the computing resources of the leaf devices.

[0037] Regarding the parsing task allocation module 116, it is used to allocate parsing tasks to devices participating in the parsing tasks so that the data to be parsed can be parsed via the devices participating in the parsing tasks. The devices participating in the parsing tasks include a main device and / or at least one leaf device.

[0038] Figure 2 FIG. 2 is a flow chart of a method 200 for parsing data according to an embodiment of the present invention. The method 200 may be performed as follows: Figure 1 The main device 110 shown in FIG. Figure 7 The method 200 is executed at the electronic device 700. It should be understood that the method 200 may further include additional steps not shown and / or may omit the steps shown, and the scope of the present invention is not limited in this respect.

[0039] In step 210, at the main device 110, based on a predetermined parameter or in response to a user's operation, data to be parsed is determined, wherein the data to be parsed includes a plurality of data segments, and the plurality of data segments come from at least one input source.

[0040] Regarding the input source, it includes, for example, CAN bus protocol data (CAN, Controller Area Network, controller area network bus), USB bus data (USB, Universal Serial Bus, universal serial bus), serial port protocol data, EPA protocol data (EPA, Ethernet for Plant Automation, industrial automation Ethernet), network port protocol data and other input source data of various protocols.

[0041] In some embodiments, determining the data to be parsed includes determining a data path of the data to be parsed, where the data path indicates a storage path for local data, an acquisition path for offline data, and / or a receiving path for real-time data.

[0042] For example, the data path indicates the local address of the data to be parsed in the main device, for example, indicates the local address of the data to be parsed in the leaf device, for example, indicates the access address of the data to be parsed in a public storage device such as storage device 150.

[0043] In some embodiments, the data to be parsed includes files and / or folders. When the data to be parsed is a folder, parsing the data to be parsed includes: continuously parsing multiple files in the folder; and continuously playing the multiple files after parsing the data to be parsed. Regarding the multiple files in the folder, they can, for example, come from the same or different input sources, and they can, for example, be data of the same or different data protocols. Regarding files, for example, a file can also include data from multiple data sources. Therefore, this solution can support automatic parsing of data from multiple input sources, different protocols, single or multiple files, thereby improving the parsing efficiency and flexibility of the data to be parsed; and it also supports automatic playback after data parsing, thereby improving the efficiency of data analysis.

[0044] In step 220, the master device 110 generates a parsing task for the determined data to be parsed based on the computing resources of the master device and / or the computing resources of the leaf devices.

[0045] In some embodiments, please refer to Figure 3 The method for generating parsing tasks according to an embodiment of the present invention is illustrated, wherein step 220 generates a parsing task about the data to be parsed based on the computing resources of the main device and / or the computing resources of the leaf device, and further includes the following steps.

[0046] Step 222, determine candidate devices for the parsing task based on the computing resources of the main device and / or the available computing resources of the leaf devices.

[0047] Regarding computing resources, for example, they include CPU (Central Processing Unit), GPU (Graphics Processing Unit), TPU (Tensor Processing Unit), NPU (Neural Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), etc. Regarding the amount of available computing resources, such as the amount of available computing resources (i.e., idle computing resources) of the main device and leaf device, for example, the amount of computing resources is calculated, measured, compared, etc. in common units of computing resource types.

[0048] Step 224, based on the data type, data volume, parsing requirements and / or data path of the data to be parsed, determine the device that participates in the parsing task from the candidate devices.

[0049] For example, if the amount of data to be parsed is small, the parsing task can be assigned to a certain device; if the amount of data to be parsed is large and one device cannot complete the parsing task, the parsing task can be distributed to multiple devices; for example, the data M to be parsed is divided into three data, M1, M2 and M3, where the main device 110 completes the parsing of data M1, the leaf device 130-1 completes the parsing of data M2, and the leaf device 130-3 completes the parsing of data M3.

[0050] For example, if the data to be parsed is stored locally on a device, the parsing task will be completed preferentially by the device that locally stores the data to be parsed. When the device that locally stores the data to be parsed does not have sufficient computing resources to complete all parsing tasks, the unfinished parsing tasks will be assigned to other devices.

[0051] For example, if the data type of the data to be parsed makes it more efficient to parse it using a GPU, the parsing task will be prioritized to a device with abundant GPU computing resources; if the data type of the data to be parsed makes it more efficient to parse it using a CPU, the parsing task will be prioritized to a device with abundant CPU computing resources.

[0052] For example, in the scenario where real-time CAN data analysis is required, in certain industrial automation applications, the master device 110 needs to parse sensor data from the CAN bus in real time. The amount of such data is large and updated frequently, and the analysis task is required to be completed within milliseconds. For example, the master device determines to assign the analysis task to the leaf device 130-1 (for example, 130-1 has abundant GPU resources) by analyzing the characteristics of the data stream, so as to utilize its powerful GPU resources to quickly process high-frequency data streams.

[0053] For example, in a scenario where multi-folder data parsing is required, assume that the data to be parsed is stored in a folder containing multiple subfolders, each of which contains protocol data from different input sources; its parsing requirements require the system to be able to parse all files in these folders at the same time and merge the parsing results; for example, the main device 110 allocates parsing tasks according to the storage path and computing power of each leaf device, so that each device processes the data of one or more subfolders, and finally summarizes the results to the main device for subsequent analysis.

[0054] For example, in the scenario of heterogeneous protocol parsing requirements for large data sets, when the data to be parsed contains data from multiple heterogeneous protocols (such as USB, serial port and EPA protocols), the parsing requirements need to ensure that the data of each protocol can be parsed independently and accurately; for example, the main device 110 first evaluates the protocol type and amount of the data to be parsed, and determines the corresponding parsing requirements, and then allocates parsing tasks based on the optimization capabilities of the leaf devices for specific protocols, such as making the leaf device 130-2 responsible for USB data parsing and the leaf device 130-3 responsible for EPA protocol data parsing, thereby improving the overall parsing efficiency.

[0055] For example, in a scenario where complex data structure parsing is required, such as in some applications, the data to be parsed may contain complex data structures, such as nested JSON formats or XML documents; such parsing requirements require the system to be able to handle hierarchical structures and data dependencies; for example, the main device 110 analyzes the structure of the data to be parsed and decides to prioritize the parsing task to a device with corresponding parsing capabilities, and monitors the parsing progress and dependencies during the parsing process to ensure the accuracy and completeness of the data.

[0056] Step 226, at least determine the data path, data volume and data identifier of the data to be parsed that the device participating in the parsing task needs to parse, so as to generate a parsing task for the data to be parsed.

[0057] For example, continuing with the above example, it is determined that the leaf device 130-1 completes the parsing of the data M2, and the leaf device 130-3 completes the parsing of the data M3; the data path, data volume, and data identifier of the data M to be parsed are determined; when the data to be parsed is divided into multiple parts, the data paths, data volumes, and data identifiers of the multiple sub-data to be played are determined respectively, for example, in the above example, the data paths, data volumes, and data identifiers of the data M1, M2, and M3 are determined; thereby generating the parsing task R corresponding to the data M to be parsed M , R M Including parsing task R M1 , R M2 , R M3 The three subtasks correspond to data M1, M2 and M3 respectively.

[0058] In step 230, the master device 110 assigns the parsing task to the devices participating in the parsing task so that the data to be parsed is parsed via the devices participating in the parsing task. The devices participating in the parsing task include the master device and / or at least one leaf device.

[0059] Continuing the above example, the data to be parsed M (parsing task R) will be generated M Includes: sub-analysis task R M1 , R M2 , R M3 ) to allocate sub-analysis tasks R M1 Assign the sub-analysis task R to the master device 110. M2 Assign the sub-analysis task R to the leaf device 130-1. M3 Assign to leaf device 130-3, thus, we know that the participating parsing task R M The devices are main device 110, leaf device 130-1, and leaf device 130-3.

[0060] For example, in another embodiment, based on the data to be parsed Q, a parsing task R is generated. Q , the parsing task R Q Assign to the master device 110 to participate in the analysis task R Q The device is the master device 110. At this time, the data Q to be parsed is completely parsed by the master device 110, and other devices do not participate in the parsing task R. Q .

[0061] The following will combine Figure 4 The method of allocating parsing tasks to the devices participating in the parsing tasks is described in detail and will not be repeated here.

[0062] In the above scheme, the data to be parsed contains multiple data segments, and the multiple data segments come from at least one input source, so that it can support the parsing of mixed data of multiple data sources and multiple protocols; and it can flexibly allocate parsing tasks according to the computing resources in the system, so that the data to be parsed can be parsed in parallel on one device or multiple devices, greatly improving the parsing efficiency, so that multi-source data can be parsed in time, thereby promoting the subsequent data analysis. In addition, the parsed data can be further used for data analysis, for example, by playing the parsed data to show the data parsing results to the user.

[0063] Figure 4 FIG. 3 is a flowchart of a method 300 for allocating parsing tasks to devices participating in parsing tasks according to an embodiment of the present invention. Figure 1 The main device 110 shown in FIG. Figure 7 The method 300 is executed at the electronic device 700 shown. It should be understood that the method 300 may further include additional steps not shown and / or may omit the steps shown, and the scope of the present invention is not limited in this respect.

[0064] In step 310, the master device 110 determines a parsing parameter table that matches the parsing task, where the parsing parameter table includes at least a data type, a data quantity, a parsing order of the data, and a parsing method of the data.

[0065] In some embodiments, the data type includes one or more of the following: bit, byte, integer, character, single-precision floating point, double-precision floating point, long integer, Boolean, array, and structure. For example, bit, byte, integer, character, single-precision floating point, double-precision floating point, long integer, Boolean, array, and structure.

[0066] Regarding the data parsing method, it includes, for example, loop parsing. Regarding loop parsing, loop parsing indicates that based on the data type and data quantity in the parsing parameter table, the data to be parsed is parsed for the number of times indicated by the loop number parameter in the parsing order of the data.

[0067] Regarding the data analysis method, it also includes judgment analysis. Regarding judgment analysis, judgment analysis indicates to make a judgment based on the stage of the current cycle analysis to determine to enter the next state, and the next state includes the following: the next analysis stage; stop the current cycle analysis; and data analysis is completed.

[0068] In step 320 , the master device 110 sends a parsing parameter table to the devices participating in the parsing task, so that each device participating in the parsing task parses the data to be parsed based on the parsing parameter table.

[0069] For ease of understanding, the following further describes the parsing parameter table and loop parsing in conjunction with Table 1 and Table 2. Please refer to Table 1 and Table 2, Table 1 illustrates a parsing parameter table according to an embodiment of the present invention, and Table 2 illustrates field names and field descriptions corresponding to the data types in Table 1.

[0070] Table 1

[0071]

[0072] Table 2

[0073]

[0074] 40 FD 1F D7 41 12 8F 5C 41 21 D7 0A 41 B6 66 66 00 32 00 00 64 65 76 69 63 65 30 32 40 D9 70 A4 40 FD 1F D7 41 0F 5C 29 41 12 8F 5C 41 21 D7 0A 41 B6 66 66 00 32 00 00 64 65 76 69 63 65 30 32 40 D9 70 A4 40 FD 1F D7 41 0F 5C 29 41 12 8F 5C 41 21 D7 0A 41 B6

[0075] Based on Table 1 and Table 2, the device involved in the parsing (eg, the master device 110 ) automatically parses the data to be parsed in the data source 01 , determines to parse in a circular parsing manner, and determines to use 18 bytes as a circular unit.

[0076] Parse the first 18 bytes of the concatenated data according to the offset and data type to obtain parsed data packet 1:

[0077] Temperature: 41 F2 66 66 → 30.2°C;

[0078] Humidity: 00 2D → 45%;

[0079] Device status: 00 01 → Running (True);

[0080] Device ID: 64 65 76 69 63 65 30 31 → device01;

[0081] Data block:

[0082] 3F 9D 70 A4 → 1.23,

[0083] 40 15 1E B8 → 2.34,

[0084] 40 5A 3D 71 → 3.45,

[0085] 40 91 EB 85 → 4.56,

[0086] 40 B5 20 9A → 5.67;

[0087] Packet 1: {

[0088] 'Temperature': 30.2,

[0089] 'Humidity': 45,

[0090] 'Status': True,

[0091] 'DeviceID': 'device01',

[0092] 'DataBlock': [1.23, 2.34, 3.45, 4.56, 5.67]

[0093] }.

[0094] Then, loop through the next 18 bytes to get parsed packet 2:

[0095] Temperature: 41 B6 66 66 → 22.8°C;

[0096] Humidity: 00 32 → 50%;

[0097] Device status: 00 → Stop state (False);

[0098] Device ID: 64 65 76 69 63 65 30 32 → device02;

[0099] Data block:

[0100] 40 D9 70 A4 → 6.78,

[0101] 40 FD 1F D7 → 7.89,

[0102] 41 0F 5C 29 → 8.90,

[0103] 41 12 8F 5C → 9.01,

[0104] 41 21 D7 0A → 10.12;

[0105] Packet 2: {

[0106] 'Temperature': 22.8,

[0107] 'Humidity': 50,

[0108] 'Status': False,

[0109] 'DeviceID': 'device02',

[0110] 'DataBlock': [6.78, 7.89, 8.90, 9.01, 10.12]

[0111] }.

[0112] Based on the above scheme, through the parameter parsing table, the devices involved in the parsing task can perform cyclic parsing based on the parameters in the parameter parsing table for the data to be parsed related to their respective tasks, so as to obtain multiple sets of parsed data. In addition, the parameter parsing table can support customized settings in many aspects such as data type, data quantity, data length, etc., and can also support the combination of multiple data types; therefore, based on the parameter parsing table, the main device and / or leaf device can automatically complete the parsing of multi-source data, greatly improving the efficiency of data parsing.

[0113] In addition, the parameter parsing table is generated, for example, based on predetermined parameters, for example, in response to user operations, by selecting, combining and / or modifying predetermined parameters to be selected, and for example, in response to user operations.

[0114] In some embodiments, Figure 5 The method for loop parsing is illustrated, and judging based on the current loop parsing stage to determine the next state to enter also includes Figure 5 The following steps are illustrated in FIG.

[0115] In step 322, the device executing the parsing task determines the entry condition of the loop parsing.

[0116] In step 324, during the loop parsing process, the device executing the parsing task obtains the calculation results of the numerical values, characters, operators, formulas, encryption methods and / or decryption methods, and performs comparison based on the obtained calculation results to determine whether to enter the next parsing stage, stop the current loop parsing, enter the next loop parsing and / or complete the data parsing.

[0117] In step 326, the device executing the parsing task makes an abnormality determination for the current cycle parsing based on the comparison result.

[0118] For example, in the parameter analysis table, the determination type, the number of determinations, and the operation after a successful determination may be added according to each group of data types to perform a complexity determination combination.

[0119] Regarding judgment types or precondition types, such as: none, first judgment, multiple judgments.

[0120] Judgment basis: equality, greater than, less than, greater than or equal to, less than or equal to, mathematical judgment (such as cos, sin, cot, sec, csc, tan, etc., support for advanced mathematics, etc.), custom formula judgment (such as through custom input formula, addition, subtraction, multiplication and division mathematical symbols, support for advanced mathematics, and judgment based on the numerical value under the referenced group, etc.), encryption and decryption judgment (base64, crc, etc.).

[0121] Regarding the comparison content: numbers (binary, decimal, hexadecimal, etc.), characters, mathematical calculation results (mathematical operators such as cos, sin, cot, sec, csc, tan, support for advanced mathematics, etc.), custom formula results (for example, through custom input formulas, addition, subtraction, multiplication and division mathematical symbols, support for advanced mathematics, such as calculations based on the values ​​under the referenced group, etc.).

[0122] Regarding the number of judgments: 0, 1, 2, 3, etc. or unlimited number of times.

[0123] Regarding the operations after successful judgment: none, end, report warning, report error, classification tag1, classification tag2, classification tag3, classification tag1, etc., execution group 1, execution group 2, execution group 3, etc. (where tag is a mark, which can be set in the customization later to identify or record the operation).

[0124] Regarding the device that performs the parsing task, for example, the master device and / or leaf device assigned to the parsing task. In order to facilitate understanding of the above steps, the following further describes the cyclic parsing, judgment parsing and specific judgment methods in combination with Table 3, Table 4 and Table 5.

[0125] Table 3

[0126]

[0127] As shown in the parameter parsing table in Table 3, the parsing method on the far left contains four execution groups, and the loop order is:

[0128] ① Read the data source and execute group 1 first. When the judgment condition of group 1 is that two adjacent bytes are equal to 0xAABB and 0xCCDD, group 2 is executed after the judgment is successful; if the judgment fails, group 1 is looped until the loop ends;

[0129] ② The precondition of group 2 is 1, that is, group 2 can only be executed after group 1 is judged successfully. When the value of group 2 is greater than 80.54 and the type is equal to Device001, the judgment loop ends, group 3 is executed, and an alarm prompts;

[0130] ③ Group 3 is used to read and parse data in a loop;

[0131] ④ Group 4 is used to determine the end of parsing. When the data 0xDDCC 0xBBAA is read, the entire parsing process is completed.

[0132] The parameter parsing table shown in Table 3 is further described below in conjunction with specific scenarios. For example, the parameter parsing table provided in Table 3 can be used for data flow parsing under a combination of multiple protocols and a relatively complex judgment and parsing process.

[0133] Assume a usage scenario where the data to be parsed contains data of four protocols, protocol A, protocol B, protocol C, and protocol D. The data of each protocol is described as follows.

[0134] ① Protocol A (Group 1): Packet Header

[0135] - Header1: `0xAABB` (2 bytes);

[0136] - Header2: `0xCCDD` (2 bytes).

[0137] ② Protocol B (Group 2), see Table 4 below:

[0138] Table 4

[0139]

[0140] ③ Protocol C (Group 3), see Table 5 below:

[0141] Table 5

[0142]

[0143] ④ Protocol D: Packet tail

[0144] - Header1: `0xDDCC` (2 bytes);

[0145] - Header2: `0xBBAA` (2 bytes).

[0146] 44 65 76 69 63 65 30 30 32 00 00 0003 44 65 76 69 63 65 30 30 32 00 00 0004 44 65 76 69 63 65 30 30 32 00 00 0005 44 65 76 69 63 65 30 30 32 00 00 0006 44 65 76 69 63 65 30 30 32 00 00 0007 44 65 76 69 63 65 30 30 32 00 00 0008 44 65 76 69 63 65 30 30 32

[0147] The following is an illustration of the analysis based on the above parameter analysis table 3:

[0148] 00 00 AA BB 00 00 CC DD | (Execute group 1, identify the packet header, and enter the loop parsing);

[0149] 00 00 42 29 44 65 76 69 63 65 30 30 31 | (Execute 2-1 of group 2, first group: 40.2 and Device001);

[0150] 00 00 42 C7 44 65 76 69 63 65 30 30 31 | (Execute 2-2 of group 2, second group: 99.5 and Device001, enter the next parsing stage);

[0151] 00 00 00 01 44 65 76 69 63 65 30 30 32 | (Execution Group 3, Third Group: 1 and Device002);

[0152] 00 00 00 02 44 65 76 69 63 65 30 30 32 | (fourth group: 2 and Device002);

[0153] 00 00 00 03 44 65 76 69 63 65 30 30 32 | (Fifth group: 3 and Device002);

[0154] 00 00 DD CC 00 00 BB AA | (Recognize the bracket and end the parsing).

[0155] Therefore, the above solution can parse mixed protocol data streams from multiple data sources (such as sensors). Each data packet contains different protocol identifiers (packet header and packet trailer) and supports embedding complex logical judgments in the middle, thereby completing the automatic parsing of complex data.

[0156] Figure 6 FIG. 5 is a flowchart of a method 500 for storing data to be parsed according to an embodiment of the present invention. The method 500 may be performed as follows: Figure 1 The main device 110 shown in FIG. Figure 7 The method 500 is executed at the electronic device 700. It should be understood that the method 500 may further include additional steps not shown and / or may omit the steps shown, and the scope of the present invention is not limited in this respect.

[0157] In step 502, the master device 110 pre-stores the data to be parsed in the master device and / or at least one leaf device based at least on the device type and the parsing target, via predetermined parameters or in response to user operations.

[0158] For example, in the network, the master device and the leaf device distribute and store the target data to be parsed on each machine according to the pre-set master-slave configuration. The master device is the scheduling center of the parsing task, responsible for collecting and analyzing the data storage situation in the current network, and sending parsing tasks to the leaf devices according to different parsing goals.

[0159] For example, continuing with the above example, the data to be parsed can be stored in two ways: first, according to the device type and the parsing target, the required data is uploaded to the corresponding host and leaf nodes in advance to reduce the burden of real-time parsing; second, when the network receives specific target data (such as collected data from certain devices under test), the master and leaf devices automatically store the data related to these targets and maintain a local task data list.

[0160] For example, when allocating parsing tasks, the master device allocates the parsing tasks to relevant devices in the network (including master devices and / or leaf devices) based on the parsing goals; the device that receives the parsing task will check its own data storage to confirm whether the files stored in it meet the requirements of the parsing task; if necessary, the current device can also obtain the required data to be parsed from other devices.

[0161] In step 504, the master device 110 stores the data to be parsed in the public storage device, and the master device and / or the leaf device can access and obtain the data to be parsed stored on the public storage device through the data path.

[0162] For example, one or more public storage devices (such as hard disks, NAS, etc.) are deployed in a local area network. The main device and the leaf device can access the storage device together. After the parsing task is assigned, each device obtains the required parsing data from the public storage device according to the task.

[0163] Specifically, regarding the public storage configuration: the public storage device is used as the central storage point of the local area network to store all data to be parsed, and each device can access the storage device through a unified interface or protocol.

[0164] Regarding task allocation: When the master device completes task allocation, it will pass the parsing task list to at least the leaf devices that need to participate in the parsing task. The parsing task list includes various information such as the data path, data type, and task priority of the data to be parsed.

[0165] Regarding data reading: Each device reads the required files from the public storage device through the network according to the parsing task list, avoiding repeated data transmission between each node and improving the efficiency of data reading. Regarding parsing execution: Each device executes the parsing task locally and sends the results to the master device for aggregation or further processing.

[0166] In step 506, the master device 110 determines a receiving path for the real-time data and a storage location for the real-time data based at least on the device type, the parsing task, and the real-time data source.

[0167] For real-time data sources, both the host and leaf nodes participate in receiving and storing data in the network. The host sends parsing tasks to the leaf nodes based on the current network load and parsing requirements, and the leaf nodes perform data parsing based on the assigned tasks to improve real-time processing capabilities.

[0168] Regarding real-time data reception and storage: The master device and leaf devices simultaneously receive real-time data sources in the network and store the data in their respective local storage in a distributed manner. The data can contain collected data from multiple real-time sources, such as sensors, devices, or systems.

[0169] Regarding task allocation and load balancing: The master device calculates and allocates parsing tasks based on the real-time status of each leaf device (such as CPU usage, storage capacity, etc.); it supports dynamic adjustment of the allocation ratio of parsing tasks to ensure balanced parsing load.

[0170] Regarding data analysis and result aggregation: Leaf devices analyze corresponding data segments according to the tasks assigned by the master device. After the analysis is completed, for example, each device aggregates the results to the master device, which can be further used to integrate, analyze and display the analysis results.

[0171] In order to further understand the solution provided by the embodiment of the present invention, the application scenarios and usage methods of the present invention are further described below.

[0172] The embodiment of the present invention provides two deployment methods, namely, stand-alone version deployment and master-slave version deployment scheme. Different deployment schemes have slight differences in the execution of parsing tasks. The parsing method provided by the embodiment of the present invention can be deployed on the parsing device in the form of software, application, etc.

[0173] First, regarding the stand-alone version deployment plan: For stand-alone deployment, select the stand-alone deployment mode when installing; the stand-alone version does not need to be used to monitor each leaf device node in the local area network. The device installed by the stand-alone version is the main device mentioned in the embodiment of the present invention, and data parsing and parsed data playback are performed on the main device.

[0174] About the data parsing steps in the stand-alone version:

[0175] 1. Determine the data to be parsed (local file, offline file or real-time input source data, or folder, if a folder is selected, continuous parsing is performed) based on predetermined parameters or in response to user operations;

[0176] 2. Generate a parameter parsing table based on the data to be parsed (based on predetermined parameters and / or corresponding to user operations);

[0177] 3. Set the summary device and display area for the parsed data, such as selecting the display window. If multiple sets of data need to be compared, set parameters such as the number of split-screen comparison windows;

[0178] 4. Further set the playback parameters after analyzing the data (e.g. customizing the color of charts, warnings, errors, differences, etc.; customizing the automatic storage of data under abnormal conditions, screenshots, screen captures, etc.; if the user makes additional settings, the parameters are the default parameters);

[0179] 5. Based on the display settings, the parsed data is displayed (the display process can interact with the user, for example, in response to receiving a user click on a play icon or button, data is displayed in the selected display window, such as playing data; it can also respond to user operation instructions such as pause and screenshot).

[0180] Second, regarding the master-slave version deployment plan: when installing the master device, select the master device mode and install the program corresponding to the master device, which can be used to monitor the leaf device nodes connected to each LAN; when installing the leaf device, select the leaf mode and install the function corresponding to the leaf application process, which can connect and bind the master device program; multiple leaf devices can be installed in the LAN to balance the analysis pressure; in addition, both the leaf device and the master device can load online and offline files. When all devices are started (master and leaf devices), the leaf device actively scans the master device in the LAN and automatically connects to the master device.

[0181] About the data parsing steps under the master-slave version:

[0182] 1. At the master device, based on predetermined parameters or in response to user operations, determine the data to be parsed (local files on the master device, local files on a leaf device, offline files, or real-time input source data, or folders. If a folder is selected, continuous parsing is performed);

[0183] 2. At the master device, based on the data to be parsed, a parameter parsing table is generated (based on predetermined parameters and / or corresponding to user operations);

[0184] 3. On the main device, set the summary device and display area for the parsed data, such as selecting a display window. If multiple sets of data need to be compared, set parameters such as the number of split-screen comparison windows;

[0185] 4. On the main device, further set the playback parameters after analyzing the data (e.g. customizing the color of charts, alarms, errors, differences, etc.; customizing the automatic storage of data under abnormal conditions, screenshots, screen captures, etc.; if the user makes additional settings, the parameters are the default parameters);

[0186] 5. At the main device, based on the display settings, the parsed data is displayed (the display process can interact with the user, for example, in response to receiving a user click on a play icon or button, data is displayed in the selected display window such as playing data; it can also respond to user operation instructions such as pause and screenshot).

[0187] In summary, the above scheme has the following technical effects and improvements:

[0188] 1. Supports custom parsing of data to be parsed of various protocol types, and can perform grouped and multi-segment data parsing on the data to be parsed according to the combination of various parsing methods;

[0189] 2. Supports analysis with judgments and custom analysis logic. You can add mathematical operations, conditional judgments, calculations and other methods, multiple comparisons to determine the analysis data, and support nested analysis methods that loop once or multiple times;

[0190] 3. Support multiple analysis methods including online analysis and offline analysis, and support the integration of multi-protocol data, such as TCP, UDP, HTTP, CAN data, etc.

[0191] Figure 7 Schematic diagram of an example electronic device 700 that can be used to implement an embodiment of the present specification is shown. Figure 1 The main device 110 shown can be implemented by an electronic device 700. As shown, the electronic device 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 702 or computer program instructions loaded from a storage unit 708 to a random access memory (RAM) 703. In the random access memory 703, various programs and data required for the operation of the electronic device 700 can also be stored. The central processing unit 701, the read-only memory 702, and the random access memory 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0192] Multiple components in the electronic device 700 are connected to the input / output interface 705, including: an input unit 706, such as a keyboard, a mouse, a microphone, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the electronic device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0193] The various processes and processing described above, such as methods 200 to 500, may be performed by the central processing unit 701. For example, in some embodiments, methods 200 to 500 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 700 via the read-only memory 702 and / or the communication unit 709. When the computer program is loaded into the random access memory 703 and executed by the central processing unit 701, one or more actions of the methods 200 to 500 described above may be performed.

[0194] The present invention relates to methods, apparatuses, systems, electronic devices, computer-readable storage media and / or computer program products. The computer program products may include computer-readable program instructions for executing various aspects of the present invention.

[0195] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the above. The computer-readable storage medium used herein is not to be interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0196] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge computing devices. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0197] The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect through the Internet). In some embodiments, the electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be executed by using the state information of the computer-readable program instructions to personalize and customize the electronic circuit, thereby implementing various aspects of the present invention.

[0198] Various aspects of the present invention are described herein with reference to flow charts and / or step diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each of the steps in the flow charts and / or step diagrams and the combination of the steps in the flow charts and / or step diagrams can be implemented by computer-readable program instructions.

[0199] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more steps in the flowchart and / or step diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more steps in the flowchart and / or step diagram.

[0200] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more steps in the flowchart and / or step diagram.

[0201] The flowchart and step diagram in the accompanying drawings show the possible architecture, functions and operations of the system, method and computer program product according to multiple embodiments of the present invention. In this regard, each square step in the flowchart or step diagram can represent a part of a module, program segment or instruction, and a part of a module, program segment or instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the square steps can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive square steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square step in the step diagram and / or the flowchart, and the combination of the square steps in the step diagram and / or the flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0202] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for parsing data, characterized in that: include: At the main device, based on a predetermined parameter or in response to a user's operation, determining data to be parsed, wherein the data to be parsed includes a plurality of data segments, and the plurality of data segments come from at least one input source; For the determined data to be parsed, generating a parsing task for the data to be parsed based on the computing resources of the master device and / or the computing resources of the leaf device; Allocating a parsing task to a device participating in the parsing task so that the data to be parsed is parsed via the device participating in the parsing task includes: determining a parsing parameter table matching the parsing task, the parsing parameter table at least including a data type, a data quantity, a parsing order of the data, and a parsing method of the data; the parsing method of the data includes a loop parsing and a judgment parsing; the device participating in the parsing task includes a master device and / or at least one leaf device; During the cyclic parsing process, the calculation results of the numerical values, characters, operators, formulas, encryption methods and / or decryption methods are obtained, and a comparison is performed based on the obtained calculation results to determine whether to enter the next state; and In the parameter analysis table, according to the type of each group of data, add the corresponding judgment type, judgment number, and operation after judgment to perform complex judgment combinations; Among them, the judgment types include none, first judgment, and multiple judgments; the judgment basis includes one or more of the following: equality, greater than, less than, greater than or equal to, less than or equal to, mathematical judgment, custom formula judgment, encryption and decryption judgment; the judgment comparison content includes one or more of the following: numbers, characters, mathematical calculation results, and custom formula results; the number of judgments includes 0 times, 1 time, or multiple times; the operations after the judgment include: none, end, alarm, error, classification mark, and number of execution groups.

2. The method according to claim 1, characterized in that Allocating the parsing task to the devices participating in the parsing task so that the data to be parsed is parsed via the devices participating in the parsing task further comprises: A parsing parameter table is sent to the devices participating in the parsing task, so that each device participating in the parsing task parses the data to be parsed based on the parsing parameter table.

3. The method according to claim 2, characterized in that Data types include one or more of the following: Bit, Byte, Int, Char, Float, Double, Long, Boolean, Array, and Structure.

4. The method according to claim 2, characterized in that: The loop parsing instruction is based on the data type and data quantity in the parsing parameter table, and performs parsing for the number of times indicated by the loop number parameter according to the parsing order of the data to be parsed.

5. The method according to claim 4, characterized in that The judgment and analysis instruction is to make a judgment based on the stage of the current loop analysis so as to determine to enter the next state, and the next state includes one of the following: Next parsing stage; Stop the current parsing cycle; and Data parsing completed.

6. The method according to claim 5, characterized in that The decision to enter the next state based on the current loop parsing stage includes: Determine the entry conditions for loop parsing; During the loop parsing process, the calculation results of the numerical values, characters, operators, formulas, encryption methods and / or decryption methods are obtained, and comparison is performed based on the obtained calculation results to determine whether to enter the next parsing stage, stop the current loop parsing, enter the next loop parsing and / or complete the data parsing; Based on the comparison results, an exception judgment is made for the current loop analysis.

7. The method according to claim 1, characterized in that Generating a parsing task about the data to be parsed based on the computing resources of the master device and / or the computing resources of the leaf device includes: Determining candidate devices for the parsing task based on computing resources of the master device and / or available computing resources of the leaf device; Determining a device that participates in the parsing task from the candidate devices based on the data type, data volume, parsing requirements and / or data path of the data to be parsed; and At least the data path, data volume and data identification of the data to be parsed that the device participating in the parsing task needs to parse are determined, so as to generate a parsing task for the data to be parsed.

8. The method according to claim 1, characterized in that Determining the data to be parsed includes determining a data path of the data to be parsed, where the data path indicates a storage path of local data, an acquisition path of offline data, and / or a receiving path of real-time data.

9. The method according to claim 1, characterized in that The data to be parsed includes files and / or folders. When the data to be parsed is a folder, Parsing the data to be parsed includes: continuously parsing multiple files in a folder; and The multiple parsed files are played continuously.

10. The method according to claim 8, characterized in that Also include one or more of the following: Based at least on the device type and the parsing target, the data to be parsed is pre-stored in the main device and / or at least one leaf device via predetermined parameters or in response to a user operation; The data to be parsed is stored in a public storage device, and the master device and / or the leaf device can access and obtain the data to be parsed stored in the public storage device through a data path; Based at least on the device type, the parsing task and the real-time data source, a receiving path for the real-time data and a storage location for the real-time data are determined.

11. A computing device, characterized in that: include: at least one processing unit; At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions, when executed by the at least one processing unit, causing the device to perform the steps of the method according to any one of claims 1 to 10.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a machine, the method according to any one of claims 1 to 10 is implemented.

13. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a machine, the method according to any one of claims 1 to 10 is performed.

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