阅读说明：本技术 数据处理方法、装置、服务器及存储介质 (Data processing method, device, server and storage medium ) 是由 曹智辉 于 2021-09-29 设计创作，主要内容包括：本申请适用于人工智能技术领域,提供了一种数据处理方法、装置、服务器及存储介质,该方法包括：接收用户配置的至少一个操作指令,其中,操作指令包括操作标识及操作标识所指示的操作涉及的字段标识；从各操作指令中解析得到字段标识,存入字段标识集；响应于接收到目标报文,根据字段标识集,对目标报文进行部分报文解析,得到解析数据,以及将解析数据存入目标数据集；针对各操作指令,从目标数据集中、提取相应操作指令中的字段标识所指示的解析数据,以及根据所提取的解析数据执行相应操作指令中的操作标识所指示的操作。本申请只对目标报文进行一次解析即可,比及针对每个操作指令对目标报文解析一次,可以极大地提高数据处理效率。(The application is applicable to the technical field of artificial intelligence, and provides a data processing method, a device, a server and a storage medium, wherein the method comprises the following steps: receiving at least one operation instruction configured by a user, wherein the operation instruction comprises an operation identifier and a field identifier related to an operation indicated by the operation identifier; analyzing each operation instruction to obtain a field identifier, and storing the field identifier into a field identifier set; in response to receiving the target message, according to the field identification set, carrying out partial message analysis on the target message to obtain analysis data, and storing the analysis data into a target data set; for each operation instruction, extracting the analysis data indicated by the field identifier in the corresponding operation instruction from the target data set, and executing the operation indicated by the operation identifier in the corresponding operation instruction according to the extracted analysis data. According to the method and the device, the target message is analyzed only once, and compared with the method and the device for analyzing the target message once aiming at each operation instruction, the data processing efficiency can be greatly improved.)

1. A method of data processing, the method comprising:

receiving at least one operation instruction configured by a user, wherein the operation instruction comprises an operation identifier and a field identifier related to an operation indicated by the operation identifier;

analyzing each operation instruction to obtain a field identifier, and storing the field identifier into a field identifier set;

in response to receiving a target message, according to the field identification set, carrying out partial message analysis on the target message to obtain analysis data, and storing the analysis data into a target data set;

for each operation instruction, extracting the analysis data indicated by the field identifier in the corresponding operation instruction from the target data set, and executing the operation indicated by the operation identifier in the corresponding operation instruction according to the extracted analysis data.

2. The data processing method according to claim 1, wherein the performing partial packet parsing on the target packet according to the field identifier set to obtain parsed data includes:

and if the target message is an extensible markup language message, extracting data adaptive to the corresponding field identifier from the target message aiming at each field identifier in the field identifier set to obtain analysis data aiming at the corresponding field identifier.

3. The data processing method according to claim 1, wherein the number of the target data sets is plural, and the data structure of each target data set includes any one of: array, linked list, queue, stack, set.

4. The data processing method of claim 3, wherein storing the parsed data into a target data set comprises:

determining a field type corresponding to each field identifier in the field identifier set;

determining a data structure corresponding to each field identification according to the field type corresponding to each field identification;

and respectively storing the analytical data aiming at each field identifier into a target data set with a corresponding data structure.

5. The data processing method of claim 3, wherein storing the parsed data into a target data set comprises:

when the analytic data aiming at the target field identification are obtained, the analytic data which are consistent with the target field value are continuously extracted from the obtained analytic data and are marked as target analytic data, and all the target analytic data are stored in the same target data set.

6. The data processing method according to any of claims 1-5, wherein prior to said receiving at least one user-configured operational instruction, the method further comprises:

in response to the detection of the format selection operation, determining the instruction input format selected by the format selection operation as a target input format;

wherein the target input format comprises: expression format, function format.

7. The data processing method of claim 6, wherein the receiving at least one operation instruction configured by a user comprises:

acquiring at least one operation instruction input by a user, and determining whether the corresponding operation instruction is matched with the target input format or not according to characters included in each operation instruction;

and if the operation instructions are matched with the target input format, receiving the at least one operation instruction.

8. A data processing apparatus, characterized in that the apparatus comprises:

the device comprises an instruction receiving unit, a processing unit and a processing unit, wherein the instruction receiving unit is used for receiving at least one operation instruction configured by a user, and the operation instruction comprises an operation identifier and a field identifier related to an operation indicated by the operation identifier;

the identification storage unit is used for analyzing each operation instruction to obtain a field identification and storing the field identification into a field identification set;

the message analysis unit is used for responding to the received target message, performing partial message analysis on the target message according to the field identification set to obtain analysis data, and storing the analysis data into a target data set;

and the operation execution unit is used for extracting the analysis data indicated by the field identification in the corresponding operation instruction from the target data set aiming at each operation instruction, and executing the operation indicated by the operation identification in the corresponding operation instruction according to the extracted analysis data.

9. A server comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 7 are implemented when the computer program is executed by the processor.

10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.

Technical Field

The present application relates to the field of artificial intelligence technologies, and in particular, to a data processing method, an apparatus, a server, and a storage medium.

Background

Extensible Markup Language (XML), which is a Markup for marking electronic documents to have a structure, can be used for marking data and defining data types, is a source Language allowing a user to define the Markup Language of the user, and is widely applied to a plurality of scenarios such as data exchange between different application software.

Open source rule engines, such as DROOLS, typically process data based on a series of operational instructions or rules. In the related art, when a rule engine parses a message, such as an XML message, it is usually necessary to repeatedly parse an original message under different operation instructions or rules to obtain message data corresponding to each operation instruction or rule, and the larger the number of operation instructions or rules in the rule engine is, the more times the original message is repeatedly parsed is, which results in lower data processing efficiency.

Disclosure of Invention

In view of this, embodiments of the present application provide a data processing method, an apparatus, a server, and a storage medium, so as to solve the problem in the related art that the greater the number of operation instructions or rules in a rule engine, the greater the number of times of repeatedly parsing an original packet, and the lower the data processing efficiency.

A first aspect of an embodiment of the present application provides a data processing method, including:

receiving at least one operation instruction configured by a user, wherein the operation instruction comprises an operation identifier and a field identifier related to an operation indicated by the operation identifier;

analyzing each operation instruction to obtain a field identifier, and storing the field identifier into a field identifier set;

in response to receiving the target message, according to the field identification set, carrying out partial message analysis on the target message to obtain analysis data, and storing the analysis data into a target data set;

for each operation instruction, extracting the analysis data indicated by the field identifier in the corresponding operation instruction from the target data set, and executing the operation indicated by the operation identifier in the corresponding operation instruction according to the extracted analysis data.

Further, according to the field identifier set, performing partial message parsing on the target message to obtain parsed data, including:

and if the target message is an extensible markup language message, extracting data adaptive to the corresponding field identifier from the target message aiming at each field identifier in the field identifier set to obtain analysis data aiming at the corresponding field identifier.

Further, the number of the target data sets is plural, and the data structure of each target data set includes any one of: array, linked list, queue, stack, set.

Further, storing the parsed data into a target data set, including:

determining a field type corresponding to each field identifier in the field identifier set;

determining a data structure corresponding to each field identification according to the field type corresponding to each field identification;

and respectively storing the analytical data aiming at each field identifier into a target data set with a corresponding data structure.

Further, storing the parsed data into a target data set, including:

when the analytic data aiming at the target field identification are obtained, the analytic data which are consistent with the target field value are continuously extracted from the obtained analytic data and are marked as target analytic data, and all the target analytic data are stored in the same target data set.

Further, before receiving at least one operation instruction configured by a user, the method further comprises:

in response to the detection of the format selection operation, determining the instruction input format selected by the format selection operation as a target input format;

wherein the target input format comprises: expression format, function format.

Further, receiving at least one operation instruction configured by a user, including:

acquiring at least one operation instruction input by a user, and determining whether the corresponding operation instruction is matched with a target input format or not according to characters included in each operation instruction;

and receiving at least one operation instruction if each operation instruction is matched with the target input format.

A second aspect of an embodiment of the present application provides a data processing apparatus, including:

the device comprises an instruction receiving unit, a processing unit and a processing unit, wherein the instruction receiving unit is used for receiving at least one operation instruction configured by a user, and the operation instruction comprises an operation identifier and a field identifier related to an operation indicated by the operation identifier;

the identification storage unit is used for analyzing each operation instruction to obtain a field identification and storing the field identification into a field identification set;

the message analysis unit is used for responding to the received target message, performing partial message analysis on the target message according to the field identification set to obtain analysis data, and storing the analysis data into a target data set;

and the operation execution unit is used for extracting the analysis data indicated by the field identification in the corresponding operation instruction from the target data set aiming at each operation instruction, and executing the operation indicated by the operation identification in the corresponding operation instruction according to the extracted analysis data.

A third aspect of embodiments of the present application provides a server, which includes a memory, a processor, and a computer program stored in the memory and executable on the server, and when the processor executes the computer program, the processor implements the steps of the data processing method provided in the first aspect.

A fourth aspect of embodiments of the present application provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the data processing method provided by the first aspect.

The data processing method, the data processing device, the server and the storage medium provided by the embodiment of the application have the following beneficial effects: and performing partial message analysis on the target message once based on the field identifications corresponding to all the operation instructions to obtain analysis data aiming at all the operation instructions. After the analysis data for all the operation instructions are obtained, for each operation instruction, the analysis data corresponding to the operation instruction can be directly extracted from the stored analysis data to be operated. According to the method and the device, the target message is analyzed only once, and compared with the method and the device for analyzing the target message once aiming at each operation instruction, the data processing efficiency can be greatly improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the embodiments or the related technical descriptions will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.

Fig. 1 is a flowchart of an implementation of a data processing method provided in an embodiment of the present application;

fig. 2 is a schematic diagram of an XML message according to an embodiment of the present application;

FIG. 3 is a flow chart illustrating an implementation of storing parsed data into a target data set according to an embodiment of the present application;

FIG. 4 is a flow chart of another implementation of a data processing method provided by an embodiment of the present application;

fig. 5 is a block diagram of a data processing apparatus according to an embodiment of the present application;

fig. 6 is a block diagram of a server according to an embodiment of the present disclosure.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

In the embodiment of the application, the data in the target message is processed based on the artificial intelligence technology.

The data processing method according to the embodiment of the present application may be executed by a server. When the data processing method is executed by the server, the execution subject is the server.

It should be noted that the server may include, but is not limited to, a server, a mobile phone, a tablet, a wearable smart device, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a web service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), and a big data and artificial intelligence platform.

Referring to fig. 1, fig. 1 shows a flowchart of an implementation of a data processing method according to an embodiment of the present application, including:

step 101, at least one operation instruction configured by a user is received.

The operation instruction comprises an operation identifier and a field identifier related to the operation indicated by the operation identifier.

The operation instruction is generally information for instructing to operate on certain data or some data. The operation identifier is generally information for indicating an operation. The field identification is generally information for indicating a field.

As an example, the operation instruction may be: select cName from Course sphere score >90and sName ═ Zhang-three. In the operation instruction, the field identifier may be cName, Course, score, and siname. The operation flag may be select. In this example, "zhang san" is a value of a field indicated by the field identification, and for convenience of description, the value of the field may be referred to as a field value.

Here, the execution body may receive at least one operation instruction input by a user through a user terminal.

It should be noted that, a user may pre-configure a plurality of operation instructions, and the configured plurality of operation instructions are used to automatically process subsequently received messages, so that the same operation may be performed on one or more received messages.

In practice, one or more operation instructions configured by the user usually have a sequential execution identifier. For example, if there are three operation instructions, i.e. a, b, and c, the execution sequence id of a may be 1 to indicate that the first one is executed, the execution sequence id of b may be 3 to indicate that the third one is executed, and the execution sequence id of c may be 2 to indicate that the second one is executed.

And 102, analyzing each operation instruction to obtain a field identifier, and storing the field identifier into a field identifier set.

Here, since the operation instruction generally has a fixed format. Therefore, the execution body may parse the field identifier from each operation instruction based on the format of the operation instruction, and store the parsed field identifier into the field identifier set. It is noted that the field identification set is by default an empty set.

In practice, the format of the operation instruction is usually a Structured Query Language (SQL) format. The operation instruction with the SQL format has a simple structure, is convenient to analyze, and is beneficial to improving the data processing efficiency.

And 103, responding to the received target message, performing partial message analysis on the target message according to the field identification set to obtain analysis data, and storing the analysis data into the target data set.

The analysis data is usually data obtained by analysis. The number of target data sets is usually plural.

In practice, the data structure of the target data set may include, but is not limited to, any of: array, linked list, queue, stack, set, etc.

The target message may be various messages. In practice, the target message is typically an XML message.

Here, when receiving the target message, the execution body may parse only a message portion of the target message, which is related to each field identifier in the field identifier set, and then store parsed data obtained by parsing in the target data set.

And 104, extracting the analysis data indicated by the field identification in the corresponding operation instruction from the target data set aiming at each operation instruction, and executing the operation indicated by the operation identification in the corresponding operation instruction according to the extracted analysis data.

Here, for each operation instruction, the execution main body may directly extract, from the target data set, the analysis data corresponding to the field identifier in the operation instruction, and then execute the operation corresponding to the operation instruction using the extracted analysis data.

In this embodiment, a partial message analysis is performed on the target message once based on the field identifiers corresponding to all the operation instructions, so as to obtain analysis data for all the operation instructions. After the analysis data for all the operation instructions are obtained, for each operation instruction, the analysis data corresponding to the operation instruction can be directly extracted from the stored analysis data to be operated. According to the method and the device, the target message is analyzed only once, and compared with the method and the device for analyzing the target message once aiming at each operation instruction, the data processing efficiency can be greatly improved.

In some optional implementation manners of this embodiment, the performing partial packet parsing on the target packet according to the field identifier set to obtain parsing data includes: and if the target message is an XML message, extracting data adaptive to the corresponding field identifier from the target message aiming at each field identifier in the field identifier set to obtain analysis data aiming at the corresponding field identifier.

Here, when the target message is an XML message, the syntax rule of the XML message is simple, and the execution body may extract data adapted to the corresponding field identifier from the target message based on the syntax rule of the XML message. In practice, the data adapted to the corresponding field identifier is usually data with the same attribute as the corresponding field identifier in the XML message. The execution main body may obtain corresponding analysis data for each field identifier, and a set of analysis data corresponding to all the field identifiers is analysis data corresponding to the field identifier set.

Fig. 2 is a schematic diagram of an XML message provided in the embodiment of the present application. As shown in fig. 2, the XML message records the score information of zhang san and lie san. Wherein, the number of Zhang III is 10001, the Chinese score is 90 points, the math score is 100 points, and the English score is 80 points. Li IV is numbered 10002, with a Chinese score of 90.

In some alternative implementations, storing the parsed data into the target data set may include the following steps 301 to 303. Fig. 3 is a flowchart of an implementation of storing parsed data into a target data set according to an embodiment of the present application.

Step 301, determining a field type corresponding to each field identifier in the field identifier set.

The field identifier corresponds to a field type, and generally refers to a type of a field indicated by the field identifier. In practice, each field id may correspond to a field type, for example, the name field id corresponds to a field type of student name, and the Score field id corresponds to a field type of Score.

Here, the execution body may determine the field type corresponding to each field identifier in various ways.

As an example, the execution body may determine the field type corresponding to each field identifier by: for each field identifier, the execution main body may compare the field identifier with preset identifiers in a preset identifier set, and if a preset identifier consistent with the field identifier exists in the preset identifier set, determine a field type corresponding to the consistent preset identifier as the field type corresponding to the field identifier. Wherein, each preset identification in the preset identification set corresponds to a field type.

As another example, the execution body may determine the field type corresponding to each field identifier as follows: for each field identifier, the execution body may search for a field type corresponding to the field identifier from a pre-stored table of correspondence between the field identifier and the field type by using the field identifier. The field id-field type correspondence table is generally used to describe the correspondence between the field id and the field type.

Step 302, determining the data structure corresponding to each field identifier according to the field type corresponding to each field identifier.

Here, for each field identifier, the execution body may use the field type corresponding to the field identifier to find the data structure corresponding to the character type from the preset field type-data structure correspondence table. The field type-data structure correspondence table is generally used to describe the correspondence between the field type and the data structure. The data structures may include, but are not limited to, arrays, linked lists, queues, stacks, collections, and the like.

Step 303, storing the analysis data for each field identifier into a target data set having a corresponding data structure.

Here, for each field-identified parsed data, the execution body may store the field-identified parsed data into a target data set for data storage in a corresponding data structure.

In this implementation manner, when analyzing the target packet, the execution main body may analyze the field type corresponding to each field identifier, and then store the analysis data extracted for the same field type into the data structure of the same type. For example, the extracted parsed data for the student name field type may be stored in a queue and the extracted parsed data for the score field type may be stored in an array. It should be noted that storing the analytic data corresponding to the same field type into the data structure of the same kind and storing the analytic data corresponding to the different field types into the data structure of the different kind can facilitate the subsequent rapid extraction of the data.

In some alternative implementations, storing the parsed data into the target data set may include: when the analytic data aiming at the target field identification are obtained, the analytic data which are consistent with the target field value are continuously extracted from the obtained analytic data and are marked as target analytic data, and all the target analytic data are stored in the same target data set.

The target field id may be a preset field id, such as sName, and the target field value may be a preset field value, such as "zhang san".

For example, the execution agent may store all the parsed data associated with "zhang san" in a linked list. When the analytical data related to Zhang III is required to be acquired subsequently, the data can be directly extracted from the linked list, and the data query efficiency is improved.

Here, the execution agent may store all the analysis data related to the target field value in the same target data set, so that the data query efficiency when the analysis data related to the target field value is subsequently extracted may be further improved, thereby further improving the data processing efficiency.

Referring to fig. 4, fig. 4 is a flowchart illustrating an implementation of a data processing method according to an embodiment of the present disclosure. The data processing method provided by this embodiment may include the following steps:

step 401, in response to detecting the format selection operation, determining the instruction input format selected by the format selection operation as the target input format.

Wherein the target input format comprises: expression format, function format.

The format selection operation is generally an operation for selecting an instruction input format. The format selection operation may be an operation of clicking a preset control. The format selection operation may be one operation or a series of operations.

Here, the execution main body may detect, through the user terminal, a format selection operation performed by the user, and then set, as the target input format, an instruction input format selected by the user through the format selection operation. In this way, the user can input the operation instruction in an instruction input format that the user excels in.

In practice, the target input format may include, but is not limited to, any of: expression format, function format.

As an example, the operation instruction in the expression format may be: select cName from Course sphere score >90and sName ═ Zhang-three. As another example, the operation instructions in the function format may be: select (field, table, condition).

At step 402, at least one operation instruction configured by a user is received.

The operation instruction comprises an operation identifier and a field identifier related to the operation indicated by the operation identifier.

And step 403, analyzing the operation instructions to obtain field identifications, and storing the field identifications into a field identification set.

Step 404, in response to receiving the target message, according to the field identifier set, performing partial message parsing on the target message to obtain parsed data, and storing the parsed data in a target data set.

Step 405, for each operation instruction, extracting the parsing data indicated by the field identifier in the corresponding operation instruction from the target data set, and executing the operation indicated by the operation identifier in the corresponding operation instruction according to the extracted parsing data.

In the present embodiment, the specific operations of steps 402-405 are substantially the same as the operations of steps 101-104 in the embodiment shown in fig. 1, and are not repeated herein.

The embodiment can realize that the user sets the input format of the operation instruction by himself, can realize that the user inputs the operation instruction by adopting the input format which is good in adequacy, and has higher flexibility. In addition, all the operation instructions for one user are in the same format, so that the operation instructions can be quickly analyzed, and the data processing efficiency can be further improved.

In some optional implementations, receiving at least one operation instruction configured by a user includes:

firstly, at least one operation instruction input by a user is obtained, and whether the corresponding operation instruction is matched with a target input format or not is determined according to characters included in each operation instruction.

The adaptation of the operation command to the target input format generally means that the input format of the operation command is the same as the target input format.

Here, the characters included in the operation instruction are generally different in different input formats, for example, the operation instruction input in the function format generally includes a left bracket and a right bracket, and the operation instruction input in the expression format generally does not include a left bracket and a right bracket. Therefore, the execution body may determine whether the input format of the operation instruction is the target input format by analyzing the characters included in the operation instruction.

And then, if each operation instruction is matched with the target input format, receiving at least one operation instruction.

Here, when the input format of each operation instruction is the target input format, the execution main body may receive at least one operation instruction input by the user. On the contrary, if the input format of one operation instruction is wrong, the user can refuse to receive at least one operation instruction input by the user. In addition, when refusing to receive at least one operation instruction input by the user, the execution main body can output prompt information for prompting format input error, so that the user can find problems in time and input the operation instruction by adopting a correct input format.

Referring to fig. 5, fig. 5 is a block diagram of a data processing apparatus 500 according to an embodiment of the present disclosure. The data processing apparatus in this embodiment comprises units for performing the steps in the embodiments corresponding to fig. 1-4. Please refer to fig. 1-4 and the related descriptions of the embodiments corresponding to fig. 1-4. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 5, the data processing apparatus 500 includes:

an instruction receiving unit 501, configured to receive at least one operation instruction configured by a user, where the operation instruction includes an operation identifier and a field identifier related to an operation indicated by the operation identifier;

an identifier storage unit 502, configured to obtain a field identifier through parsing in each operation instruction, and store the field identifier into a field identifier set;

a message parsing unit 503, configured to, in response to receiving the target message, perform partial message parsing on the target message according to the field identifier set to obtain parsed data, and store the parsed data in a target data set;

an operation executing unit 504, configured to, for each operation instruction, extract, from the target data set, the parsing data indicated by the field identifier in the corresponding operation instruction, and execute, according to the extracted parsing data, the operation indicated by the operation identifier in the corresponding operation instruction.

As an embodiment of the present application, the message parsing unit 503 is specifically configured to: and if the target message is an extensible markup language message, extracting data adaptive to the corresponding field identifier from the target message aiming at each field identifier in the field identifier set to obtain analysis data aiming at the corresponding field identifier.

As an embodiment of the present application, there are a plurality of target data sets, and the data structure of each target data set includes any one of: array, linked list, queue, stack, set.

As an embodiment of the present application, the message parsing unit 503 is specifically configured to: determining a field type corresponding to each field identifier in the field identifier set; determining a data structure corresponding to each field identification according to the field type corresponding to each field identification; and respectively storing the analytical data aiming at each field identifier into a target data set with a corresponding data structure.

As an embodiment of the present application, the message parsing unit 503 is specifically configured to: when the analytic data aiming at the target field identification are obtained, the analytic data which are consistent with the target field value are continuously extracted from the obtained analytic data and are marked as target analytic data, and all the target analytic data are stored in the same target data set.

As an embodiment of the present application, the apparatus may further include a format determination unit (not shown in the figure). Wherein the format determination unit is configured to: in response to the detection of the format selection operation, determining the instruction input format selected by the format selection operation as a target input format; wherein the target input format comprises: expression format, function format.

As an embodiment of the present application, the instruction receiving unit 501 is specifically configured to: acquiring at least one operation instruction input by a user, and determining whether the corresponding operation instruction is matched with a target input format or not according to characters included in each operation instruction; and receiving at least one operation instruction if each operation instruction is matched with the target input format.

The apparatus provided in this embodiment performs partial packet parsing on the target packet once based on the field identifiers corresponding to all the operation instructions, so as to obtain parsing data for all the operation instructions. After the analysis data for all the operation instructions are obtained, for each operation instruction, the analysis data corresponding to the operation instruction can be directly extracted from the stored analysis data to be operated. According to the method and the device, the target message is analyzed only once, and compared with the method and the device for analyzing the target message once aiming at each operation instruction, the data processing efficiency can be greatly improved.

It should be understood that, in the structural block diagram of the data processing apparatus shown in fig. 5, each unit is used to execute each step in the embodiment corresponding to fig. 1 to 4, and each step in the embodiment corresponding to fig. 1 to 4 has been explained in detail in the above embodiment, and please refer to the relevant description in the embodiments corresponding to fig. 1 to 4 and fig. 1 to 4 specifically, which is not described again here.

Fig. 6 is a block diagram of a server according to another embodiment of the present application. As shown in fig. 6, the server 600 of this embodiment includes: a processor 601, a memory 602, and a computer program 603, such as a program of a data processing method, stored in the memory 602 and executable on the processor 601. The processor 601 executes the computer program 603 to implement the steps in the embodiments of the data processing methods, such as the steps 101 to 104 shown in fig. 1. Alternatively, when the processor 601 executes the computer program 603, the functions of the units in the embodiment corresponding to fig. 5, for example, the functions of the units 501 to 504 shown in fig. 5, are implemented, and please refer to the related description in the embodiment corresponding to fig. 5, which is not described herein again.

Illustratively, the computer program 603 may be partitioned into one or more units, which are stored in the memory 602 and executed by the processor 601 to complete the present application. One or more elements may be a sequence of computer program instruction segments for describing the execution of the computer program 603 in the server 600, which can perform certain functions. For example, the computer program 603 may be divided into an instruction receiving unit, an identification storage unit, a message parsing unit, and an operation execution unit, and the specific functions of each unit are as described above.

The server may include, but is not limited to, a processor 601, a memory 602. Those skilled in the art will appreciate that fig. 6 is merely an example of a server 600, and does not constitute a limitation on server 600, and may include more or fewer components than shown, or some components in combination, or different components, e.g., a turntable device may also include input output devices, network access devices, buses, etc.

The Processor 601 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The storage 602 may be an internal storage unit of the server 600, such as a hard disk or a memory of the server 600. The memory 602 may also be an external storage device of the server 600, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the server 600. Further, the memory 602 may also include both internal storage units of the server 600 and external storage devices. The memory 602 is used to store computer programs and other programs and data required by the turntable device. The memory 602 may also be used to temporarily store data that has been output or is to be output.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated module, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable storage medium. The computer readable storage medium may be non-volatile or volatile. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like. It should be noted that the computer readable storage medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable storage media that does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.

The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.