A data concurrent processing method, data concurrent processing device, equipment and medium

Abstract

The present application provides a data concurrent processing method, data concurrent processing device, equipment and medium, the method comprising: receiving multiple external input data, and performing data message parsing on each external input data to obtain multiple message data streams; combining and matching and mapping each message data stream based on the characteristic information and matching rules of each message data stream to form multiple data stream groups; sending multiple data stream groups to different concurrent transmission paths, so that each concurrent transmission path simultaneously sends the message data stream received in the data stream group to the corresponding data processing unit, so as to realize the concurrent processing of multiple external input data. Through the method and device, the requirements of data on CPU and interface processing capabilities are reduced, the CPU processing load is reduced, the CPU utilization rate is effectively improved, and the problem of limited processing capabilities caused by excessive data volume under high-bandwidth transmission interfaces is solved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data concurrent processing method, data concurrent processing device, equipment and medium. Background Art

[0002] With the development of Internet technology, more and more devices are connected, especially the explosion of video services, which has led to an exponential growth in network data flow.

[0003] In the face of so much high-bandwidth real-time network data, it is necessary to continuously improve the processing capabilities of the CPU and interfaces to cope with the processing requirements of large traffic. This places higher and higher requirements on the CPU performance for directly processing data streams, which makes CPU performance a bottleneck, prone to congestion, packet loss, data omissions, and incomplete data. Summary of the invention

[0004] In view of this, the purpose of the present application is to provide a data concurrent processing method, a data concurrent processing device, equipment and medium, by grouping multiple data streams to obtain multiple data stream groups, and the multiple data stream groups are respectively diverted to different concurrent transmission paths for transmission, so as to send the data streams to different data processing units for concurrent processing, so as to reduce the data requirements on the CPU and interface processing capabilities, reduce the CPU processing load, effectively improve the CPU utilization rate, and solve the problem of limited processing capabilities due to excessive data volume under high-bandwidth transmission interfaces.

[0005] In a first aspect, an embodiment of the present application provides a data concurrent processing method, the data concurrent processing method comprising:

[0006] Receive multiple external input data, and perform data message parsing on each external input data to obtain multiple message data streams;

[0007] Based on the characteristic information and matching rules of each message data flow, each message data flow is combined, matched and mapped to form multiple data flow groups; wherein each data flow group includes multiple message data flows with a mapping relationship;

[0008] The multiple data stream groups are sent to different concurrent transmission paths, so that each concurrent transmission path simultaneously sends the message data stream received in the data stream group to the corresponding data processing unit, so as to realize the concurrent processing of multiple external input data.

[0009] Furthermore, after receiving multiple pieces of external input data, the data concurrent processing method further includes:

[0010] For each piece of external input data, a corresponding timestamp tag is added to the external input data based on the time when the external input data is received.

[0011] Furthermore, after sending the multiple data stream groups to different concurrent transmission paths, the data concurrent processing method further includes:

[0012] For each message data stream in each data stream group, receiving target data after the data processing unit performs data processing on the message data stream;

[0013] The target data corresponding to each piece of external input data is mirrored and restored according to the timestamp label corresponding to each piece of external input data.

[0014] Further, when the matching rule is based on a specific feature value in the feature information, the feature information of each message data flow and the matching rule are combined and matched and mapped to form multiple data flow groups, including:

[0015] For each specific characteristic value, the message data flows including the specific characteristic value in the characteristic information are combined and matched to obtain a data flow group corresponding to the specific characteristic value.

[0016] Further, when the matching rule is based on a combination of different specific feature values ​​for matching, the feature information of each message data flow and the matching rule are combined and matched and mapped to each message data flow to form multiple data flow groups, including:

[0017] Combining different specific eigenvalues ​​to obtain a combined eigenvalue;

[0018] For each combined characteristic value, the message data flows including the combined characteristic value in the characteristic information are combined and matched to obtain a data flow group corresponding to the combined characteristic value.

[0019] Furthermore, the data concurrent processing method also includes:

[0020] The flow of each concurrent transmission channel is monitored in real time, and each concurrent transmission channel is load balanced based on the current flow of each concurrent transmission channel.

[0021] In a second aspect, an embodiment of the present application further provides a data concurrent processing device, the data concurrent processing device comprising:

[0022] A data receiving module, used for receiving multiple external input data, and performing data message parsing on each external input data to obtain multiple message data streams;

[0023] A data distribution module, used for combining, matching and mapping each message data flow based on the characteristic information and matching rules of each message data flow to form multiple data flow groups; wherein each data flow group includes multiple message data flows with a mapping relationship;

[0024] The concurrent transmission module is used to send multiple data stream groups to different concurrent transmission paths, so that each concurrent transmission path simultaneously sends the message data stream received in the data stream group to the corresponding data processing unit, so as to realize concurrent processing of multiple external input data.

[0025] Furthermore, the data concurrent processing device further includes a label adding module. After receiving a plurality of external input data, the label adding module is used to:

[0026] For each piece of external input data, a corresponding timestamp tag is added to the external input data based on the time when the external input data is received.

[0027] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the above-mentioned data concurrency processing method are performed.

[0028] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the data concurrency processing method as described above are executed.

[0029] The embodiments of the present application provide a data concurrent processing method, a data concurrent processing device, a device and a medium. First, multiple external input data are received, and each external input data is parsed as a data message to obtain multiple message data streams; then, each message data stream is combined, matched and mapped based on the characteristic information and matching rules of each message data stream to form multiple data stream groups; wherein each data stream group includes multiple message data streams with a mapping relationship; finally, the multiple data stream groups are sent to different concurrent transmission paths, so that each concurrent transmission path simultaneously sends the message data streams received in the data stream group to the corresponding data processing unit, so as to realize concurrent processing of multiple external input data.

[0030] The present application obtains multiple data stream groups by grouping multiple data streams, and the multiple data stream groups are respectively diverted to different concurrent transmission paths for transmission, so as to send the data streams to different data processing units for concurrent processing, thereby reducing the data requirements on the CPU and interface processing capabilities, reducing the CPU processing load, and effectively improving the CPU utilization rate, thereby solving the problem of limited processing capabilities due to excessive data volume under high-bandwidth transmission interfaces.

[0031] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0033] Figure 1 A flowchart of a data concurrent processing method provided in an embodiment of the present application;

[0034] Figure 2 One of the structural schematic diagrams of a data concurrent processing device provided in an embodiment of the present application;

[0035] Figure 3 A second structural diagram of a data concurrent processing device provided in an embodiment of the present application;

[0036] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0037] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work belongs to the scope of protection of the present application.

[0038] First, the application scenarios to which the present application is applicable are introduced. The present application can be applied in the field of computer technology.

[0039] With the development of Internet technology, more and more devices are connected, especially the explosion of video services, which has led to an exponential growth in network data flow.

[0040] In the face of so much high-bandwidth real-time network data, it is necessary to continuously improve the processing capabilities of the CPU and interfaces to cope with the processing requirements of large traffic. This places higher and higher requirements on the CPU performance for directly processing data streams, which makes CPU performance a bottleneck, prone to congestion, packet loss, data omissions, and incomplete data.

[0041] Based on this, an embodiment of the present application provides a method for concurrent data processing to reduce the data requirements on the CPU and interface processing capabilities, thereby solving the problem of limited processing capabilities due to excessive data volume under a high-bandwidth transmission interface.

[0042] See also Figure 1 , Figure 1 This is a flow chart of a data concurrent processing method provided in an embodiment of the present application. Figure 1 As shown in , the data concurrent processing method provided in the embodiment of the present application includes:

[0043] S101, receiving a plurality of external input data, and performing data message parsing on each piece of external input data to obtain a plurality of message data streams.

[0044] Here, firstly, multiple external input data are received on the network device. For the above step S101, in the specific implementation, firstly, multiple external input data are received, such as receiving a gigabit or 10 gigabit network data stream on a receiving interface. Then, data message parsing is performed on each received external input data to obtain multiple message data streams.

[0045] As an optional embodiment, after receiving multiple external input data, the data concurrent processing method provided by the present application further includes:

[0046] For each piece of external input data, a corresponding timestamp tag is added to the external input data based on the time when the external input data is received.

[0047] For the above steps, in the specific implementation, after receiving multiple external input data, for each external input data, the time when the external input data is received is determined, and a corresponding timestamp label is added to the external input data based on the time when the external input data is received. In this way, the application synchronizes the data diverted based on the Ethernet physical layer signal, and adds a timestamp label on the basis of forming a message to mark the precise time when each external input data is received, which is also a real transmission data sequence on the network.

[0048] S102, combining, matching and mapping each message data flow based on the characteristic information and matching rules of each message data flow to form a plurality of data flow groups.

[0049] Specifically, each data flow group includes a plurality of message data flows having a mapping relationship.

[0050] Here, the characteristic information of the message data flow may be MAC address, EthType type, IP address protocol, UDP data message / TCP session flow, etc., and this application does not make any specific limitation on this.

[0051] With respect to the above step S102, in a specific implementation, each message data stream is combined, matched and mapped based on the characteristic information of each message data stream and the matching rule to form a plurality of data stream groups. Here, each data stream group includes a plurality of message data streams with a mapping relationship. In this way, each message data stream is combined and matched based on a specific matching rule to form a plurality of data stream groups with a mapping relationship.

[0052] As an optional embodiment, for the above step S102, when the matching rule is to match based on a specific feature value in the feature information, the feature information of each message data flow and the matching rule are combined and matched and mapped to form multiple data flow groups, including:

[0053] For each specific characteristic value, the message data flows including the specific characteristic value in the characteristic information are combined and matched to obtain a data flow group corresponding to the specific characteristic value.

[0054] With respect to the above steps, in specific implementation, when the matching rule is to match based on a specific feature value in the feature information, for each specific feature value, the message data flows including the specific feature value in the feature information are combined and matched to obtain a data flow group corresponding to the specific feature value. In this way, there are multiple message data flows in the data flow group that have the same specific feature value in the feature information.

[0055] As an optional embodiment, for the above step S102, when the matching rule is based on a combination of different specific feature values ​​for matching, the feature information of each message data flow and the matching rule are combined and matched and mapped to form multiple data flow groups, including:

[0056] Different specific characteristic values ​​are combined to obtain a combined characteristic value; for each combined characteristic value, the message data streams including the combined characteristic value in the characteristic information are combined and matched to obtain a data stream group corresponding to the combined characteristic value.

[0057] For the above two steps, when the matching rule is based on the combination of different specific feature values, the different specific feature values ​​are combined to obtain a combined feature value. For each combined feature value, the message data streams including the combined feature value in the feature information are combined and matched to obtain a data stream group corresponding to the combined feature value. In this way, there are multiple message data streams in the data stream group that have the same combined feature value in the feature information.

[0058] S103, sending multiple data stream groups to different concurrent transmission paths, so that each concurrent transmission path simultaneously sends the message data stream received in the data stream group to the corresponding data processing unit, so as to realize concurrent processing of multiple external input data.

[0059] For the above step S103, in the specific implementation, multiple data stream groups are sent to different concurrent transmission paths, so that each concurrent transmission path simultaneously sends the message data stream received in the data stream group to the corresponding data processing unit, so as to realize the concurrent processing of multiple external input data. In this way, for multiple groups of mapping relationship data streams with different combinations, each group of data streams is distributed to different concurrent transmission paths, and the corresponding concurrent transmission paths will transmit the data streams to the corresponding data processing units based on the corresponding transmission rate mode. Multiple data processing units process the received message data streams at the same time, so as to realize the concurrent processing of multiple external input data. According to the data concurrent processing method provided by the present application, the high-bandwidth data stream is shunted to form multiple concurrent low-speed data streams, thereby reducing the bandwidth of each concurrent data stream, so as to reduce the requirements of the data on the CPU and interface processing capabilities, and solve the problem of limited processing capabilities due to excessive data volume under the high-bandwidth transmission interface.

[0060] As an optional embodiment, the data concurrent processing method provided in the present application also includes:

[0061] The flow of each concurrent transmission channel is monitored in real time, and each concurrent transmission channel is load balanced based on the current flow of each concurrent transmission channel.

[0062] In the specific implementation of the above steps, when the concurrent transmission path is transmitting data, the flow of each concurrent transmission path is monitored in real time to obtain the current flow of each concurrent transmission path. Based on the current flow of each concurrent transmission path, load balancing is performed on each concurrent transmission path to distribute the flow data in real time.

[0063] As an optional embodiment, after sending the multiple data stream groups to different concurrent transmission paths, the data concurrent processing method further includes:

[0064] For each message data stream in each data stream group, the target data after the data processing unit performs data processing on the message data stream is received; and the target data corresponding to each external input data is mirror-restored according to the timestamp label corresponding to each external input data.

[0065] For the above two steps, in the specific implementation, after sending multiple data stream groups to different concurrent transmission paths, for each message data stream in each data stream group, the target data after the data processing unit performs data processing on the message data stream is received. Since each external input data carries a corresponding timestamp label, the target data corresponding to each external input data can be mirrored and restored according to the timestamp label corresponding to each external input data. In this way, by adding timestamp labels to external input data, the ability to mirror and restore data based on timestamps for separated data streams is supported, historical traffic data can be effectively mirrored and restored, and strict serialization of data streams can be achieved, so that data after multi-channel concurrent processing can be mirrored and restored according to timestamps.

[0066] The data concurrent processing method provided in the embodiment of the present application can be embedded as an embedded module inside the data processing device, and concurrently transmitted to the data processing unit module through the corresponding multi-channel data transmission interface. It can also be used as an independent device, using different grading devices according to the different processing capabilities of the data processing device. When processing high-bandwidth data streams, a cascade diverter device can be used to effectively reduce the speed to meet the data processing capability requirements. It can be divided into a general diversion device or a cascade diversion device. In the cascade device, the timestamp of the primary device will be used in the secondary diversion through the synchronous cascade message to ensure the consistency of time synchronization.

[0067] The data concurrent processing method provided in the embodiment of the present application first receives multiple external input data, and performs data message parsing on each external input data to obtain multiple message data streams; then, based on the characteristic information and matching rules of each message data stream, each message data stream is combined, matched and mapped to form multiple data stream groups; wherein each data stream group includes multiple message data streams with mapping relationships; finally, the multiple data stream groups are sent to different concurrent transmission paths, so that each concurrent transmission path simultaneously sends the message data streams received in the data stream group to the corresponding data processing unit, so as to realize concurrent processing of multiple external input data.

[0068] This application uses traffic data features based on certain rule methods to divert high-bandwidth data streams to form multiple concurrent low-speed data streams, thereby reducing the bandwidth of each concurrent data stream. And through the timestamp label, strict serialization of the data stream can be achieved, and the data after multiple concurrent processing can be restored according to the timestamp mirror. In this way, this application obtains multiple data stream groups by grouping multiple data streams, and the multiple data stream groups are diverted to different concurrent transmission paths for transmission, so as to send the data streams to different data processing units for concurrent processing, so as to reduce the data requirements for the CPU and interface processing capabilities, reduce the CPU processing load, effectively improve the CPU utilization rate, and solve the problem of limited processing capabilities due to excessive data volume under high-bandwidth transmission interfaces.

[0069] See also Figure 2 and Figure 3 , Figure 2 This is one of the structural diagrams of a data concurrent processing device provided in an embodiment of the present application. Figure 3 This is a second structural diagram of a data concurrent processing device provided in an embodiment of the present application. Figure 2 As shown in , the data concurrent processing device 200 provided in the embodiment of the present application includes:

[0070] The data receiving module 201 is used to receive multiple external input data and perform data message parsing on each external input data to obtain multiple message data streams;

[0071] The data distribution module 202 is used to combine, match and map each message data flow based on the characteristic information and matching rules of each message data flow to form multiple data flow groups; wherein each data flow group includes multiple message data flows with a mapping relationship;

[0072] The concurrent transmission module 203 is used to send multiple data stream groups to different concurrent transmission paths, so that each concurrent transmission path simultaneously sends the message data stream received in the data stream group to the corresponding data processing unit, so as to realize concurrent processing of multiple external input data.

[0073] See also Figure 3 The data concurrent processing device 200 further includes a label adding module 204. After receiving a plurality of external input data, the label adding module 204 is used to:

[0074] For each piece of external input data, a corresponding timestamp tag is added to the external input data based on the time when the external input data is received.

[0075] See also Figure 3 The data concurrent processing device 200 further includes a mirror image restoration module 205. After sending the multiple data stream groups to different concurrent transmission paths, the mirror image restoration module 205 is used to:

[0076] For each message data stream in each data stream group, receiving target data after the data processing unit performs data processing on the message data stream;

[0077] The target data corresponding to each piece of external input data is mirrored and restored according to the timestamp label corresponding to each piece of external input data.

[0078] Further, when the matching rule is to match based on a specific feature value in the feature information, the data distribution module 202 is used to perform combined matching mapping on each message data flow based on the feature information of each message data flow and the matching rule to form multiple data flow groups, and the data distribution module 202 is also used to:

[0079] For each specific characteristic value, the message data flows including the specific characteristic value in the characteristic information are combined and matched to obtain a data flow group corresponding to the specific characteristic value.

[0080] Further, when the matching rule is based on a combination of different specific feature values ​​for matching, the data diversion module 202 is used to perform combination matching mapping on each message data stream based on the feature information of each message data stream and the matching rule to form multiple data stream groups, and the data diversion module 202 is also used to:

[0081] Combining different specific eigenvalues ​​to obtain a combined eigenvalue;

[0082] For each combined characteristic value, the message data flows including the combined characteristic value in the characteristic information are combined and matched to obtain a data flow group corresponding to the combined characteristic value.

[0083] See also Figure 3 The data concurrent processing device 200 further includes a flow monitoring module 206, and the flow monitoring module 206 is used to:

[0084] The flow of each concurrent transmission channel is monitored in real time, and each concurrent transmission channel is load balanced based on the current flow of each concurrent transmission channel.

[0085] See also Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown in , the electronic device 400 includes a processor 410 , a memory 420 and a bus 430 .

[0086] The memory 420 stores machine-readable instructions executable by the processor 410. When the electronic device 400 is running, the processor 410 communicates with the memory 420 via the bus 430. When the machine-readable instructions are executed by the processor 410, the above-mentioned Figure 1 The steps of the data concurrent processing method in the method embodiment shown, the specific implementation method can be found in the method embodiment, and will not be repeated here.

[0087] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 The steps of the data concurrent processing method in the method embodiment shown, the specific implementation method can be found in the method embodiment, and will not be repeated here.

[0088] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0089] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0090] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0092] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0093] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A data concurrent processing method, characterized in that: The data concurrent processing method comprises: Receive multiple external input data, and perform data message parsing on each external input data to obtain multiple message data streams; Based on the characteristic information and matching rules of each message data flow, each message data flow is combined, matched and mapped to form multiple data flow groups; wherein each data flow group includes multiple message data flows with a mapping relationship; Sending multiple data stream groups to different concurrent transmission paths, so that each concurrent transmission path simultaneously sends the message data stream received in the data stream group to the corresponding data processing unit, so as to realize concurrent processing of multiple external input data; When the matching rule is to match based on a specific feature value in the feature information, the feature information of each message data flow and the matching rule are combined and matched and mapped to form multiple data flow groups, including: For each specific characteristic value, the message data streams including the specific characteristic value in the characteristic information are combined and matched to obtain a data stream group corresponding to the specific characteristic value; When the matching rule is based on a combination of different specific feature values, the combination matching mapping is performed on each message data flow based on the feature information of each message data flow and the matching rule to form multiple data flow groups, including: Combining different specific eigenvalues ​​to obtain a combined eigenvalue; For each combined characteristic value, the message data flows including the combined characteristic value in the characteristic information are combined and matched to obtain a data flow group corresponding to the combined characteristic value.

2. The data concurrent processing method according to claim 1, characterized in that: After receiving multiple pieces of external input data, the data concurrent processing method further includes: For each piece of external input data, a corresponding timestamp tag is added to the external input data based on the time when the external input data is received.

3. The data concurrent processing method according to claim 2, characterized in that: After sending the multiple data stream groups to different concurrent transmission paths, the data concurrent processing method further includes: For each message data stream in each data stream group, receiving target data after the data processing unit performs data processing on the message data stream; The target data corresponding to each piece of external input data is mirrored and restored according to the timestamp label corresponding to each piece of external input data.

4. The data concurrent processing method according to claim 1, characterized in that: The data concurrent processing method also includes: The flow of each concurrent transmission channel is monitored in real time, and each concurrent transmission channel is load balanced based on the current flow of each concurrent transmission channel.

5. A data concurrent processing device, characterized in that: The data concurrent processing device comprises: A data receiving module, used for receiving multiple external input data, and performing data message parsing on each external input data to obtain multiple message data streams; A data distribution module, used for combining, matching and mapping each message data flow based on the characteristic information and matching rules of each message data flow to form multiple data flow groups; wherein each data flow group includes multiple message data flows with a mapping relationship; A concurrent transmission module, used for sending multiple data stream groups to different concurrent transmission paths, so that each concurrent transmission path simultaneously sends the message data stream received in the data stream group to the corresponding data processing unit, so as to realize concurrent processing of multiple external input data; When the matching rule is to match based on a specific feature value in the feature information, the data distribution module is used to perform combined matching mapping on each message data flow based on the feature information of each message data flow and the matching rule to form multiple data flow groups, and the data distribution module is also used to: For each specific characteristic value, the message data streams including the specific characteristic value in the characteristic information are combined and matched to obtain a data stream group corresponding to the specific characteristic value; When the matching rule is based on a combination of different specific feature values ​​for matching, the data diversion module is used to perform combination matching mapping on each message data flow based on the feature information of each message data flow and the matching rule to form multiple data flow groups, and the data diversion module is also used to: Combining different specific eigenvalues ​​to obtain a combined eigenvalue; For each combined characteristic value, the message data flows including the combined characteristic value in the characteristic information are combined and matched to obtain a data flow group corresponding to the combined characteristic value.

6. The data concurrent processing device according to claim 5, characterized in that: The data concurrent processing device further includes a label adding module. After receiving a plurality of external input data, the label adding module is used to: For each piece of external input data, a corresponding timestamp tag is added to the external input data based on the time when the external input data is received.

7. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the data concurrent processing method as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the concurrent data processing method according to any one of claims 1 to 4 are executed.

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