CN112565404A - Data processing method, edge server, center server and medium - Google Patents
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Abstract
The present disclosure provides a data processing method applied to an edge server, including: receiving a data processing request uploaded by industrial equipment; judging whether to perform data modeling according to the application type corresponding to the industrial data; if the industrial data are judged not to be modeled, the industrial data are sent to a central server through message middleware; and if the data modeling is judged, modeling operation is carried out on the industrial data according to a pre-configured training model, a data model is generated and is sent to the central server through the message middleware. The present disclosure also provides an edge server, a central server, and a computer readable medium.
Description
Technical Field
The present disclosure relates to the field of communications technologies, and in particular, to a data processing method, an edge server, a center server, and a computer-readable medium.
Background
In the industrial internet and intelligent transition, industrial data and application thereof play a significant role. In recent years, the edge computing technology is continuously developed and widely applied to architectures such as the internet of vehicles and the internet of things, and the instant response capability of applications in various industries is continuously improved by matching with the rapid low-delay response of the mobile network. There are also numerous connected devices in a plant enterprise that can generate data that has a corresponding data value for industrial applications, but is not fully utilized due to the lack of processing and management mechanisms.
Disclosure of Invention
The present disclosure is directed to at least one of the technical problems occurring in the prior art, and provides a data processing method, an edge server, a central server, and a computer-readable medium.
In order to achieve the above object, in a first aspect, an embodiment of the present disclosure provides a data processing method applied to an edge server, including:
receiving a data processing request uploaded by an industrial device, wherein the data processing request comprises: industrial data;
judging whether to perform data modeling according to the application type corresponding to the industrial data;
if the industrial data is judged not to be modeled, the industrial data is sent to a central server through message middleware;
and if the data modeling is judged, modeling operation is carried out on the industrial data according to a pre-configured training model, a data model is generated and is sent to the central server through the message middleware.
In some embodiments, before the step of receiving industrial data uploaded by an industrial device, the method further comprises:
and analyzing the industrial protocol of the industrial equipment, and recording the data type corresponding to the industrial protocol.
In some embodiments, the step of determining whether to perform data modeling according to the application type corresponding to the industrial data includes:
if the application type corresponding to the industrial data is a first type, judging that data modeling is not performed, wherein the first type comprises the following steps: equipment monitoring class and video image class;
if the application type corresponding to the industrial data is a second type, judging to perform data modeling, wherein the second type comprises: machine vision class and predictive maintenance class.
In a second aspect, an embodiment of the present disclosure further provides a data processing method applied to a central server, including:
responding to industrial data sent by an edge server through message middleware, storing the industrial data, and uploading the industrial data to a public cloud;
responding to a data model sent by the edge server through the message middleware, storing the data model, and uploading model algorithm information corresponding to the data model to a public cloud.
In some embodiments, prior to the uploading the industrial data to a public cloud, further comprising:
desensitizing filtering the industrial data;
before the step of uploading the model algorithm information corresponding to the data model to a public cloud, the method further comprises the following steps:
desensitizing filtering the model algorithm information.
In some embodiments, prior to the uploading the industrial data to a public cloud, further comprising:
encrypting the industrial data;
before the step of uploading the model algorithm information corresponding to the data model to a public cloud, the method further comprises the following steps:
encrypting the model algorithm information.
In a third aspect, an embodiment of the present disclosure further provides an edge server, including:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the data processing method as described in the first aspect above.
In a fourth aspect, an embodiment of the present disclosure further provides a central server, including:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a data processing method as described in the second aspect above.
In a fifth aspect, the disclosed embodiments also provide a computer readable medium, on which a computer program is stored, where the program, when executed by a processor, implements the steps in the data processing method as described in the above first aspect.
In a sixth aspect, the disclosed embodiments also provide a computer readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the steps in the data processing method according to the second aspect.
The present disclosure has the following beneficial effects:
the embodiment of the disclosure provides a data processing method, an edge server, a central server and a computer readable medium, which can judge whether to perform data modeling according to an application type and optimize a data processing flow; the time delay of task request response is reduced and the instantaneity of task processing is guaranteed through the edge server which is correspondingly deployed with the industrial equipment; meanwhile, the edge server uploads the processed data to the central server, and the central server stores the data and uploads the data to the public cloud to realize data storage and sharing.
Drawings
FIG. 1 is a block diagram of a data processing system according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a data processing method provided by an embodiment of the present disclosure;
FIG. 3 is a flow chart of another data processing method provided by the embodiments of the present disclosure;
FIG. 4 is a flowchart illustrating a method of step S2 according to an embodiment of the present disclosure;
FIG. 5 is a flow chart of yet another data processing method provided by the embodiments of the present disclosure;
FIG. 6 is a flowchart illustrating a method of step S5 according to an embodiment of the present disclosure;
fig. 7 is a flowchart illustrating a specific implementation method of step S6 in the embodiment of the present disclosure.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present disclosure, the data processing method, the edge server, the center server and the computer readable medium provided by the present disclosure are described in detail below with reference to the accompanying drawings.
The data processing method, the edge server, the central server and the computer readable medium provided by the disclosure can be used for judging whether to perform data modeling according to the application type and optimizing a data processing flow; the time delay of task request response is reduced and the instantaneity of task processing is guaranteed through the edge server which is correspondingly deployed with the industrial equipment; meanwhile, the edge server uploads the processed data to the central server, and the central server stores the data and uploads the data to the public cloud to realize data storage and sharing.
Fig. 1 is a block diagram of a data processing system according to an embodiment of the present disclosure. As shown in fig. 1, the data processing system includes: n industrial devices and their respective corresponding edge servers, i.e., industrial devices 1 to n and edge servers 1 to n; a message middleware; a central server, or a central server cluster. And the central server is also connected with the public cloud.
Fig. 2 is a flowchart of a data processing method according to an embodiment of the present disclosure. As shown in fig. 2, the method is applied to an edge server, and includes:
and step S1, receiving a data processing request uploaded by the industrial equipment.
Wherein the data processing request includes industrial data. In some embodiments, the industrial equipment is located one-to-one with the edge server.
And step S2, judging whether to carry out data modeling according to the application type corresponding to the industrial data.
The industrial data uploaded by the industrial equipment comes from corresponding applications, for example, an industrial camera can be related to applications such as instrument monitoring, predictive maintenance and machine vision, an industrial sensor is related to applications such as threshold control, and virtual equipment maintenance and equipment twinning are mostly used by virtual reality or augmented reality equipment. Or, in some embodiments, the application type is explicitly an immediate response type, a data synchronization type, and the like, and when the application type is the immediate response type, data modeling is not performed, and when the application type is the data synchronization type, data modeling is performed.
Specifically, the application type corresponding to the industrial data can be determined by the application identifier carried in the data processing request, or the data type of the industrial data, or the application type reported in advance by the industrial device.
In step S2, if it is determined that data modeling is not to be performed, step S3 is executed; if it is determined to perform data modeling, step S4 is performed.
And step S3, sending the industrial data to the central server through message middleware.
After the corresponding data processing process is completed according to the data processing request, the industrial data is sent to the central server through the message middleware. The message middleware is applicable to a supporting software system for message transmission, and can adopt message middleware such as ActiveMQ, RabbitMQ, Kafka, RocktetMQ and the like.
And step S4, carrying out modeling operation on the industrial data according to the pre-configured training model, generating a data model and sending the data model to the central server through the message middleware.
The industrial data is subjected to modeling operation according to a pre-configured training model, and the generated data model is result data. In some embodiments, the method further comprises perfecting the training model through continuous data interaction, and optimizing the workflow of the corresponding application and the industrial equipment.
The embodiment of the disclosure provides a data processing method, which can be used for reducing the time delay of task request response and ensuring the instantaneity of task processing through an edge server deployed corresponding to industrial equipment, and judging whether to perform data modeling or not according to an application type on the edge server side to optimize a data processing flow.
Fig. 3 is a flowchart of another data processing method provided in the embodiment of the present disclosure. As shown in fig. 3, the method is an embodied alternative embodiment based on the method shown in fig. 2. Specifically, the method includes not only steps S1 to S4, but also, before the step of receiving the data processing request uploaded by the industrial device at step S1:
and step S01, analyzing the industrial protocol of the industrial equipment, and recording the data type corresponding to the industrial protocol.
The industrial protocols for analyzing the industrial equipment include Modbus protocols, Message Queue Telemetry Transport (MQTT) protocols and other industrial communication protocols.
The embodiment of the disclosure provides a data processing method, which can be used for completing analysis of an industrial protocol at an edge server, recording a corresponding data type, avoiding an unnecessary data type conversion process during data acquisition, and facilitating operation, maintenance and repair if data problems occur in the industrial equipment and a specific industrial protocol.
Fig. 4 is a flowchart illustrating a specific implementation method of step S2 in the embodiment of the present disclosure. Specifically, as shown in fig. 4, the step S2 of determining whether to perform data modeling according to the application type corresponding to the industrial data includes:
step S201, if the application type corresponding to the industrial data is the first type, determining that data modeling is not performed.
Wherein the first type comprises a device monitoring class and a video image class. Specifically, the data processing request corresponding to the equipment monitoring application does not need to be responded, and the industrial data is simply processed and then directly uploaded to the central server without being fed back to the industrial equipment; the industrial data corresponding to the video image application is the video image data, and the industrial data is correspondingly post-processed according to the data processing request without modeling.
And S202, if the application type corresponding to the industrial data is the second type, judging to perform data modeling.
Wherein the second type includes a machine vision class and a predictive maintenance class. For the second type of application, the corresponding industrial data has no practical significance after being collected, and the result data is obtained after modeling processing is carried out on the industrial data. Specifically, for the predictive maintenance application, after modeling the corresponding industrial data, the generated data model needs to be fed back to the corresponding industrial equipment, where the data model is a predictive analysis result, and the industrial equipment performs corresponding equipment maintenance according to the data model.
The embodiment of the disclosure provides a data processing method, which can be used for determining whether data modeling is needed according to an application type and carrying out instant response or modeling analysis according to application and equipment requirements.
Fig. 5 is a flowchart of another data processing method provided in the embodiment of the present disclosure. As shown in fig. 5, the method is applied to a central server, and includes:
and step S5, responding to the industrial data sent by the edge server through the message middleware, storing the industrial data, and uploading the industrial data to the public cloud.
In some embodiments, the step of storing industrial data comprises: the industrial data is stored in a database or stored persistently based on a Distributed File System (HDFS).
In some embodiments, the public cloud is a public cloud for an industry, a cloud platform that can enable storage and sharing.
And step S6, responding to the data model sent by the edge server through the message middleware, storing the data model, and uploading model algorithm information corresponding to the data model to the public cloud.
The embodiment of the disclosure provides a data processing method, which can be used for storing and uploading processed data and data models to a public cloud by an edge server to realize data storage and sharing.
Fig. 6 is a flowchart illustrating a specific implementation method of step S5 in the embodiment of the present disclosure. Specifically, as shown in fig. 6, in step S5, before the step of uploading the industrial data to the public cloud, the method further includes:
and step S501, desensitizing and filtering the industrial data.
Wherein the desensitization filtering process filters out invalid, sensitive and private fields.
And S502, encrypting the industrial data.
The industrial data is encrypted and uploaded to the public cloud, and other data demanders can obtain the industrial data after obtaining decryption modes through sharing, trading and the like.
Fig. 7 is a flowchart illustrating a specific implementation method of step S6 in the embodiment of the present disclosure. Specifically, as shown in fig. 7, in step S6, before the step of uploading the model algorithm information corresponding to the data model to the public cloud, the method further includes:
and S601, desensitizing and filtering the model algorithm information.
Therein, the desensitization filtering process filters out model algorithm information, in particular, invalid fields in the code information corresponding to the model.
And step S602, encrypting the model algorithm information.
The trained model algorithm information is encrypted and uploaded to the public cloud, and other data model demanders can obtain the data model after obtaining a decryption mode through sharing, trading and the like.
An embodiment of the present disclosure further provides an edge server, including:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a data processing method as any one of the above embodiments applied to an edge server.
The embodiment of the present disclosure further provides a central server, including:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a data processing method as any one of the above embodiments applied to a central server.
The embodiments of the present disclosure also provide a computer readable medium, on which a computer program is stored, where the program, when executed by a processor, implements the steps in any of the data processing methods applied to the edge server as in the above embodiments.
The embodiments of the present disclosure also provide a computer readable medium, on which a computer program is stored, where the program, when executed by a processor, implements the steps in any of the data processing methods applied to the central server as in the above embodiments.
It is to be understood that the above embodiments are merely exemplary embodiments that are employed to illustrate the principles of the present disclosure, and that the present disclosure is not limited thereto. It will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the disclosure, and these are to be considered as the scope of the disclosure.
Claims (10)
1. A data processing method is applied to an edge server and comprises the following steps:
receiving a data processing request uploaded by an industrial device, wherein the data processing request comprises: industrial data;
judging whether to perform data modeling according to the application type corresponding to the industrial data;
if the industrial data is judged not to be modeled, the industrial data is sent to a central server through message middleware;
and if the data modeling is judged, modeling operation is carried out on the industrial data according to a pre-configured training model, a data model is generated and is sent to the central server through the message middleware.
2. The data processing method of claim 1, further comprising, prior to the step of receiving a data processing request uploaded by an industrial device:
and analyzing the industrial protocol of the industrial equipment, and recording the data type corresponding to the industrial protocol.
3. The data processing method according to claim 1, wherein the step of determining whether to perform data modeling according to the application type corresponding to the industrial data comprises:
if the application type corresponding to the industrial data is a first type, judging that data modeling is not performed, wherein the first type comprises the following steps: equipment monitoring class and video image class;
if the application type corresponding to the industrial data is a second type, judging to perform data modeling, wherein the second type comprises: machine vision class and predictive maintenance class.
4. A data processing method is applied to a central server and comprises the following steps:
responding to industrial data sent by an edge server through message middleware, storing the industrial data, and uploading the industrial data to a public cloud;
responding to a data model sent by the edge server through the message middleware, storing the data model, and uploading model algorithm information corresponding to the data model to a public cloud.
5. The data processing method of claim 4, further comprising, prior to the step of uploading the industrial data to a public cloud:
desensitizing filtering the industrial data;
before the step of uploading the model algorithm information corresponding to the data model to a public cloud, the method further comprises the following steps:
desensitizing filtering the model algorithm information.
6. The data processing method of claim 4, further comprising, prior to the step of uploading the industrial data to a public cloud:
encrypting the industrial data;
before the step of uploading the model algorithm information corresponding to the data model to a public cloud, the method further comprises the following steps:
encrypting the model algorithm information.
7. An edge server, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a data processing method as claimed in any one of claims 1 to 3.
8. A central server, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a data processing method as claimed in any one of claims 4-6.
9. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the data processing method of one of claims 1 to 3.
10. A computer-readable medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the data processing method of any one of claims 4 to 6.
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