CN110677295A - Data processing method, device and system for industrial Internet of things - Google Patents
Data processing method, device and system for industrial Internet of things Download PDFInfo
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- CN110677295A CN110677295A CN201910931439.9A CN201910931439A CN110677295A CN 110677295 A CN110677295 A CN 110677295A CN 201910931439 A CN201910931439 A CN 201910931439A CN 110677295 A CN110677295 A CN 110677295A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H04L41/069—Management of faults, events, alarms or notifications using logs of notifications; Post-processing of notifications
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- H—ELECTRICITY
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- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
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Abstract
The disclosure provides a data processing method for an industrial internet of things. The method comprises the following steps: acquiring operation data of the industrial Internet of things, wherein the operation data is processing data of at least one of multiple stages of data processing of the industrial Internet of things, and the multiple stages comprise a field acquisition stage, a server processing stage, a remote data center processing stage and a storage and tray dropping stage; and outputting the operation condition of the industrial Internet of things based on the operation data. The disclosure also provides a data processing device and system for the industrial Internet of things.
Description
Technical Field
The disclosure relates to a data processing method, device and system for an industrial Internet of things.
Background
Currently, the operation of the industrial internet of things only displays input data and output data of the industrial internet of things. Therefore, when the output data of the industrial internet of things has a problem, the problem of which link appears cannot be quickly located, and the problem is often checked one by one.
Disclosure of Invention
In one aspect of the disclosure, a data processing method for an industrial internet of things is provided. The method comprises the following steps: acquiring operation data of the industrial Internet of things, wherein the operation data is processing data of at least one of multiple stages of data processing of the industrial Internet of things, and the multiple stages comprise a field acquisition stage, a server processing stage, a remote data center processing stage and a storage and tray dropping stage; and outputting the operation condition of the industrial Internet of things based on the operation data.
Optionally, the outputting the operating condition of the industrial internet of things based on the operating data includes: determining whether first data meets a first operating condition, wherein the first data is processing data of the first stage, the first stage is any one stage in the at least one stage, and the first condition is a data processing specification of the first stage; if yes, outputting information representing normal operation of the first stage; if not, outputting information representing that the first stage is not normal.
Optionally, the outputting the operating condition of the industrial internet of things based on the operating data includes outputting an operating log of the at least one stage.
Optionally, the obtaining of the operation data of the industrial internet of things includes continuously obtaining processing data of a second stage within a period of time, where the second stage is any one of the stages except for the storage landing stage. And outputting the operation condition of the industrial Internet of things based on the operation data, wherein the operation condition comprises the statistics of the data output quantity of the processing data of the second stage in the period of time, and the data pressure of the stage after the second stage is output based on the data output quantity.
Optionally, the obtaining of the operation data of the industrial internet of things includes obtaining time information and marking information of second data, where the second data is any one of the operation data, and the marking information is used for marking that the second data is obtained by processing specific original data in input data of the industrial internet of things. The outputting the operation condition of the industrial internet of things based on the operation data comprises outputting evolution process information of the specific original data based on the marking information and the time information.
Optionally, the obtaining the operation data of the industrial internet of things includes setting a data buried point at an output position of the processing data of at least one of the plurality of stages, so as to collect the operation data by using the data buried point.
In another aspect of the disclosure, a data processing device for industrial internet of things is provided. The device comprises an acquisition module and an output module. The acquisition module is used for acquiring the operation data of the industrial Internet of things, the operation data is the processing data of at least one of the multiple stages of data processing of the industrial Internet of things, and the multiple stages comprise a field acquisition stage, a server processing stage, a remote data center processing stage and a storage and landing stage. The output module is used for outputting the operation condition of the industrial Internet of things based on the operation data.
Optionally, the output module is specifically configured to: determining whether first data meets a first operating condition, wherein the first data is processing data of the first stage, the first stage is any one stage in the at least one stage, and the first condition is a data processing specification of the first stage; if yes, outputting information representing normal operation of the first stage; or if not, outputting information representing that the first stage is not normal.
Optionally, the obtaining module is specifically configured to continuously obtain processing data of a second stage within a period of time, where the second stage is any one stage of the plurality of stages except for the storage landing stage. The output module is specifically configured to count a data output amount of the processing data of the second stage in the period of time, and output the data pressure of the stage subsequent to the second stage based on the data output amount.
In another aspect of the disclosure, a data processing system for an industrial internet of things is provided. The data processing system includes a sensor, a processor, and a memory. The sensor is used for collecting data. The memory has stored thereon executable instructions. The instructions, when executed by the processor, cause the processor to perform the data processing method as described above.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
Drawings
For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
fig. 1 schematically shows an application system architecture of a data processing method and apparatus for industrial internet of things according to an embodiment of the present disclosure;
fig. 2 schematically shows a flow chart of a data processing method for industrial internet of things according to an embodiment of the present disclosure;
fig. 3 schematically shows a flow chart of a data processing method for industrial internet of things according to another embodiment of the present disclosure;
fig. 4 schematically shows a flow chart of a data processing method for industrial internet of things according to yet another embodiment of the present disclosure;
fig. 5 schematically shows a flow chart of a data processing method for industrial internet of things according to yet another embodiment of the present disclosure;
fig. 6 schematically shows a flow chart of a data processing method for industrial internet of things according to yet another embodiment of the present disclosure;
fig. 7 schematically illustrates a block diagram of a data processing device for an industrial internet of things, in accordance with an embodiment of the present disclosure; and
fig. 8 schematically illustrates a block diagram of a computer system suitable for implementing a data processing method for the industrial internet of things according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon for use by or in connection with an instruction execution system.
The embodiment of the disclosure provides a data processing method for an industrial Internet of things, and a data processing device and system for the industrial Internet of things. The method comprises the steps of obtaining operation data of the industrial Internet of things, and outputting operation conditions of the industrial Internet of things based on the operation data. The operation data is processing data of at least one stage in multiple stages of industrial internet of things data processing, and the multiple stages comprise a field acquisition stage, a server processing stage, a remote data center processing stage and a storage and landing stage. In this way, the data processing method of the embodiment of the disclosure can analyze and process data of the intermediate link of the industrial internet of things so as to monitor the internal operation process of the industrial internet of things. Therefore, once the operation of the industrial Internet of things has a problem, the problem of the data processing process at which stage can be quickly positioned, and a user is helped to quickly process the problem.
Fig. 1 schematically illustrates an application system architecture 100 of a data processing method and apparatus for industrial internet of things according to an embodiment of the present disclosure.
As shown in fig. 1, the system architecture 100 may include an industrial internet of things 101, a server 102, and a terminal device 103. The server 102 can be in communication connection with the industrial internet of things 101 in a wired or wireless mode, and the terminal device 103 can be in communication connection with the server 102 in a wired or wireless mode.
The data processing phase of the industrial internet of things 101 may include a plurality of phases such as a field acquisition phase, a server processing phase, a remote data center processing phase, and a storage and landing phase. The hardware facilities respectively corresponding to the field acquisition stage, the server processing stage, the remote data center processing stage and the storage and landing stage can be a sensor for acquiring original data of an industrial production line, a factory-side server Agent for performing primary processing on the data acquired by the sensor, a remote server cluster for performing deep processing on the data, a database cluster for storing and landing the data processed by the remote data center and the like.
The server 102 may obtain processing data (i.e., operation data according to the embodiment of the present disclosure) of at least one of the multiple stages of data processing of the industrial internet of things 101, and then process the operation data to obtain an operation status of the industrial internet of things 101. In one embodiment, the server 102 may output the operation status of the industrial internet of things 101 to the terminal device 103 for displaying, so as to help the user monitor and manage the operation status of the industrial internet of things 101 in real time.
The server 102 may execute the data processing method for the industrial internet of things 101 provided by the embodiment of the disclosure to monitor the operating condition of the industrial internet of things 101. Correspondingly, the data processing device for the industrial internet of things 101 provided by the embodiment of the disclosure can also be arranged in the server 102. It is understood that in some embodiments, the server 102 may be part of a remote server cluster of the industrial internet of things 101.
It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
The following describes a data processing method, an apparatus, and a system for an industrial internet of things according to the embodiments of the present disclosure with reference to the system architecture of fig. 1.
Fig. 2 schematically shows a flow chart of a data processing method for the industrial internet of things 101 according to an embodiment of the present disclosure.
As shown in fig. 2, the method may include operation S210 and operation S220.
In operation S210, operation data of the industrial internet of things 101 is obtained, where the operation data is processing data of at least one of multiple stages of data processing of the industrial internet of things 101, and the multiple stages may include, for example, a field acquisition stage, a server processing stage, a remote data center processing stage, and a storage landing stage shown in fig. 1.
In operation S220, the operation condition of the industrial internet of things 101 is output based on the operation data.
According to an embodiment of the present disclosure, a data burial point may be set at an output position of the processing data of at least one stage of the plurality of stages to collect the operation data using the data burial point in operation S210. Alternatively, according to another embodiment of the present disclosure, in operation S210, a subscription protocol may be configured in at least one of the multiple stages, and the operation data may be acquired in a subscription manner.
With reference to the system architecture shown in fig. 1, a detailed implementation of the operation S210 that the server 102 obtains the processing data of each of the multiple stages of the data processing of the industrial internet of things 101 is briefly described as follows.
For the field acquisition phase, the server 102 may obtain data output by the sensors. In the industrial internet of things 101, the sensor can sense physical quantities on an industrial production line through the sensing unit and output various parameters (such as temperature, speed, gas components and the like) to the factory-side server Agent connected with the sensor. According to embodiments of the present disclosure, the server 102 may obtain data output by the sensors for uploading to the factory side server Agent. For example, a data buried point may be set at a position where the sensor outputs data for data acquisition, or the sensor may upload a copy to the server 102 while uploading the output data to the factory-side server Agent through protocol subscription.
In addition, for the field acquisition stage, different sensor data transmission protocols may be different, and therefore the server 102 needs to match the data transmission protocol of the sensor when acquiring the data of the sensor. The data transmission protocol may specify, for example, units of data, acquisition frequency, measurement scale, and the like. When the protocol of the sensor in the industrial internet of things 101 changes, the protocol for reading data from the sensor can be extended in the form of a plug-in, so that the server 102 can acquire the data of the sensor. As can be seen, according to the method of the embodiment of the present disclosure, in the process of acquiring data of the industrial internet of things 101, multiple data transmission protocols can be compatible, and the method can be applied to the industrial internet of things 101 with various transmission protocols.
For the server process phase, the server 102 may obtain data of the process or output in the factory side server Agent. In the industrial internet of things 101, a factory-side server Agent can perform primary processing on data uploaded by sensors, for example, uniformly process various types or formats of data transmitted by the sensors into a format capable of being uniformly processed by a remote data center. Such as unity (e.g., unified as international system of units), binary unity (e.g., decimal or binary), identifying data uniformly by time of acquisition, etc. The factory side server Agent can be a small embedded device deployed near an industrial production line. After the factory-side server Agent performs data processing, the processed data can be transmitted to the remote server cluster. According to an embodiment of the disclosure, the server 102 may obtain data obtained after processing by the factory-side server Agent. For example, a program may be written to set a data embedding point in a final link of the factory-side server Agent in the process of processing data to collect data, or the factory-side server Agent may upload a copy of data to the server 102 when sending the data out in a protocol subscription manner.
The remote data center processing stage can comprise two major links of MQTT message queue telemetry transmission and remote server cluster processing. For the remote data center processing phase, the server 102 may obtain data telemetered by MQTT message queues and/or data resulting from deep processing by a cluster of remote servers. In the remote data center processing stage of the industrial internet of things 101, data of a plurality of factory-side server agents (for example, one factory area may have a plurality of workshops, and each workshop has one Agent) may be summarized. The summary process may be, for example, data cleaning, summing, fitting, percentage calculation, etc. (e.g., calculating product yield, etc.). According to an embodiment of the disclosure, the server 102 may obtain data telemetered by MQTT message queues and/or data resulting from remote server cluster processing. For example, data collection can be performed by setting data embedding points at corresponding data transmission positions through writing programs; alternatively, the MQTT message queue may send a copy of the data transmitted therein to the server 102 by way of the protocol subscription, or may also upload the data to the server 102 when the remote server cluster sends the processed data to the database cluster.
For the data storage phase, server 102 may retrieve data ultimately stored in the database cluster. The data storage phase of the industrial internet of things 101 may be a disk-dropping of data subjected to deep processing by a remote server cluster, for example, a disk-dropping into a database such as HDFS, MySQL, Oricle, Redis, etc. according to a predetermined address. According to the embodiment of the present disclosure, the server 102 may obtain the data of the final tray drop, for example, a data buried point may be set at the corresponding database interface for data acquisition, or the data of the tray drop may be sent to one server 102 while the data of the tray drop is performed by the corresponding database in the protocol subscription.
Therefore, according to the data processing method disclosed by the embodiment of the disclosure, the data of the intermediate link of the industrial internet of things 101 can be analyzed and processed so as to monitor the internal operation process of the industrial internet of things 101.
Fig. 3 schematically shows a flow chart of a data processing method for the industrial internet of things 101 according to another embodiment of the present disclosure.
As shown in fig. 3, the method may include operation S210 and operation S320.
In operation S210, operation data of the industrial internet of things 101 is obtained, where the operation data is processing data of at least one of multiple stages of data processing of the industrial internet of things 101. As described in detail above.
Operation S320 may be a specific embodiment of operation S220. Wherein, in operation S320, the operation log of the at least one stage is output. For example, the operation log is output to the terminal device 103 to be presented to the user through a display interface of the terminal device 103.
Fig. 4 schematically shows a flow chart of a data processing method for the industrial internet of things 101 according to still another embodiment of the present disclosure.
As shown in fig. 4, the method may include operation S210 and operations S421 to S423. Operation S210 as described above, operations S421 to S423 may be a specific embodiment of operation S220. That is, operation S220 may include operations S421 to S423 in the embodiment of the present disclosure.
In operation S210, operation data of the industrial internet of things 101 is obtained, where the operation data is processing data of at least one of multiple stages of data processing of the industrial internet of things 101. As described in detail above.
In operation S421, it is determined whether first data meets a first operating condition, where the first data is processing data of the first phase, the first phase is any one of the at least one phase, and the first condition is a data processing specification (e.g., a data range requirement, a data format requirement, etc.) of the first phase. If yes, operation S422 is performed, and if not, operation S423 is performed.
In operation S422, if the first data satisfies the first operating condition, information indicating that the first stage is operating normally is output.
In operation S423, if the first data does not satisfy the first operating condition, information indicating that the first stage is not operating normally is output.
In one embodiment, only the result of the determination of whether the first stage is operating normally may be output. In another embodiment, the operational details of the first stage may be output, including, for example, what the input data for the first stage is, what the output data is, and/or where the error occurred, etc.
Fig. 5 schematically shows a flow chart of a data processing method for the industrial internet of things 101 according to yet another embodiment of the present disclosure.
As shown in fig. 5, the method may include operations S510, S521 to S522. Operation S510 is an embodiment of operation S210, and operations S521 to S522 are embodiments of operation S220.
In operation S510, processing data of a second phase is continuously acquired for a period of time, wherein the second phase is any one of the plurality of phases except for the storage landing phase.
In operation S521, the data output amount of the second stage of the processed data in the period of time is counted.
In operation S522, the data pressure of the stage subsequent to the second stage is output based on the data output amount. Wherein the data pressure can be characterized by the ratio of the data input quantity to the data output quantity in each data processing stage.
According to the embodiment of the disclosure, the data pressure of the data stream of the industrial internet of things 101 can be monitored in real time. By statistically analyzing the data output quantity of a certain stage in the industrial internet of things 101 in a period of time and prejudging the operating pressure of each stage in the operation of the industrial internet of things 101, a user can be helped to more accurately master the operating condition of the industrial internet of things 101. For example, when the industrial internet of things 101 runs slowly, if the data pressure just indicating the server processing stage on the terminal device 103 is too large, the user can accurately know that the slow running of the industrial internet of things 101 is caused by the data pressure of the factory-side server Agent being too large, but not the factory-side server Agent failing. In this way, the user can select a solution in a targeted manner.
Fig. 6 schematically shows a flow chart of a data processing method for industrial internet of things according to yet another embodiment of the present disclosure.
As shown in fig. 6, the method may include operations S610 and S620. Operation S610 is an embodiment of operation S210, and operation S620 is an embodiment of operation S220.
In operation S610, time information and mark information of second data are obtained, where the second data is any one of the operation data, and the mark information is used to mark that the second data is obtained by processing specific raw data in the input data of the industrial internet of things 101. For example, in the industrial internet of things 101, each data collected by the sensor at the initial stage may be provided with unique identification information, and the identification information may serve as an identity when each data is transferred at the subsequent stages. By tracking the marker information, the evolution process of the corresponding data can be tracked.
In operation S620, evolution process information of the specific raw data is output based on the flag information and the time information. According to the embodiment of the disclosure, various important indexes such as time or state change of data can be extracted according to the identification information of the data, and the data can be tracked by visually displaying the circulation mode of the data. Such as tracking the progress of a stream of data, etc. Thus, if a certain data processing stage in the industrial internet of things 101 does not obtain a result in a half day, the data can be checked forward whether the data to be processed is not transmitted yet.
Fig. 7 schematically illustrates a block diagram of a data processing device 700 for the industrial internet of things 101, according to an embodiment of the disclosure.
As shown in fig. 7, the apparatus 700 may include an obtaining module 710 and an outputting module 720. The obtaining module 710 is configured to obtain operation data of the industrial internet of things 101, where the operation data is processing data of at least one of multiple stages of data processing of the industrial internet of things 101, and the multiple stages include a field acquisition stage, a server processing stage, a remote data center processing stage, and a storage stage. The output module 720 can be used for outputting the operation condition of the industrial internet of things 101 based on the operation data. The apparatus 700 may be used to perform the methods described with reference to fig. 2-6.
According to an embodiment of the present disclosure, the output module 720 may be specifically configured to determine whether first data meets a first operating condition, where the first data is processing data of the first stage, the first stage is any one stage of the at least one stage, and the first condition is a data processing specification of the first stage; if yes, outputting information representing normal operation of the first stage; if not, outputting information representing that the first stage is not normal.
According to an embodiment of the present disclosure, the output module 720 may be specifically configured to: determining whether first data meets a first operating condition, wherein the first data is processing data of the first stage, the first stage is any one stage in the at least one stage, and the first condition is a data processing specification of the first stage; if yes, outputting information representing normal operation of the first stage; or if not, outputting information representing that the first stage is not normal.
According to an embodiment of the present disclosure, the obtaining module 710 may be specifically configured to continuously obtain the processing data of a second phase within a period of time, where the second phase is any one of the multiple phases except for the storage landing phase. Accordingly, the output module 720 may be specifically configured to count the data output amount of the processing data of the second stage in the period of time, and output the data pressure of the stage after the second stage based on the data output amount.
According to an embodiment of the present disclosure, the obtaining module 710 may be specifically configured to obtain time information and marking information of second data, where the second data is any one of the operation data, and the marking information is used to mark that the second data is obtained by processing specific raw data in input data of the industrial internet of things 101. The output module 720 may be specifically configured to output evolution process information of the specific raw data based on the flag information and the time information.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, the obtaining module 710 and the outputting module 720 may be combined and implemented in one module, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the obtaining module 710 and the outputting module 720 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware by any other reasonable manner of integrating or packaging a circuit, or may be implemented in any one of three implementations of software, hardware, and firmware, or in a suitable combination of any of them. Alternatively, at least one of the obtaining module 710 and the output module 720 may be at least partially implemented as computer program modules, which, when executed, may perform corresponding functions.
Fig. 8 schematically illustrates a block diagram of a computer system 800 suitable for implementing a data processing method for the industrial internet of things 101 according to an embodiment of the present disclosure. The computer system 800 illustrated in FIG. 8 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
As shown in fig. 8, computer system 800 includes a processor 810, a computer-readable storage medium 820, and a sensor 830. The computer system 800 may perform a method according to an embodiment of the disclosure. The computer system 800 may be located in the server 102.
In particular, processor 810 may include, for example, a general purpose microprocessor, an instruction set processor and/or related chip set and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), and/or the like. The processor 810 may also include on-board memory for caching purposes. Processor 810 may be a single processing unit or a plurality of processing units for performing different actions of a method flow according to embodiments of the disclosure.
Computer-readable storage medium 820, for example, may be a non-volatile computer-readable storage medium, specific examples including, but not limited to: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and so on.
The computer-readable storage medium 820 may include a computer program 821, which computer program 821 may include code/computer-executable instructions that, when executed by the processor 810, cause the processor 810 to perform a method according to an embodiment of the present disclosure, or any variation thereof.
The computer program 821 may be configured with, for example, computer program code comprising computer program modules. For example, in an example embodiment, code in computer program 821 may include one or more program modules, including for example 821A, modules 821B, … …. It should be noted that the division and number of modules are not fixed, and those skilled in the art may use suitable program modules or program module combinations according to actual situations, and when the program modules are executed by the processor 810, the processor 810 may execute the method according to the embodiment of the present disclosure or any variation thereof.
According to embodiments of the present disclosure, the sensor 830 may be used to collect data, for example, data of at least one of a plurality of stages of data processing of the industrial internet of things 101. The processor 810 may implement various operations in a method according to an embodiment of the present disclosure through interaction with the sensor 830.
According to an embodiment of the present invention, at least one of the obtaining module 710 and the outputting module 720 may be implemented as a computer program module described with reference to fig. 8, which, when executed by the processor 810, may implement the respective operations described above.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the disclosure can be made without conflict, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
While the disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (10)
1. A data processing method for an industrial Internet of things comprises the following steps:
acquiring operation data of the industrial Internet of things, wherein the operation data is processing data of at least one of multiple stages of data processing of the industrial Internet of things, and the multiple stages comprise a field acquisition stage, a server processing stage, a remote data center processing stage and a storage and tray dropping stage; and
and outputting the operation condition of the industrial Internet of things based on the operation data.
2. The method of claim 1, wherein the outputting of the operational status of the industrial internet of things based on the operational data comprises:
determining whether first data meets a first operating condition, wherein the first data is processing data of the first stage, the first stage is any one stage in the at least one stage, and the first condition is a data processing specification of the first stage; and
if yes, outputting information representing normal operation of the first stage; if not, outputting information representing that the first stage is not normal.
3. The method of claim 1, wherein the outputting the operating condition of the industrial internet of things based on the operating data comprises:
and outputting the running log of the at least one stage.
4. The method of claim 1, wherein:
the acquiring of the operation data of the industrial internet of things comprises the following steps: continuously acquiring processing data of a second stage within a period of time, wherein the second stage is any one stage of the plurality of stages except the storage landing stage; and
the outputting the operating condition of the industrial internet of things based on the operating data comprises: counting a data output amount of the processing data of the second stage in the period of time, and outputting data pressure of a stage subsequent to the second stage based on the data output amount.
5. The method of claim 1, wherein:
the operation data of the industrial internet of things is obtained, and the method comprises the following steps: acquiring time information and marking information of second data, wherein the second data is any one of the operation data, and the marking information is used for marking that the second data is obtained by processing specific original data in input data of the industrial Internet of things;
the outputting the operating condition of the industrial internet of things based on the operating data comprises: outputting evolution process information of the specific original data based on the marker information and the time information.
6. The method of claim 1, wherein the obtaining operational data of the industrial internet of things comprises:
setting a data burial point at an output position of the processing data of at least one stage of the plurality of stages to collect the operation data by using the data burial point.
7. A data processing apparatus for industrial internet of things, comprising:
the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring operation data of the industrial Internet of things, the operation data is processing data of at least one stage in a plurality of stages of data processing of the industrial Internet of things, and the plurality of stages comprise a field acquisition stage, a server processing stage, a remote data center processing stage and a storage and landing stage; and
and the output module is used for outputting the operation condition of the industrial Internet of things based on the operation data.
8. The apparatus of claim 7, wherein the output module is specifically configured to:
determining whether first data meets a first operating condition, wherein the first data is processing data of the first stage, the first stage is any one stage in the at least one stage, and the first condition is a data processing specification of the first stage; and
if yes, outputting information representing normal operation of the first stage; if not, outputting information representing that the first stage is not normal.
9. The apparatus of claim 7, wherein:
the obtaining module is specifically configured to continuously obtain processing data of a second stage within a period of time, where the second stage is any one of the stages except the storage landing stage;
the output module is specifically configured to count a data output amount of the processing data of the second stage in the period of time, and output the data pressure of the stage subsequent to the second stage based on the data output amount.
10. A data processing system for an industrial internet of things, comprising:
the sensor is used for collecting data on site;
a processor; and
a memory having stored thereon executable instructions that, when executed by the processor, cause the processor to perform the method of any of claims 1-6.
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