CN112019626B - Industrial Internet of things system and data processing method - Google Patents
Industrial Internet of things system and data processing method Download PDFInfo
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- CN112019626B CN112019626B CN202010897319.4A CN202010897319A CN112019626B CN 112019626 B CN112019626 B CN 112019626B CN 202010897319 A CN202010897319 A CN 202010897319A CN 112019626 B CN112019626 B CN 112019626B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
- H04L67/125—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
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- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/10—Detection; Monitoring
Abstract
The invention discloses an industrial Internet of things system and a data processing method. The system includes a controller and a cloud server. The controller is used for acquiring the data of the Internet of things and marking the data of the Internet of things as real-time data or trigger data. The controller is also used for sending the real-time data to the cloud server. The controller is also used for reading the state of the trigger identification bit corresponding to the trigger data. And when the trigger identification bit is in the collection state, the controller is also used for sampling the trigger data and storing the trigger data in a trigger data storage area corresponding to the trigger data. When the state of the trigger identification bit is triggered, the controller is further used for sending data stored in the trigger data storage area corresponding to the trigger data to the cloud server. The cloud server is used for receiving the real-time data and the trigger data. The cloud server is further used for processing the real-time data and the trigger data and generating a processing result. The system can meet the processing requirements of different types of data by respectively processing the different types of data, and simultaneously ensures the integrity and the real-time performance of the data.
Description
Technical Field
The invention relates to the field of industrial network Internet of things, in particular to an industrial Internet of things system and a data processing method.
Background
The industrial internet of things is a new stage which continuously integrates various acquisition or control sensors or controllers with sensing and monitoring capabilities, ubiquitous technologies, mobile communication, intelligent analysis and other technologies into each link of an industrial production process, so that the manufacturing efficiency is greatly improved, the product quality is improved, the product cost and the resource consumption are reduced, and the traditional industry is finally improved to intellectualization.
With the development of the technology of the internet of things, more kinds of equipment are accessed to the internet of things and upload a large amount of data in real time. The traditional internet of things needs a long time for collecting and processing a large amount of data, and the requirements of equipment cannot be met generally. The frequency of generation and variation of different types of data may vary. In the current common application of the internet of things, all generated data are usually collected and analyzed, and in order to meet the real-time performance under the condition of a data volume peak value, a cloud server needs a large amount of operation resources, so that the cost is high, and the resource waste can be caused when the data volume is not large.
Therefore, how to establish an industrial internet of things system which meets the processing requirements of different types of data and ensures the data integrity and real-time performance becomes a key point for technical problems to be solved and research all the time by technical personnel in the field.
Disclosure of Invention
In view of this, embodiments of the present invention provide an industrial internet of things system and a data processing method, so as to solve the problems of poor data integrity and poor real-time performance caused by large data volume and different processing requirements of different types of data.
Therefore, the embodiment of the invention provides the following technical scheme:
the invention provides an industrial Internet of things system in a first aspect, which comprises a controller and a cloud server;
the controller is used for acquiring the data of the Internet of things and marking the data of the Internet of things as real-time data or trigger data;
the controller is further used for sending the real-time data to the cloud server;
the controller is also used for reading the trigger mark bit state corresponding to the trigger data;
when the trigger identification bit is in an acquisition state, the controller is further used for sampling the trigger data and storing the trigger data into a trigger data storage area corresponding to the trigger data;
when the trigger identification bit state is triggered, the controller is further configured to send data stored in a trigger data storage region corresponding to the trigger data to the cloud server;
the cloud server is used for receiving the real-time data and the trigger data;
the cloud server is further used for processing the real-time data and the trigger data and generating a processing result.
Further, the cloud server comprises a collector, a timing analysis module, a trigger analysis module, a mapping layer and an application module;
the collector is used for receiving the real-time data and the trigger data;
the collector is also used for sending the real-time data to the timing analysis module;
the timing analysis module is used for analyzing the real-time data according to a set time interval and storing the analyzed data into the mapping layer;
the collector is also used for sending the trigger data to the trigger analysis module;
the trigger analysis module is used for analyzing the trigger data and storing the analyzed trigger data into the mapping layer;
the application module is used for acquiring data from the mapping layer and generating a processing result.
Further, the controller sends the real-time data and the trigger data to the cloud server according to a set data frame format;
the set data frame format includes the following information:
controller identification, frame data designation, length, data type, data content, and CRC check.
Further, when the data sent to the cloud server by the controller is real-time data, the data content includes: frame timestamp, data length, variable group 1 data, variable group 2 data … … variable group N data;
wherein N is a positive integer; the variable group N data represents the continuous I/O data of the Nth group of variables of the same type; the variable group N data includes the following information:
the I/O point type of the Nth group of data, the offset of the Nth group of data, the number of point positions in the Nth group of data, the quality label of the Nth group of data and the variable value of the Nth group of data.
Further, when the data sent to the cloud server by the controller is trigger data, the data content includes: frame time stamp, data length, variable data;
wherein the variable data includes the following information:
I/O point type, offset, number of included time points, time resolution unit, time resolution, quality label of all time points, variable value of all time points.
Further, the timing module comprises a mapping layer;
the timing module is further used for storing the quality label of the Nth group of data and the variable value of the Nth group of data into the corresponding storage area of the mapping layer according to the type of the Nth group of data I/O points and the offset of the Nth group of data and covering original data.
The second aspect of the invention provides an industrial Internet of things data processing method, which comprises the following steps:
collecting internet of things data;
marking the data of the Internet of things as real-time data or trigger data;
sending the real-time data to a cloud server;
when the trigger identification bit state corresponding to the trigger data is acquisition, sampling the trigger data and storing the trigger data in a trigger data storage area corresponding to the trigger data;
when the trigger identification bit state is triggered, sending data stored in a trigger data storage region corresponding to the trigger data to the cloud server;
and processing the real-time data and the trigger data and generating a processing result.
Further, before sampling the trigger data and storing the trigger data in a trigger data storage area corresponding to the trigger data, the method further comprises the following steps:
judging whether a trigger data storage area corresponding to the trigger data is empty or not to obtain a first judgment result;
and if the first judgment result is yes, recording the current time as the starting time to a trigger data storage area corresponding to the trigger data.
Further, when the status of the trigger flag bit is triggered, the method further includes:
sending the start time to the cloud server;
and emptying the trigger data storage area corresponding to the trigger data.
Further, the step of analyzing the trigger data and generating a processing result comprises the following steps:
sending the trigger data to a trigger analysis module;
writing the trigger data into a buffer;
analyzing the trigger data, and storing the analyzed trigger data into the mapping layer;
and acquiring data from the mapping layer and generating a processing result.
The technical scheme of the embodiment of the invention has the following advantages:
the invention provides an industrial Internet of things system, which is characterized in that the data of the Internet of things is marked as real-time data and trigger data, and the real-time data and the trigger data are respectively processed, so that the processing requirements of different types of data can be met, the data storage capacity of the system is reduced, and the data integrity and the real-time performance are ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a block diagram of an industrial internet of things system according to an embodiment of the present invention.
Fig. 2 is a flowchart of an industrial internet of things data processing method according to an embodiment of the invention.
FIG. 3 is a flow chart of the controller process according to the embodiment of the present invention.
Fig. 4 is a flow chart of server processing according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description of the present application, it is to be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the present application and for simplicity in description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated in a particular manner, and are not to be construed as limiting the present application. Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
In the description of the present application, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; may be mechanically connected, may be electrically connected or may be in communication with each other; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.
In this application, unless expressly stated or limited otherwise, the first feature "on" or "under" the second feature may comprise direct contact of the first and second features, or may comprise contact of the first and second features not directly but through another feature in between. Also, the first feature being "on," "above" and "over" the second feature includes the first feature being directly on and obliquely above the second feature, or merely indicating that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature includes the first feature being directly under and obliquely below the second feature, or simply meaning that the first feature is at a lesser elevation than the second feature.
The following disclosure provides many different embodiments or examples for implementing different features of the application. In order to simplify the disclosure of the present application, specific example components and arrangements are described below. Of course, they are merely examples and are not intended to limit the present application. Moreover, the present application may repeat reference numerals and/or letters in the various examples, such repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. In addition, examples of various specific processes and materials are provided herein, but one of ordinary skill in the art may recognize applications of other processes and/or use of other materials.
Fig. 1 is a block diagram of an industrial internet of things system according to an embodiment of the present invention. As shown in fig. 1, the present invention provides an industrial internet of things system, which includes a controller 11 and a cloud server 12. The controller 11 is configured to obtain data of the internet of things, and mark the data of the internet of things as real-time data or trigger data. The controller 11 is also configured to send real-time data to the cloud server 12. The controller 11 is further configured to read a trigger flag bit state corresponding to the trigger data. When the trigger flag is in the collection state, the controller 11 is further configured to sample the trigger data and store the sample in the trigger data storage area corresponding to the trigger data. When the trigger flag state is a trigger, the controller 11 is further configured to send data stored in a trigger data storage area corresponding to the trigger data to the cloud server 12. The cloud server 12 is configured to receive real-time data and trigger data. The cloud server 12 is further configured to process the real-time data and the trigger data and generate a processing result.
In this embodiment, the Controller 11 is preferably a small Programmable Logic Controller (PLC) 11, and is configured to collect data of an internet of things, communicate with the cloud server 12, and perform edge computing. The controller 11 is preferably a plurality of controllers, each connected to a device or sensor, for collecting data. The controller 11 acts as an edge computing gateway and determines and marks the data type in a programmed or configured manner. The controller 11 preferably marks data that needs to be permanently saved as trigger data and data that needs to be updated in real time as real-time data. For data labeled as "real-time data" type, the controller 11 continuously sends data frames to the cloud server 12 at configured time intervals. For the I/O point data marked as "trigger data" type, the controller 11 reads the trigger flag bit state corresponding to each I/O point in a loop. When the trigger identifier of a point location is in the acquisition state, the controller 11 will continuously sample the point location according to the configured sampling interval (time resolution), and store the acquired data in the trigger data storage area corresponding to the I/O point. When the trigger mark bit state of the point location is triggered, the controller 11 reads out all data from the trigger data storage area corresponding to the I/O point, sends the data to the cloud server 12, then clears the trigger data storage area, and resets the trigger mark. The cloud server 12 parses the received real-time data and trigger data and provides the parsed data to the application module 125. The application module 125 preferably includes an alarm module and a statistical analysis module.
Compared with the prior art, the method and the device have the advantages that the data of the Internet of things are marked as the real-time data and the trigger data, and the real-time data and the trigger data are respectively processed, so that the processing requirements of different types of data can be met, the data storage capacity of a system is reduced, and the data integrity and the real-time performance are guaranteed.
In a specific embodiment, cloud server 12 includes collector 121, timing resolution module 122, trigger resolution module 123, mapping layer 124, and application module 125. The collector 121 is used for receiving real-time data and trigger data. The collector 121 is further configured to send real-time data to the timing analysis module 122. The timing analysis module 122 is configured to analyze the real-time data according to a set time interval and store the analyzed data in the mapping layer 124. The collector 121 is further configured to send the trigger data to the trigger parsing module 123. The trigger parsing module 123 is configured to parse the trigger data, and store the parsed trigger data in the mapping layer 124. The application module 125 is used to obtain data from the mapping layer 124 and generate processing results.
In this embodiment, after receiving the real-time data, the collector 121 on the cloud server 12 sends the data frame content to the timing analysis module 122, and temporarily stores the quality flag and the data value of each variable group to the corresponding storage area of the mapping layer according to the type and the offset of each group of I/O points, so as to cover the original data. The timing analysis module 122 regularly traverses the storage areas of the point locations of the mapping layer at set minimum intervals, reads and analyzes the latest data of the point locations one by one, and stores the processing result in the mapping layer 124 for the application module 125 to use, for example, to display real-time data and status, store historical data, determine and trigger events such as alarms, perform statistical analysis and calculation, and the like. During traversal, if new data received by the processor exists at each point, the existing data is overwritten. The point location storage area only reserves the latest piece of data for reading processing in the next traversal. The timing analysis module 122 should start each traversal operation as much as possible at a set interval, and start the next traversal after one traversal is finished and the set interval is reached. The time required by traversal depends on the number of point locations and the complexity of analysis operation, so when the point locations have large scale or the operation is complex, and the time of traversal once is greater than or equal to the configured minimum interval, the analysis module will start the next traversal immediately after the end of traversal once. The collector 121 sends the trigger data to a buffer of the trigger data parsing module. The trigger parsing module 123 fetches and parses each packet data in the buffer as soon as possible within the given computational resource limit. The time stamp of each piece of data in each packet is restored according to the start time and the number of time points in each packet, and the processing result is written into the mapping layer 124 one by one for use by the application module 125.
Compared with the prior art, the method reduces the computation amount of the system by processing the latest real-time data at regular time, and improves the real-time property by analyzing the trigger data in real time. Different index requirements such as integrity or instantaneity of different data can be met by fully utilizing operation resources.
In a specific embodiment, the controller sends the real-time data and the trigger data to the cloud server according to a set data frame format. The set data frame format includes the following information. Controller identification, frame data designation, length, data type, data content, and CRC check.
In this embodiment, the data frame for the communication between the controller and the cloud server preferably adopts the following format:
the data content differs depending on the data frame type.
In a specific embodiment, when the data sent to the cloud server by the controller is real-time data, the data content includes: frame timestamp, data length, variable group 1 data, variable group 2 data … … variable group N data. Wherein N is a positive integer; the variable group N data represents the continuous I/O data of the Nth group of variables of the same type; the variable group N data includes the following information. The I/O point type of the Nth group of data, the offset of the Nth group of data, the number of point positions in the Nth group of data, the quality label of the Nth group of data and the variable value of the Nth group of data.
In this embodiment, for data marked as "real-time data" type, the controller continuously sends data frames to the cloud server at configured intervals, where the data content part is preferably in the following format:
| frame time stamp | Data length | Variable group 1 data | .... | Variable group N data |
The variable group N data represents a group of consecutive I/O point data of the same type, preferably in the format:
in a specific embodiment, when the data sent by the controller to the cloud server is the trigger data, the data content includes: frame time stamp, data length, variable data. The variable data includes the following information. I/O point type, offset, number of included time points, time resolution unit, time resolution, quality label of all time points, variable value of all time points.
In this embodiment, when the trigger identifier of the point location is in a trigger state, the controller reads out the start time and all data from the trigger data storage area corresponding to the I/O point, encapsulates the start time and all data, sends the encapsulated start time and all data to the cloud server, then clears the storage area, and resets the trigger identifier. In the data frame sent to the cloud server, the data content part preferably adopts the following format:
| frame time stamp | Data length | Variable data |
Wherein "variable data" represents data that the single I/O variable holds during the trigger being in the acquisition state in the format:
in a particular embodiment, the timing module includes an image layer. The timing module is also used for storing the quality label of the Nth group of data and the variable value of the Nth group of data into the corresponding storage area of the mapping layer according to the type of the Nth group of data I/O points and the offset of the Nth group of data and covering the original data.
Fig. 2 is a flowchart of an industrial internet of things data processing method according to an embodiment of the invention. As shown in fig. 1, the invention further provides an industrial internet of things data processing method, which includes the following steps:
s201: and collecting the data of the Internet of things.
S202: and marking the data of the internet of things as real-time data or trigger data.
S203: and sending the real-time data to a cloud server.
S204: and when the trigger mark bit state corresponding to the trigger data is acquisition, sampling the trigger data and storing the trigger data in a trigger data storage area corresponding to the trigger data.
S205: and when the trigger identification bit state is triggered, sending the data stored in the trigger data storage region corresponding to the trigger data to the cloud server.
S206: and processing the real-time data and the trigger data and generating a processing result.
Compared with the prior art, the method and the device have the advantages that the data of the Internet of things are marked as the real-time data and the trigger data, and the real-time data and the trigger data are respectively processed, so that the processing requirements of different types of data can be met, the data storage capacity of a system is reduced, and the data integrity and the real-time performance are guaranteed.
In a specific embodiment, the step of sampling the trigger data and storing the trigger data in the trigger data storage area corresponding to the trigger data further includes: and judging whether the trigger data storage area corresponding to the trigger data is empty or not to obtain a first judgment result. And if the first judgment result is yes, recording the current time as the starting time to a trigger data storage area corresponding to the trigger data. When the trigger bit state is triggered, the method further comprises the following steps: the start time is sent to the cloud server. And clearing the trigger data storage area corresponding to the trigger data.
In this embodiment, when the trigger identifier of the point location is in the acquisition state, the controller continuously samples the point location according to the configured sampling interval (time resolution), and stores the acquired data in the trigger data storage area corresponding to the I/O point. If the storage area is empty, recording the current time stamp as the starting time and storing the point bit data, otherwise, directly adding new data after the existing point bit data. When the trigger mark of the point location is in a trigger state, the controller reads out the start time and all data from the trigger data storage area corresponding to the I/O point, packages the data and sends the data to the cloud server, then clears the storage area and resets the trigger mark.
In a specific embodiment, the analyzing the trigger data and generating the processing result includes the following steps: and sending the trigger data to a trigger analysis module. The trigger data is written to the buffer. And analyzing the trigger data, and storing the analyzed trigger data into the mapping layer. Data is obtained from the mapping layer and processing results are generated.
FIG. 3 is a flow chart of the controller process according to the embodiment of the present invention. In one specific embodiment, as shown in fig. 3, the process flow of the controller includes the following steps:
s301: data is collected from devices or sensors.
S302: the I/Os are traversed and data types are marked.
S303: and judging whether the I/O point is trigger data or not to obtain a second judgment result.
S304: and if the second judgment result is negative, sending the data to the cloud server according to the configured communication interval.
S305: if the second determination result is yes, the I/O point trigger flag is read.
S306: and judging whether the state of the trigger mark bit is an acquisition state or not to obtain a third judgment result.
S307, if the third judgment result is yes, judging whether the corresponding trigger data storage area is empty or not to obtain a fourth judgment result.
S308: and if the fourth judgment result is yes, recording the current time and point location data into the storage area.
S309: and if the fourth judgment result is no, adding and storing point location data to the storage area.
S310: and if the third judgment result is negative, judging whether the state of the trigger identification bit is a trigger state or not, and obtaining a fifth judgment result.
S311: and if the fifth judgment result is yes, reading the starting time and all the data from the storage area, and sending the data to the cloud server.
S312: and clearing the storage area and resetting the state of the trigger bit.
Fig. 4 is a flow chart of server processing according to an embodiment of the present invention. As shown in fig. 4, in a specific embodiment, the processing flow of the cloud server includes the following steps:
s401: the collector receives the data sent by the controller.
S402: the data content is read.
S403: and judging whether the data is trigger data or not to obtain a sixth judgment result.
S404: and if the sixth judgment result is yes, sending the data content to the trigger analysis module and writing the data content into the buffer area.
S405: and taking out the data from the buffer one by one, and restoring the time stamp of each piece of data according to the starting time and the offset.
S406: and analyzing the data, and writing the data into the mapping layer for use by a subsequent module.
S407: and if the sixth judgment result is negative, sending the data content to the timing analysis module to cover the corresponding area data.
S408: and traversing at regular time, reading the latest data corresponding to the current timestamp, and executing the step S406.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (9)
1. An industrial Internet of things system is characterized by comprising a controller and a cloud server;
the controller is used for acquiring the data of the Internet of things and marking the data of the Internet of things as real-time data or trigger data, wherein the trigger data are data needing to be stored permanently;
the controller is further used for continuously sending the real-time data to the cloud server;
the controller is also used for reading the trigger mark bit state corresponding to the trigger data;
when the trigger identification bit is in an acquisition state, the controller is further used for sampling the trigger data and storing the trigger data into a trigger data storage area corresponding to the trigger data;
when the trigger identification bit state is triggered, the controller is further configured to send data stored in a trigger data storage region corresponding to the trigger data to the cloud server;
the cloud server is used for receiving the real-time data and the trigger data;
the cloud server is further used for processing the real-time data and the trigger data and generating a processing result;
the cloud server comprises a collector, a timing analysis module, a trigger analysis module, a mapping layer and an application module;
the collector is used for receiving the real-time data and the trigger data;
the collector is also used for sending the real-time data to the timing analysis module;
the timing analysis module is used for analyzing the real-time data according to a set time interval and storing the analyzed data into the mapping layer;
the timing analysis module temporarily stores the real-time data to a corresponding storage area of the mapping layer, the timing analysis module regularly traverses the storage areas of all point locations of the mapping layer according to a set interval, reads the latest data of all the point locations one by one and analyzes the latest data, a processing result is stored in the mapping layer for the module to use, during traversal of the timing analysis module, the new data received by all the point location storage areas cover the existing data, and only the latest data is reserved in the point location storage areas for the next time of reading processing of the timing analysis module;
the collector is also used for sending the trigger data to the trigger analysis module;
the trigger analysis module is used for analyzing the trigger data and storing the analyzed trigger data into the mapping layer, and the trigger analysis module analyzes each trigger data as fast as possible within given calculation resource limit;
the application module is used for acquiring data from the mapping layer and generating a processing result.
2. The industrial internet of things system according to claim 1, wherein the controller transmits the real-time data and the trigger data to the cloud server according to a set data frame format;
the set data frame format includes the following information:
controller identification, frame data designation, length, data type, data content, and CRC check.
3. The industrial internet of things system according to claim 2, wherein when the data sent by the controller to the cloud server is real-time data, the data content comprises: frame timestamp, data length, variable group 1 data, variable group 2 data … … variable group N data;
wherein N is a positive integer; the variable group N data represents the continuous I/O data of the Nth group of variables of the same type; the variable group N data includes the following information:
the I/O point type of the Nth group of data, the offset of the Nth group of data, the number of point positions in the Nth group of data, the quality label of the Nth group of data and the variable value of the Nth group of data.
4. The industrial internet of things system according to claim 3, wherein when the data sent by the controller to the cloud server is trigger data, the data content comprises: frame time stamp, data length, variable data;
wherein the variable data includes the following information:
I/O point type, offset, number of included time points, time resolution unit, time resolution, quality label of all time points, variable value of all time points.
5. The industrial internet of things system of claim 3, wherein the timing module comprises an image layer;
the timing module is further used for storing the quality label of the Nth group of data and the variable value of the Nth group of data into the corresponding storage area of the mapping layer according to the type of the Nth group of data I/O points and the offset of the Nth group of data and covering original data.
6. An industrial Internet of things data processing method is characterized by comprising the following steps:
collecting internet of things data;
marking the data of the Internet of things as real-time data or trigger data, wherein the trigger data are data needing to be stored permanently;
continuously sending the real-time data to a cloud server;
when the trigger identification bit state corresponding to the trigger data is acquisition, sampling the trigger data and storing the trigger data in a trigger data storage area corresponding to the trigger data;
when the trigger identification bit state is triggered, sending data stored in a trigger data storage region corresponding to the trigger data to the cloud server;
processing the real-time data and the trigger data and generating a processing result;
temporarily storing the real-time data to a corresponding storage area of the mapping layer, regularly traversing the storage areas of all point locations of the mapping layer according to a set interval, reading and analyzing the latest data of all the point locations one by one, storing a processing result into the mapping layer for a module for use, covering the existing data if new data is received by all the point location storage areas during traversal, and only retaining the latest data in the point location storage areas for reading and processing during the next traversal;
each trigger datum is parsed as quickly as possible within given computational resource constraints.
7. The industrial internet of things data processing method according to claim 6, wherein the step of sampling the trigger data and storing the trigger data in the trigger data storage area corresponding to the trigger data further comprises the following steps:
judging whether a trigger data storage area corresponding to the trigger data is empty or not to obtain a first judgment result;
and if the first judgment result is yes, recording the current time as the starting time to a trigger data storage area corresponding to the trigger data.
8. The industrial internet of things data processing method of claim 7, wherein when the trigger bit state is a trigger, the method further comprises:
sending the start time to the cloud server;
and emptying the trigger data storage area corresponding to the trigger data.
9. The industrial internet of things data processing method according to claim 6, wherein the step of analyzing the trigger data and generating the processing result comprises the following steps:
sending the trigger data to a trigger analysis module;
writing the trigger data into a buffer;
analyzing the trigger data, and storing the analyzed trigger data into the mapping layer;
and acquiring data from the mapping layer and generating a processing result.
Priority Applications (1)
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