CN111459104A

CN111459104A - Data tracking method based on industrial Internet and electronic equipment

Info

Publication number: CN111459104A
Application number: CN202010237242.8A
Authority: CN
Inventors: 林细兵
Original assignee: Individual
Current assignee: Changzhou Longyu Tianzheng New Energy Industry Co ltd; Changzhou Tianzheng Industrial Development Co ltd
Priority date: 2020-03-30
Filing date: 2020-03-30
Publication date: 2020-07-28
Anticipated expiration: 2040-03-30
Also published as: CN112180868A; CN112180867A; CN111459104B

Abstract

The application relates to a data tracking method based on an industrial internet and an electronic device. By applying the scheme, data distortion and transmission loss of the target production data during transmission between the industrial intelligent devices can be taken into consideration, and the target production data can be accurately and reliably tracked in the transmission process of the target production data by combining the comparison results of the target production data in the characteristic sets of the data sending end and the data receiving end, so that the second industrial intelligent device receiving actual production data corresponding to the target production data is monitored, whether the second industrial intelligent device has data receiving abnormal behaviors can be further determined, and the abnormal detection of the industrial intelligent devices is further realized by data tracking of the target production data.

Description

Data tracking method based on industrial Internet and electronic equipment

Technical Field

The present application relates to the field of data processing technologies applied to industrial internet, and in particular, to a data tracking method and an electronic device based on industrial internet.

Background

With the development of communication technology, the application range of the industrial internet is more and more extensive. The essence and core of the industrial internet is that the equipment, production lines, factories, suppliers, products and customers are tightly connected and converged through an industrial internet platform. The method can help the manufacturing industry to elongate an industrial chain, and form cross-equipment, cross-system, cross-factory and cross-regional interconnection and intercommunication, thereby improving the efficiency and promoting the intellectualization of the whole manufacturing service system.

In practical application, the industrial intelligent devices cooperate with each other through data transmission and interaction to ensure efficient and smooth operation of the whole industrial production line. However, as the number and types of industrial smart devices increase, it is difficult to accurately and reliably track industrial production data.

Disclosure of Invention

The application provides a data tracking method based on an industrial internet and an electronic device, so as to solve the technical problems in the prior art.

In a first aspect, an industrial internet-based data tracking method is applied to an electronic device communicating with a plurality of industrial intelligent devices communicating with each other, and the electronic device and the plurality of industrial intelligent devices communicating with each other form a data tracking system, and the method includes:

determining whether target production data exist in a first industrial intelligent device in a data tracking system, and acquiring the target production data when the target production data exist in the first industrial intelligent device; the first industrial intelligent device is an industrial intelligent device with a fault in a set time period, the set time period is a time period before the current moment, and the target production data is data of interaction between the first industrial intelligent device and other devices in the data tracking system;

dividing the target production data into a first data set and a second data set; extracting first data features of each first data segment in the first data set one by one to form a first feature set of the first data set; extracting second data features of each second data segment in the second data set one by one, and comparing and correcting the second data features with the first feature set to form a second feature set of the second data set; the first data set and the second data set respectively comprise different types of data segments, the first data set is used for representing data flow direction information of the target production data, and the second data set is used for representing data fluctuation information of the target production data;

acquiring a data transmission path of the first industrial intelligent device for transmitting the target production data; determining at least a plurality of second industrial intelligent devices for receiving the target production data according to the data transmission path;

response information fed back by each second industrial intelligent device when receiving the target production data is obtained from the first industrial intelligent device, and actual production data corresponding to the target production data received by each second industrial intelligent device is determined based on the response information;

dividing the actual production data into a third data set and a fourth data set, and determining a third characteristic set of the third data set and a fourth characteristic set of the fourth data set according to the step of determining that the first characteristic set is similar to the second characteristic set;

for each second industrial intelligent device, judging whether the first feature set is the same as a third feature set corresponding to the industrial intelligent device; if the first characteristic set is different from the third characteristic set, judging that the second industrial intelligent device has an abnormal industrial data receiving behavior; if the first feature set is the same as the third feature set, judging whether the second feature set is the same as a fourth feature set corresponding to the industrial intelligent device, and if the second feature set is different from the fourth feature set, judging that the second industrial intelligent device has an abnormal industrial data receiving behavior.

Optionally, the dividing the target production data into a first data set and a second data set includes:

partitioning the target production data into a plurality of initial data segments based on data segment identifications in the target production data; each group of data segment identifications comprise a start identification and an end identification, the start identification and the end identification in each group of data segment identifications have the same segment weight value, and data between the start identification and the end identification corresponding to the same segment weight is a data segment;

acquiring data thread information of the target production data, wherein the data thread information is used for representing the generation process of the target production data;

judging whether a first data segment type corresponding to the target production data and used for representing data flow information of the target production data and a second data segment type corresponding to data fluctuation information of the target production data exist or not according to the data thread information; when the first data segment category and the second data segment category exist, determining a first data comparison result between each initial data segment of the target production data under the second data segment category and each initial data segment of the target production data under the first data segment category according to the initial data segment of the target production data under the first data segment category and a segmentation weight value of the initial data segment, and transferring the initial data segments of the target production data under the second data segment category, which are identical to the first data comparison result corresponding to the initial data segment under the first data segment category, to the first data segment category; the data comparison result is used for representing data characteristic directions of different initial data segments;

if the target production data has a plurality of initial data segments under the second data segment category, determining a second data comparison result between the initial data segments under the second data segment category according to the initial data segments under the first data segment category and the segmentation weight values of the target production data, and recombining the initial data segments under the second data segment category according to the second data comparison result between the initial data segments to obtain at least part of recombined data segments;

adding a data recombination number to each recombined data segment according to the initial data segment of the target production data in the first data segment category and the segmentation weight value of the initial data segment, and transferring each recombined data segment to the first data segment category according to the size sequence of the data recombination number;

determining a plurality of data segments corresponding to the first data segment category as the first data set and a plurality of data segments corresponding to the second data segment category as the second data set.

Optionally, the step of extracting the first data features of each first data segment in the first data set one by one to form a first feature set of the first data set includes:

determining data direction information of each first data segment, and generating a target information list according to the determined data direction information; the data pointing information is used for representing the continuity of a data segment in a first data set, the target information list is a partitioned area list, each area corresponds to one area identifier, each area identifier corresponds to at least one piece of data pointing information, and the area identifiers of each area in the target information list have a sorting relation from low to high;

determining data coding information of each first data segment, establishing a corresponding relation between each data coding information and the target information list, and generating a feature extraction logic according to the corresponding relation; generating a feature extraction logic according to the corresponding relationship, specifically comprising: converting each first data segment into a characteristic array mode; respectively determining at least one array sorting mode of each characteristic array mode; acquiring an array sorting mode of each first data segment, which is not repeated with each other, to form an array reconstruction mode; mapping each array sorting mode in each array reconstruction mode to the target information list to generate the feature extraction logic;

according to the feature extraction logic, performing feature extraction on the data coding information in each first data segment to obtain a first data feature corresponding to each first data segment; wherein the first data feature is a data feature vector;

and sequencing each first data feature according to the sequencing relation of the region identifier corresponding to the region of the first data segment corresponding to each first data feature in the target information list to obtain a sequencing sequence, and generating a first feature set of the first data set according to the sequencing sequence.

Optionally, the extracting second data features of each second data segment in the second data set one by one and comparing and correcting the second data features with the first feature set to form a second feature set of the second data set, includes:

determining second data characteristics of each second data segment one by one;

determining cosine similarity of each second data feature and each first data feature in the first feature set and determining a cosine similarity mean value corresponding to each second data feature;

correcting a vector value in a second data feature of which the cosine similarity mean value is smaller than a set value according to the feature weight corresponding to each first data feature in the first feature set; and generating a second feature set of the second data set based on the second data features of which the cosine similarity values are greater than or equal to the set value and the second data features of which the modified cosine similarity mean values are smaller than the set value.

Optionally, the obtaining a data transmission path of the first industrial intelligent device for transmitting the target production data includes:

calling an equipment running log of the first industrial intelligent equipment from a preset storage area of the first industrial intelligent equipment; the preset storage area is used for storing the operation condition of the first industrial intelligent device, and the operation condition comprises the device operation log;

determining first log information used for representing a directional connection and second log information used for representing a connection object from the running log; the directional connection line is used for representing the data transmission flow direction of the first industrial intelligent equipment, and the connection object is used for representing the data transmission object of the first industrial intelligent equipment;

listing each first log information in a form of directed connection and listing second log information corresponding to each first log information in a form of transmission nodes; the system comprises a transmission node, a data transmission object and a data transmission system, wherein the transmission node is used for representing a data transmission object, and the data transmission object is second industrial intelligent equipment;

and generating the data transmission path according to the transmission node corresponding to each directed connection line.

Optionally, the determining, according to the data transmission path, at least a plurality of second industrial intelligent devices that receive the target production data includes:

acquiring a target directed connection line in the data transmission path; the target directed connection line is used for connecting a transmission node and a sending node;

determining at least a plurality of corresponding target industrial intelligent devices according to the node identification of the transmission node corresponding to each target directional connection line;

determining at least a plurality of second industrial intelligent devices used for receiving the target production data in at least a plurality of target industrial intelligent devices according to the connecting line identification corresponding to each target directional connecting line; and the connecting line identification is obtained through the data structure identification of the target production data.

Optionally, the obtaining, from the first industrial intelligent device, response information fed back by each second industrial intelligent device when receiving the target production data includes:

sending an information acquisition request to the first industrial intelligent device; wherein, the information acquisition request carries a check field;

enabling the first industrial intelligent device to verify the information acquisition request based on the verification field to obtain a verification result; enabling the first industrial intelligent device to send response information carrying the device model of the second industrial intelligent device when the verification result represents that the information acquisition request passes verification;

and acquiring each response message sent by the first industrial intelligent device.

In a second aspect, an electronic device includes:

a processor, and

a memory and a network interface connected with the processor;

the network interface is connected with a nonvolatile memory in the electronic equipment;

when the processor is operated, the computer program is called from the nonvolatile memory through the network interface, and the computer program is operated through the memory so as to execute the method.

In a third aspect, a readable storage medium applied to a computer is recorded with a computer program, and the computer program implements the method when running in a memory of an electronic device.

When the data tracking method based on the industrial internet and the electronic device are applied, data distortion and transmission loss of target production data during transmission between the industrial intelligent devices can be taken into consideration, and the target production data can be accurately and reliably tracked in the transmission process of the target production data by combining the comparison results of the feature sets of the target production data at the data sending end and the data receiving end, so that the second industrial intelligent device receiving actual production data corresponding to the target production data is monitored, whether data receiving abnormity exists in the second industrial intelligent device can be further determined, and the abnormity detection of the industrial intelligent device is further realized by data tracking of the target production data.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

FIG. 1 is a communication connection diagram of a data tracking system shown in the present application according to an exemplary embodiment.

FIG. 2 is a flow chart illustrating a data tracking method according to an exemplary embodiment of the present application.

FIG. 3 is a block diagram illustrating one embodiment of a data tracking device according to one exemplary embodiment of the present application.

Fig. 4 is a hardware structure diagram of an electronic device in which the data tracking apparatus of the present application is located.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.

In order to solve the technical problem that the accurate and reliable tracking of industrial production data is difficult, the invention discloses a data tracking method based on an industrial internet and an electronic device. Please refer to fig. 1, which is a communication connection diagram of an industrial internet-based data tracking system 100 according to the present invention.

As seen in FIG. 1, the data tracking system 100 includes an electronic device 200 and a plurality of industrial intelligence devices 300 communicatively coupled to each other. The electronic device 200 is used for tracking industrial production data of the industrial intelligent device 300 to ensure the normal operation of the industrial intelligent device 300, and the industrial intelligent device 300 can be different types of production devices in an automation plant.

On the basis of the above, please refer to fig. 2, which is a flowchart illustrating a data tracking method based on the industrial internet according to the present invention, the method can be applied to the electronic device 200 in fig. 1, and specifically includes the following steps.

Step S201, determining whether target production data exist in a first industrial intelligent device in a data tracking system, and acquiring the target production data when determining that the target production data exist in the first industrial intelligent device.

In an embodiment of the present invention, the first industrial intelligent device is an industrial intelligent device having a fault within a set time period, the set time period is a time period before a current time, and the target production data is data of interaction between the first industrial intelligent device and other devices in the data tracking system.

Step S202, dividing the target production data into a first data set and a second data set; extracting first data features of each first data segment in the first data set one by one to form a first feature set of the first data set; and extracting second data characteristics of each second data segment in the second data set one by one, and comparing and correcting the second data characteristics with the first characteristic set to form a second characteristic set of the second data set.

In an embodiment of the present invention, the first data set and the second data set respectively include different categories of data segments, the first data set is used for characterizing data flow direction information of the target production data, and the second data set is used for characterizing data fluctuation information of the target production data.

Step S203, acquiring a data transmission path of the first industrial intelligent device for transmitting the target production data; and determining at least a plurality of second industrial intelligent devices for receiving the target production data according to the data transmission path.

Step S204, obtaining, from the first industrial intelligent device, response information fed back by each second industrial intelligent device when receiving the target production data, and determining, based on the response information, actual production data corresponding to the target production data received by each second industrial intelligent device.

In this embodiment, the target production data may be understood as production data transmitted from the first industrial intelligent device, and the actual production data may be understood as production data that is finally received by each second industrial intelligent device in the actual transmission process of the target production data.

In specific implementation, the target production data may be the same as or different from the actual production data, and when the target production data is different from the actual production data, it indicates that the target production data has problems of data distortion, transmission interference and the like in the transmission process.

Step S205, dividing the actual production data into a third data set and a fourth data set, and determining the third feature set of the third data set and the fourth feature set of the fourth data set according to the steps of determining that the first feature set and the second feature set are similar.

Step S206, aiming at each second industrial intelligent device, judging whether the first feature set is the same as a third feature set corresponding to the industrial intelligent device; if the first characteristic set is different from the third characteristic set, judging that the second industrial intelligent device has an abnormal industrial data receiving behavior; if the first feature set is the same as the third feature set, judging whether the second feature set is the same as a fourth feature set corresponding to the industrial intelligent device, and if the second feature set is different from the fourth feature set, judging that the second industrial intelligent device has an abnormal industrial data receiving behavior.

When the content described in the above steps is applied, firstly, the acquired target production data of the first industrial intelligent device is subjected to data set division to obtain a first data set and a second data set, and a first feature set of the first data set and a second feature set of the second data set are determined. Secondly, determining actual production data received by each second industrial intelligent device according to the obtained response information, and determining a third feature set of a third data set corresponding to the actual production data and a fourth feature set corresponding to a fourth data set. Finally, by carrying out consistency comparison on the first characteristic set and the third characteristic set, and the second characteristic set and the fourth characteristic set, whether the second industrial intelligent device has the behavior of industrial data receiving abnormity can be determined.

Therefore, data distortion and transmission loss of target production data during transmission between the industrial intelligent devices can be taken into consideration, and the target production data can be accurately and reliably tracked in the transmission process of the target production data by combining the comparison results of the target production data in the characteristic sets of the data sending end and the data receiving end, so that the second industrial intelligent device receiving actual production data corresponding to the target production data is monitored, whether the second industrial intelligent device has data receiving abnormity behavior can be further determined, and the abnormity detection of the industrial intelligent devices is realized by data tracking of the target production data.

In one possible example, in order to ensure accurate partitioning of the first data set and the second data set, in step S202, the step of partitioning the target production data into the first data set and the second data set may specifically include the following.

Step S2021, dividing the production target data into a plurality of initial data segments based on data segment identifiers in the production target data; each group of data segment identifications comprise a start identification and an end identification, the start identification and the end identification in each group of data segment identifications have the same segment weight value, and data between the start identification and the end identification corresponding to the same segment weight is a data segment.

In this embodiment, the start flag may be "| luminance_n", the suspension flag may be". Oct ""_n". Wherein. n may represent a segment weight value, and n may be a rational number, which is not limited herein.

Step S2022, acquiring data thread information of the target production data, where the data thread information is used to characterize a generation process of the target production data.

Step S2023, judging whether a first data segment category corresponding to the target production data and used for representing data flow information of the target production data and a second data segment category corresponding to data fluctuation information of the target production data exist or not according to the data thread information; when the first data segment category and the second data segment category exist, determining a first data comparison result between each initial data segment of the target production data under the second data segment category and each initial data segment of the target production data under the first data segment category according to the initial data segment of the target production data under the first data segment category and a segmentation weight value of the initial data segment, and transferring the initial data segments of the target production data under the second data segment category, which are identical to the first data comparison result corresponding to the initial data segment under the first data segment category, to the first data segment category; and the data comparison result is used for representing the data characteristic directions of different initial data segments.

Step S2024, if the target production data has multiple initial data segments in the second data segment category, determining a second data comparison result between the initial data segments in the second data segment category according to the initial data segments in the first data segment category of the target production data and the segment weight value thereof, and recombining the initial data segments in the second data segment category according to the second data comparison result between the initial data segments to obtain at least a partially recombined data segment.

Step S2025, adding a data reassembly number to each reassembly data segment according to the initial data segment of the target production data in the first data segment category and the segmentation weight value thereof, and transferring each reassembly data segment to the first data segment category according to the size sequence of the data reassembly number.

Step S2026, determining a plurality of data segments corresponding to the first data segment category as the first data set and a plurality of data segments corresponding to the second data segment category as the second data set.

It can be understood that through the descriptions in the foregoing steps S2021 to S2026, the target production data can be segmented based on the data segment identifier, and the first data segment class and the second data end class corresponding to the target production data are determined based on the data thread information, so that the initial data segments under the first data segment class and the second data end class are analyzed to implement the transfer and adjustment of the data segments under the first data segment class and the second data segment class. In this way, an accurate partitioning of the first data set and the second data set can be ensured.

In the embodiment of the present application, the step of extracting the first data feature of each first data segment in the first data set one by one to form the first feature set of the first data set described in step S202 specifically includes what is described in the following sub-steps.

Determining data direction information of each first data segment, and generating a target information list according to the determined data direction information; the data pointing information is used for representing the continuity of the data segment in the first data set, the target information list is a partitioned area list, each area corresponds to one area identifier, each area identifier corresponds to at least one piece of data pointing information, and the area identifiers of each area in the target information list have a sorting relation from low to high.

In the next step, determining data coding information of each first data segment, establishing a corresponding relation between each data coding information and the target information list, and generating a feature extraction logic according to the corresponding relation; generating a feature extraction logic according to the corresponding relationship, specifically comprising: converting each first data segment into a characteristic array mode; respectively determining at least one array sorting mode of each characteristic array mode; acquiring an array sorting mode of each first data segment, which is not repeated with each other, to form an array reconstruction mode; mapping each array ordering mode in each array reconstruction mode to the target information list to generate the feature extraction logic.

In the next step, according to the feature extraction logic, performing feature extraction on the data coding information in each first data segment to obtain a first data feature corresponding to each first data segment; wherein the first data feature is a data feature vector.

In the next step, according to the ordering relationship of the area identifiers corresponding to the areas where the first data segments corresponding to the first data features are located in the target information list, ordering the first data features to obtain an ordering sequence, and according to the ordering sequence, generating a first feature set of the first data set.

When the contents described in the above steps are applied, the correspondence relationship between the data encoding information of each first data segment and the target information list can be determined based on the generated target information list, so that the feature extraction logic is generated. In this way, the data coding information of each first data segment can be subjected to feature extraction according to the feature extraction logic so as to accurately obtain the first data features. And then ranking the first data features based on the ranking relation of the region identifiers corresponding to the first data features to obtain a first feature set. In this way, when the first data features are determined one by one, different data coding information of different first data segments can be taken into account, and the accuracy and reliability of the first feature set are further ensured.

On the basis, the step S202 of extracting the second data features of each second data segment in the second data set one by one and comparing the second data features with the first feature set to correct the second data feature set to form the second feature set of the second data set may specifically include the contents described in the following steps.

The second data characteristic of each second data segment is determined one by one.

In this embodiment, the step of determining the second data characteristic may refer to the step of determining the first data characteristic, and therefore will not be further described herein.

In the next step, the cosine similarity between each second data feature and each first data feature in the first feature set is determined, and the cosine similarity mean value corresponding to each second data feature is determined.

In the next step, according to the feature weight corresponding to each first data feature in the first feature set, correcting the vector value in the second data feature of which the cosine similarity mean value is smaller than a set value; and generating a second feature set of the second data set based on the second data features of which the cosine similarity values are greater than or equal to the set value and the second data features of which the modified cosine similarity mean values are smaller than the set value.

It will be appreciated that, with the above, the second data characteristic can be modified to eliminate errors between the second set of characteristics and the first set of characteristics.

In a possible implementation manner, the step of acquiring the data transmission path of the first industrial intelligent device for transmitting the target production data described in step S203 may specifically include the following.

Calling an equipment running log of the first industrial intelligent equipment from a preset storage area of the first industrial intelligent equipment; the preset storage area is used for storing the operation condition of the first industrial intelligent device, and the operation condition comprises the device operation log.

Further, first log information used for representing the directional connection and second log information used for representing the connection object are determined from the running log; the directional connection line is used for representing the data transmission flow direction of the first industrial intelligent device, and the connection object is used for representing the data transmission object of the first industrial intelligent device.

In the embodiment of the invention, the first log information and the second log information are in one-to-one correspondence, and at least one group of the first log information exists.

Further, listing each first log information in a form of directed connection and listing second log information corresponding to each first log information in a form of transmission nodes; and one transmission node is used for representing one data transmission object, and the data transmission object is a second industrial intelligent device.

And further, generating the data transmission path according to the transmission node corresponding to each directed connection line.

In this embodiment, the data transmission path includes a transmission node and a transmission node, and the transmission node is configured to characterize the first industrial intelligent device.

Based on the content described in the above steps, different directed lines and transmission nodes can be separated from the device operation log of the first industrial intelligent device, so that a data transmission path can be determined quickly, conveniently and accurately based on the directed lines and the transmission nodes corresponding to the directed lines.

On the basis, the step of determining at least a plurality of second industrial intelligent devices receiving the target production data according to the data transmission path described in step S203 may specifically include the content described in the following steps.

Acquiring a target directed connection line in the data transmission path; the target directed connection is used for connecting the transmission node and the sending node.

In the next step, determining at least a plurality of corresponding target industrial intelligent devices according to the node identification of the transmission node corresponding to each target directed connection line;

in the next step, at least a plurality of second industrial intelligent devices used for receiving the target production data in at least a plurality of target industrial intelligent devices are determined according to the connecting line identification corresponding to each target directional connecting line; and the connecting line identification is obtained through the data structure identification of the target production data.

It can be understood that by executing the above steps, hierarchical screening can be performed on the transmission nodes and the directed connection lines, and then at least a plurality of second industrial intelligent devices receiving the target production data are accurately determined.

In an alternative embodiment, the step of obtaining, from the first industrial intelligent device, the response information fed back by each second industrial intelligent device when receiving the target production data described in step S204 may specifically include the content described in the following steps.

Step S2041, sending an information acquisition request to the first industrial intelligent device; wherein, the information acquisition request carries a check field.

Step S2042, the first industrial intelligent device verifies the information acquisition request based on the verification field to obtain a verification result; and the first industrial intelligent equipment sends response information carrying the equipment model of the second industrial intelligent equipment when the verification result represents that the information acquisition request passes verification.

Step S2043, obtaining each response message sent by the first industrial intelligent device.

In specific implementation, through the contents described in the above steps S2041 to S2043, a check field can be carried in the information acquisition request, so that the first industrial intelligent device checks the information acquisition request based on the check field, the first industrial intelligent device is ensured to be able to safely send response information, and a phenomenon that the first industrial intelligent device sends response information when receiving any information acquisition request is avoided.

On the basis of the above, the determining, based on the response information, the actual production data corresponding to the target production data received by each second industrial intelligent device in step S204 may further include the following steps.

(1) Determining the generation time of the response information; and the generation time is the time when the second industrial intelligent device receives the target production data sent by the first industrial intelligent device.

(2) And determining the time-consuming duration of the target production data transmitted from the first industrial intelligent device to the second industrial intelligent device according to the data receiving and transmitting structure information and the data size information of the target production data, and determining the expected time of the second industrial intelligent device for receiving the target production data based on the transmitting time of the first industrial intelligent device for transmitting the target production data.

(3) And adjusting the target production data according to the comparison result of the expected time and the generation time to obtain the actual production data.

In the specific implementation, if the expected time and the generation time are the same, the actual production data and the target production data are the same. And if the expected time is different from the generation time, the actual production data is different from the target production data.

It can be understood that, through the content described in the above steps (1) to (3), the actual production data corresponding to the target production data received by each second industrial intelligent device can be accurately determined.

On the basis of the above content, the present invention also provides:

A1. an industrial internet-based data tracking system comprises an electronic device and a plurality of industrial intelligent devices which are communicated with each other, wherein the plurality of industrial intelligent devices comprise a first industrial intelligent device which has a fault within a set time period.

The first industrial intelligent device is used for generating target production data when interacting with other devices in the plurality of industrial intelligent devices.

And the electronic equipment is used for acquiring the target production data when the first industrial intelligent equipment is determined to have the target production data.

An electronic device to divide the target production data into a first data set and a second data set; extracting first data features of each first data segment in the first data set one by one to form a first feature set of the first data set; extracting second data features of each second data segment in the second data set one by one, and comparing and correcting the second data features with the first feature set to form a second feature set of the second data set; the first data set and the second data set respectively comprise different types of data segments, the first data set is used for representing data flow direction information of the target production data, and the second data set is used for representing data fluctuation information of the target production data.

And the first industrial intelligent equipment is used for transmitting the target production data.

The electronic equipment is used for acquiring a data transmission path of the first industrial intelligent equipment, wherein the data transmission path is used for transmitting the target production data; and determining at least a plurality of second industrial intelligent devices for receiving the target production data according to the data transmission path.

And the first industrial intelligent equipment is used for receiving response information fed back by each second industrial intelligent equipment when receiving the target production data.

And the electronic equipment is used for acquiring the response information from the first industrial intelligent equipment and determining the actual production data corresponding to the target production data received by each second industrial intelligent equipment based on the response information.

The electronic equipment is used for dividing the actual production data into a third data set and a fourth data set and determining a third characteristic set of the third data set and a fourth characteristic set of the fourth data set according to the step of determining that the first characteristic set is similar to the second characteristic set;

the electronic equipment is used for judging whether the first feature set is the same as a third feature set corresponding to the industrial intelligent equipment or not aiming at each second industrial intelligent equipment; if the first characteristic set is different from the third characteristic set, judging that the second industrial intelligent device has an abnormal industrial data receiving behavior; if the first feature set is the same as the third feature set, judging whether the second feature set is the same as a fourth feature set corresponding to the industrial intelligent device, and if the second feature set is different from the fourth feature set, judging that the second industrial intelligent device has an abnormal industrial data receiving behavior.

A2. The data tracking system of a1, the electronic device being configured to:

A3. The data tracking system of a1, the electronic device being configured to:

A4. The data tracking system of a3, the electronic device being configured to:

determining second data characteristics of each second data segment one by one;

A5. The data tracking system of any one of a1-a4, the electronic device being further configured to:

A6. The data tracking system of a1, the electronic device being configured to:

A7. The data tracking system of a1, the electronic device being configured to:

Alternatively, in the data tracking system of a1, the electronic device may be further configured to: and determining the generation time of the response information. And the generation time is the time when the second industrial intelligent device receives the target production data sent by the first industrial intelligent device. And determining the time-consuming duration of the target production data transmitted from the first industrial intelligent device to the second industrial intelligent device according to the data receiving and transmitting structure information and the data size information of the target production data, and determining the expected time of the second industrial intelligent device for receiving the target production data based on the transmitting time of the first industrial intelligent device for transmitting the target production data. And adjusting the target production data according to the comparison result of the expected time and the generation time to obtain the actual production data.

Based on the above, as shown in fig. 3, a functional block diagram of an industrial internet-based data tracking apparatus 400 according to an embodiment of the present invention is provided, where the data tracking apparatus 400 includes the following functional blocks.

B1. A data tracking device comprises the following modules.

A data obtaining module 401, configured to determine whether target production data exists in a first industrial intelligent device in a data tracking system, and obtain the target production data when it is determined that the target production data exists in the first industrial intelligent device; the first industrial intelligent device is an industrial intelligent device with a fault in a set time period, the set time period is a time period before the current moment, and the target production data is data of interaction between the first industrial intelligent device and other devices in the data tracking system.

A first data partitioning module 402, configured to partition the target production data into a first data set and a second data set; extracting first data features of each first data segment in the first data set one by one to form a first feature set of the first data set; extracting second data features of each second data segment in the second data set one by one, and comparing and correcting the second data features with the first feature set to form a second feature set of the second data set; the first data set and the second data set respectively comprise different types of data segments, the first data set is used for representing data flow direction information of the target production data, and the second data set is used for representing data fluctuation information of the target production data.

A path obtaining module 403, configured to obtain a data transmission path of the first industrial intelligent device, where the data transmission path is used for transmitting the target production data; and determining at least a plurality of second industrial intelligent devices for receiving the target production data according to the data transmission path.

A data determining module 404, configured to obtain, from the first industrial intelligent device, response information fed back by each second industrial intelligent device when receiving the target production data, and determine, based on the response information, actual production data corresponding to the target production data received by each second industrial intelligent device.

A second data dividing module 405, configured to divide the actual production data into a third data set and a fourth data set, and determine a third feature set of the third data set and a fourth feature set of the fourth data set according to the above-mentioned steps of determining that the first feature set and the second feature set are similar.

A second data dividing module 406, configured to determine, for each second industrial intelligent device, whether the first feature set is the same as a third feature set corresponding to the industrial intelligent device; if the first characteristic set is different from the third characteristic set, judging that the second industrial intelligent device has an abnormal industrial data receiving behavior; if the first feature set is the same as the third feature set, judging whether the second feature set is the same as a fourth feature set corresponding to the industrial intelligent device, and if the second feature set is different from the fourth feature set, judging that the second industrial intelligent device has an abnormal industrial data receiving behavior.

B2. According to the data tracking apparatus of B1, the first data partitioning module 402 is specifically configured to:

B3. According to the data tracking apparatus of B1, the first data partitioning module 402 is specifically configured to:

B4. According to the data tracking system of B3, the first data partitioning module 402 is specifically configured to:

determining second data characteristics of each second data segment one by one;

B5. The data tracking device of any one of claims B1-B4, the path acquisition module 403 being specifically configured to:

B6. According to the data tracking apparatus in B1, the path obtaining module 403 is specifically configured to:

B7. According to the data tracking apparatus of B1, the data determining module 404 is specifically configured to:

B8. The data tracking device of B7, the data determination module 404 further configured to:

and determining the generation time of the response information. And the generation time is the time when the second industrial intelligent device receives the target production data sent by the first industrial intelligent device. And determining the time-consuming duration of the target production data transmitted from the first industrial intelligent device to the second industrial intelligent device according to the data receiving and transmitting structure information and the data size information of the target production data, and determining the expected time of the second industrial intelligent device for receiving the target production data based on the transmitting time of the first industrial intelligent device for transmitting the target production data. And adjusting the target production data according to the comparison result of the expected time and the generation time to obtain the actual production data.

On the basis of the above, as shown in fig. 4, an electronic device 200 is provided, which includes a processor 501, and a memory 502 and a network interface 503 connected to the processor 501. The network interface 503 is connected to a non-volatile memory 504 in the electronic device 200. The processor 501 retrieves a computer program from the non-volatile memory 504 via the network interface 503 and runs the computer program via the memory 502 to perform the data tracking method described above.

On the basis of fig. 4, a readable storage medium applied to a computer is further recorded with a computer program, and the computer program implements the data tracking method when running in the memory 502 of the electronic device 200.

The various technical features in the above embodiments can be arbitrarily combined, so long as there is no conflict or contradiction between the combinations of the features, but the combination is limited by the space and is not described one by one, and therefore, any combination of the various technical features in the above embodiments also belongs to the scope disclosed in the present specification.

For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, wherein the modules described as separate parts may or may not be physically separate, and the parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. An industrial internet-based data tracking method, which is applied to an electronic device communicating with a plurality of industrial intelligent devices communicating with each other, wherein the electronic device and the plurality of industrial intelligent devices communicating with each other form a data tracking system, and the method comprises the following steps:

2. The data tracking method of claim 1, wherein the dividing the production target data into a first data set and a second data set comprises:

3. The data tracking method of claim 1, wherein the step of extracting first data features of each first data segment in the first data set one by one to form a first feature set of the first data set comprises:

4. The method for data tracking according to claim 3, wherein the extracting second data features of each second data segment in the second data set one by one and comparing the extracted second data features with the first feature set to form a second feature set of the second data set includes:

determining second data characteristics of each second data segment one by one;

5. The data tracking method according to any one of claims 1 to 4, wherein the obtaining of the data transmission path of the first industrial intelligent device for transmitting the target production data comprises:

6. The data tracking method of claim 1, wherein the determining at least a second plurality of industrial intelligent devices receiving the target production data according to the data transmission path comprises:

7. The data tracking method according to claim 1, wherein the obtaining response information from the first industrial intelligent device fed back by each second industrial intelligent device when receiving the target production data comprises:

8. An electronic device, comprising:

a processor, and

a memory and a network interface connected with the processor;

the processor, when running, retrieves a computer program from the non-volatile memory via the network interface and runs the computer program via the memory to perform the method of any of claims 1-7.

9. A readable storage medium applied to a computer, wherein the readable storage medium is burned with a computer program, and the computer program realizes the method of any one of the above claims 1-7 when running in a memory of an electronic device.