CN115221135B - Sharing method and system for industrial Internet data - Google Patents
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Abstract
According to the industrial Internet data sharing method and system, the local industrial Internet data corresponding to the data sharing event and the cloud industrial Internet data corresponding to the data sharing event are combined to conduct independent data induction knowledge feature mining, two first data induction knowledge features focusing on the local industrial Internet data and the cloud industrial Internet data can be obtained, the data sharing indication label of the industrial Internet data set to be shared is further determined through the first data induction knowledge features, the defect that the data sharing knowledge label of the industrial Internet data set is poor in searching process is overcome, and accordingly the corresponding data sharing indication label can be configured for the industrial Internet data set to be shared to achieve induction processing of the industrial Internet data set to be shared, and targeted and safe data sharing processing is facilitated according to the data sharing indication label.
Description
Technical Field
The invention relates to the technical field of industrial Internet, in particular to a sharing method and a sharing system of industrial Internet data.
Background
The industrial Internet comprises four large systems of network, platform, data and safety, which is not only an infrastructure for industrial digitization, networking and intelligent transformation, but also an application mode for deep integration of Internet, big data, artificial intelligence and entity economy, and is a new industry state and new industry, and the enterprise morphology, supply chain and industry chain are remodeled. Based on this, the data sharing has a small specific weight in the operation process of the industrial internet, and how to implement the induction processing of the data to be shared and the tag configuration to ensure the pertinence and the security of the data sharing is a barrier which is difficult to overcome at present.
Disclosure of Invention
In order to improve the technical problems in the related art, the invention provides a sharing method and a sharing system of industrial Internet data.
In a first aspect, an embodiment of the present invention provides a method for sharing industrial internet data, which is applied to a data sharing system, where the method includes:
obtaining a plurality of groups of industrial Internet data of an industrial Internet data set to be shared, and carrying out shared data induction processing on the plurality of groups of industrial Internet data to obtain a first common data type of the plurality of groups of industrial Internet data, wherein the first common data type comprises local industrial Internet data corresponding to a data sharing event and cloud industrial Internet data corresponding to the data sharing event;
Analyzing the data sharing event based on the local industrial Internet data corresponding to the data sharing event to obtain a first data induction knowledge feature of the local industrial Internet data corresponding to the data sharing event; analyzing the data sharing event based on cloud industrial internet data corresponding to the data sharing event to obtain a first data induction knowledge feature of the cloud industrial internet data corresponding to the data sharing event;
and determining a data sharing indication label of the industrial internet data set to be shared based on the first data induction knowledge feature.
Optionally, the performing the sharing data induction processing on the plurality of groups of industrial internet data to obtain a first common data category of the plurality of groups of industrial internet data includes:
carrying out data sharing event identification on the industrial Internet data to obtain at least one data sharing event window;
determining a global credibility score of the data sharing event window based on the credibility score of the data sharing event window, the correlation degree of the data sharing event window and a designated data area of industrial internet data and the proportion of the coverage area of the data sharing event window in the industrial internet data;
And taking the common data type corresponding to the data sharing event window with the maximum global credibility score as the first common data type of the industrial Internet data.
Optionally, each first feature member in the first data summary knowledge feature corresponds to a second common data category, and the feature value of the first feature member indicates a credibility score of the industrial internet data for the second common data category corresponding to the first feature member.
Optionally, the determining, based on the first data summary knowledge feature, a data sharing indication tag of the industrial internet data set to be shared includes:
based on the first data induction knowledge features, inducing the industrial Internet data corresponding to the first data induction knowledge features into at least one second common data category; wherein each second common data type corresponds to an industrial internet data queue;
determining the credibility score of the industrial internet data set to be shared as a second common data type corresponding to the industrial internet data queue based on the first data induction knowledge characteristic of each industrial internet data in the industrial internet data queue;
A data sharing indication tag of the industrial internet data set to be shared is determined based on the trusted score of the second common data category.
Optionally, the summarizing, based on the first data summary knowledge feature, the industrial internet data corresponding to the first data summary knowledge feature into at least one second common data category includes:
obtaining a data induction variable;
comparing and analyzing the characteristic value of each first characteristic member in the first data induction knowledge characteristic with the size of the data induction variable;
and if the characteristic value of the first characteristic member is higher than the data induction variable, inducing the industrial Internet data corresponding to the first data induction knowledge characteristic into a second common data type corresponding to the first characteristic member.
Optionally, the method further comprises: analyzing keywords of the data sharing event based on the local industrial Internet data corresponding to the data sharing event to obtain keyword credibility scores of the keywords of the data sharing event included in the local industrial Internet data corresponding to the data sharing event; determining an impact index of local industrial internet data corresponding to the data sharing event based on the keyword credibility score;
The determining, based on the first data summary knowledge feature of each industrial internet data in the industrial internet data queue, the trust score of the industrial internet data set to be shared as the second common data category corresponding to the industrial internet data queue includes: based on the influence index of the local industrial Internet data corresponding to the data sharing event, carrying out fusion determination on a first data induction knowledge feature of the local industrial Internet data corresponding to the data sharing event in an industrial Internet data queue to obtain a first fused data induction knowledge feature; and determining that the industrial Internet data set to be shared is a credible score of a second common data type corresponding to the industrial Internet data queue based on the first integrated data summary knowledge features.
Optionally, the determining the impact index of the local industrial internet data corresponding to the data sharing event based on the keyword credibility score includes:
obtaining an impact index judgment value;
when the keyword credibility score is not smaller than the influence index determination value, determining an influence index of the local industrial Internet data corresponding to the data sharing event of the first influence index based on the influence index determination value and the keyword credibility score;
When the keyword credibility score is smaller than the influence index determination value, setting a set influence index as the influence index of the local industrial Internet data corresponding to the data sharing event; wherein the set impact index is not higher than the first impact index.
Optionally, the method further comprises: obtaining a data description field of the industrial internet data set to be shared; determining a first quantitative evaluation based on the data description field, wherein the value of the first quantitative evaluation is related to the accumulated value of the type description field with the second common data category in the data description field;
the determining, based on the first data summary knowledge feature of each industrial internet data in the industrial internet data queue, the trust score of the industrial internet data set to be shared as the second common data category corresponding to the industrial internet data queue includes: determining a first credibility score of the industrial internet data set to be shared as a second common data category corresponding to the industrial internet data queue based on the first data induction knowledge characteristic of each industrial internet data in the industrial internet data queue; and determining the credibility score of the second common data category corresponding to the industrial Internet data queue of the industrial Internet data set to be shared based on the first quantitative evaluation and the first credibility score of the second common data category.
Optionally, the determining the first quantitative evaluation based on the data description field includes:
pairing the data description field of the industrial Internet data set to be shared with the second common data types to obtain an accumulated value of a type description field of each second common data type in the data description field;
a first quantitative evaluation of each second generic data category is determined based on the cumulative value, wherein the first quantitative evaluation has a set mapping relationship with the cumulative value.
Optionally, the determining the data sharing indication tag of the industrial internet data set to be shared based on the confidence score of the second common data category includes:
obtaining a data induction judgment value;
comparing and analyzing the credible score of the second common data type with the data induction judgment value;
and determining the second common data type with the credibility score higher than the data induction judgment value as a data sharing indication label of the industrial Internet data set to be shared.
In a second aspect, the present invention also provides a data sharing system, including a processor and a memory; the processor is in communication with the memory, and the processor is configured to read the computer program from the memory and execute the computer program to implement the method described above.
The technical scheme provided by the embodiment of the invention obtains a plurality of groups of industrial Internet data of the industrial Internet data set to be shared; carrying out shared data induction processing on the plurality of groups of industrial Internet data to obtain a first common data type of the plurality of groups of industrial Internet data, wherein the first common data type comprises local industrial Internet data corresponding to a data sharing event and cloud industrial Internet data corresponding to the data sharing event; analyzing the data sharing event based on the local industrial Internet data corresponding to the data sharing event to obtain a first data induction knowledge feature of the local industrial Internet data corresponding to the data sharing event; analyzing the data sharing event based on cloud industrial internet data corresponding to the data sharing event to obtain a first data induction knowledge feature of the cloud industrial internet data corresponding to the data sharing event; and determining a data sharing indication label of the industrial internet data set to be shared based on the first data induction knowledge feature.
By means of the design, independent data induction knowledge feature mining is conducted through combining the local industrial Internet data corresponding to the data sharing event and the cloud industrial Internet data corresponding to the data sharing event, two first data induction knowledge features focusing on the local industrial Internet data and the cloud industrial Internet data can be obtained, the data sharing indication label of the industrial Internet data set to be shared is further determined through the first data induction knowledge features, the defect that the data sharing knowledge label of the industrial Internet data set is poor in matching searching process can be overcome, and accordingly the corresponding data sharing indication label can be configured for the industrial Internet data set to be shared to achieve induction processing of the industrial Internet data set to be shared, and targeted and safe data sharing processing can be conducted conveniently according to the data sharing indication label.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flow chart of a method for sharing industrial internet data according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a communication architecture of an application environment of a sharing method of industrial internet data according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided by the embodiments of the present invention may be performed in a data sharing system, a computer device, or similar computing apparatus. Taking the example of operating on a data sharing system, the data sharing system 10 may include one or more processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU, a programmable logic device FPGA, etc. processing means) and a memory 104 for storing data, and optionally the data sharing system may also include transmission means 106 for communication functions. It will be appreciated by those of ordinary skill in the art that the above-described architecture is merely illustrative and is not intended to limit the architecture of the data sharing system described above. For example, the data sharing system 10 may also include more or fewer components than shown above, or have a different configuration than shown above.
The memory 104 may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to a method for sharing industrial internet data in an embodiment of the present invention, and the processor 102 executes the computer program stored in the memory 104 to perform various functional applications and data processing, that is, implement the method described above. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located with respect to the processor 102, which may be connected to the data sharing system 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means 106 is arranged to receive or transmit data via a network. The specific examples of networks described above may include wireless networks provided by communication providers of data sharing system 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet wirelessly.
Based on this, referring to fig. 1, fig. 1 is a flow chart of an industrial internet data sharing method according to an embodiment of the present invention, where the method is applied to a data sharing system, and further may include the following technical solutions described below.
STEP101, obtaining several sets of industrial internet data of an industrial internet data set to be shared.
In the embodiment of the present invention, the industrial internet data set to be shared may be any kind of industrial internet data set. The industrial internet data set may be an operation data set of a digital factory or a safety detection data set of an intelligent production line, which is not limited herein.
In some examples, the industrial internet data set to be shared may be called from a setting database, or may be received through a setting data transmission interface, such as calling the industrial internet data set to be shared from a setting relational database, or may be received through a GUI data transmission interface, where the industrial internet data set to be shared is transmitted by a data sharing end, and so on.
Further, the obtaining the plurality of sets of industrial internet data of the industrial internet data set to be shared comprises: and carrying out data sampling on the industrial Internet data set to be shared based on the data sampling step length to obtain the plurality of groups of industrial Internet data. For example, the industrial internet DATA set to be shared is DATA sampled based on a step size of 2 sets of industrial internet DATA per second, resulting in an industrial internet DATA set data= [ DATA1, DATA2 ], DATAn ], where n reflects the number of industrial internet DATA.
Further, in order to facilitate the analysis of the data sharing event in the industrial internet data, the step can further comprise the adaptive processing of the industrial internet data. For example, at this step, the industrial internet data is normalized to a set window size (e.g., window transverse constraint is M, window longitudinal constraint is N).
Further, the sharing method of the industrial internet data further comprises the following content.
STEP102, carrying out sharing data induction processing on the plurality of groups of industrial Internet data to obtain a first common data type of the plurality of groups of industrial Internet data.
In the embodiment of the invention, the first common data category includes local industrial internet data corresponding to a data sharing event and cloud industrial internet data corresponding to the data sharing event.
The first common data category is to sum the industrial internet data into local industrial internet data corresponding to a data sharing event including local data features of the data sharing event and cloud industrial internet data corresponding to the data sharing event including cloud data features of the data sharing event.
The local industrial Internet data corresponds to the operation data of the local smart factory, and the cloud industrial Internet data corresponds to the operation data of the remote/remote smart factory. Further, the data sharing events include fault repair record sharing events, security early warning report sharing events, and the like, which relate to intelligent production and industrial internet, and those skilled in the art will recognize that the data sharing events may correspond to a portion of the data set.
In order to avoid analysis errors caused by too high a proportion of non-data sharing events when there are multiple data sharing events in the industrial internet data, the STEP102 further includes the following.
STEP201, carrying out data sharing event identification on the industrial Internet data to obtain at least one data sharing event window.
STEP202, determining a global confidence score for the data sharing event window based on the confidence score for the data sharing event window, a correlation of the data sharing event window to a designated data area of the industrial Internet data, and a proportion of coverage of the data sharing event window in the industrial Internet data.
STEP203, taking the common data type corresponding to the data sharing event window with the largest global credibility score as the first common data type of the industrial Internet data.
In an embodiment of the present invention, the STEP201 may be implemented by a deep learning network (DNN) that is successfully debugged in advance. The deep learning network (DNN) is used for identifying industrial Internet data of two common data types, namely local industrial Internet data and cloud industrial Internet data. In actual implementation, if the identification result of certain industrial internet data is an empty set, the group is indicated to not contain a data sharing event, so that the group can be deleted. If the identification result of certain industrial Internet data is a non-empty set, the deep learning network (DNN) outputs no less than one data sharing event window.
In STEP202, the global confidence score (integrated confidence) of each data sharing event window output by the deep learning network is obtained, which is not less than one global confidence score.
In STEP203, the at least one global confidence score is sorted based on descending order, and the common data category corresponding to the data sharing event window with the largest global confidence score is used as the first common data category of the industrial internet data.
In STEP201-STEP203, by adding the distribution area evaluation and the coverage ratio in the data sharing event recognition, the shared data induction processing (such as data classification or data clustering) can be performed on the industrial internet data as accurately as possible when a plurality of data sharing event windows are recognized.
In some examples, the method of sharing industrial internet data may further include the following.
STEP103, analyzing the data sharing event based on the local industrial Internet data corresponding to the data sharing event to obtain a first data induction knowledge feature of the local industrial Internet data corresponding to the data sharing event.
STEP104, analyzing the data sharing event based on the cloud industrial Internet data corresponding to the data sharing event to obtain a first data induction knowledge feature of the cloud industrial Internet data corresponding to the data sharing event.
Further, for STEP103, industrial internet data of the local industrial internet data corresponding to the data sharing event, which is generalized in STEP102, is input into a naive bayes algorithm of the local industrial internet data successfully debugged in advance, and the naive bayes algorithm of the local industrial internet data is used for performing sharing data generalization processing on the industrial internet data through the local industrial internet data corresponding to the data sharing event.
Further, for the STEP104, the industrial internet data which is summarized in the STEP102 as the cloud industrial internet data corresponding to the data sharing event is input into a naive bayes algorithm of the cloud industrial internet data which is successfully debugged in advance, and the naive bayes algorithm of the cloud industrial internet data is used for carrying out sharing data summarization processing on the industrial internet data through the cloud industrial internet data corresponding to the data sharing event.
In some examples, to improve data processing efficiency, the native industrial internet data of the naive bayes algorithm or the cloud industrial internet data of the native bayes algorithm may be a data set within the data sharing event window obtained in STEP 102.
Further, each first feature member (element) in the first data summary knowledge feature corresponds to a second common data category, and the feature value of the first feature member characterizes the industrial internet data as a confidence score (confidence) of the second common data category corresponding to the first feature member. For example, the first data summary knowledge feature is a normalized (normalized) one-dimensional array of naive bayes algorithm of the local industrial internet data or naive bayes algorithm output of cloud industrial internet data.
Through STEP103 and STEP104, the local industrial Internet data corresponding to the data sharing event and the cloud industrial Internet data corresponding to the data sharing event are respectively analyzed and classified to obtain first data induction knowledge features, and the processing of the cloud industrial Internet data corresponding to the data sharing event is added on the basis of the local industrial Internet data corresponding to the data sharing event, so that the global searching degree (such as recall rate) of classification of the industrial Internet data set is improved.
In some possible examples, the method of sharing industrial internet data may further include the following.
STEP105, determining a data sharing indication tag of the industrial internet data set to be shared based on the first data summary knowledge feature.
In STEP103 and STEP104, the industrial internet data of the industrial internet data set to be shared is subjected to shared data induction processing identification to obtain at least one first data induction knowledge feature, and in general, a plurality of first data induction knowledge features are obtained, and in STEP105, the data sharing indication tag of the industrial internet data set to be shared, such as the second common data category of the industrial internet data set to be shared, is determined by integrating the plurality of first data induction knowledge features.
In some possible embodiments, the STEP105 may further include what is recorded by STEP301-STEP303 as follows.
STEP301, based on the first data summary knowledge feature, summarizing the industrial internet data corresponding to the first data summary knowledge feature into at least one second common data category; wherein each second generic data class corresponds to an industrial internet data queue.
STEP302, based on the first data summary knowledge feature of each industrial Internet data in the industrial Internet data queue, determines a credibility score of the industrial Internet data set to be shared as a second common data category corresponding to the industrial Internet data queue.
STEP303, determining a data sharing indication label of the industrial internet data set to be shared based on the credibility score of the second common data category.
In the embodiment of the present invention, for the local industrial internet data corresponding to each data sharing event obtained through STEP102 and the cloud industrial internet data corresponding to the data sharing event, a corresponding first data summary knowledge feature (data summary vector/data classification vector) may be obtained through STEP103 or STEP104, and by using the data summary knowledge feature, the industrial internet data may be classified into at least one second common data type, where the second common data type of the industrial internet data is determined by the feature value of the largest first feature member in the first data summary knowledge feature. In order to improve the classification accuracy, a data induction variable can be set in advance, and the industrial internet data is subjected to shared data induction processing based on the data induction variable.
Further, the STEP301 may further include contents recorded by STEP401 to STEP403 as follows.
STEP401, obtaining data summary variables.
Wherein the data induction variable can be understood as a classification index or a cluster index.
STEP402, compare and analyze the feature value of each first feature member in the first data summary knowledge feature with the size of the data summary variable.
STEP403, if the feature value of the first feature member is higher than the data induction variable, inducing the industrial internet data corresponding to the first data induction knowledge feature into a second common data category corresponding to the first feature member.
By implementing STEP401-STEP403, industrial internet data corresponding to each common data category in the R second common data categories may be obtained, that is, each second common data category corresponds to an industrial internet data queue, where the industrial internet data queue includes local industrial internet data corresponding to the data sharing event and/or cloud industrial internet data corresponding to the data sharing event.
In STEP302, first global reliability score summary knowledge features of each second common data category are determined first, and further, the first global reliability score summary knowledge features are determined through a standardized idea after overlapping (weighting) the first data summary knowledge features of each industrial internet data in the industrial internet data queue corresponding to the second common data category.
For each second common data category, a confidence score of the second common data category may be determined, and then in STEP303, the second common data category corresponding to the confidence score of the largest second common data category may be determined as the data sharing indication tag of the industrial internet data set to be shared.
In order to reduce the computation load, before STEP302, the method may further include the following: screening the second common data category. Further, the screening includes cleaning out a second common data category with little industrial internet data. Further, a number limit value index2 is set, wherein the value range of index2 is 0-1, so that a number threshold value can be obtained: and n is index2, the second common data types corresponding to the industrial Internet data queues with the number of the industrial Internet data smaller than the number threshold are washed, and the rest second common data types participate in the determination of the first global credibility score induction knowledge characteristics and the credibility scores of the second common data types. The erroneous second generic data category may therefore be purged to reduce the amount of certainty in determining the confidence score for the second generic data category.
For STEP303, since each second common data type participating in the determination of the first global confidence score summary knowledge feature obtains a corresponding class1, in this STEP, the sizes of all classes 1 may be compared and analyzed, and the second common data type with the largest class1 value may be used as the data sharing indication tag of the industrial internet data set to be shared.
Further, as the data sharing indication tag based on the industrial internet data set to be shared is the second common data type having the largest value of all class1, it is possible that all the class1 values are not large, in which case the accuracy of the data sharing indication tag (data sharing classification tag) is controversial.
Thus, for the precision of the last data sharing indication tag, the STEP303 further includes the following: obtaining a data induction judgment value; comparing and analyzing the credible score of the second common data type with the data induction judgment value; and determining the second common data type with the credibility score higher than the data induction judgment value as a data sharing indication label of the industrial Internet data set to be shared.
In the embodiment of the present invention, the data induction determination value is a determination value index3 configured in advance, when class1> index3, it is determined that the data sharing indication label of the industrial internet data set to be shared is a class i, and in order to avoid that the industrial internet data set to be shared is induced into a plurality of second common data types, the value of index3 may be: index 3=0.9.
According to the content recorded by the STEP101-STEPS105, when the industrial Internet data sets are subjected to shared data induction processing, the cloud data characteristics of the data sharing event are added in addition to the local data characteristics of the data sharing event, so that the industrial Internet data sets comprising the cloud industrial Internet data and the local industrial Internet data are more accurately classified, the defect of poor searching accuracy caused by the fact that the industrial Internet data sets are subjected to shared data induction processing by only the industrial Internet data of the local industrial Internet data can be overcome, and meanwhile, the defect of data induction accuracy can be also improved; in addition, the distributed area and coverage of the data sharing event window can be combined, and errors of data induction can be reduced.
Further, in some embodiments, data sharing events in some industrial internet data sets are explicitly critical, which can accurately perform shared data induction processing on industrial internet data sets.
Further, the industrial internet data sharing method may further include a technical scheme recorded by STEP501-STEP 504.
STEP501, analyzing the keywords of the data sharing event based on the local industrial Internet data corresponding to the data sharing event to obtain the keyword credibility score of the keywords of the data sharing event included in the local industrial Internet data corresponding to the data sharing event.
STEP502, determining an impact index (weight value) of the local industrial internet data corresponding to the data sharing event based on the keyword credibility score.
Further, the STEP302 may further include the following.
STEP503, based on the impact index of the local industrial Internet data corresponding to the data sharing event, fuses and determines the first data summary knowledge feature of the local industrial Internet data corresponding to the data sharing event in the industrial Internet data queue to obtain a first fused data summary knowledge feature.
STEP504, based on the first integrated data summary knowledge feature, determines that the industrial Internet data set to be shared is a credibility score of a second common data category corresponding to the industrial Internet data queue.
For STEP501, the local industrial internet data corresponding to the data sharing event may be input into an identification network of a data sharing event keyword (event identifier) that is successfully debugged in advance, so as to analyze whether the local industrial internet data corresponding to the data sharing event includes the keyword of the data sharing event, where the generation content of the identification network of the data sharing event keyword is similar to the output of the identification network in STEP102, that is, one or more data sharing event windows are output. In the present invention, since the shared data induction processing is performed on the local industrial internet data corresponding to the independent thread data sharing event, in STEP501, it is possible to identify only whether the local industrial internet data corresponding to the data sharing event includes the keyword of the data sharing event, without performing the shared data induction processing. Therefore, the keyword credibility scores of the plurality of data sharing event windows can be compared and analyzed, and the maximum keyword credibility score is obtained.
Further, in STEP502, the impact index of the local industrial internet data corresponding to the data sharing event is determined based on the keyword confidence score, which may include the content recorded by STEP601-STEP 603.
STEP601, obtaining an impact index determination value.
STEP602, when the keyword credibility score is not smaller than the influence index determination value, determining an influence index of the local industrial Internet data corresponding to the data sharing event of the first influence index based on the influence index determination value and the keyword credibility score.
STEP603, setting a set impact index as the impact index of the local industrial Internet data corresponding to the data sharing event when the keyword credibility score is smaller than the impact index determination value; wherein the set impact index is not higher than the first impact index.
For example, the impact index determination value index4 is configured in advance, and the keyword confidence score of the local industrial internet data corresponding to the data sharing event obtained in STEP502 is set to be tag_value.
For STEP503, fusion determination is performed on the first data summary knowledge feature of the local industrial internet data corresponding to the data sharing event in the industrial internet data queue to obtain a first fused data summary knowledge feature. Based on the above, the first integrated data summary knowledge feature of the industrial internet data data_q in the industrial internet data queue m1 with the common data category of l may be a multidimensional vector matrix.
For STEP504, first, the integrated first fused data summary knowledge feature is derived based on the first fused data summary knowledge feature of all industrial internet data in the queue m1, whereby the confidence score of the second common data category of the final common data category 1 is: class1. The process of determining the data sharing indication tags of the industrial internet data set to be shared is then combined with STEP105.
Further, when the first global credible score is determined to induce knowledge features, the industrial internet data containing the keywords of the data sharing event are subjected to superposition processing, so that the weight of the data is increased, and the data set with the larger credible scores of the keywords is increased, when the first integrated data is determined to induce knowledge features, the feature recognition degree is increased, and therefore the noise rate of the final data sharing indication label is lower, and classification is more accurate.
In the above embodiments, the categorization process has a relationship with the characteristics of the industrial internet data; in some cases, to improve the reliability of data classification, the data sharing indication tag may also be optimized through the data description field of the industrial internet dataset to be shared.
Further, the industrial internet data sharing method may further include a technical scheme recorded by STEP701-STEP704 as follows.
STEP701, obtaining a data description field of the industrial internet data set to be shared.
STEP702, determining a first quantization evaluation based on the data description field, where the value of the first quantization evaluation is related to the accumulated value of the type description field in which the second common data category exists in the data description field.
At this time, the STEP302 further includes the following.
STEP703, determining, based on the first data summary knowledge feature of each industrial Internet data in the industrial Internet data queue, that the industrial Internet data set to be shared is a first confidence score of a second common data category corresponding to the industrial Internet data queue.
STEP704, determining, based on the first quantitative evaluation and the first confidence score of the second common data category, that the industrial internet data set to be shared is the confidence score of the second common data category corresponding to the industrial internet data queue.
In STEP702, significant vector mining is performed on the data description field of the industrial internet data set to be shared to obtain feature vectors matched with the type description field of the second common data type, where the number of feature vectors may be any number. A first quantitative evaluation of the second generic data category corresponding to the industrial internet data queue is determined based on the number of feature vectors paired with the type description field of the second generic data category.
Further, the STEP702 further includes the following: pairing the data description field of the industrial Internet data set to be shared with the second common data types to obtain an accumulated value of a type description field of each second common data type in the data description field; a first quantitative evaluation of each second generic data category is determined based on the cumulative value, wherein the first quantitative evaluation has a set mapping relationship with the cumulative value.
In STEP703, a first confidence score of a second common data type is determined, where the first confidence score of the second common data type may be the confidence score of the second common data type determined in STEP302 or the confidence score of the second common data type determined in STEP 504.
In STEP704, a confidence score of the industrial internet data set to be shared as the second common data category corresponding to the industrial internet data queue is determined based on the first quantitative evaluation and the first confidence score of the second common data category. Further, an index of the first quantitative evaluation of the first confidence score of the second common data category is used to determine a confidence score of the second common data category.
In this way, the data sharing indication tag is interfered by the data description field, so that the data sharing indication tag can be optimized by the characteristics contained in the data description field, the anti-interference performance of the final data sharing indication tag is enhanced, and the classification is more accurate.
The embodiment of the invention discloses a sharing method of industrial Internet data, which comprises the following steps: obtaining a plurality of groups of industrial internet data of an industrial internet data set to be shared; carrying out shared data induction processing on the plurality of groups of industrial Internet data to obtain a first common data type of the plurality of groups of industrial Internet data, wherein the first common data type comprises local industrial Internet data corresponding to a data sharing event and cloud industrial Internet data corresponding to the data sharing event; analyzing the data sharing event based on the local industrial Internet data corresponding to the data sharing event to obtain a first data induction knowledge feature of the local industrial Internet data corresponding to the data sharing event; analyzing the data sharing event based on cloud industrial internet data corresponding to the data sharing event to obtain a first data induction knowledge feature of the cloud industrial internet data corresponding to the data sharing event; and determining a data sharing indication label of the industrial internet data set to be shared based on the first data induction knowledge feature. According to the method, the defect of poor searching range of the data sharing knowledge tag pairing of the industrial Internet data set can be overcome by combining the local industrial Internet data corresponding to the data sharing event and the cloud industrial Internet data corresponding to the data sharing event.
In some independent implementations, after determining the data sharing indication label of the industrial internet data set to be shared, targeted data sharing can be performed according to the received sharing request, so that the intelligent degree of data sharing is improved. Based on this, the method may further include the following: determining an expected label corresponding to a data sharing request in response to the data sharing request to be processed; determining a correlation coefficient of the expected tag and the data sharing indication tag; when the correlation coefficient is greater than or equal to a set coefficient, determining the industrial Internet data set to be shared as a target data set matched with a data sharing request to be processed, and carrying out data sharing on the industrial Internet data set to be shared; and when the correlation coefficient is smaller than the set coefficient, issuing a sharing request rejection prompt to the request end of the data sharing request to be processed.
Therefore, targeted data sharing processing can be performed according to the correlation coefficient calculation among different tags, and potential safety hazards in the data sharing process are avoided.
In some independent implementations, in response to a pending data sharing request, determining a desired tag corresponding to the data sharing request may include: carrying out text analysis on the data sharing request to be processed to obtain a sharing requirement text data sequence, wherein the sharing requirement text data sequence comprises Y groups of sharing requirement text data with association, and Y is an integer greater than or equal to 1; determining an auxiliary demand text data sequence according to the sharing demand text data sequence, wherein the auxiliary demand text data sequence comprises Y groups of auxiliary demand text data with association; based on the shared demand text data sequence, acquiring a shared demand knowledge distribution sequence through a first demand detail knowledge mining model included in a shared demand analysis algorithm, wherein the shared demand knowledge distribution sequence comprises Y shared demand knowledge distributions; acquiring an auxiliary demand knowledge distribution sequence through a second demand detail knowledge mining model included in the shared demand analysis algorithm based on the auxiliary demand text data sequence, wherein the auxiliary demand knowledge distribution sequence comprises Y auxiliary demand knowledge distributions; based on the shared demand knowledge distribution sequence and the auxiliary demand knowledge distribution sequence, obtaining a shared expected score corresponding to the shared demand text data through an expected prediction module included in the shared demand analysis algorithm; and determining the expected label of the sharing requirement text data sequence according to the sharing expected score.
In the embodiment of the invention, the auxiliary demands can be obtained by prediction based on the sharing demands, the auxiliary demands can reflect demands with low importance degree except the data sharing demands, and the interference of the auxiliary demands on the sharing expected scores can be reduced by considering the auxiliary demands, so that the sharing expected scores are ensured to be concentrated on the evaluation of the sharing demands as much as possible, and the corresponding expected labels can be accurately determined.
Based on the same or similar inventive concept, please refer to fig. 2, an architecture schematic diagram of an application environment 30 of an industrial internet data sharing method is further provided, which includes a data sharing system 10 and a data sharing participant 20 that communicate with each other, where the data sharing system 10 and the data sharing participant 20 implement or partially implement the technical scheme described in the above method embodiments at runtime.
Further, there is also provided a readable storage medium having stored thereon a program which when executed by a processor implements the above-described method.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus and method embodiments described above are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a media service server 10, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method for sharing industrial internet data, applied to a data sharing system, the method comprising:
obtaining a plurality of groups of industrial Internet data of an industrial Internet data set to be shared, and carrying out shared data induction processing on the plurality of groups of industrial Internet data to obtain a first common data type of the plurality of groups of industrial Internet data, wherein the first common data type comprises local industrial Internet data corresponding to a data sharing event and cloud industrial Internet data corresponding to the data sharing event;
analyzing the data sharing event based on the local industrial Internet data corresponding to the data sharing event to obtain a first data induction knowledge feature of the local industrial Internet data corresponding to the data sharing event; analyzing the data sharing event based on cloud industrial internet data corresponding to the data sharing event to obtain a first data induction knowledge feature of the cloud industrial internet data corresponding to the data sharing event;
Determining a data sharing indication tag of the industrial internet data set to be shared based on the first data summary knowledge feature;
wherein, each first characteristic member in the first data induction knowledge characteristic corresponds to a second common data category, and the characteristic value of the first characteristic member represents the credibility score of the industrial internet data as the second common data category corresponding to the first characteristic member;
wherein the determining, based on the first data summary knowledge feature, a data sharing indication tag of the industrial internet data set to be shared comprises: based on the first data induction knowledge features, inducing the industrial Internet data corresponding to the first data induction knowledge features into at least one second common data category; wherein each second common data type corresponds to an industrial internet data queue; determining the credibility score of the industrial internet data set to be shared as a second common data type corresponding to the industrial internet data queue based on the first data induction knowledge characteristic of each industrial internet data in the industrial internet data queue; determining a data sharing indication tag of the industrial internet data set to be shared based on the trusted score of the second common data category;
The first data induction knowledge feature is a normalized one-dimensional array output by a naive Bayesian algorithm of the local industrial internet data or a naive Bayesian algorithm of the cloud industrial internet data.
2. The method for sharing industrial internet data according to claim 1, wherein said performing a shared data induction process on said plurality of sets of industrial internet data to obtain a first common data category of said plurality of sets of industrial internet data comprises:
carrying out data sharing event identification on the industrial Internet data to obtain at least one data sharing event window;
determining a global credibility score of the data sharing event window based on the credibility score of the data sharing event window, the correlation degree of the data sharing event window and a designated data area of industrial internet data and the proportion of the coverage area of the data sharing event window in the industrial internet data;
and taking the common data type corresponding to the data sharing event window with the maximum global credibility score as the first common data type of the industrial Internet data.
3. The method for sharing industrial internet data according to claim 1, wherein the step of summarizing the industrial internet data corresponding to the first data summary knowledge feature into at least one second common data category based on the first data summary knowledge feature comprises:
Obtaining a data induction variable;
comparing and analyzing the characteristic value of each first characteristic member in the first data induction knowledge characteristic with the size of the data induction variable;
and if the characteristic value of the first characteristic member is higher than the data induction variable, inducing the industrial Internet data corresponding to the first data induction knowledge characteristic into a second common data type corresponding to the first characteristic member.
4. The method for sharing industrial internet data according to claim 1, wherein the method further comprises: analyzing keywords of the data sharing event based on the local industrial Internet data corresponding to the data sharing event to obtain keyword credibility scores of the keywords of the data sharing event included in the local industrial Internet data corresponding to the data sharing event; determining an impact index of local industrial internet data corresponding to the data sharing event based on the keyword credibility score;
the determining, based on the first data summary knowledge feature of each industrial internet data in the industrial internet data queue, the trust score of the industrial internet data set to be shared as the second common data category corresponding to the industrial internet data queue includes: based on the influence index of the local industrial Internet data corresponding to the data sharing event, carrying out fusion determination on a first data induction knowledge feature of the local industrial Internet data corresponding to the data sharing event in an industrial Internet data queue to obtain a first fused data induction knowledge feature; and determining that the industrial Internet data set to be shared is a credible score of a second common data type corresponding to the industrial Internet data queue based on the first integrated data summary knowledge features.
5. The method for sharing industrial internet data as claimed in claim 4, wherein said determining an impact index of local industrial internet data corresponding to the data sharing event based on the keyword confidence score comprises:
obtaining an impact index judgment value;
when the keyword credibility score is not smaller than the influence index determination value, determining an influence index of the local industrial Internet data corresponding to the data sharing event of the first influence index based on the influence index determination value and the keyword credibility score;
when the keyword credibility score is smaller than the influence index determination value, setting a set influence index as the influence index of the local industrial Internet data corresponding to the data sharing event; wherein the set impact index is not higher than the first impact index.
6. The method for sharing industrial internet data according to claim 1, wherein the method further comprises: obtaining a data description field of the industrial internet data set to be shared; determining a first quantitative evaluation based on the data description field, wherein the value of the first quantitative evaluation is related to the accumulated value of the type description field with the second common data category in the data description field;
The determining, based on the first data summary knowledge feature of each industrial internet data in the industrial internet data queue, the trust score of the industrial internet data set to be shared as the second common data category corresponding to the industrial internet data queue includes: determining a first credibility score of the industrial internet data set to be shared as a second common data category corresponding to the industrial internet data queue based on the first data induction knowledge characteristic of each industrial internet data in the industrial internet data queue; and determining the credibility score of the second common data category corresponding to the industrial Internet data queue of the industrial Internet data set to be shared based on the first quantitative evaluation and the first credibility score of the second common data category.
7. The method of sharing industrial internet data as claimed in claim 6, wherein said determining a first quantitative rating based on said data description field comprises:
pairing the data description field of the industrial Internet data set to be shared with the second common data types to obtain an accumulated value of a type description field of each second common data type in the data description field;
Determining a first quantitative evaluation of each second common data category based on the accumulated value, wherein the first quantitative evaluation has a set mapping relationship with the accumulated value;
wherein the determining the data sharing indication tag of the industrial internet data set to be shared based on the trust score of the second common data category comprises: obtaining a data induction judgment value; comparing and analyzing the credible score of the second common data type with the data induction judgment value; and determining the second common data type with the credibility score higher than the data induction judgment value as a data sharing indication label of the industrial Internet data set to be shared.
8. A data sharing system comprising a processor and a memory; the processor being communicatively connected to the memory, the processor being adapted to read a computer program from the memory and execute it to carry out the method of any of the preceding claims 1-7.
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