CN116383742B - Rule chain setting processing method, system and medium based on feature classification - Google Patents
Rule chain setting processing method, system and medium based on feature classification Download PDFInfo
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Abstract
The application provides a rule chain setting processing method, a system and a medium based on feature classification, wherein the method comprises the following steps: classifying each rule node of a rule chain according to rule node characteristic data to generate rule node information clusters of different types, adding class labels to generate a class label database, standardizing telemetry data after abnormal data are removed, inputting the class label database for matching identification, indexing the telemetry data operation semantic information to a target rule node information cluster according to a class label matching result, sequentially comparing the matching degree of the telemetry data operation semantic information with characteristic data of all rule nodes in the target rule node information cluster, and judging whether to execute operation behaviors defined by each rule node according to the matching degree comparison result; therefore, the target rule node is accurately and rapidly positioned, accurate and effective telemetry data is obtained through processing, so that the rule node processing efficiency is improved, and the purpose of high-efficiency operation of a rule chain is achieved.
Description
Technical Field
The application relates to the technical field of big data and rule chains, in particular to a rule chain setting processing method, a system and a medium based on feature classification.
Background
Because of various kinds of butted equipment, the IOT platform of the Internet of things uses complex service scenes, and the code is needed to be modified or the service logic is needed to be adjusted when one equipment or a certain service scene is butted, so that time and labor are wasted, development cost is high, the service and data are separated by a rule chain technology, the complex service scenes can be flexibly processed, frequent service adjustment can be dealt with, the service processing process can be intuitively known, verification can be carried out, and development efficiency and quality can be improved. The existing rule chain technology is to sequentially check the input data according to the matching sequence in the rule chain, namely, the rule nodes which are matched are found out, so that the problem of low operation efficiency exists, the problem that the input data of the rule chain possibly has abnormal data such as data repetition and data deletion, and the problem that the rule nodes cannot perform unified standardized processing due to inconsistent dimensions caused by different data sources can also exist.
In view of the above problems, an effective technical solution is currently needed.
Disclosure of Invention
The application aims to provide a rule chain setting processing method, a system and a medium based on feature classification, which are used for classifying each rule node of a rule chain according to rule node feature data to generate rule node information clusters of different categories, carrying out standardization processing on telemetry data after abnormal data are removed, sequentially carrying out matching degree comparison on telemetry data operation semantic information and feature data of all rule nodes in a target rule node information cluster to obtain a plurality of corresponding matching degree values, further obtaining a matching degree comparison result, judging whether to execute operation behaviors defined by each rule node according to the matching degree comparison result, and realizing accurate and rapid positioning of target rule nodes, and obtaining accurate and effective telemetry data through processing so as to improve rule node processing efficiency and further realize the purpose of high-efficiency operation of the rule chain.
The application also provides a rule chain setting processing method, a system and a medium based on feature classification, which comprise the following steps:
acquiring rule node information of each rule node of a rule chain;
extracting features of the rule node information to obtain feature data, classifying each rule node of a rule chain according to the feature data to generate rule node information clusters of different categories, adding class labels into the rule node information clusters, and generating a class label database for all class label sets;
acquiring telemetry data of gateway equipment, and analyzing the telemetry data to acquire data characteristic information;
identifying abnormal data of the data characteristic information, eliminating the identified abnormal data, and carrying out standardized processing on telemetry data after eliminating the abnormal data to obtain standardized telemetry data;
analyzing the standardized telemetry data to obtain operation semantic information, inputting the operation semantic information into the class label database for matching and identifying, and obtaining a target rule node information cluster corresponding to the index according to a matching result;
sequentially comparing the operation semantic information with the characteristic data of all rule nodes in the target rule node information cluster to obtain a plurality of corresponding matching degree values, and comparing the matching degree values with a preset matching degree threshold value to obtain a matching degree comparison result;
And judging whether to execute the operation behaviors defined by each rule node according to the matching degree comparison result.
Optionally, in the method for setting and processing a rule chain based on feature classification according to the present application, feature extraction is performed on the rule node information to obtain feature data, each rule node of the rule chain is classified according to the feature data to generate rule node information clusters of different categories, class labels are added to the rule node information clusters, and a class label database is generated for all class label sets, including:
inputting rule node information into a preset feature recognition model for analysis and recognition to obtain feature data, wherein the feature data comprises node type data, parameter configuration data, execution rule data and execution process data;
classifying each rule node of the rule chain according to the node type data to obtain a classification result;
aggregating the rule nodes according to the classification result to obtain rule node information clusters of different categories;
class labels are added to the regular node information clusters, and a class label database is generated by collecting all class labels.
Optionally, in the method for processing rule chain setting based on feature classification according to the present application, the obtaining telemetry data of gateway equipment, and analyzing the telemetry data to obtain data feature information specifically includes:
Acquiring telemetry data of gateway equipment;
analyzing the telemetry data through a preset data characteristic analysis model to obtain data characteristic information, wherein the data characteristic information comprises characteristic parameter information, attribute information and data quantity information.
Optionally, in the rule chain setting processing method based on feature classification of the present application, the identifying abnormal data of the data feature information, removing the identified abnormal data, and performing standardized processing on telemetry data after removing the abnormal data to obtain standardized telemetry data specifically includes:
inputting the characteristic parameter information, the attribute information and the data quantity information into an abnormal data detection model for analysis and identification to obtain abnormal data;
removing the abnormal data, and performing standardized processing on telemetry data after removing to obtain standardized telemetry data;
the processing program formula of the standardized telemetry data is as follows:
；
wherein ,for standardizing telemetry data->For telemetry after rejection +.>For telemetry data mean>For telemetry data standard deviation>、/>、/>Characteristic parameter information, attribute information, data amount information, respectively +. >、、/>Is a preset characteristic coefficient.
Optionally, in the feature classification-based rule chain setting processing method of the present application, the analyzing the standardized telemetry data to obtain operation semantic information, inputting the operation semantic information into the class label database to perform matching recognition, and obtaining a target rule node information cluster corresponding to the index according to a matching result, specifically including:
inputting the standardized telemetry data into a preset semantic recognition model for analysis and recognition, and obtaining operation semantic information comprising program execution category data, feature quantity data, static semantic data and dynamic semantic data;
and inputting the program execution category information into the category label database for matching identification, obtaining a corresponding index path according to a matching result of the category label, and guiding to a corresponding target rule node information cluster according to the index path.
Optionally, in the feature classification-based rule chain setting processing method of the present application, the matching degree comparing the operation semantic information with feature data of all rule nodes in the target rule node information cluster sequentially to obtain a plurality of corresponding matching degree values, and comparing the matching degree values with a preset matching degree threshold to obtain a matching degree comparison result, which specifically includes:
Respectively inputting the feature quantity data, static semantic data and dynamic semantic data and parameter configuration data, execution rule data and execution process data of each rule node in the target rule node information cluster into a preset matching degree analysis model for processing to obtain a plurality of corresponding matching degree values;
the program processing formula of the matching degree value is as follows:
；
wherein ,for the matching degree value of the ith rule node, < +.>、/>、/>Feature data, static semantic data, dynamic semantic data, +.>、/>、/>Parameter configuration data, execution rule data, execution procedure data, respectively for the ith rule node,/for the first rule node>For presetting the matching compensation factor, < >>、/>、/>、/>、/>、/>Is a preset characteristic coefficient;
and respectively carrying out threshold comparison on the matching degree values corresponding to the rule nodes and a preset matching degree threshold value to obtain a matching degree comparison result.
Optionally, in the method for processing rule chain setting based on feature classification according to the present application, the determining whether to execute the operation behavior defined by each rule node according to the matching degree comparison result specifically includes:
if the matching degree comparison result corresponding to the rule node meets the preset threshold comparison requirement, taking the rule node as a target rule node, executing the operation behavior defined by the target rule node and outputting a result;
If the matching degree comparison result corresponding to the rule node does not meet the preset threshold comparison requirement, not executing operation behaviors and generating non-executable feedback information;
and sequencing the rule nodes according to the matching degree values corresponding to the rule nodes to obtain sequencing results, and synchronously pushing the sequencing results and the non-executable feedback information.
In a second aspect, the present application provides a rule chain setting processing system based on feature classification, the system comprising: the memory comprises a program for processing the rule chain setting based on the feature classification, and the program for processing the rule chain setting based on the feature classification realizes the following steps when being executed by the processor:
acquiring rule node information of each rule node of a rule chain;
extracting features of the rule node information to obtain feature data, classifying each rule node of a rule chain according to the feature data to generate rule node information clusters of different categories, adding class labels into the rule node information clusters, and generating a class label database for all class label sets;
acquiring telemetry data of gateway equipment, and analyzing the telemetry data to acquire data characteristic information;
Identifying abnormal data of the data characteristic information, eliminating the identified abnormal data, and carrying out standardized processing on telemetry data after eliminating the abnormal data to obtain standardized telemetry data;
analyzing the standardized telemetry data to obtain operation semantic information, inputting the operation semantic information into the class label database for matching and identifying, and obtaining a target rule node information cluster corresponding to the index according to a matching result;
sequentially comparing the operation semantic information with the characteristic data of all rule nodes in the target rule node information cluster to obtain a plurality of corresponding matching degree values, and comparing the matching degree values with a preset matching degree threshold value to obtain a matching degree comparison result;
and judging whether to execute the operation behaviors defined by each rule node according to the matching degree comparison result.
Optionally, in the rule chain setting processing system based on feature classification of the present application, feature extraction is performed on the rule node information to obtain feature data, each rule node of the rule chain is classified according to the feature data to generate rule node information clusters of different categories, class labels are added to the rule node information clusters, and a class label database is generated for all class label sets, including:
Inputting rule node information into a preset feature recognition model for analysis and recognition to obtain feature data, wherein the feature data comprises node type data, parameter configuration data, execution rule data and execution process data;
classifying each rule node of the rule chain according to the node type data to obtain a classification result;
aggregating the rule nodes according to the classification result to obtain rule node information clusters of different categories;
class labels are added to the regular node information clusters, and a class label database is generated by collecting all class labels.
In a third aspect, the present application also provides a computer-readable storage medium having embodied therein a rule chain setting processing method program based on feature classification, which when executed by a processor, implements the steps of the rule chain setting processing method based on feature classification as described in any one of the above.
As can be seen from the above, the rule chain setting processing method, system and medium based on feature classification provided by the application are used for classifying each rule node of the rule chain according to the rule node feature data to generate rule node information clusters of different types, performing standardization processing on telemetry data after abnormal data are removed, sequentially performing matching degree comparison on telemetry data operation semantic information and feature data of all rule nodes in the target rule node information cluster to obtain a plurality of corresponding matching degree values, further obtaining a matching degree comparison result, judging whether to execute operation behaviors defined by each rule node according to the matching degree comparison result, realizing high-efficiency operation of the rule chain, and realizing more accurate data processing by pre-removing the abnormal data and performing standardization processing on telemetry data after the abnormal data are removed.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a rule chain setting processing method based on feature classification according to an embodiment of the present application;
FIG. 2 is a flowchart of a rule chain setting processing method based on feature classification for generating a class label database according to an embodiment of the present application;
FIG. 3 is a flowchart of a method for processing rule chain setting based on feature classification to obtain data feature information according to an embodiment of the present application;
Fig. 4 is a schematic structural diagram of a rule chain setting processing system based on feature classification according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart of a rule chain setting processing method based on feature classification according to some embodiments of the application. The rule chain setting processing method based on the feature classification is used in terminal equipment, such as a computer, a mobile phone terminal and the like. The rule chain setting processing method based on feature classification comprises the following steps:
s101, acquiring rule node information of each rule node of a rule chain;
s102, extracting features of the rule node information to obtain feature data, classifying each rule node of a rule chain according to the feature data to generate rule node information clusters of different categories, adding class labels to the rule node information clusters, and generating a class label database for all class label sets;
s103, acquiring telemetry data of gateway equipment, and analyzing the telemetry data to acquire data characteristic information;
s104, identifying abnormal data of the data characteristic information, eliminating the identified abnormal data, and carrying out standardized processing on telemetry data after eliminating the abnormal data to obtain standardized telemetry data;
s105, analyzing the standardized telemetry data to obtain operation semantic information, inputting the operation semantic information into the class label database for matching and identifying, and obtaining a target rule node information cluster corresponding to the index according to a matching result;
S106, matching degree comparison is sequentially carried out on the operation semantic information and the characteristic data of all rule nodes in the target rule node information cluster to obtain a plurality of corresponding matching degree values, and the matching degree values are compared with a preset matching degree threshold value to obtain a matching degree comparison result;
and S107, judging whether to execute the operation behaviors defined by the rule nodes according to the matching degree comparison result.
In order to more rapidly identify a corresponding target rule node, obtain more accurate and effective telemetry data and eliminate dimension influence among different telemetry data, so that comparability among different data indexes is achieved, and further rule node processing efficiency is improved.
Referring to fig. 2, fig. 2 is a flowchart of a method for generating a class label database according to a rule chain setting processing method based on feature classification in some embodiments of the application. According to the embodiment of the application, the feature extraction is performed on the rule node information to obtain feature data, each rule node of the rule chain is classified according to the feature data to generate rule node information clusters of different categories, class labels are added to the rule node information clusters, and a class label database is generated for all class label sets, and the method specifically comprises the following steps:
s201, inputting rule node information into a preset feature recognition model for analysis and recognition to obtain feature data, wherein the feature data comprises node type data, parameter configuration data, execution rule data and execution process data;
s202, classifying each rule node of a rule chain according to the node type data to obtain a classification result;
s203, aggregating the rule nodes according to the classification result to obtain rule node information clusters of different categories;
s204, class labels are added to the regular node information clusters, and a class label database is generated by collecting all class labels.
The method includes the steps that rule node information is input into a preset feature recognition model to be analyzed and recognized, feature data is obtained, the feature data comprises node type data, parameter configuration data, execution rule data and execution process data, the feature recognition model is a model obtained by training rule node information and feature data of a large number of historical samples, the corresponding output feature data can be obtained through input of relevant information to be processed, each rule node of a rule chain is classified according to the node type to obtain a classification result, each rule node of the rule chain is classified according to the classification result to generate rule node information clusters of different types, class labels are added to the rule node information clusters, and a class label database is generated by all class label sets.
Referring to fig. 3, fig. 3 is a flowchart of a method for processing rule chain setting based on feature classification to obtain data feature information according to an embodiment of the present application. According to an embodiment of the present application, the obtaining telemetry data of gateway equipment, and analyzing the telemetry data to obtain data feature information specifically includes:
s301, acquiring telemetry data of gateway equipment;
s302, analyzing the telemetry data through a preset data characteristic analysis model to obtain data characteristic information, wherein the data characteristic information comprises characteristic parameter information, attribute information and data quantity information.
It should be noted that, obtaining telemetry data of gateway equipment, inputting the telemetry data into a feature extraction model for analysis to obtain data feature information, where the data feature information includes feature parameter information, attribute information and data volume information, the feature extraction model is a model obtained by obtaining telemetry data of a large number of historical samples and training the data feature information, and the data feature information corresponding to output can be obtained by inputting related information for processing.
According to an embodiment of the present application, the identifying the abnormal data of the data feature information, removing the identified abnormal data, and performing standardization processing on telemetry data after removing the abnormal data to obtain standardized telemetry data specifically includes:
Inputting the characteristic parameter information, the attribute information and the data quantity information into an abnormal data detection model for analysis and identification to obtain abnormal data;
removing the abnormal data, and performing standardized processing on telemetry data after removing to obtain standardized telemetry data;
the processing program formula of the standardized telemetry data is as follows:
；
wherein ,for standardizing telemetry data->For telemetry after rejection +.>For telemetry data mean>For telemetry data standard deviation>、/>、/>Characteristic parameter information, attribute information, data amount information, respectively +.>、、/>The characteristic coefficient is preset (the characteristic coefficient is obtained by inquiring a preset rule chain data information base).
In order to obtain more accurate and effective telemetry data and eliminate dimension influence among different data so as to improve rule node processing efficiency, firstly, characteristic parameter information, attribute information and data quantity information are input into an abnormal behavior detection model for analysis and identification, so that abnormal data is obtained, the abnormal behavior detection model is a model obtained by training the characteristic parameter information, the attribute information, the data quantity information and the abnormal data of a large number of historical samples, the correspondingly output abnormal data can be obtained by inputting relevant information for processing, then the abnormal data is removed, and the telemetry data after the abnormal data is removed is subjected to standardized processing so as to eliminate dimension influence among different source data, so that comparability among different data indexes is provided, and more accurate and effective telemetry data is obtained.
According to an embodiment of the present invention, the analyzing the standardized telemetry data to obtain operation semantic information, inputting the operation semantic information into the class label database to perform matching recognition, and obtaining a target rule node information cluster of a corresponding index according to a matching result, specifically includes:
inputting the standardized telemetry data into a preset semantic recognition model for analysis and recognition, and obtaining operation semantic information comprising program execution category data, feature quantity data, static semantic data and dynamic semantic data;
and inputting the program execution category information into the category label database for matching identification, obtaining a corresponding index path according to a matching result of the category label, and guiding to a corresponding target rule node information cluster according to the index path.
In order to match the telemetry data to the corresponding category rule node information cluster, firstly inputting the telemetry data into a preset semantic recognition model for analysis and recognition to obtain operation semantic information, then carrying out matching recognition on a category label database according to the operation semantic information, and indexing the category label database to a target rule node information cluster according to a category label matching result, wherein the semantic recognition model is a model obtained by training the telemetry data and the operation semantic information of a large number of historical samples, and can obtain the operation semantic information which is correspondingly output through inputting related information for processing.
According to the embodiment of the invention, the matching degree comparison is sequentially performed on the operation semantic information and the feature data of all rule nodes in the target rule node information cluster to obtain a plurality of corresponding matching degree values, and the matching degree values are compared with a preset matching degree threshold value to obtain a matching degree comparison result, which specifically comprises the following steps:
respectively inputting the feature quantity data, static semantic data and dynamic semantic data and parameter configuration data, execution rule data and execution process data of each rule node in the target rule node information cluster into a preset matching degree analysis model for processing to obtain a plurality of corresponding matching degree values;
the program processing formula of the matching degree value is as follows:
；
wherein ,for the matching degree value of the ith rule node, < +.>、/>、/>Feature data, static semantic data, dynamic semantic data, +.>、/>、/>Parameter configuration data, execution rule data, execution procedure data, respectively for the ith rule node,/for the first rule node>For presetting the matching compensation factor, < >>、/>、/>、/>、/>、/>The characteristic coefficient is preset (the characteristic coefficient is obtained by inquiring a preset rule chain data information base);
and respectively carrying out threshold comparison on the matching degree values corresponding to the rule nodes and a preset matching degree threshold value to obtain a matching degree comparison result.
It should be noted that, matching degree comparison is performed in sequence according to the operation semantic information and the feature data of all rule nodes in the target rule node information cluster respectively, so as to obtain a plurality of corresponding matching degree values, and threshold comparison is performed according to each matching degree value and a preset matching degree threshold value respectively, so as to obtain each corresponding matching degree comparison result.
According to an embodiment of the present invention, the determining whether to execute the operation behavior defined by each rule node according to the matching degree comparison result specifically includes:
if the matching degree comparison result corresponding to the rule node meets the preset threshold comparison requirement, taking the rule node as a target rule node, executing the operation behavior defined by the target rule node and outputting a result;
if the matching degree comparison result corresponding to the rule node does not meet the preset threshold comparison requirement, not executing operation behaviors and generating non-executable feedback information;
and sequencing the rule nodes according to the matching degree values corresponding to the rule nodes to obtain sequencing results, and synchronously pushing the sequencing results and the non-executable feedback information.
It should be noted that, if the rule node information corresponds to the matching degree comparison result and accords with the preset comparison result, the corresponding rule node is taken as the target rule node, the operation behavior defined by the target rule node is executed and the result is output, if the rule node information corresponds to the matching degree comparison result and does not accord with the preset comparison result, the operation is not executed, non-executable feedback information is generated, the rule node information is ordered according to the matching degree value, and the ordering result is pushed.
According to an embodiment of the present invention, further comprising:
acquiring rule chain logic operation information, including behavior path information, time instruction parameter information, rule execution log information and security log information;
inputting behavior path information, operation time-consuming information, rule execution log information and safety log information into a fault diagnosis model to obtain a rule chain fault diagnosis result;
and positioning the fault rule nodes according to the rule chain fault diagnosis result.
It should be noted that, through carrying out full-flow monitoring to the rule chain operation process, in order to realize carrying out the purpose of accurate location to the fault node, obtain rule chain logic operation information at first, input logic operation information into the fault diagnosis model, obtain rule chain fault diagnosis result, and then position the fault rule node according to the fault diagnosis result.
As shown in fig. 4, the invention also discloses a rule chain setting processing system 4 based on feature classification, which comprises a memory 41 and a processor 42, wherein the memory comprises a rule chain setting processing method program based on feature classification, and the rule chain setting processing method program based on feature classification realizes the following steps when being executed by the processor:
Acquiring rule node information of each rule node of a rule chain;
extracting features of the rule node information to obtain feature data, classifying each rule node of a rule chain according to the feature data to generate rule node information clusters of different categories, adding class labels into the rule node information clusters, and generating a class label database for all class label sets;
acquiring telemetry data of gateway equipment, and analyzing the telemetry data to acquire data characteristic information;
identifying abnormal data of the data characteristic information, eliminating the identified abnormal data, and carrying out standardized processing on telemetry data after eliminating the abnormal data to obtain standardized telemetry data;
analyzing the standardized telemetry data to obtain operation semantic information, inputting the operation semantic information into the class label database for matching and identifying, and obtaining a target rule node information cluster corresponding to the index according to a matching result;
sequentially comparing the operation semantic information with the characteristic data of all rule nodes in the target rule node information cluster to obtain a plurality of corresponding matching degree values, and comparing the matching degree values with a preset matching degree threshold value to obtain a matching degree comparison result;
And judging whether to execute the operation behaviors defined by each rule node according to the matching degree comparison result.
In order to more rapidly identify a corresponding target rule node, obtain more accurate and effective telemetry data and eliminate dimension influence among different telemetry data, so that comparability among different data indexes is achieved, and further rule node processing efficiency is improved.
According to the embodiment of the invention, the feature extraction is performed on the rule node information to obtain feature data, each rule node of the rule chain is classified according to the feature data to generate rule node information clusters of different categories, class labels are added to the rule node information clusters, and a class label database is generated for all class label sets, and the method specifically comprises the following steps:
Inputting rule node information into a preset feature recognition model for analysis and recognition to obtain feature data, wherein the feature data comprises node type data, parameter configuration data, execution rule data and execution process data;
classifying each rule node of the rule chain according to the node type data to obtain a classification result;
aggregating the rule nodes according to the classification result to obtain rule node information clusters of different categories;
class labels are added to the regular node information clusters, and a class label database is generated by collecting all class labels.
The method includes the steps that rule node information is input into a preset feature recognition model to be analyzed and recognized, feature data is obtained, the feature data comprises node type data, parameter configuration data, execution rule data and execution process data, the feature recognition model is a model obtained by training rule node information and feature data of a large number of historical samples, the corresponding output feature data can be obtained through input of relevant information to be processed, each rule node of a rule chain is classified according to the node type to obtain a classification result, each rule node of the rule chain is classified according to the classification result to generate rule node information clusters of different types, class labels are added to the rule node information clusters, and a class label database is generated by all class label sets.
According to an embodiment of the present invention, the obtaining telemetry data of gateway equipment, and analyzing the telemetry data to obtain data feature information specifically includes:
acquiring telemetry data of gateway equipment;
analyzing the telemetry data through a preset data characteristic analysis model to obtain data characteristic information, wherein the data characteristic information comprises characteristic parameter information, attribute information and data quantity information.
It should be noted that, obtaining telemetry data of gateway equipment, inputting the telemetry data into a feature extraction model for analysis to obtain data feature information, where the data feature information includes feature parameter information, attribute information and data volume information, the feature extraction model is a model obtained by obtaining telemetry data of a large number of historical samples and training the data feature information, and the data feature information corresponding to output can be obtained by inputting related information for processing.
According to an embodiment of the present invention, the identifying the abnormal data of the data feature information, removing the identified abnormal data, and performing standardization processing on telemetry data after removing the abnormal data to obtain standardized telemetry data specifically includes:
Inputting the characteristic parameter information, the attribute information and the data quantity information into an abnormal data detection model for analysis and identification to obtain abnormal data;
removing the abnormal data, and performing standardized processing on telemetry data after removing to obtain standardized telemetry data;
the processing program formula of the standardized telemetry data is as follows:
；
wherein ,for standardizing telemetry data->For telemetry after rejection +.>For telemetry data mean>For telemetry data standard deviation>、/>、/>Characteristic parameter information, attribute information, data amount information, respectively +.>、、/>The characteristic coefficient is preset (the characteristic coefficient is obtained by inquiring a preset rule chain data information base).
In order to obtain more accurate and effective telemetry data and eliminate dimension influence among different data so as to improve rule node processing efficiency, firstly, characteristic parameter information, attribute information and data quantity information are input into an abnormal behavior detection model for analysis and identification, so that abnormal data is obtained, the abnormal behavior detection model is a model obtained by training the characteristic parameter information, the attribute information, the data quantity information and the abnormal data of a large number of historical samples, the correspondingly output abnormal data can be obtained by inputting relevant information for processing, then the abnormal data is removed, and the telemetry data after the abnormal data is removed is subjected to standardized processing so as to eliminate dimension influence among different source data, so that comparability among different data indexes is provided, and more accurate and effective telemetry data is obtained.
According to an embodiment of the present invention, the analyzing the standardized telemetry data to obtain operation semantic information, inputting the operation semantic information into the class label database to perform matching recognition, and obtaining a target rule node information cluster of a corresponding index according to a matching result, specifically includes:
inputting the standardized telemetry data into a preset semantic recognition model for analysis and recognition, and obtaining operation semantic information comprising program execution category data, feature quantity data, static semantic data and dynamic semantic data;
and inputting the program execution category information into the category label database for matching identification, obtaining a corresponding index path according to a matching result of the category label, and guiding to a corresponding target rule node information cluster according to the index path.
In order to match the telemetry data to the corresponding category rule node information cluster, firstly inputting the telemetry data into a preset semantic recognition model for analysis and recognition to obtain operation semantic information, then carrying out matching recognition on a category label database according to the operation semantic information, and indexing the category label database to a target rule node information cluster according to a category label matching result, wherein the semantic recognition model is a model obtained by training the telemetry data and the operation semantic information of a large number of historical samples, and can obtain the operation semantic information which is correspondingly output through inputting related information for processing.
According to the embodiment of the invention, the matching degree comparison is sequentially performed on the operation semantic information and the feature data of all rule nodes in the target rule node information cluster to obtain a plurality of corresponding matching degree values, and the matching degree values are compared with a preset matching degree threshold value to obtain a matching degree comparison result, which specifically comprises the following steps:
respectively inputting the feature quantity data, static semantic data and dynamic semantic data and parameter configuration data, execution rule data and execution process data of each rule node in the target rule node information cluster into a preset matching degree analysis model for processing to obtain a plurality of corresponding matching degree values;
the program processing formula of the matching degree value is as follows:
；
wherein ,for the matching degree value of the ith rule node, < +.>、/>、/>Feature data, static semantic data, dynamic semantic data, +.>、/>、/>Parameter configuration data, execution rule data, execution procedure data, respectively for the ith rule node,/for the first rule node>For presetting the matching compensation factor, < >>、/>、/>、/>、/>、/>The characteristic coefficient is preset (the characteristic coefficient is obtained by inquiring a preset rule chain data information base);
and respectively carrying out threshold comparison on the matching degree values corresponding to the rule nodes and a preset matching degree threshold value to obtain a matching degree comparison result.
It should be noted that, matching degree comparison is performed in sequence according to the operation semantic information and the feature data of all rule nodes in the target rule node information cluster respectively, so as to obtain a plurality of corresponding matching degree values, and threshold comparison is performed according to each matching degree value and a preset matching degree threshold value respectively, so as to obtain each corresponding matching degree comparison result.
According to an embodiment of the present invention, the determining whether to execute the operation behavior defined by each rule node according to the matching degree comparison result specifically includes:
if the matching degree comparison result corresponding to the rule node meets the preset threshold comparison requirement, taking the rule node as a target rule node, executing the operation behavior defined by the target rule node and outputting a result;
if the matching degree comparison result corresponding to the rule node does not meet the preset threshold comparison requirement, not executing operation behaviors and generating non-executable feedback information;
and sequencing the rule nodes according to the matching degree values corresponding to the rule nodes to obtain sequencing results, and synchronously pushing the sequencing results and the non-executable feedback information.
It should be noted that, if the rule node information corresponds to the matching degree comparison result and accords with the preset comparison result, the corresponding rule node is taken as the target rule node, the operation behavior defined by the target rule node is executed and the result is output, if the rule node information corresponds to the matching degree comparison result and does not accord with the preset comparison result, the operation is not executed, non-executable feedback information is generated, the rule node information is ordered according to the matching degree value, and the ordering result is pushed.
According to an embodiment of the present invention, further comprising:
acquiring rule chain logic operation information, including behavior path information, time instruction parameter information, rule execution log information and security log information;
inputting behavior path information, operation time-consuming information, rule execution log information and safety log information into a fault diagnosis model to obtain a rule chain fault diagnosis result;
and positioning the fault rule nodes according to the rule chain fault diagnosis result.
It should be noted that, through carrying out full-flow monitoring to the rule chain operation process, in order to realize carrying out the purpose of accurate location to the fault node, obtain rule chain logic operation information at first, input logic operation information into the fault diagnosis model, obtain rule chain fault diagnosis result, and then position the fault rule node according to the fault diagnosis result.
A third aspect of the present invention provides a readable storage medium having embodied therein a rule chain setting processing method program based on feature classification, which when executed by a processor, implements the steps of the rule chain setting processing method based on feature classification as described in any one of the above.
In order to more rapidly identify a corresponding target rule node, acquire more accurate and effective telemetry data and eliminate dimension influence among different telemetry data, so that comparability among different data indexes is achieved, and further rule node processing efficiency is improved.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
Claims (7)
1. The rule chain setting processing method based on feature classification is characterized by comprising the following steps of:
acquiring rule node information of each rule node of a rule chain;
extracting features of the rule node information to obtain feature data, classifying each rule node of a rule chain according to the feature data to generate rule node information clusters of different categories, adding class labels into the rule node information clusters, and generating a class label database for all class label sets;
Acquiring telemetry data of gateway equipment, and analyzing the telemetry data to acquire data characteristic information;
identifying abnormal data of the data characteristic information, eliminating the identified abnormal data, and carrying out standardized processing on telemetry data after eliminating the abnormal data to obtain standardized telemetry data;
analyzing the standardized telemetry data to obtain operation semantic information, inputting the operation semantic information into the class label database for matching and identifying, and obtaining a target rule node information cluster corresponding to the index according to a matching result;
sequentially comparing the operation semantic information with the characteristic data of all rule nodes in the target rule node information cluster to obtain a plurality of corresponding matching degree values, and comparing the matching degree values with a preset matching degree threshold value to obtain a matching degree comparison result;
judging whether to execute the operation behaviors defined by each rule node according to the matching degree comparison result;
the method comprises the steps of analyzing the standardized telemetry data to obtain operation semantic information, inputting the operation semantic information into the class label database to carry out matching identification, and obtaining a target rule node information cluster of a corresponding index according to a matching result, wherein the method specifically comprises the following steps:
Inputting the standardized telemetry data into a preset semantic recognition model for analysis and recognition, and obtaining operation semantic information comprising program execution category data, feature quantity data, static semantic data and dynamic semantic data;
inputting the program execution category data into the category label database for matching identification, obtaining a corresponding index path according to a matching result of the category label, and guiding to a corresponding target rule node information cluster according to the index path;
the matching degree comparison is sequentially performed on the operation semantic information and the characteristic data of all rule nodes in the target rule node information cluster to obtain a plurality of corresponding matching degree values, and the matching degree values are compared with a preset matching degree threshold value to obtain a matching degree comparison result, which specifically comprises the following steps:
respectively inputting the feature quantity data, static semantic data and dynamic semantic data and parameter configuration data, execution rule data and execution process data of each rule node in the target rule node information cluster into a preset matching degree analysis model for processing to obtain a plurality of corresponding matching degree values;
the program processing formula of the matching degree value is as follows:
；
wherein ,for the matching degree value of the ith rule node, < +. >、/>、/>Feature data, static semantic data, dynamic semantic data, +.>、/>、/>Parameter configuration data, execution rule data, execution procedure data, respectively for the ith rule node,/for the first rule node>For presetting the matching compensation factor, < >>、/>、/>、/>、/>、/>Is a preset characteristic coefficient;
respectively carrying out threshold comparison on the matching degree values corresponding to the rule nodes and a preset matching degree threshold value to obtain a matching degree comparison result;
the step of judging whether to execute the operation behaviors defined by the rule nodes according to the matching degree comparison result specifically comprises the following steps:
if the matching degree comparison result corresponding to the rule node meets the preset threshold comparison requirement, taking the rule node as a target rule node, executing the operation behavior defined by the target rule node and outputting a result;
if the matching degree comparison result corresponding to the rule node does not meet the preset threshold comparison requirement, not executing operation behaviors and generating non-executable feedback information;
and sequencing the rule nodes according to the matching degree values corresponding to the rule nodes to obtain sequencing results, and synchronously pushing the sequencing results and the non-executable feedback information.
2. The method for processing the rule chain setting based on the feature classification according to claim 1, wherein the feature extraction is performed on the rule node information to obtain feature data, each rule node of the rule chain is classified according to the feature data to generate rule node information clusters of different categories, class labels are added to the rule node information clusters, and a class label database is generated for all class label sets, and the method specifically comprises the steps of:
Inputting rule node information into a preset feature recognition model for analysis and recognition to obtain feature data, wherein the feature data comprises node type data, parameter configuration data, execution rule data and execution process data;
classifying each rule node of the rule chain according to the node type data to obtain a classification result;
aggregating the rule nodes according to the classification result to obtain rule node information clusters of different categories;
class labels are added to the regular node information clusters, and a class label database is generated by collecting all class labels.
3. The rule chain setting processing method based on feature classification according to claim 2, wherein the obtaining telemetry data of gateway equipment, analyzing the telemetry data to obtain data feature information, specifically comprises:
acquiring telemetry data of gateway equipment;
analyzing the telemetry data through a preset data characteristic analysis model to obtain data characteristic information, wherein the data characteristic information comprises characteristic parameter information, attribute information and data quantity information.
4. The rule chain setting processing method based on feature classification according to claim 3, wherein the identifying the data feature information as abnormal data, rejecting the identified abnormal data, and performing standardized processing on telemetry data after rejecting the abnormal data to obtain standardized telemetry data, specifically comprises:
Inputting the characteristic parameter information, the attribute information and the data quantity information into an abnormal data detection model for analysis and identification to obtain abnormal data;
removing the abnormal data, and performing standardized processing on telemetry data after removing to obtain standardized telemetry data;
the processing program formula of the standardized telemetry data is as follows:
；
wherein ,for standardizing telemetry data->For telemetry after rejection +.>For telemetry data mean>For telemetry data standard deviation>、/>、/>Characteristic parameter information, attribute information, data amount information, respectively +.>、/>、/>Is a preset characteristic coefficient.
5. The rule chain setting processing system based on the feature classification is characterized by comprising a memory and a processor, wherein the memory comprises a rule chain setting processing method program based on the feature classification, and the rule chain setting processing method program based on the feature classification realizes the following steps when being executed by the processor:
acquiring rule node information of each rule node of a rule chain;
extracting features of the rule node information to obtain feature data, classifying each rule node of a rule chain according to the feature data to generate rule node information clusters of different categories, adding class labels into the rule node information clusters, and generating a class label database for all class label sets;
Acquiring telemetry data of gateway equipment, and analyzing the telemetry data to acquire data characteristic information;
identifying abnormal data of the data characteristic information, eliminating the identified abnormal data, and carrying out standardized processing on telemetry data after eliminating the abnormal data to obtain standardized telemetry data;
analyzing the standardized telemetry data to obtain operation semantic information, inputting the operation semantic information into the class label database for matching and identifying, and obtaining a target rule node information cluster corresponding to the index according to a matching result;
sequentially comparing the operation semantic information with the characteristic data of all rule nodes in the target rule node information cluster to obtain a plurality of corresponding matching degree values, and comparing the matching degree values with a preset matching degree threshold value to obtain a matching degree comparison result;
judging whether to execute the operation behaviors defined by each rule node according to the matching degree comparison result;
the method comprises the steps of analyzing the standardized telemetry data to obtain operation semantic information, inputting the operation semantic information into the class label database to carry out matching identification, and obtaining a target rule node information cluster of a corresponding index according to a matching result, wherein the method specifically comprises the following steps:
Inputting the standardized telemetry data into a preset semantic recognition model for analysis and recognition, and obtaining operation semantic information comprising program execution category data, feature quantity data, static semantic data and dynamic semantic data;
inputting the program execution category data into the category label database for matching identification, obtaining a corresponding index path according to a matching result of the category label, and guiding to a corresponding target rule node information cluster according to the index path;
the matching degree comparison is sequentially performed on the operation semantic information and the characteristic data of all rule nodes in the target rule node information cluster to obtain a plurality of corresponding matching degree values, and the matching degree values are compared with a preset matching degree threshold value to obtain a matching degree comparison result, which specifically comprises the following steps:
respectively inputting the feature quantity data, static semantic data and dynamic semantic data and parameter configuration data, execution rule data and execution process data of each rule node in the target rule node information cluster into a preset matching degree analysis model for processing to obtain a plurality of corresponding matching degree values;
the program processing formula of the matching degree value is as follows:
；
wherein ,for the matching degree value of the ith rule node, < +. >、/>、/>Feature data, static semantic data, dynamic semantic data, +.>、/>、/>Parameter configuration data, execution rule data, execution procedure data, respectively for the ith rule node,/for the first rule node>For presetting the matching compensation factor, < >>、/>、/>、/>、/>、/>Is a preset characteristic coefficient;
respectively carrying out threshold comparison on the matching degree values corresponding to the rule nodes and a preset matching degree threshold value to obtain a matching degree comparison result;
the step of judging whether to execute the operation behaviors defined by the rule nodes according to the matching degree comparison result specifically comprises the following steps:
if the matching degree comparison result corresponding to the rule node meets the preset threshold comparison requirement, taking the rule node as a target rule node, executing the operation behavior defined by the target rule node and outputting a result;
if the matching degree comparison result corresponding to the rule node does not meet the preset threshold comparison requirement, not executing operation behaviors and generating non-executable feedback information;
and sequencing the rule nodes according to the matching degree values corresponding to the rule nodes to obtain sequencing results, and synchronously pushing the sequencing results and the non-executable feedback information.
6. The feature classification-based rule chain setting processing system according to claim 5, wherein the feature extraction is performed on the rule node information to obtain feature data, each rule node of the rule chain is classified according to the feature data to generate rule node information clusters of different categories, class labels are added to the rule node information clusters, and a class label database is generated for all class label sets, and the method specifically comprises:
Inputting rule node information into a preset feature recognition model for analysis and recognition to obtain feature data, wherein the feature data comprises node type data, parameter configuration data, execution rule data and execution process data;
classifying each rule node of the rule chain according to the node type data to obtain a classification result;
aggregating the rule nodes according to the classification result to obtain rule node information clusters of different categories;
class labels are added to the regular node information clusters, and a class label database is generated by collecting all class labels.
7. A computer-readable storage medium, wherein a rule chain setting processing method program based on feature classification is included in the computer-readable storage medium, which when executed by a processor, implements the steps of the rule chain setting processing method based on feature classification as claimed in any one of claims 1 to 4.
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