CN111813593B - Data processing method, device, server and storage medium - Google Patents
Data processing method, device, server and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a data processing method, equipment, a server and a storage medium, wherein the method comprises the following steps: acquiring abnormal test data generated during the test of the interface; testing the abnormal test data through an interface test program to obtain abnormal field data; identifying the abnormal field data according to an abnormal identification algorithm to determine the actual result characteristic representation corresponding to the abnormal field data; inputting the actual result characteristic representation into a rule recognition model for processing so as to obtain an actual abnormal business rule representation; and comparing the actual abnormal business rule representation with the expected abnormal business rule representation, and if the comparison result is that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, improving the rule recognition model until the actual abnormal business rule representation in the comparison result is matched with the expected abnormal business rule representation. By the implementation mode, model autonomous learning and maintenance efficiency of automatic cases are improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data processing method, a device, a server, and a storage medium.
Background
In order to reduce the frequency of faults in a product during use, each interface needs to be tested, and with the continuous development of automatic testing technology, more and more automatic testing tools are layered, such as a Jmeter for the practical tool of interface automation, and other secondary development based on the tool. The tools can perform a large amount of repeated labor, and can replace manpower to realize the timing regression and maintenance work of automatic cases. But presents a significant disadvantage in many respects, such as the original test case will not be usable when the demand is changed, and the large number of stock cases makes maintenance costs prohibitive. Therefore, how to improve the efficiency of writing and maintaining cases is a problem to be solved.
Disclosure of Invention
The embodiment of the invention provides a data processing method, data processing equipment, a server and a storage medium, wherein a rule learning model is improved in real time according to a test result, so that the autonomous learning of the model is greatly improved, the generation, storage and maintenance of basic business rules are automatically completed, and the maintenance efficiency of an automatic case is improved.
In a first aspect, an embodiment of the present invention provides a data processing method, including:
acquiring abnormal test data generated during the test of the interface;
testing the abnormal test data through an interface test program to obtain abnormal field data in the abnormal test data;
identifying the abnormal field data according to an abnormal identification algorithm to determine the actual result characteristic representation corresponding to the abnormal field data;
inputting the actual result characteristic representation of the abnormal field data into a rule recognition model for processing so as to obtain an actual abnormal business rule representation;
and comparing the actual abnormal business rule representation with the expected abnormal business rule representation, and if the comparison result shows that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, improving the rule identification model until the actual abnormal business rule representation in the comparison result is matched with the expected abnormal business rule representation.
Further, before the actual result characteristic representation of the abnormal field data is input into the rule recognition model for processing, the method further comprises the following steps:
Acquiring sample training data, wherein the sample training data comprises sample data of expected abnormal business rule representation and actual result characteristic representation;
and inputting the sample training data into a neural network model for training to obtain the rule recognition model.
Further, the training the sample training data into a neural network model to obtain the rule recognition model includes:
inputting the expected abnormal business rule representation and the actual result characteristic representation in the sample training data into a first level of the neural network model for training;
inputting the training result of the first level into a second level of the neural network model for training, and inputting the training result of the second level into a third level of the neural network model for training to obtain an actual abnormal business rule representation;
outputting the actual abnormal business rule representation obtained by training of the third layer through a fourth layer of the neural network model, and determining the rule recognition model according to a comparison result of the actual abnormal business rule representation and the expected abnormal business rule representation, wherein the first layer is a feature set layer, the second layer is a hidden layer, the third layer is an abnormal business rule representation set layer, and the fourth layer is an output layer.
Further, if the comparison result is that the actual abnormal business rule representation does not match the expected abnormal business rule representation, the rule identification model is improved, including:
if the comparison result shows that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, detecting whether the nodes in the rule identification model are abnormal or not;
if the abnormal occurrence of the nodes in the rule recognition model is detected, the nodes in the rule recognition model are adjusted according to the abnormal occurrence, and the adjusted rule recognition model is retrained to improve the rule recognition model.
Further, the adjusting the nodes in the rule recognition model according to the anomaly includes:
detecting whether all nodes represented by the actual abnormal business rules exist in a third level of the rule identification model;
and if the detection result is that all the nodes represented by the actual abnormal business rules do not exist in the third hierarchy, adding the nodes represented by the actual abnormal business rules which do not exist in the third hierarchy into the third hierarchy.
Further, the adjusting the nodes in the rule recognition model according to the anomaly includes:
detecting whether all nodes in a third level of the rule recognition model are triggered;
if the detection result is that all the nodes of the third level are not triggered, adding a compensating node in the third level;
and acquiring a type determining operation of the compensation node input by a user, and determining the type of the compensation node according to the type determining operation.
Further, the adjusting the nodes in the rule recognition model according to the anomaly includes:
detecting whether nodes represented by the actual result features exist in a first level of the rule recognition model;
if the detection result is that the node represented by the actual result feature does not exist in the first hierarchy, adding the node represented by the actual result feature in the first hierarchy.
In a second aspect, an embodiment of the present invention provides a data processing apparatus, including:
the acquisition unit is used for acquiring abnormal test data generated when the interface is tested;
the test unit is used for testing the abnormal test data through an interface test program to obtain abnormal field data in the abnormal test data;
The identification unit is used for identifying the abnormal field data according to an abnormal identification algorithm so as to determine the actual result characteristic representation corresponding to the abnormal field data;
the processing unit is used for processing the actual result characteristic representation input rule recognition model of the abnormal field data to obtain an actual abnormal business rule representation;
and the improvement unit is used for comparing the actual abnormal business rule representation with the expected abnormal business rule representation, and if the comparison result shows that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, the rule identification model is improved until the actual abnormal business rule representation in the comparison result is matched with the expected abnormal business rule representation.
In a third aspect, an embodiment of the present invention provides a server, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, and the memory is configured to store a computer program that supports a data processing device to execute the method described above, where the computer program includes a program, and where the processor is configured to invoke the program to execute the method of the first aspect described above.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program for execution by a processor to implement the method of the first aspect described above.
In the embodiment of the invention, the server can acquire the abnormal test data generated during the interface test, test the abnormal test data through an interface test program to obtain the abnormal field data in the abnormal test data, identify the abnormal field data according to an abnormal identification algorithm to determine the actual result characteristic representation corresponding to the abnormal field data, process the actual result characteristic representation of the abnormal field data into a rule identification model to obtain the actual abnormal business rule representation, compare the actual abnormal business rule representation with the expected abnormal business rule representation, and if the comparison result is that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, improve the rule identification model until the actual abnormal business rule representation in the comparison result is matched with the expected abnormal business rule representation. By the method, basic business rules are generated, stored and maintained autonomously, and model autonomous learning and automatic case maintenance efficiency are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a data processing method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a data processing method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an abnormal business rule representation according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a hierarchical structure of a rule recognition model according to an embodiment of the present invention;
FIG. 5 is a schematic block diagram of a data processing apparatus provided by an embodiment of the present invention;
fig. 6 is a schematic block diagram of a server according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Some embodiments of the present invention are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
The data processing method provided in the embodiment of the invention can be executed by a server, and in particular, can be executed by data processing equipment in the server.
In the process of the data processing method provided by the embodiment of the invention, as shown in fig. 1, fig. 1 is a schematic flow chart of the data processing method provided by the embodiment of the invention, as shown in fig. 1, a server may generate abnormal test data according to an abnormal data generating program, test the abnormal test data through an interface test program to obtain abnormal field data in the abnormal test data, identify the abnormal field data through an abnormal identification algorithm to obtain an actual result characteristic representation, process the actual result characteristic representation of the abnormal field data into a rule identification model to obtain an actual abnormal business rule representation, compare the actual abnormal business rule representation with an expected abnormal business rule representation through a result comparison program, and if the actual abnormal business rule representation in the comparison result does not match with the expected abnormal business rule representation, modify weights in a rule learning model to improve a rule learning model. By the method, basic business rules can be generated, stored and maintained autonomously, automatic optimization and improvement of the model are realized, and maintenance efficiency of an automatic case is improved.
The following is a schematic description of a data processing method according to an embodiment of the present invention with reference to fig. 2 to fig. 4.
Referring to fig. 2, fig. 2 is a schematic flowchart of a data processing method according to an embodiment of the present invention, and as shown in fig. 2, the method may be performed by a data processing device, where the data processing device is disposed in a server. Specifically, the method of the embodiment of the invention comprises the following steps.
S201: and acquiring abnormal test data generated when the interface is tested.
In the embodiment of the invention, the data processing equipment can acquire the abnormal test data generated during the test of the interface.
In one embodiment, the data processing apparatus may generate the abnormal test data according to a preset abnormal data generation program when acquiring the abnormal test data.
In one embodiment, the data processing apparatus may set an expected abnormal business rule representation prior to acquiring the abnormal test data, wherein the expected abnormal business rule representation is a standard representation of the abnormal business rule.
In some embodiments, the expected abnormal business rule representation may be represented by a tree structure, wherein the expected abnormal business rule representation is a classification of the abnormal business rule by a tree structure hierarchy. In some embodiments, the method may start from a root node of the tree structure, where an uppermost node where the root node is located is used to store abnormal traffic; from the root node, the method can be divided into field length exception, field type exception, field coding exception and other exception types; the anomaly type for each node may continue to be sorted down again until no sorting can continue.
Specifically, as can be illustrated by taking fig. 3 as an example, fig. 3 is a schematic structural diagram of an abnormal service rule representation provided by an embodiment of the present invention, and as shown in fig. 3, the abnormal service rule representation is expected, where the root node 31 is a node of an abnormal service type, the child nodes of the root node 31 include a child node 311 of a field length abnormality, a child node 312 of a field type abnormality, and a child node 313 of a field coding abnormality, where the child nodes of the child node 312 of the field type abnormality include a child node 3121 of an Int type abnormality, the child node 3121 of the Int type abnormality includes a child node 31211 of Int32 and a child node 31212 of Int64, and the child node 31212 of Int64 includes a child node 312121 of a special character abnormality, a child node 312122 of a chinese abnormality, and a child node 312123 of a key word abnormality.
In some embodiments, various exception types may be represented as binary symbol 0, 1 types, as shown in fig. 3, the exception traffic of root node 31 may be represented as 00, the field length exception of child node 311 may be represented as 00000, the field type exception of child node 312 may be represented as 00001, the field encoding exception of child node 313 may be represented as 00010, the Int type exception of child node 3121 may be represented as 00000, the Int32 of child node 31211 may be represented as 00000, the Int64 of child node 31212 may be represented as 00001, the special character exception of child node 312121 may be represented as 00000, the chinese exception of child node 312122 may be represented as 00001, the key exception of child node 312123 may be represented as 00010, wherein "...
Taking the keyword anomalies shown in fig. 3 as an example, experience starts from the root node: abnormal business- > field type abnormality- > Int64 abnormality- > keyword abnormality, the keyword abnormality is expressed in the form of 01: 00_00001_00001_00010.
By this representation of the abnormal business rules in binary form, the machine is facilitated to better identify the abnormal business rule representation.
S202: and testing the abnormal test data through an interface test program to obtain abnormal field data in the abnormal test data.
In the embodiment of the invention, the data processing equipment can test the abnormal test data through the interface test program to obtain the abnormal field data in the abnormal test data.
In one embodiment, the abnormal test data may be used as a request message parameter, and the interface test program is used to test the request message parameter to obtain the abnormal field data in the abnormal test data.
S203: and identifying the abnormal field data according to an abnormal identification algorithm to determine the actual result characteristic representation corresponding to the abnormal field data.
In the embodiment of the invention, the data processing equipment can identify the abnormal field data according to the abnormal identification algorithm so as to determine the actual result characteristic representation corresponding to the abnormal field data.
In one embodiment, the anomaly identification algorithm may be a natural language identification algorithm, and the data processing device may identify the anomaly field data according to the natural language identification algorithm to determine an actual resulting feature representation corresponding to the anomaly field data. In one example, assuming that the exception field data is a message field, if the message field cannot be of the int type, the actual result feature is expressed as message (set) not (logic) int (type).
S204: and processing the actual result characteristic representation input rule identification model of the abnormal field data to obtain an actual abnormal business rule representation.
In the embodiment of the invention, the data processing equipment can process the actual result characteristic representation input rule identification model of the abnormal field data to obtain the actual abnormal business rule representation.
In one embodiment, the data processing apparatus may acquire sample training data before processing the actual result characteristic representation of the anomaly field data into a rule recognition model, the sample training data including sample data of an expected anomaly traffic rule representation and an actual result characteristic representation, and input the sample training data into a neural network model to train the neural network model to obtain the rule recognition model. In some embodiments, the neural network model comprises an existing neural network model such as a convolutional neural network.
In one embodiment, when the sample training data is input into a neural network model to train to obtain the rule identification model, the data processing device may train the expected abnormal service rule representation and the actual result feature representation in the sample training data into a first level of the neural network model, train the training result of the first level into a second level of the neural network model, train the training result of the second level into a third level of the neural network model, and obtain the actual abnormal service rule representation, so that the actual abnormal service rule representation obtained by training the third level is output through a fourth level of the neural network model, and determine the rule identification model according to a comparison result of the actual abnormal service rule representation and the expected abnormal service rule representation, where the first level is a feature set level, the second level is a hidden layer, the third level is an abnormal service rule representation set layer, and the fourth level is an output layer.
In one embodiment, when the data processing device determines the rule recognition model according to the comparison result of the actual abnormal service rule representation and the expected abnormal service rule representation, the actual abnormal service rule representation and the expected abnormal service rule representation may be compared, if the comparison result is matched, the rule recognition model is determined to be obtained through current training, if the comparison result is not matched, the rule recognition model is required to be improved, and when the improved model is determined to be matched with the actual abnormal service rule representation and the expected abnormal service rule representation, the rule recognition model is determined to be obtained through training after improvement.
Specifically, as shown in fig. 4, fig. 4 is a schematic hierarchical structure diagram of a rule recognition model provided by an embodiment of the present invention, where, as shown in fig. 4, the rule recognition model includes 4 levels, a first level 41 is a feature set level, including nodes of an expected abnormal service rule representation and a plurality of feature nodes, a second level 42 is a hidden layer, including a plurality of hidden layer nodes, a third level 43 is an abnormal service rule representation set level, including a plurality of abnormal service rule representation nodes, and a fourth level 44 is an output layer, including an output node.
S205: and comparing the actual abnormal business rule representation with the expected abnormal business rule representation, and if the comparison result shows that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, improving the rule identification model until the actual abnormal business rule representation in the comparison result is matched with the expected abnormal business rule representation.
In the embodiment of the invention, the data processing device can compare the actual abnormal business rule representation with the expected abnormal business rule representation, and if the comparison result is that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, the rule identification model is improved until the actual abnormal business rule representation in the comparison result is matched with the expected abnormal business rule representation.
In one embodiment, when the comparison result is that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, the data processing device may detect whether an abnormality occurs in a node in the rule recognition model if the comparison result is that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, adjust the node in the rule recognition model according to the abnormality if the abnormality occurs in the node in the rule recognition model, and retrain the adjusted rule recognition model to improve the rule recognition model.
In one embodiment, the data processing apparatus may detect whether all nodes represented by the actual abnormal business rule exist in a third hierarchy of the rule recognition model when adjusting the nodes in the rule recognition model according to the abnormality, and if the detection result is that all nodes represented by the actual abnormal business rule do not exist in the third hierarchy, may add the nodes represented by the actual abnormal business rule that do not exist in the third hierarchy.
Specifically, since many unknown results are generated in the actual process, the number of nodes corresponding to the third level in the rule recognition model is continuously increased in the actual process, when the rule recognition model is improved, the third level of the rule recognition model can be modified, and the number of nodes of the third level can be changed.
For example, assuming that the actual abnormal business rule representation includes a Chinese abnormal representation, if no node of the Chinese abnormal is detected in the third level of the rule recognition model, a node of the Chinese abnormal representation may be added in the third level of the rule recognition model.
In one embodiment, when the data processing device adjusts the nodes in the rule recognition model according to the anomaly, the data processing device may detect whether all the nodes in a third level of the rule recognition model are triggered, if the detection result is that all the nodes in the third level are not triggered, a compensating node may be added in the third level, a type determining operation input by a user on the compensating node may be acquired, and the type of the compensating node may be determined according to the type determining operation. By adding the compensation node, a user can determine the type of the compensation node, and the rule recognition model can be improved on the basis of meeting the requirement of the user on the type of the node, so that the user experience is improved.
In one embodiment, the data processing apparatus may detect whether a node represented by the actual result feature exists in a first hierarchy of the rule recognition model when adjusting the node in the rule recognition model according to the anomaly, and may add the node represented by the actual result feature in the first hierarchy if the detection result is that the node represented by the actual result feature does not exist in the first hierarchy. In particular, the type of the node of the actual result feature representation may be determined from the actual abnormal business rule representation.
In one embodiment, after adjusting the nodes in the rule recognition model according to the anomalies, the adjusted rule model may be retrained to reset the weights in the rule recognition model to enable an improvement to the rule recognition model.
In one embodiment, the weights in the entire rule recognition model may be retrained when the adjusted rule model is retrained to reset the weights in the rule recognition model.
In one embodiment, when the adjusted rule model is retrained to reset the weights in the rule recognition model, a three-layer network model may be formed by taking the second level in the rule recognition model before modification as the first level, and the weights between the second level and the third level in the rule recognition model are reset to update the weights. By resetting weights between the second level and the third level in the rule recognition model, the efficiency of resetting weights in the rule recognition model is facilitated.
In the embodiment of the invention, the data processing equipment can acquire the abnormal test data generated during the interface test, test the abnormal test data through an interface test program to obtain abnormal field data in the abnormal test data, identify the abnormal field data according to an abnormal identification algorithm to determine the actual result characteristic representation corresponding to the abnormal field data, process the actual result characteristic representation of the abnormal field data into a rule identification model to obtain an actual abnormal business rule representation, compare the actual abnormal business rule representation with an expected abnormal business rule representation, and if the comparison result is that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, improve the rule identification model until the actual abnormal business rule representation in the comparison result is matched with the expected abnormal business rule representation. By the method, basic business rules are generated, stored and maintained autonomously, and model autonomous learning and automatic case maintenance efficiency are improved.
The embodiment of the invention also provides data processing equipment, which is used for executing the unit of the method. In particular, referring to fig. 5, fig. 5 is a schematic block diagram of a data processing apparatus according to an embodiment of the present invention. The data processing apparatus of the present embodiment includes: an acquisition unit 501, a test unit 502, an identification unit 503, and a processing unit 504.
An acquiring unit 501 configured to acquire abnormal test data generated when testing the interface;
the test unit 502 is configured to test the abnormal test data through an interface test program to obtain abnormal field data in the abnormal test data;
a recognition unit 503, configured to recognize the abnormal field data according to an abnormal recognition algorithm, so as to determine an actual result feature representation corresponding to the abnormal field data;
the processing unit 504 is configured to process the actual result feature representation of the abnormal field data with an input rule recognition model to obtain an actual abnormal service rule representation;
and an improvement unit 505, configured to compare the actual abnormal business rule representation with an expected abnormal business rule representation, and if the comparison result is that the actual abnormal business rule representation does not match with the expected abnormal business rule representation, improve the rule recognition model until the actual abnormal business rule representation matches with the expected abnormal business rule representation in the comparison result.
Further, before the processing unit 504 processes the actual result feature representation of the anomaly field data into a rule recognition model, the processing unit is further configured to:
acquiring sample training data, wherein the sample training data comprises sample data of expected abnormal business rule representation, actual result characteristic representation and actual abnormal business rule representation;
and inputting the sample training data into a neural network model to train the neural network model to obtain the rule recognition model.
Further, the processing unit 504 is specifically configured to, when inputting the sample training data into a neural network model for training to obtain the rule recognition model:
inputting the expected abnormal business rule representation and the actual result characteristic representation in the sample training data into a first level of the neural network model for training;
inputting the training result of the first level into a second level of the neural network model for training, and inputting the training result of the second level into a third level of the neural network model for training to obtain an actual abnormal business rule representation;
outputting the actual abnormal business rule representation obtained by training of the third layer through a fourth layer of the neural network model, and determining the rule recognition model according to a comparison result of the actual abnormal business rule representation and the expected abnormal business rule representation, wherein the first layer is a feature set layer, the second layer is a hidden layer, the third layer is an abnormal business rule representation set layer, and the fourth layer is an output layer.
Further, if the comparison result is that the actual abnormal business rule representation does not match the expected abnormal business rule representation, the improving unit 505 is specifically configured to:
if the comparison result shows that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, detecting whether the nodes in the rule identification model are abnormal or not;
if the abnormal occurrence of the nodes in the rule recognition model is detected, the nodes in the rule recognition model are adjusted according to the abnormal occurrence, and the adjusted rule recognition model is retrained to improve the rule recognition model.
Further, when the improvement unit 505 adjusts the nodes in the rule recognition model according to the anomaly, the improvement unit is specifically configured to:
detecting whether all nodes represented by the actual abnormal business rules exist in a third level of the rule identification model;
and if the detection result is that all the nodes represented by the actual abnormal business rules do not exist in the third hierarchy, adding the nodes represented by the actual abnormal business rules which do not exist in the third hierarchy into the third hierarchy.
Further, when the improvement unit 505 adjusts the nodes in the rule recognition model according to the anomaly, the improvement unit is specifically configured to:
detecting whether all nodes in a third level of the rule recognition model are triggered;
if the detection result is that all the nodes of the third level are not triggered, adding a compensating node in the third level;
and acquiring a type determining operation of the compensation node input by a user, and determining the type of the compensation node according to the type determining operation.
Further, when the improvement unit 505 adjusts the nodes in the rule recognition model according to the anomaly, the improvement unit is specifically configured to:
detecting whether nodes represented by the actual result features exist in a first level of the rule recognition model;
if the detection result is that the node represented by the actual result feature does not exist in the first hierarchy, adding the node represented by the actual result feature in the first hierarchy.
In the embodiment of the invention, the data processing equipment can acquire the abnormal test data generated during the interface test, test the abnormal test data through an interface test program to obtain abnormal field data in the abnormal test data, identify the abnormal field data according to an abnormal identification algorithm to determine the actual result characteristic representation corresponding to the abnormal field data, process the actual result characteristic representation of the abnormal field data into a rule identification model to obtain an actual abnormal business rule representation, compare the actual abnormal business rule representation with an expected abnormal business rule representation, and if the comparison result is that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, improve the rule identification model until the actual abnormal business rule representation in the comparison result is matched with the expected abnormal business rule representation. By the method, basic business rules are generated, stored and maintained autonomously, and model autonomous learning and automatic case maintenance efficiency are improved.
Referring to fig. 6, fig. 6 is a schematic block diagram of a server according to an embodiment of the present invention. The server in the present embodiment as shown in the drawings may include: one or more processors 601; one or more input devices 602, one or more output devices 603, and a memory 604. The processor 601, input device 602, output device 603, and memory 604 are connected by a bus 605. The memory 604 is used for storing a computer program comprising a program, and the processor 601 is used for executing the program stored in the memory 604. Wherein the processor 601 is configured to invoke the program execution:
acquiring abnormal test data generated during the test of the interface;
testing the abnormal test data through an interface test program to obtain abnormal field data in the abnormal test data;
identifying the abnormal field data according to an abnormal identification algorithm to determine the actual result characteristic representation corresponding to the abnormal field data;
inputting the actual result characteristic representation of the abnormal field data into a rule recognition model for processing so as to obtain an actual abnormal business rule representation;
and comparing the actual abnormal business rule representation with the expected abnormal business rule representation, and if the comparison result shows that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, improving the rule identification model until the actual abnormal business rule representation in the comparison result is matched with the expected abnormal business rule representation.
Further, before the processor 601 processes the actual result feature representation of the anomaly field data into a rule recognition model, the processor is further configured to:
acquiring sample training data, wherein the sample training data comprises sample data of expected abnormal business rule representation, actual result characteristic representation and actual abnormal business rule representation;
and inputting the sample training data into a neural network model to train the neural network model to obtain the rule recognition model.
Further, if the comparison result shows that the actual abnormal business rule representation does not match the expected abnormal business rule representation, the processor 601 is specifically configured to, when improving the rule recognition model:
if the comparison result shows that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, detecting whether the nodes in the rule identification model are abnormal or not;
if the abnormal occurrence of the nodes in the rule recognition model is detected, the nodes in the rule recognition model are adjusted according to the abnormal occurrence, and the adjusted rule recognition model is retrained to improve the rule recognition model.
Further, when the processor 601 adjusts the node in the rule recognition model according to the anomaly, the method specifically is used for:
detecting whether all nodes represented by the actual abnormal business rules exist in a third level of the rule identification model;
and if the detection result is that all the nodes represented by the actual abnormal business rules do not exist in the third hierarchy, adding the nodes represented by the actual abnormal business rules which do not exist in the third hierarchy into the third hierarchy.
Further, when the processor 601 adjusts the node in the rule recognition model according to the anomaly, the method specifically is used for:
detecting whether all nodes in a third level of the rule recognition model are triggered;
if the detection result is that all the nodes of the third level are not triggered, adding a compensating node in the third level;
and acquiring a type determining operation of the compensation node input by a user, and determining the type of the compensation node according to the type determining operation.
Further, when the processor 601 adjusts the node in the rule recognition model according to the anomaly, the method specifically is used for:
detecting whether all nodes in a third level of the rule recognition model are triggered;
If the detection result is that all the nodes of the third level are not triggered, adding a compensating node in the third level;
and acquiring a type determining operation of the compensation node input by a user, and determining the type of the compensation node according to the type determining operation.
Further, when the processor 601 adjusts the node in the rule recognition model according to the anomaly, the method specifically is used for:
detecting whether nodes represented by the actual result features exist in a first level of the rule recognition model;
if the detection result is that the node represented by the actual result feature does not exist in the first hierarchy, adding the node represented by the actual result feature in the first hierarchy.
In the embodiment of the invention, the server can acquire the abnormal test data generated during the interface test, test the abnormal test data through an interface test program to obtain the abnormal field data in the abnormal test data, identify the abnormal field data according to an abnormal identification algorithm to determine the actual result characteristic representation corresponding to the abnormal field data, process the actual result characteristic representation of the abnormal field data into a rule identification model to obtain the actual abnormal business rule representation, compare the actual abnormal business rule representation with an expected abnormal business rule representation, and if the comparison result is that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, improve the rule identification model until the actual abnormal business rule representation in the comparison result is matched with the expected abnormal business rule representation. By the method, basic business rules are generated, stored and maintained autonomously, and model autonomous learning and automatic case maintenance efficiency are improved.
It should be appreciated that in embodiments of the present invention, the processor 601 may be a central processing unit (CenSral Processing UniS, CPU), which may also be other general purpose processors, digital signal processors (DigiSal Signal Processor, DSPs), application Specific Integrated Circuits (ASICs), off-the-shelf programmable gate arrays (Field-Programmable GaSe Array, FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 602 may include a touch pad, microphone, etc., and the output device 603 may include a display (LCD, etc.), speaker, etc.
The memory 604 may include read only memory and random access memory and provides instructions and data to the processor 601. A portion of memory 604 may also include non-volatile random access memory. For example, the memory 604 may also store information of device type.
In a specific implementation, the processor 601, the input device 602, and the output device 603 described in the embodiments of the present invention may perform the implementation described in the embodiment of the method described in fig. 2 provided in the embodiments of the present invention, and may also perform the implementation of the data processing device described in fig. 5 in the embodiments of the present invention, which is not described herein again.
The embodiment of the present invention further provides a computer readable storage medium, where the computer readable storage medium stores a computer program, where the computer program when executed by a processor implements a data processing method described in the embodiment corresponding to fig. 2, and may also implement a data processing device according to the embodiment corresponding to fig. 5 of the present invention, which is not described herein again.
The computer readable storage medium may be an internal storage unit of the data processing apparatus according to any of the foregoing embodiments, for example, a hard disk or a memory of the data processing apparatus. The computer readable storage medium may also be an external storage device of the data processing apparatus, such as a plug-in hard disk, a smart Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the data processing apparatus. Further, the computer readable storage medium may also include both an internal storage unit and an external storage device of the data processing apparatus. The computer readable storage medium is used to store the computer program and other programs and data required by the data processing apparatus. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention is essentially or a part contributing to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a computer-readable storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a terminal, 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 computer-readable 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.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention.
Claims (9)
1. A method of data processing, comprising:
acquiring abnormal test data generated during the test of the interface;
testing the abnormal test data through an interface test program to obtain abnormal field data in the abnormal test data;
identifying the abnormal field data according to an abnormal identification algorithm to determine the actual result characteristic representation corresponding to the abnormal field data;
the anomaly recognition algorithm is a natural language recognition algorithm, and the method for recognizing the anomaly field data according to the anomaly recognition algorithm to determine the actual result feature representation corresponding to the anomaly field data comprises the following steps:
identifying the abnormal field data according to the natural language identification algorithm to determine an actual result characteristic representation corresponding to the abnormal field data, wherein the abnormal field data is a message field, and if the message field cannot be of the type int, the actual result characteristic representation is a message (set) not (logic) int (type);
inputting the actual result characteristic representation of the abnormal field data into a rule recognition model for processing so as to obtain an actual abnormal business rule representation;
Comparing the actual abnormal business rule representation with the expected abnormal business rule representation, and if the comparison result shows that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, improving the rule recognition model until the actual abnormal business rule representation in the comparison result is matched with the expected abnormal business rule representation;
and if the comparison result shows that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, improving the rule identification model, wherein the method comprises the following steps:
if the comparison result shows that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, detecting whether the nodes in the rule identification model are abnormal or not;
if the abnormal occurrence of the nodes in the rule recognition model is detected, the nodes in the rule recognition model are adjusted according to the abnormal occurrence, and the adjusted rule recognition model is retrained to improve the rule recognition model.
2. The method of claim 1, wherein said processing the actual resulting feature representation of the anomaly field data prior to inputting a rule recognition model further comprises:
Acquiring sample training data, wherein the sample training data comprises sample data of expected abnormal business rule representation and actual result characteristic representation;
and inputting the sample training data into a neural network model for training to obtain the rule recognition model.
3. The method of claim 2, wherein the training the sample training data into a neural network model to obtain the rule recognition model comprises:
inputting the expected abnormal business rule representation and the actual result characteristic representation in the sample training data into a first level of the neural network model for training;
inputting the training result of the first level into a second level of the neural network model for training, and inputting the training result of the second level into a third level of the neural network model for training to obtain an actual abnormal business rule representation;
outputting the actual abnormal business rule representation obtained by training of the third layer through a fourth layer of the neural network model, and determining the rule recognition model according to a comparison result of the actual abnormal business rule representation and the expected abnormal business rule representation, wherein the first layer is a feature set layer, the second layer is a hidden layer, the third layer is an abnormal business rule representation set layer, and the fourth layer is an output layer.
4. A method according to claim 3, wherein said adjusting nodes in said rule recognition model according to said anomalies comprises:
detecting whether all nodes represented by the actual abnormal business rules exist in a third level of the rule identification model;
and if the detection result is that all the nodes represented by the actual abnormal business rules do not exist in the third hierarchy, adding the nodes represented by the actual abnormal business rules which do not exist in the third hierarchy into the third hierarchy.
5. A method according to claim 3, wherein said adjusting nodes in said rule recognition model according to said anomalies comprises:
detecting whether all nodes in a third level of the rule recognition model are triggered;
if the detection result is that all the nodes of the third level are not triggered, adding a compensating node in the third level;
and acquiring a type determining operation of the compensation node input by a user, and determining the type of the compensation node according to the type determining operation.
6. A method according to claim 3, wherein said adjusting nodes in said rule recognition model according to said anomalies comprises:
Detecting whether nodes represented by the actual result features exist in a first level of the rule recognition model;
if the detection result is that the node represented by the actual result feature does not exist in the first hierarchy, adding the node represented by the actual result feature in the first hierarchy.
7. A data processing apparatus, comprising:
the acquisition unit is used for acquiring abnormal test data generated when the interface is tested;
the test unit is used for testing the abnormal test data through an interface test program to obtain abnormal field data in the abnormal test data;
the identification unit is used for identifying the abnormal field data according to an abnormal identification algorithm so as to determine the actual result characteristic representation corresponding to the abnormal field data;
the anomaly recognition algorithm is a natural language recognition algorithm, and the recognition unit is specifically configured to, when recognizing the anomaly field data according to the anomaly recognition algorithm to determine an actual result feature representation corresponding to the anomaly field data:
identifying the abnormal field data according to the natural language identification algorithm to determine an actual result characteristic representation corresponding to the abnormal field data, wherein the abnormal field data is a message field, and if the message field cannot be of the type int, the actual result characteristic representation is a message (set) not (logic) int (type);
The processing unit is used for processing the actual result characteristic representation input rule recognition model of the abnormal field data to obtain an actual abnormal business rule representation;
the improvement unit is used for comparing the actual abnormal business rule representation with the expected abnormal business rule representation, and if the comparison result shows that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, the rule identification model is improved until the actual abnormal business rule representation in the comparison result is matched with the expected abnormal business rule representation;
the improvement unit is specifically configured to, when the rule recognition model is improved if the comparison result indicates that the actual abnormal business rule representation does not match the expected abnormal business rule representation:
if the comparison result shows that the actual abnormal business rule representation is not matched with the expected abnormal business rule representation, detecting whether the nodes in the rule identification model are abnormal or not;
if the abnormal occurrence of the nodes in the rule recognition model is detected, the nodes in the rule recognition model are adjusted according to the abnormal occurrence, and the adjusted rule recognition model is retrained to improve the rule recognition model.
8. A server comprising a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory being interconnected, wherein the memory is adapted to store a computer program, the computer program comprising a program, the processor being configured to invoke the program to perform the method of any of claims 1-6.
9. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, which is executed by a processor to implement the method of any of claims 1-6.
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