CN111309331A - Service processing method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a business processing method and a business processing device, which are used for automatically generating business check rules to establish a rule engine by converting business parameter relationships with higher confidence coefficient into the business check rules after performing association relationship algorithm processing on business data, so that a business system can utilize the rule engine to perform business check.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a service processing method and apparatus, an electronic device, and a storage medium.
Background
In the field of software quality assurance, a main means for judging whether the application service processing of the server side meets expectations is deployment monitoring and checking. For different service scenarios, software development/testers are required to analyze service processing logic and manually write service checking scripts or codes. However, manually writing a service check script or code may cause analysis omission, resulting in insufficient service check point coverage.
Disclosure of Invention
In view of the above, an object of one or more embodiments of the present disclosure is to provide a service processing method and apparatus, an electronic device, and a storage medium, so as to solve the above problems.
In view of the above, one or more embodiments of the present specification provide a service processing method, including:
acquiring service data of a service system;
obtaining a business parameter relation of the business data and a confidence coefficient corresponding to the business parameter relation through an incidence relation algorithm model based on the business data;
converting the business parameter relationship with the confidence coefficient larger than a preset confidence coefficient threshold value into a business checking rule;
and establishing a rule engine by using the business checking rule for providing the business system with the rule engine for business checking.
One or more embodiments of the present specification provide a service processing apparatus, including:
the acquisition module is used for acquiring the service data of the service system;
the relation generation module is used for obtaining a business parameter relation of the business data and a confidence coefficient corresponding to the business parameter relation based on the business data through an incidence relation algorithm model;
the rule conversion module is used for converting the business parameter relation with the confidence coefficient larger than a preset confidence coefficient threshold value into a business check rule;
and the engine establishing module is used for establishing a rule engine by using the business checking rule and providing the rule engine for the business system to carry out business checking.
One or more embodiments of the present specification provide an electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method when executing the program.
One or more embodiments of the present specification provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method.
As can be seen from the foregoing, in the service processing method and apparatus, the electronic device, and the storage medium provided in one or more embodiments of the present disclosure, after performing association algorithm processing on service data, a service parameter relationship with a higher confidence is obtained and then converted into a service checking rule, so that the service checking rule is automatically generated to construct a rule engine, and a service system can perform service checking by using the rule engine.
Drawings
In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, and it is obvious that the drawings in the following description are only one or more embodiments of the present specification, and that other drawings may be obtained by those skilled in the art without inventive effort from these drawings.
FIG. 1 is a schematic structural diagram of a business processing system provided in one or more embodiments of the present disclosure;
fig. 2 is a schematic flow chart of a service processing method provided in one or more embodiments of the present disclosure;
fig. 3 is a schematic flow chart of a service processing method provided in one or more embodiments of the present disclosure;
fig. 4 is a schematic block diagram of a service processing apparatus according to one or more embodiments of the present disclosure;
fig. 5 is a hardware structural diagram of an electronic device provided in one or more embodiments of the present disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present specification should have the ordinary meaning as understood by those of ordinary skill in the art to which this disclosure belongs. The use of "first," "second," and similar terms in one or more embodiments of the specification is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
Fig. 1 shows a schematic structural diagram of a service processing system provided in one or more embodiments of the present specification.
As shown in fig. 1, the business processing system includes a business system, a business processing device, and a rule engine.
In one or more embodiments of the present specification, the service processing apparatus obtains service data from a service system, and obtains a service parameter relationship of the service data and a confidence corresponding to the service parameter relationship through a correlation algorithm model; and finally, establishing a rule engine by using the business checking rule to provide for the business system to carry out business checking. In this way, after the incidence relation algorithm processing is carried out on the business data, the business parameter relation with higher confidence coefficient is obtained and then converted into the business check rule, so that the business check rule is automatically generated to establish a rule engine, and further, the business system can carry out business check or business inspection (whether the flow processing of the business accords with the expected information flow check) by using the rule engine.
In one or more embodiments of the present description, the business data can be automatically acquired by performing a site-embedding process in the business system. Optionally, the acquired service data may be stored offline, so as to be called at any time when needed.
In one or more embodiments of the present description, the business system may invoke the rule engine, and check the business data by using the business check rule in the rule engine.
In one or more embodiments of the present specification, the business processing apparatus may further distinguish the business parameter into a first data set and a second data set; wherein the first data set contains upstream parameters and the second data set contains downstream parameters; and then, data cleaning is carried out on the service parameters in the first data set and the second data set, strong attribute service parameters are filtered out, and weak attribute service parameters are reserved.
For example, the service data is generated by a payment action, the upstream parameter is a service parameter in the payment data generated by the user, and the downstream parameter is a service parameter in the payment data generated by the merchant. It is assumed that the service parameter in the payment data generated by the user side includes abcd, and the service parameter in the payment data generated by the merchant side includes efg. The parameters a and b belong to the strong attribute service parameters if the parameters a and b are generated in each payment action, and the parameter e also belongs to the strong attribute service parameters if the parameter e is generated in each payment action. Assuming that the parameters c and d are generated only in a specific payment behavior, the parameters c and d belong to weak attribute service parameters; assuming that the parameters f and g are generated only in a specific charging action, the parameters f and g also belong to the weak attribute service parameters. For the association relationship between the strong attribute service parameters, because each transaction is generated and cannot or cannot completely reflect the real situation when being used for service check, filtering can be performed, and finally the weak attribute service parameters are reserved.
In one or more embodiments of the present specification, the service processing apparatus may further process the weak attribute service parameter as a Key-Value (Key-Value) pair; simplifying the weak attribute service parameters processed as key-value pairs to obtain simplified weak attribute service parameters; and then inputting the simplified weak attribute service parameters into the incidence relation algorithm model to obtain the service parameter relation of the service data and the confidence corresponding to the service parameter relation.
For example, in the key-value pair, the key includes attribute information corresponding to the service data, and the value represents a value of the service data. For example, assuming a class with a class name of a, an attribute of B in the class, and an attribute of C in the class of B, C is described as a.b.c, where a is assumed to be a transaction, B is the transaction initiated by a shopping App, and C is the amount of the transaction. Here, the a.b.c may be treated as a.b.c ═ 1, where C is the parameter name, and a.b represents a kind of attribute information (or path) of the C parameter, that is, the C parameter belongs to the class B, and B belongs to the class a, and 1 is the value of the parameter C. By means of the processing, the data processing speed can be greatly improved, and therefore the overall operation efficiency is improved.
For another example, if a parameter is described as a.b.c.d.e.f.g.h being 1, the path length of the parameter H is a.b.c.d.e.f.g., which affects the data processing speed. Therefore, the service data can be more easily calculated by simplifying the processing. Optionally, after the business parameter relationship is obtained subsequently, the data can be mapped back to the expression form of the key-value pair. Alternatively, the simplified processing may adopt any method for simplifying the data length, for example, an MD5 processing method, and the data may be processed into the same length, thereby facilitating the calculation.
In one or more embodiments of the present disclosure, the association algorithm model is based on an F-PGrowth algorithm, which is an association analysis algorithm proposed by korean jiawei et al in 2000, and adopts a divide and conquer strategy of compressing a database providing a Frequent item set into a Frequent Pattern Tree (FP-Tree) and still retaining item set association information, a data structure called a Frequent Pattern Tree (free Pattern Tree) is used in the algorithm, the FP-Tree is a special prefix Tree and is composed of a Frequent item header table and an item prefix Tree, the FP-Growth algorithm accelerates the entire mining process based on the above structure, wherein an item set with a support degree greater than a minimum support degree is a Frequent item set, the support degree (support) means that a probability (a) ═ B) ═ P (a ∪ B), and means a probability (B) ═ F > a/F > a), and the probability (a) of simultaneous occurrence of a and B means that a and B are present together, and ∪.
In one or more embodiments of the present specification, the service processing apparatus only retains a service parameter relationship having a frequent item set with a maximum length in a service parameter relationship and a confidence corresponding to the service parameter relationship of the service data obtained by processing.
For example, the service data is generated for a payment action, and it is assumed that the service parameter in the payment data generated by the user side includes abcd, the service parameter in the payment data generated by the merchant side includes efg, wherein the strong attribute service parameters a, b and e are filtered out, and the parameters c, d, f and g remain. It is assumed that in this transaction, the implicit business rule is that when parameters c and d are both present, parameters f and g must be present, and cd and fg have a strong relationship. However, when the parameters c, d, F, and g are all input into the F-PGrowth algorithm model, the obtained output will include the service parameter relationship cd, the service parameter relationship cf, the service parameter relationship cg, the service parameter relationship df, the service parameter relationship dg, the service parameter relationship cdf, the service parameter relationship cdg, the service parameter relationship cfg, the service parameter relationship dfg, and the service parameter relationship cdfg, but the service rule corresponding to the service parameter relationship of the parameters c, d, F, and g is that when the parameters c and d are all present, the parameters F and g are definitely present, so we are concerned about the service parameter relationship cdfg. In order to solve the problem, the service parameter relationship cdfg can be obtained from the parameters c, d, f and g by reserving the service parameter relationship with the maximum length frequent item set in the service parameter relationship of the service data obtained by processing and the confidence corresponding to the service parameter relationship.
In one or more embodiments of the present specification, the service processing apparatus may further obtain new service data of a service system; obtaining a service parameter relationship of the newly added service data and a confidence coefficient corresponding to the service parameter relationship based on the newly added service data through an incidence relation algorithm model; converting the business parameter relationship with the confidence coefficient larger than the preset confidence coefficient threshold value into a newly added business checking rule; and updating the rule engine by using the newly added service check rule, so that the rule engine is updated according to newly added data, and the rule engine can automatically adapt to new service behaviors.
In one or more embodiments of the present specification, the service processing apparatus may further acquire new service data of the service system according to a predetermined time node (for example, 0 point per day) to process the new service data as a new service checking rule.
In one or more embodiments of the present specification, the service processing apparatus may further send an update notification of the rule engine to a service system, so that after the service system receives the update notification, if the service system needs to invoke the rule engine, the service processing apparatus invokes a newly added service checking rule in the rule engine according to the update notification.
In one or more embodiments of the present description, the business processing apparatus, the business system, and the rule engine may all be executed on a server. A server refers to a computer system in a network that can provide services to other devices. The objects served by the server are generally called terminals or clients, and the server and the terminals can be connected in a wired or wireless communication mode. The implementation manner of the server is various, and may be a single computer device, or may be a combination of multiple computer devices (e.g., a cluster server, a cloud server, etc.). The server may also be referred to as a server, a cloud, etc. in some application scenarios. In another embodiment, the business processing apparatus, business system, and rules engine may also execute on a single computer (e.g., workstation).
In one or more embodiments of the present description, the rules engine may be part of the business processing apparatus without splitting the rules engine and the business processing apparatus into two parts.
Fig. 2 is a flowchart illustrating a service processing method provided in one or more embodiments of the present disclosure.
As shown in fig. 2, the service processing method includes:
step 102: and acquiring service data of the service system.
Optionally, the service data may be input/output data of an interface layer of the service system, or may also be data stored in a database, data in a cache, and a message (a call message between systems), and as long as data belonging to a big data category, the service data may be collected here.
Optionally, in one or more embodiments of the present specification, acquiring service data of a service system includes: and performing buried point processing in the service system to automatically acquire service data without actively performing data acquisition operation.
Optionally, in one or more embodiments of the present specification, acquiring service data of a service system includes: and storing the service data off line so as to call the service data at any time when the service data is needed to be used.
Optionally, in one or more embodiments of this specification, the service processing method may further include:
distinguishing the business parameters into a first data set and a second data set; wherein the first data set contains upstream parameters and the second data set contains downstream parameters;
and performing data cleaning on the service parameters in the first data set and the second data set, filtering out strong attribute service parameters and reserving weak attribute service parameters.
For example, the service data is generated by a payment action, the upstream parameter is a service parameter in the payment data generated by the user, and the downstream parameter is a service parameter in the payment data generated by the merchant. It is assumed that the service parameter in the payment data generated by the user side includes abcd, and the service parameter in the payment data generated by the merchant side includes efg. The parameters a and b belong to the strong attribute service parameters if the parameters a and b are generated in each payment action, and the parameter e also belongs to the strong attribute service parameters if the parameter e is generated in each payment action. Assuming that the parameters c and d are generated only in a specific payment behavior, the parameters c and d belong to weak attribute service parameters; assuming that the parameters f and g are generated only in a specific charging action, the parameters f and g also belong to the weak attribute service parameters. For the association relationship between the strong attribute service parameters, because each transaction is generated and cannot or cannot completely reflect the real situation when being used for service check, filtering can be performed, and finally the weak attribute service parameters are reserved.
Step 104: and obtaining the business parameter relationship of the business data and the confidence corresponding to the business parameter relationship through an incidence relationship algorithm model based on the business data.
Optionally, in one or more embodiments of the present specification, the step of obtaining, through an association relation algorithm model and based on the service data, a service parameter relation of the service data and a confidence corresponding to the service parameter relation may further include:
processing the weak attribute service parameters into Key-Value (Key-Value) pairs;
simplifying the weak attribute service parameters processed as key-value pairs to obtain simplified weak attribute service parameters;
and inputting the simplified weak attribute service parameters into the incidence relation algorithm model to obtain the service parameter relation of the service data and the confidence corresponding to the service parameter relation.
For example, in the key-value pair, the key includes attribute information corresponding to the service data, and the value represents a value of the service data. For example, assuming a class with a class name of a, an attribute of B in the class, and an attribute of C in the class of B, C is described as a.b.c, where a is assumed to be a transaction, B is the transaction initiated by a shopping App, and C is the amount of the transaction. Here, a.b.c may be treated as a.b.c ═ 1, where C is the parameter name, a.b represents one attribute information (or called path) of the C parameter, i.e., the C parameter belongs to the class B, and B belongs to the class a, and 1 is the value of the parameter C, i.e., one parameter is described by three dimensions of the parameter name, the attribute information, and the value. By means of the processing, the data processing speed can be greatly improved, and therefore the overall operation efficiency is improved.
For another example, if a parameter is described as a.b.c.d.e.f.g.h being 1, the path length of the parameter H is a.b.c.d.e.f.g., which affects the data processing speed. Therefore, the service data can be more easily calculated by simplifying the processing. Optionally, after the business parameter relationship is obtained subsequently, the data can be mapped back to the expression form of the key-value pair. Alternatively, the simplified processing may adopt any method for simplifying the data length, for example, an MD5 processing method, and the data may be processed into the same length, thereby facilitating the calculation.
In one or more embodiments of the present disclosure, the association algorithm model is based on an F-PGrowth algorithm, which is an association analysis algorithm proposed by korou et al in 2000, and adopts a divide and conquer strategy of compressing a database providing a Frequent item set into a Frequent Pattern Tree (FP-Tree) and still retaining item set association information, a data structure called a Frequent Pattern Tree (frequency Pattern Tree) is used in the algorithm, the FP-Tree is a special prefix Tree composed of a Frequent item head table and an item prefix Tree, the FP-Growth algorithm accelerates the entire mining process based on the above structure, wherein an item set with a support greater than a minimum support is a Frequent item set, a support means a support (a) is P (a ∪ B), and a probability of occurrence of a and B is represented by a probability of occurrence of a being equal to P (a) ∪ B), a confidence is represented by a probability of a occurrence of a and B being equal to a probability of occurrence of a, and a probability of occurrence of B being equal to a being equal to B (35probability of a occurrence of a).
Optionally, in one or more embodiments of the present specification, the inputting the simplified weak attribute service parameter into the association relation algorithm model to obtain a service parameter relation of the service data and a confidence corresponding to the service parameter relation includes: and reserving the business parameter relation with the maximum length frequent item set.
For example, the service data is generated for a payment action, and it is assumed that the service parameter in the payment data generated by the user side includes abcd, the service parameter in the payment data generated by the merchant side includes efg, wherein the strong attribute service parameters a, b and e are filtered out, and the parameters c, d, f and g remain. It is assumed that in this transaction, the implicit business rule is that when parameters c and d are both present, parameters f and g must be present, and cd and fg have a strong relationship. However, when the parameters c, d, F, and g are all input into the F-PGrowth algorithm model, the obtained output will include the service parameter relationship cd, the service parameter relationship cf, the service parameter relationship cg, the service parameter relationship df, the service parameter relationship dg, the service parameter relationship cdf, the service parameter relationship cdg, the service parameter relationship cfg, the service parameter relationship dfg, and the service parameter relationship cdfg, but the service rule corresponding to the service parameter relationship of the parameters c, d, F, and g is that when the parameters c and d are all present, the parameters F and g are definitely present, so we are concerned about the service parameter relationship cdfg. In order to solve the problem, the service parameter relationship cdfg can be obtained from the parameters c, d, f and g by reserving the service parameter relationship with the maximum length frequent item set in the service parameter relationship of the service data obtained by processing and the confidence corresponding to the service parameter relationship.
Optionally, the association algorithm may also be an Apriori algorithm.
Step 106: and converting the business parameter relationship with the confidence coefficient larger than the preset confidence coefficient threshold value into a business checking rule.
In this step, the business parameter relationship with the confidence level greater than the preset confidence level threshold is considered as an effective relationship that can be converted into a business check rule. Optionally, the preset confidence threshold may be 0.99. Of course, it can be understood that the preset confidence threshold may have a different value for different scenarios, and the value is not limited to 0.99, but is merely an exemplary description.
Optionally, the service processing method may further include: and converting the business parameter relation with the confidence coefficient smaller than the preset confidence coefficient threshold value into a suspicious business checking rule for identifying the suspicious business.
For example, in the foregoing step, the service parameter relationship cdfg output by the association algorithm model only indicates that the parameters f and g must have such a relationship when the parameters c and d occur. In this step, the relationship needs to be converted into a rule that can be understood by the rule engine, for example, the rule is expressed as: if c is not equal to null and d is not equal to null, then is not equal to null and g is not equal to null.
Step 108: and establishing a rule engine by using the business checking rule for providing the business system with the rule engine for business checking. The business system may invoke the rules engine and use business collation rules in the rules engine to collate business data.
In the service processing method provided in one or more embodiments of the present specification, after the association relation algorithm processing is performed on the service data, the service parameter relation with higher confidence is obtained and then converted into the service check rule, so that the service check rule is automatically generated to construct a rule engine, and the service system can perform service check or service inspection (whether the flow processing of the service conforms to the expected information flow check) by using the rule engine. The business processing method can dig out the relevance of various rule relations by using an algorithm so as to automatically generate the business check rule and automatically deploy the business check rule, the whole process does not need manual intervention, the automatic discovery capability of a full-automatic system is achieved, and the defects of manual check, analysis omission and the like can be overcome.
Optionally, in one or more embodiments of this specification, as shown in fig. 3, the service processing method may further include:
step 202: acquiring newly added service data of a service system;
step 204: obtaining a service parameter relationship of the newly added service data and a confidence coefficient corresponding to the service parameter relationship based on the newly added service data through an incidence relation algorithm model;
step 206: converting the business parameter relationship with the confidence coefficient larger than the preset confidence coefficient threshold value into a newly added business checking rule;
step 208: and updating the rule engine by using the newly added service check rule.
And updating the rule engine according to the newly added data, so that the rule engine can automatically adapt to the new business behavior.
Optionally, in one or more embodiments of this specification, the service processing method may further include:
step 210: and sending the update notification of the rule engine to a service system, so that after the service system receives the update notification, if the service system needs to call the rule engine, a newly-added service check rule is called in the rule engine according to the update notification.
Optionally, in one or more embodiments of this specification, the service processing method may further include:
and acquiring newly added service data of the service system according to the preset time node to process the newly added service data into a newly added service check rule. For example, new service data of the service system is acquired at 0 point every day, and processing of new service parameters is started at 3 points every day, and the new service parameters are processed into new service parameter relationships and further processed into new service check rules. Optionally, the update notification of the newly added service checking rule may be automatically pushed to the service system by the rule background, so as to complete automatic deployment of the newly added service checking rule.
It should be noted that the method of one or more embodiments of the present disclosure may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may perform only one or more steps of the method of one or more embodiments of the present disclosure, and the devices may interact with each other to complete the method.
Optionally, in one embodiment, the method is performed on a server. A server refers to a computer system in a network that can provide services to other devices. The objects served by the server are generally called terminals or clients, and the server and the terminals can be connected in a wired or wireless communication mode. The implementation manner of the server is various, and may be a single computer device, or may be a combination of multiple computer devices (e.g., a cluster server, a cloud server, etc.). The server may also be referred to as a server, a cloud, etc. in some application scenarios. In another embodiment, the method may also be performed on a single computer (e.g., a workstation).
Fig. 4 is a schematic block diagram illustrating a service processing apparatus according to one or more embodiments of the present disclosure.
As shown in fig. 4, the service processing apparatus includes:
an obtaining module 301, configured to obtain service data of a service system;
a relationship generation module 302, configured to obtain, through an association relationship algorithm model, a service parameter relationship of the service data and a confidence corresponding to the service parameter relationship based on the service data;
the rule conversion module 303 is configured to convert the service parameter relationship with the confidence level greater than the preset confidence level threshold into a service checking rule;
an engine establishing module 304, configured to establish a rule engine using the business checking rule, so as to provide the rule engine for the business system to perform business checking.
The service processing apparatus provided in one or more embodiments of the present specification performs association algorithm processing on service data, obtains a service parameter relationship with a higher confidence level, and converts the service parameter relationship into a service check rule, so as to automatically generate a service check rule for building a rule engine, and further enable a service system to perform service check or service inspection (whether the flow processing of the service conforms to expected information flow check) by using the rule engine. The business processing device can dig out the relevance of various rule relations by using an algorithm so as to automatically generate the business check rule and automatically deploy the business check rule, the whole process does not need manual intervention, the automatic discovery capability of a full-automatic system is achieved, and the defects of manual check, analysis omission and the like can be overcome.
Optionally, the relationship generating module 302 is configured to:
distinguishing the business parameters into a first data set and a second data set; wherein the first data set contains upstream parameters and the second data set contains downstream parameters;
and performing data cleaning on the service parameters in the first data set and the second data set, filtering out strong attribute service parameters and reserving weak attribute service parameters.
Optionally, the relationship generating module 302 is configured to:
processing the weak attribute service parameters into key-value pairs;
simplifying the weak attribute service parameters processed as key-value pairs to obtain simplified weak attribute service parameters;
and inputting the simplified weak attribute service parameters into the incidence relation algorithm model to obtain the service parameter relation of the service data and the confidence corresponding to the service parameter relation.
Optionally, the association relation algorithm model is a model based on an F-PGrowth algorithm.
Optionally, the relationship generating module 302 is configured to retain a service parameter relationship with a maximal length frequent item set.
Optionally, the obtaining module 301 is configured to perform a site burying process in the service system to automatically obtain service data.
Optionally, the obtaining module 301 is configured to store the service data offline.
Optionally, the obtaining module 301 is configured to obtain new service data of a service system;
the relationship generating module 302 is configured to obtain, through an association relationship algorithm model, a service parameter relationship of the newly added service data and a confidence corresponding to the service parameter relationship based on the newly added service data;
the rule conversion module 303 is configured to convert the service parameter relationship with the confidence level greater than the preset confidence level threshold into a newly added service checking rule;
the engine building module 304 is configured to update the rule engine by using the new service checking rule.
Optionally, the engine building module 304 is configured to send an update notification of the rule engine to a business system.
Optionally, the obtaining module 301 is configured to obtain, according to a predetermined time node, new service data of the service system to be processed as a new service checking rule.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the modules may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.
The apparatus of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Fig. 5 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 401, a memory 402, an input/output interface 403, a communication interface 404, and a bus 405. Wherein the processor 401, the memory 401, the input/output interface 403 and the communication interface 404 are communicatively connected to each other within the device via a bus 405.
The processor 401 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present specification.
The Memory 402 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random access Memory), a static storage device, a dynamic storage device, or the like. The memory 402 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 402 and called to be executed by the processor 401.
The input/output interface 403 is used for connecting an input/output module to realize information input and output. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 404 is used to connect a communication module (not shown in the figure) to implement communication interaction between the present device and other devices. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
The bus 405 includes a path that transfers information between the various components of the device, such as the processor 401, memory 402, input/output interface 403, and communication interface 404.
It should be noted that although the above-mentioned device only shows the processor 401, the memory 402, the input/output interface 403, the communication interface 404 and the bus 405, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the idea of the invention, also features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
In addition, well known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures for simplicity of illustration and discussion, and so as not to obscure the invention. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the invention, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the present invention is to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that the invention can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The embodiments of the invention are intended to embrace all such alternatives, modifications and variances that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements and the like that may be made without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (22)
1. A service processing method comprises the following steps:
acquiring service data of a service system;
obtaining a business parameter relation of the business data and a confidence coefficient corresponding to the business parameter relation through an incidence relation algorithm model based on the business data;
converting the business parameter relationship with the confidence coefficient larger than a preset confidence coefficient threshold value into a business checking rule;
and establishing a rule engine by using the business checking rule for providing the business system with the rule engine for business checking.
2. The method of claim 1, wherein the method comprises:
distinguishing the business parameters into a first data set and a second data set; wherein the first data set contains upstream parameters and the second data set contains downstream parameters;
and performing data cleaning on the service parameters in the first data set and the second data set, filtering out strong attribute service parameters and reserving weak attribute service parameters.
3. The method of claim 2, wherein obtaining the business parameter relationship of the business data and the confidence corresponding to the business parameter relationship based on the business data through an incidence relation algorithm model comprises:
processing the weak attribute service parameters into key-value pairs;
simplifying the weak attribute service parameters processed as key-value pairs to obtain simplified weak attribute service parameters;
and inputting the simplified weak attribute service parameters into the incidence relation algorithm model to obtain the service parameter relation of the service data and the confidence corresponding to the service parameter relation.
4. The method of claim 3, wherein the associative relationship algorithm model is a model based on the F-PGrowth algorithm.
5. The method of claim 4, wherein inputting the simplified weak attribute service parameters into the association algorithm model to obtain the service parameter relationship of the service data and the confidence corresponding to the service parameter relationship comprises:
and reserving the business parameter relation with the maximum length frequent item set.
6. The method of claim 1, wherein obtaining service data for a service system comprises:
and performing embedded point processing in the service system to automatically acquire service data.
7. The method of claim 6, wherein obtaining service data for a service system comprises:
and storing the service data off line.
8. The method of claim 1, further comprising:
acquiring newly added service data of a service system;
obtaining a service parameter relationship of the newly added service data and a confidence coefficient corresponding to the service parameter relationship based on the newly added service data through an incidence relation algorithm model;
converting the business parameter relationship with the confidence coefficient larger than the preset confidence coefficient threshold value into a newly added business checking rule;
and updating the rule engine by using the newly added service check rule.
9. The method of claim 8, further comprising:
and sending the update notice of the rule engine to a business system.
10. The method of claim 8, further comprising:
and acquiring newly added service data of the service system according to the preset time node for processing the newly added service check rule.
11. A traffic processing apparatus, comprising:
the acquisition module is used for acquiring the service data of the service system;
the relation generation module is used for obtaining a business parameter relation of the business data and a confidence coefficient corresponding to the business parameter relation based on the business data through an incidence relation algorithm model;
the rule conversion module is used for converting the business parameter relation with the confidence coefficient larger than a preset confidence coefficient threshold value into a business check rule;
and the engine establishing module is used for establishing a rule engine by using the business checking rule and providing the rule engine for the business system to carry out business checking.
12. The apparatus of claim 11, wherein the relationship generation module is to:
distinguishing the business parameters into a first data set and a second data set; wherein the first data set contains upstream parameters and the second data set contains downstream parameters;
and performing data cleaning on the service parameters in the first data set and the second data set, filtering out strong attribute service parameters and reserving weak attribute service parameters.
13. The apparatus of claim 12, wherein the relationship generation module is to:
processing the weak attribute service parameters into key-value pairs;
simplifying the weak attribute service parameters processed as key-value pairs to obtain simplified weak attribute service parameters;
and inputting the simplified weak attribute service parameters into the incidence relation algorithm model to obtain the service parameter relation of the service data and the confidence corresponding to the service parameter relation.
14. The apparatus of claim 13, wherein the associative relationship algorithm model is a model based on the F-PGrowth algorithm.
15. The apparatus of claim 14, wherein the relationship generation module is configured to retain business parameter relationships having a maximal length frequent item set.
16. The apparatus of claim 11, wherein the obtaining module is configured to perform a site-based process in the business system to automatically obtain business data.
17. The apparatus of claim 16, wherein the obtaining module is configured to store the service data offline.
18. The apparatus of claim 11, wherein the obtaining module is configured to obtain new service data of a service system;
the relation generating module is used for obtaining the service parameter relation of the newly added service data and the confidence corresponding to the service parameter relation based on the newly added service data through an incidence relation algorithm model;
the rule conversion module is used for converting the business parameter relationship with the confidence coefficient greater than the preset confidence coefficient threshold value into a newly-added business check rule;
and the engine establishing module is used for updating the rule engine by using the newly added service checking rule.
19. The apparatus of claim 18, wherein the engine component module is configured to send an update notification of the rules engine to a business system.
20. The apparatus of claim 18, wherein the obtaining module is configured to obtain new service data of the service system according to a predetermined time node for processing as a new service checking rule.
21. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 10 when executing the program.
22. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 10.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2002218519A1 (en) * | 2000-12-01 | 2002-08-15 | F. Hoffmann-La Roche Ag | Data Verification Supporting Server |
| CN104731669A (en) * | 2015-03-12 | 2015-06-24 | 深圳供电局有限公司 | Method and system for automatically checking defect data of power equipment |
| CN106326219A (en) * | 2015-06-16 | 2017-01-11 | 阿里巴巴集团控股有限公司 | Business system data check method, apparatus and system |
| CN107291716A (en) * | 2016-03-30 | 2017-10-24 | 阿里巴巴集团控股有限公司 | A kind of link data method of calibration and device |
| CN109558134A (en) * | 2017-09-27 | 2019-04-02 | 北京京东尚科信息技术有限公司 | The method and apparatus of business datum verification |
| CN110162566A (en) * | 2019-04-15 | 2019-08-23 | 平安普惠企业管理有限公司 | Association analysis method, device, computer equipment and the storage medium of business datum |
| CN110175182A (en) * | 2019-05-30 | 2019-08-27 | 口碑(上海)信息技术有限公司 | Verification of data method and device |
| CN110263071A (en) * | 2019-05-31 | 2019-09-20 | 口碑(上海)信息技术有限公司 | Business datum checking method, device, equipment and system architecture |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU1851902A (en) * | 2000-12-01 | 2002-06-11 | Fujitsu Fip Corp | Data check supporting server |
-
2020
- 2020-02-11 CN CN202010086363.7A patent/CN111309331B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2002218519A1 (en) * | 2000-12-01 | 2002-08-15 | F. Hoffmann-La Roche Ag | Data Verification Supporting Server |
| CN104731669A (en) * | 2015-03-12 | 2015-06-24 | 深圳供电局有限公司 | Method and system for automatically checking defect data of power equipment |
| CN106326219A (en) * | 2015-06-16 | 2017-01-11 | 阿里巴巴集团控股有限公司 | Business system data check method, apparatus and system |
| CN107291716A (en) * | 2016-03-30 | 2017-10-24 | 阿里巴巴集团控股有限公司 | A kind of link data method of calibration and device |
| CN109558134A (en) * | 2017-09-27 | 2019-04-02 | 北京京东尚科信息技术有限公司 | The method and apparatus of business datum verification |
| CN110162566A (en) * | 2019-04-15 | 2019-08-23 | 平安普惠企业管理有限公司 | Association analysis method, device, computer equipment and the storage medium of business datum |
| CN110175182A (en) * | 2019-05-30 | 2019-08-27 | 口碑(上海)信息技术有限公司 | Verification of data method and device |
| CN110263071A (en) * | 2019-05-31 | 2019-09-20 | 口碑(上海)信息技术有限公司 | Business datum checking method, device, equipment and system architecture |
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