CN112698971A - Rule engine based parameter conversion method, device, equipment and medium - Google Patents

Rule engine based parameter conversion method, device, equipment and medium Download PDF

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Publication number
CN112698971A
CN112698971A CN202011643304.1A CN202011643304A CN112698971A CN 112698971 A CN112698971 A CN 112698971A CN 202011643304 A CN202011643304 A CN 202011643304A CN 112698971 A CN112698971 A CN 112698971A
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target
rule
parameter
parameter conversion
parameters
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CN112698971B (en
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王涛
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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Priority to PCT/CN2021/090556 priority patent/WO2022142016A1/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/54—Interprogram communication
    • G06F9/543—User-generated data transfer, e.g. clipboards, dynamic data exchange [DDE], object linking and embedding [OLE]

Abstract

The application relates to a data processing technology, and provides a parameter conversion method, a device, computer equipment and a storage medium based on a rule engine, which comprises the following steps: when a rule issuing instruction is received, acquiring a target parameter conversion rule corresponding to the rule issuing instruction; analyzing the target parameter conversion rule to obtain a target parameter set; inputting the target parameter set into a link calculation model trained in advance to obtain an optimal execution link; receiving and analyzing a parameter conversion request to obtain a target interface parameter corresponding to the parameter conversion request; calling the optimal execution link according to the target interface parameter to obtain a parameter conversion result; and outputting the parameter conversion result. This application can improve parameter conversion efficiency, promotes the rapid development in wisdom city.

Description

Rule engine based parameter conversion method, device, equipment and medium
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for parameter transformation based on a rule engine, a computer device, and a medium.
Background
With the development of communication technology, various service systems are becoming more and more widely applied. And data interaction is carried out among different service systems in a mode of interface butt joint. In the interface docking process, parameter conversion between different service systems is often involved.
For the parameter conversion situation in the interface docking process, in the process of implementing the present application, the inventor finds that at least the following problems exist in the prior art: most of the existing schemes realize parameter conversion aiming at specific interface parameter configuration, thereby wasting a large amount of development and test resources and increasing the time cost of parameter conversion; and by using the existing scheme, a plurality of if and else codes exist in a service system, the complexity of the system is increased, and the parameter conversion efficiency is reduced.
Therefore, it is necessary to provide a parameter transformation method based on a rule engine, which can reduce the time cost of parameter transformation and improve the parameter transformation efficiency.
Disclosure of Invention
In view of the above, it is desirable to provide a parameter conversion method based on a rule engine, a parameter conversion device based on a rule engine, a computer device and a medium, which can improve the parameter conversion efficiency.
A first aspect of an embodiment of the present application provides a method for parameter transformation based on a rule engine, where the method for parameter transformation based on a rule engine includes:
when a rule issuing instruction is received, acquiring a target parameter conversion rule corresponding to the rule issuing instruction;
analyzing the target parameter conversion rule to obtain a target parameter set;
inputting the target parameter set into a link calculation model trained in advance to obtain an optimal execution link;
receiving and analyzing a parameter conversion request to obtain a target interface parameter corresponding to the parameter conversion request;
calling the optimal execution link according to the target interface parameter to obtain a parameter conversion result;
and outputting the parameter conversion result.
Further, in the above method for converting parameters based on a rule engine provided in an embodiment of the present application, before the receiving a rule issuing instruction, the method further includes:
acquiring first service data of a target interface in a target service system;
analyzing the first service data to obtain a first attention attribute corresponding to the target service system;
acquiring second service data corresponding to the target interface in other service systems;
analyzing the second service data to obtain a second attention attribute corresponding to the other service systems;
and acquiring the incidence relation between the first attention attribute and the second attention attribute, and determining a parameter conversion rule between the target service system and the other service systems according to the incidence relation.
Further, in the rule engine-based parameter transformation method provided in an embodiment of the present application, the parsing the target parameter transformation rule to obtain a target parameter set includes:
analyzing the target parameter conversion rule;
detecting whether the target parameter conversion rule contains a target rule parameter or not;
when the detection result is that the target parameter conversion rule contains target rule parameters, positioning all the target rule parameters and acquiring target attribute parameters connected with the target rule parameters;
and constructing a target parameter set according to the target rule parameters and the target attribute parameters.
Further, in the rule engine-based parameter transformation method provided in the embodiment of the present application, the inputting the target parameter set into a pre-trained link calculation model to obtain an optimal execution link includes:
calling the link calculation model to obtain the logic relation of each target parameter in the target parameter set;
determining the priority order of the target parameters according to the logic relation;
and combining the target parameters based on the priority order to construct an optimal execution link.
Further, in the rule engine-based parameter transformation method provided in an embodiment of the present application, the receiving and analyzing a parameter transformation request to obtain target interface parameters corresponding to the parameter transformation request includes:
analyzing the parameter conversion request and detecting whether the parameter conversion request carries a preset identifier or not;
when the detection result is that the parameter conversion request carries a preset identifier, acquiring the preset identifier;
and determining target interface parameters corresponding to the preset identification.
Further, in the above method for converting parameters based on a rule engine provided in an embodiment of the present application, the method further includes:
acquiring the data volume of the parameter conversion request;
determining the magnitude corresponding to the parameter conversion request according to the data volume;
the number of rule engines to invoke is determined based on the magnitude.
Further, in the above method for converting parameters based on a rule engine provided in the embodiment of the present application, after determining the number of rule engines to be invoked based on the order of magnitude, the method further includes:
acquiring the number of the rule engines to be called;
splitting the parameter conversion request according to a preset rule based on the number of the rule engines to obtain a target parameter conversion request of each rule engine;
and distributing the target parameter conversion request to the rule engine.
The second aspect of the embodiments of the present application further provides a rule engine-based parameter transformation apparatus, where the rule engine-based parameter transformation apparatus includes:
the rule obtaining module is used for obtaining a target parameter conversion rule corresponding to a rule issuing instruction when the rule issuing instruction is received;
the rule analysis module is used for analyzing the target parameter conversion rule to obtain a target parameter set;
the link acquisition module is used for inputting the target parameter set into a link calculation model trained in advance to obtain an optimal execution link;
the parameter acquisition module is used for receiving and analyzing a parameter conversion request to obtain a target interface parameter corresponding to the parameter conversion request;
the link calling module is used for calling the optimal execution link according to the target interface parameter to obtain a parameter conversion result;
and the result output module is used for outputting the parameter conversion result.
The third aspect of the embodiments of the present application further provides a computer device, where the computer device includes a processor, and the processor is configured to implement the rule engine-based parameter transformation method according to any one of the above items when executing the computer program stored in the memory.
The fourth aspect of the embodiments of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when being executed by a processor, the computer program implements any one of the rule engine-based parameter transformation methods described above.
The rule engine-based parameter conversion method, the rule engine-based parameter conversion device, the computer equipment and the computer-readable storage medium provided by the embodiment of the application solve the problem of data coupling between the business rules and the business system, dynamically execute the variable business rule conversion by the rule engine, reduce the time cost required by the parameter conversion and improve the parameter conversion efficiency. In addition, in the process of executing parameter conversion, the optimal execution link is obtained according to the target rule parameter set, the optimal execution link is called according to the target interface parameter to obtain a parameter conversion result, and parameter conversion is completed through the optimal execution link, so that the response time of parameter conversion can be shortened, and the parameter conversion efficiency is improved. The application can be applied to various functional modules of smart cities such as smart government affairs and smart traffic, for example, the parameter conversion module based on the rule engine of the smart government affairs can promote the rapid development of the smart cities.
Drawings
Fig. 1 is a flowchart of a method for converting parameters based on a rule engine according to an embodiment of the present application.
Fig. 2 is a structural diagram of a rule engine-based parameter transformation apparatus according to a second embodiment of the present application.
Fig. 3 is a schematic structural diagram of a computer device provided in the third embodiment of the present application.
The following detailed description will further illustrate the present application in conjunction with the above-described figures.
Detailed Description
In order that the above objects, features and advantages of the present application can be more clearly understood, a detailed description of the present application will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth to provide a thorough understanding of the present application, and the described embodiments are a part, but not all, of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
The parameter conversion method based on the rule engine provided by the embodiment of the application is executed by computer equipment, and correspondingly, the parameter conversion device based on the rule engine is suitable for running in the computer equipment.
Fig. 1 is a flowchart of a method for converting parameters based on a rule engine according to an embodiment of the present application. As shown in FIG. 1, the rule engine-based parameter transformation method may include the following steps, and the order of the steps in the flowchart may be changed and some may be omitted according to different requirements:
s11, when receiving the rule issuing instruction, obtaining the target parameter conversion rule corresponding to the rule issuing instruction.
In at least one embodiment of the present application, a target service system may be associated with one or more other service systems through an interface, and when the target service system performs information interaction with the other service systems, because the target service system and the other service systems have different points of interest for interface data of the same interface, when the interface data in the target service system is transmitted to the other service systems, parameter conversion needs to be performed on the interface data transmitted by the target service system to obtain data corresponding to the points of interest of the other service systems.
Before the target business system and the other business systems carry out data communication, a parameter conversion rule between the target business system and the other business systems needs to be determined, and the mode for determining the parameter conversion rule can be confirmed in a manual confirmation mode or a machine learning mode. When the mode of the parameter transformation rule is determined to be a machine learning mode, optionally before the rule issuing instruction is received, the method further includes:
acquiring first service data of a target interface in a target service system;
analyzing the first service data to obtain a first attention attribute corresponding to the target service system;
acquiring second service data corresponding to the target interface in other service systems;
analyzing the second service data to obtain a second attention attribute corresponding to the other service systems;
and acquiring the incidence relation between the first attention attribute and the second attention attribute, and determining a parameter conversion rule between the target service system and the other service systems according to the incidence relation.
The target business system comprises a plurality of interfaces, any interface is selected as a target interface, first business data corresponding to the target interface is collected, and sales product data, month data and region data are selected as the first business data exemplarily. The first interest attribute refers to a business attribute contained in the first business data, for example, the sold product, month and region are all first interest attributes. Similarly, second attention attributes of the other service systems are obtained, and for example, when the other service system is a product system a, for the target interface, the corresponding second attention attributes are a product name, a product model, a month, and a sales volume. That is, for the target business system, the interest points are the product sale, month and region, and for the other business systems, the interest points are the product name, product model, month and sales volume, so that when the target business system calls the data in the other business systems, the product name, product model, month and sales volume are required to be converted into the product name, product model, month and sales volume through parameters.
Optionally, the correlation of attributes with the same or similar semantics may be determined by performing semantic analysis on the first attention attribute and the second attention attribute. For example, the obtaining the association relationship between the first attention attribute and the second attention attribute may include: calling a pre-trained semantic analysis model to process the first concern attribute and the second concern attribute to obtain a plurality of associated packets of which the semantic similarity reaches a preset similarity threshold; detecting whether a confirmation instruction sent by a preset channel is received; when the detection result is that a confirmation instruction sent by a preset channel is received, determining that the association packet is correct; and modifying the association packet when the detection result is that a confirmation instruction sent by a preset channel is not received.
The preset similarity threshold is preset and is used for determining the value of the semantic similarity. The association package includes a plurality of attributes having association relationship, for example, if the product name and the product model are similar to the semantics of the sold product, the association package includes the product name, the product model and the sold product. The preset channel can be a mail channel, a short message channel or a website channel, and the preset channel is used for receiving a correlation packet confirmation instruction output by preset staff. And the confirmation instruction is used for determining whether the association relation of the attributes in each association package is correct.
Optionally, after obtaining the association relationship between the first interest attribute and the second interest attribute, the determining a parameter conversion rule between the target service system and the other service systems according to the association relationship includes: analyzing the association package to obtain the data type and the data format of the target attribute in the association package; and establishing a parameter conversion rule according to the data type and the data format.
The data types comprise int, long, float, char and other types. The data format may be a storage format of data, for example, for a product to be sold, the corresponding associated attribute includes a product name and a product type, and the data format may be a storage format of a product name and a product type, which is not limited herein.
In at least one embodiment of the present application, the association relationship between the first attention attribute and the second attention attribute may be displayed in a manner of building a relationship tree, and optionally, the method further includes:
determining the target interface and constructing a target relation tree with the target interface as a father node;
acquiring the first concern attribute, and determining the first concern attribute as a first child node of the target relation tree according to a first preset identifier;
acquiring the second attention attribute and the incidence relation between the second attention attribute and the first attention attribute;
and determining the second interest attribute as a secondary child node of the first child node according to the incidence relation and a second preset identifier.
The first preset identifier and the second preset identifier may be a numeric identifier, an alphabetic identifier, or a color identifier, which is not limited herein. For example, when the first preset identifier is yellow, the second preset identifier may be red. By checking the child node relationship of the target relationship tree of the same father node, the incidence relationship between the first attention attribute and the second attention attribute can be visually obtained, the time for determining the incidence relationship is reduced, and the parameter conversion efficiency is improved.
In at least one embodiment of the present application, after determining the parameter transformation rule between the target service system and the other service systems, the parameter transformation rule needs to be checked to satisfy a rule issuing condition, and then the parameter transformation rule is issued. Optionally, verifying the parameter conversion rule includes:
acquiring a target parameter conversion rule through a preset data format;
checking whether the target parameter conversion rule is correct or not;
when the verification result shows that the target parameter conversion rule is correct, generating a latest conversion rule version number according to the existing parameter conversion rule version, and storing the target parameter conversion rule carrying the latest conversion rule version number into a preset database;
and acquiring an approval chain corresponding to the target parameter conversion rule, and issuing the target parameter conversion rule after approval is passed.
The target parameter conversion rule can be obtained by editing excel on line on a visual interface, and the rule configuration mode can be a manual configuration mode or a machine learning mode, which is not limited herein. When the configuration rule is configured manually, the authority of the configuration personnel needs to be detected, for example, the identity information of the configuration personnel is obtained, the authority information corresponding to the identity information is matched, and whether the configuration personnel has the configuration authority of the parameter conversion rule is detected, so that the personnel having the configuration authority of the rule can configure the parameter conversion rule. Checking whether the target parameter conversion rule is correct, namely checking whether each parameter conversion rule meets the requirement of a preset attribute, and determining that the target parameter conversion rule is correct when each parameter conversion rule meets the preset attribute as a checking result; and outputting an error prompt when the verification result shows that the parameter conversion rule does not accord with the preset attribute. The preset attribute requirement refers to the preset attribute requirement which is required to be met by the parameters. The preset database comprises a file system and a DB database, the target conversion rule is stored in the file system firstly by the application to obtain the ID number corresponding to the target conversion rule, and the ID number is stored in the DB database to reduce the waste of storage resources in the DB database.
In at least one embodiment of the present application, the target service system further establishes a heartbeat connection with a rule engine, and when a parameter conversion rule of the target service system changes, a heartbeat packet may be sent to the rule engine in a manner that an updated parameter conversion rule is carried in the heartbeat connection. According to the method and the device, the heartbeat connection is established between the target business system and the rule engine, so that the normal connection between the target business system and the rule engine can be ensured, and the parameter conversion rule can be sent through the heartbeat packet.
Optionally, when a rule issuing instruction is received, acquiring a target parameter conversion rule corresponding to the rule issuing instruction includes:
acquiring a heartbeat packet sent by a target service system;
analyzing the heartbeat packet, and detecting whether the heartbeat packet contains a preset label or not;
when the detection result is that the heartbeat packet contains a preset label, determining that a rule issuing instruction is received;
and acquiring a target parameter conversion rule according to the preset label, and updating the target parameter conversion rule into a rule engine.
The preset label can be a letter label, a number label or a color label. The target parameter conversion rule can be carried at a preset position of the heartbeat packet, and whether the heartbeat packet carries the target parameter conversion rule is determined by detecting whether relevant data exists at the preset position of the heartbeat packet.
And S12, analyzing the target parameter conversion rule to obtain a target parameter set.
In at least one embodiment of the present application, the target parameter transformation rule refers to an interface parameter transformation rule between the target service system and another service system. The target parameter conversion rule includes a plurality of rule parameters and attribute parameters, the rule parameters are used for connecting the attribute parameters, the rule parameters may refer to parameters for identifying a conversion relationship, and the rule parameters are stored in target nodes of a block chain. The attribute parameter refers to an attribute parameter corresponding to the first attention attribute and the second attention attribute. The target parameter set refers to a set of the rule parameters and the attribute parameters after normalization processing, the target parameter set includes a plurality of target parameters, and each target parameter includes a rule parameter and an attribute parameter.
Optionally, the analyzing the target parameter conversion rule to obtain a target parameter set includes:
analyzing the target parameter conversion rule;
detecting whether the target parameter conversion rule contains a target rule parameter or not;
when the detection result is that the target parameter conversion rule contains target rule parameters, positioning all the target rule parameters and acquiring target attribute parameters connected with the target rule parameters;
and constructing a target parameter set according to the target rule parameters and the target attribute parameters.
And S13, inputting the target parameter set into a pre-trained link calculation model to obtain an optimal execution link.
In at least one embodiment of the present application, a logic relationship of each parameter in the target parameter set is obtained through a pre-trained link calculation model, a priority relationship between the parameters is determined according to the logic relationship of each parameter, and an optimal execution link is determined based on the priority relationship, where the time consumed by the optimal execution link in executing parameter conversion is the shortest.
Optionally, the inputting the target parameter set into a pre-trained link calculation model to obtain an optimal execution link includes:
calling the link calculation model to obtain the logic relation of each target parameter in the target parameter set;
determining the priority order of the target parameters according to the logic relation;
and combining the target parameters based on the priority order to construct an optimal execution link.
The priority order may be determined according to the complexity and the importance of the logical relationship, where the complexity refers to the complexity of the attribute parameter conversion in the target parameter, and the importance refers to the importance of the attribute parameter in the target parameter in the service system. The complexity degree can be used as an evaluation criterion according to the number of rule parameters, and generally, the more the number of the rule parameters is, the higher the complexity degree is; the less the number of rule parameters, the less complex it is. In other embodiments, the length of the target parameter may also be used as an evaluation criterion, and the longer the length of the target parameter is, the higher the complexity thereof is; the smaller the length of the target parameter, the lower the complexity thereof, which is not limited herein. The importance level may be determined by means of pre-marking, for example, for attribute parameters with higher importance levels, the importance level may be marked with red, and the like.
And the link calculation model acquires the indexes of the complexity and the importance of the target parameters and determines the link which takes the shortest time for executing parameter conversion as the optimal execution link. Wherein the link calculation model is pre-trained, optionally, the training step of the link calculation model includes:
dividing a target parameter sample set into a training set and a testing set;
inputting the training set into an initialized neural network model for training to obtain a trained link calculation model;
inputting the test set into a trained link calculation model to obtain an evaluation index of the model;
detecting whether the evaluation index of the model exceeds a preset index threshold value;
when the detection result is that the evaluation index of the model exceeds a preset index threshold value, determining that the model training is finished to obtain a link calculation model;
and when the detection result shows that the evaluation index of the model does not exceed the preset index threshold value, adding a training set, and retraining the model until the evaluation index of the model exceeds the preset index threshold value.
The evaluation index may refer to time required by the optimal execution link obtained by the link calculation model when parameter conversion is executed, and the preset index threshold is a preset time threshold.
And S14, receiving and analyzing the parameter conversion request to obtain the target interface parameters corresponding to the parameter conversion request.
In at least one embodiment of the present application, the parameter conversion request refers to a request for parameter conversion sent by the target service system or the other service system. The parameter conversion request carries an identifier of a target interface parameter, and the target interface parameter corresponding to the parameter conversion request can be obtained by determining the identifier. The target interface parameter refers to an attribute parameter required to be requested. For example, when the target service system initiates a parameter conversion request to the other service systems, the target interface parameter refers to an attribute parameter included in the other service systems that require the request.
Optionally, the receiving and analyzing the parameter conversion request to obtain the target interface parameter corresponding to the parameter conversion request includes:
analyzing the parameter conversion request and detecting whether the parameter conversion request carries a preset identifier or not;
when the detection result is that the parameter conversion request carries a preset identifier, acquiring the preset identifier;
and determining target interface parameters corresponding to the preset identification.
The preset identifier can be a digital identifier, an alphabetic identifier and the like, a preset mapping relation exists between the preset identifier and the interface parameters, and the mapping relation is inquired according to the preset identifier, so that target interface parameters corresponding to the preset identifier can be obtained.
In at least one embodiment of the present application, the number of the rule engines may be 1, or may be multiple. When the number of the rule engines is 1, the parameter conversion rules between the target business system and the plurality of other business systems may be processed by 1 rule engine. When the number of the rule engines is plural, each rule engine may process a parameter transformation rule between the target business system and one of the other business systems, which is not limited herein. Optionally, 1 rule engine processes the parameter conversion rule between the target service system and the plurality of other service systems, and when the number of parameter conversion requests sent by the target service system or the other service systems is large, a large number of parameter conversion requests cannot be satisfied by one rule engine, so that a plurality of rule engines in a rule engine cluster need to be called to process the parameter conversion requests in parallel, so as to improve the efficiency of the parameter conversion requests.
Optionally, the method further comprises:
acquiring the data volume of the parameter conversion request;
determining the magnitude corresponding to the parameter conversion request according to the data volume;
the number of rule engines to invoke is determined based on the magnitude.
The data size of the parameter conversion request and the order of magnitude have a first mapping relation, the order of magnitude and the number of the rule engines to be called have a second mapping relation, the corresponding order of magnitude can be obtained by inquiring the first mapping relation according to the data size, and the corresponding number of the rule engines to be called can be obtained by inquiring the second mapping relation according to the order of magnitude.
Optionally, after determining the number of rule engines to be invoked based on the magnitude, the method further includes:
acquiring the number of the rule engines to be called;
splitting the parameter conversion request according to a preset rule based on the number of the rule engines to obtain a target parameter conversion request of each rule engine;
and distributing the target parameter conversion request to the rule engine.
The rule engine is divided into a main rule engine and sub-rule engines, when the parameter conversion request is a request between the target business system and one of the other business systems, the main rule engine sends the target parameter conversion rule to the sub-rule engines, then splits a large number of parameter conversion requests, and each sub-rule engine respectively processes the target parameter conversion request. When the parameter conversion request is a request between the target service system and a plurality of other service systems, the general rule engine firstly determines information of the other service systems corresponding to the parameter conversion request, and respectively sends the parameter conversion rules corresponding to the other service systems to the sub-rule engines, and the sub-rule engines process the corresponding target parameter conversion request, so that the condition that the plurality of rule engines process the requests between different other service systems in a crossed manner is avoided, and the parameter conversion efficiency can be improved.
And S15, calling the optimal execution link according to the target interface parameters to obtain a parameter conversion result.
In at least one embodiment of the present application, an optimal execution link corresponding to the target interface parameter is obtained, and the target interface parameter is processed based on the optimal execution link to obtain a parameter conversion result.
Optionally, the invoking the optimal execution link according to the target interface parameter to obtain a parameter conversion result includes:
acquiring an optimal execution link corresponding to the target interface parameter;
determining execution logic in the optimal execution link and a priority of each of the execution logic;
and processing the target interface parameters through the execution logic according to the priority order to obtain a parameter conversion result.
The execution logic includes a filtering logic, a merging logic, a splitting logic, and the like, which is not limited herein.
In at least one embodiment of the present application, the method further comprises: and visualizing the optimal execution link, and directly viewing the visualized link to obtain the execution state of the current link. Illustratively, when a certain link node of the optimal execution link operates abnormally, the abnormal parameter conversion can be highlighted in a marking mode, so that related personnel can visually find the abnormal parameter conversion and repair the problem in time.
And S16, outputting the parameter conversion result.
In at least one embodiment of the present application, when a parameter conversion request is output as a target service system, outputting a parameter conversion result to a corresponding other service system; and when the output parameter conversion request is other service systems, outputting the parameter conversion result to the corresponding target service system.
The parameter conversion method based on the rule engine provided by the embodiment of the application solves the problem of business rules and a data coupling business system, and meanwhile, the variable business rule conversion is dynamically executed by the rule engine, so that the time cost required by parameter conversion can be reduced, and the parameter conversion efficiency is improved. In addition, in the process of executing parameter conversion, the optimal execution link is obtained according to the target parameter set, the optimal execution link is called according to the target interface parameter, the parameter conversion result is obtained, and the parameter conversion is completed through the optimal execution link, so that the response time of the parameter conversion can be shortened, and the parameter conversion efficiency is improved. The application can be applied to various functional modules of smart cities such as smart government affairs and smart traffic, for example, the parameter conversion module based on the rule engine of the smart government affairs can promote the rapid development of the smart cities.
Fig. 2 is a structural diagram of a rule engine-based parameter transformation apparatus according to a second embodiment of the present application.
In some embodiments, the rule engine based parameter transformation apparatus 20 may include a plurality of functional modules comprising computer program segments. The computer program of each program segment in the rule engine based parameter transformation apparatus 20 may be stored in a memory of a computer device and executed by at least one processor to perform (see detailed description of fig. 1) the functions of the rule engine based parameter transformation process.
In this embodiment, the rule engine based parameter transformation apparatus 20 may be divided into a plurality of functional modules according to the functions performed by the apparatus. The functional module may include: the rule obtaining module 201, the rule parsing module 202, the link obtaining module 203, the parameter obtaining module 204, the link calling module 205, and the result output module 206. A module as referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in a memory. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.
The rule obtaining module 201 is configured to, when a rule issuing instruction is received, obtain a target parameter conversion rule corresponding to the rule issuing instruction.
In at least one embodiment of the present application, a target service system may be associated with one or more other service systems through an interface, and when the target service system performs information interaction with the other service systems, because the target service system and the other service systems have different points of interest for interface data of the same interface, when the interface data in the target service system is transmitted to the other service systems, parameter conversion needs to be performed on the interface data transmitted by the target service system to obtain data corresponding to the points of interest of the other service systems.
Before the target business system and the other business systems carry out data communication, a parameter conversion rule between the target business system and the other business systems needs to be determined, and the mode for determining the parameter conversion rule can be confirmed in a manual confirmation mode or a machine learning mode. When the mode of determining the parameter transformation rule is the machine learning mode, optionally before the receiving the rule issuing instruction, the rule obtaining module further includes:
acquiring first service data of a target interface in a target service system;
analyzing the first service data to obtain a first attention attribute corresponding to the target service system;
acquiring second service data corresponding to the target interface in other service systems;
analyzing the second service data to obtain a second attention attribute corresponding to the other service systems;
and acquiring the incidence relation between the first attention attribute and the second attention attribute, and determining a parameter conversion rule between the target service system and the other service systems according to the incidence relation.
The target business system comprises a plurality of interfaces, any interface is selected as a target interface, first business data corresponding to the target interface is collected, and sales product data, month data and region data are selected as the first business data exemplarily. The first interest attribute refers to a business attribute contained in the first business data, for example, the sold product, month and region are all first interest attributes. Similarly, second attention attributes of the other service systems are obtained, and for example, when the other service system is a product system a, for the target interface, the corresponding second attention attributes are a product name, a product model, a month, and a sales volume. That is, for the target business system, the interest points are the product sale, month and region, and for the other business systems, the interest points are the product name, product model, month and sales volume, so that when the target business system calls the data in the other business systems, the product name, product model, month and sales volume are required to be converted into the product name, product model, month and sales volume through parameters.
Optionally, the correlation of attributes with the same or similar semantics may be determined by performing semantic analysis on the first attention attribute and the second attention attribute. For example, the obtaining the association relationship between the first attention attribute and the second attention attribute may include: calling a pre-trained semantic analysis model to process the first concern attribute and the second concern attribute to obtain a plurality of associated packets of which the semantic similarity reaches a preset similarity threshold; detecting whether a confirmation instruction sent by a preset channel is received; when the detection result is that a confirmation instruction sent by a preset channel is received, determining that the association packet is correct; and modifying the association packet when the detection result is that a confirmation instruction sent by a preset channel is not received.
The preset similarity threshold is preset and is used for determining the value of the semantic similarity. The association package includes a plurality of attributes having association relationship, for example, if the product name and the product model are similar to the semantics of the sold product, the association package includes the product name, the product model and the sold product. The preset channel can be a mail channel, a short message channel or a website channel, and the preset channel is used for receiving a correlation packet confirmation instruction output by preset staff. And the confirmation instruction is used for determining whether the association relation of the attributes in each association package is correct.
Optionally, after obtaining the association relationship between the first interest attribute and the second interest attribute, the determining a parameter conversion rule between the target service system and the other service systems according to the association relationship includes: analyzing the association package to obtain the data type and the data format of the target attribute in the association package; and establishing a parameter conversion rule according to the data type and the data format.
The data types comprise int, long, float, char and other types. The data format may be a storage format of data, for example, for a product to be sold, the corresponding associated attribute includes a product name and a product type, and the data format may be a storage format of a product name and a product type, which is not limited herein.
In at least one embodiment of the present application, the association relationship between the first attention attribute and the second attention attribute may be displayed in a manner of building a relationship tree, and optionally, the rule obtaining module further includes:
determining the target interface and constructing a target relation tree with the target interface as a father node;
acquiring the first concern attribute, and determining the first concern attribute as a first child node of the target relation tree according to a first preset identifier;
acquiring the second attention attribute and the incidence relation between the second attention attribute and the first attention attribute;
and determining the second interest attribute as a secondary child node of the first child node according to the incidence relation and a second preset identifier.
The first preset identifier and the second preset identifier may be a numeric identifier, an alphabetic identifier, or a color identifier, which is not limited herein. For example, when the first preset identifier is yellow, the second preset identifier may be red. By checking the child node relationship of the target relationship tree of the same father node, the incidence relationship between the first attention attribute and the second attention attribute can be visually obtained, the time for determining the incidence relationship is reduced, and the parameter conversion efficiency is improved.
In at least one embodiment of the present application, after determining the parameter transformation rule between the target service system and the other service systems, the parameter transformation rule needs to be checked to satisfy a rule issuing condition, and then the parameter transformation rule is issued. Optionally, verifying the parameter conversion rule includes:
acquiring a target parameter conversion rule through a preset data format;
checking whether the target parameter conversion rule is correct or not;
when the verification result shows that the target parameter conversion rule is correct, generating a latest conversion rule version number according to the existing parameter conversion rule version, and storing the target parameter conversion rule carrying the latest conversion rule version number into a preset database;
and acquiring an approval chain corresponding to the target parameter conversion rule, and issuing the target parameter conversion rule after approval is passed.
The target parameter conversion rule can be obtained by editing excel on line on a visual interface, and the rule configuration mode can be a manual configuration mode or a machine learning mode, which is not limited herein. When the configuration rule is configured manually, the authority of the configuration personnel needs to be detected, for example, the identity information of the configuration personnel is obtained, the authority information corresponding to the identity information is matched, and whether the configuration personnel has the configuration authority of the parameter conversion rule is detected, so that the personnel having the configuration authority of the rule can configure the parameter conversion rule. Checking whether the target parameter conversion rule is correct, namely checking whether each parameter conversion rule meets the requirement of a preset attribute, and determining that the target parameter conversion rule is correct when each parameter conversion rule meets the preset attribute as a checking result; and outputting an error prompt when the verification result shows that the parameter conversion rule does not accord with the preset attribute. The preset attribute requirement refers to the preset attribute requirement which is required to be met by the parameters. The preset database comprises a file system and a DB database, the target conversion rule is stored in the file system firstly by the application to obtain the ID number corresponding to the target conversion rule, and the ID number is stored in the DB database to reduce the waste of storage resources in the DB database.
In at least one embodiment of the present application, the target service system further establishes a heartbeat connection with a rule engine, and when a parameter conversion rule of the target service system changes, a heartbeat packet may be sent to the rule engine in a manner that an updated parameter conversion rule is carried in the heartbeat connection. According to the method and the device, the heartbeat connection is established between the target business system and the rule engine, so that the normal connection between the target business system and the rule engine can be ensured, and the parameter conversion rule can be sent through the heartbeat packet.
Optionally, when a rule issuing instruction is received, acquiring a target parameter conversion rule corresponding to the rule issuing instruction includes:
acquiring a heartbeat packet sent by a target service system;
analyzing the heartbeat packet, and detecting whether the heartbeat packet contains a preset label or not;
when the detection result is that the heartbeat packet contains a preset label, determining that a rule issuing instruction is received;
and acquiring a target parameter conversion rule according to the preset label, and updating the target parameter conversion rule into a rule engine.
The preset label can be a letter label, a number label or a color label. The target parameter conversion rule can be carried at a preset position of the heartbeat packet, and whether the heartbeat packet carries the target parameter conversion rule is determined by detecting whether relevant data exists at the preset position of the heartbeat packet.
The rule parsing module 202 is configured to parse the target parameter transformation rule to obtain a target rule parameter set.
In at least one embodiment of the present application, the target parameter transformation rule refers to an interface parameter transformation rule between the target service system and another service system. The target parameter conversion rule includes a plurality of rule parameters and attribute parameters, the rule parameters are used for connecting the attribute parameters, the rule parameters may refer to parameters for identifying a conversion relationship, and the rule parameters are stored in target nodes of a block chain. The attribute parameter refers to an attribute parameter corresponding to the first attention attribute and the second attention attribute. The target parameter set refers to a set of the rule parameters and the attribute parameters after normalization processing, the target parameter set includes a plurality of target parameters, and each target parameter includes a rule parameter and an attribute parameter.
Optionally, the analyzing the target parameter conversion rule to obtain a target parameter set includes:
analyzing the target parameter conversion rule;
detecting whether the target parameter conversion rule contains a target rule parameter or not;
when the detection result is that the target parameter conversion rule contains target rule parameters, positioning all the target rule parameters and acquiring target attribute parameters connected with the target rule parameters;
and constructing a target parameter set according to the target rule parameters and the target attribute parameters.
The link obtaining module 203 is configured to input the target rule parameter set into a link calculation model trained in advance, so as to obtain an optimal execution link.
In at least one embodiment of the present application, a logic relationship of each parameter in the target parameter set is obtained through a pre-trained link calculation model, a priority relationship between the parameters is determined according to the logic relationship of each parameter, and an optimal execution link is determined based on the priority relationship, where the time consumed by the optimal execution link in executing parameter conversion is the shortest.
Optionally, the inputting the target parameter set into a pre-trained link calculation model to obtain an optimal execution link includes:
calling the link calculation model to obtain the logic relation of each target parameter in the target parameter set;
determining the priority order of the target parameters according to the logic relation;
and combining the target parameters based on the priority order to construct an optimal execution link.
The priority order may be determined according to the complexity and the importance of the logical relationship, where the complexity refers to the complexity of the attribute parameter conversion in the target parameter, and the importance refers to the importance of the attribute parameter in the target parameter in the service system. The complexity degree can be used as an evaluation criterion according to the number of rule parameters, and generally, the more the number of the rule parameters is, the higher the complexity degree is; the less the number of rule parameters, the less complex it is. In other embodiments, the length of the target parameter may also be used as an evaluation criterion, and the longer the length of the target parameter is, the higher the complexity thereof is; the smaller the length of the target parameter, the lower the complexity thereof, which is not limited herein. The importance level may be determined by means of pre-marking, for example, for attribute parameters with higher importance levels, the importance level may be marked with red, and the like.
And the link calculation model acquires the indexes of the complexity and the importance of the target parameters and determines the link which takes the shortest time for executing parameter conversion as the optimal execution link. Wherein the link calculation model is pre-trained, optionally, the training step of the link calculation model includes:
dividing a target parameter sample set into a training set and a testing set;
inputting the training set into an initialized neural network model for training to obtain a trained link calculation model;
inputting the test set into a trained link calculation model to obtain an evaluation index of the model;
detecting whether the evaluation index of the model exceeds a preset index threshold value;
when the detection result is that the evaluation index of the model exceeds a preset index threshold value, determining that the model training is finished to obtain a link calculation model; and when the detection result shows that the evaluation index of the model does not exceed the preset index threshold value, adding a training set, and retraining the model until the evaluation index of the model exceeds the preset index threshold value.
The evaluation index may refer to time required by the optimal execution link obtained by the link calculation model when parameter conversion is executed, and the preset index threshold is a preset time threshold.
The parameter obtaining module 204 is configured to receive and analyze a parameter conversion request to obtain a target interface parameter corresponding to the parameter conversion request.
In at least one embodiment of the present application, the parameter conversion request refers to a request for parameter conversion sent by the target service system or the other service system. The parameter conversion request carries an identifier of a target interface parameter, and the target interface parameter corresponding to the parameter conversion request can be obtained by determining the identifier. The target interface parameter refers to an attribute parameter required to be requested. For example, when the target service system initiates a parameter conversion request to the other service systems, the target interface parameter refers to an attribute parameter included in the other service systems that require the request.
Optionally, the receiving and analyzing the parameter conversion request to obtain the target interface parameter corresponding to the parameter conversion request includes:
analyzing the parameter conversion request and detecting whether the parameter conversion request carries a preset identifier or not;
when the detection result is that the parameter conversion request carries a preset identifier, acquiring the preset identifier;
and determining target interface parameters corresponding to the preset identification.
The preset identifier can be a digital identifier, an alphabetic identifier and the like, a preset mapping relation exists between the preset identifier and the interface parameters, and the mapping relation is inquired according to the preset identifier, so that target interface parameters corresponding to the preset identifier can be obtained.
In at least one embodiment of the present application, the number of the rule engines may be 1, or may be multiple. When the number of the rule engines is 1, the parameter conversion rules between the target business system and the plurality of other business systems may be processed by 1 rule engine. When the number of the rule engines is plural, each rule engine may process a parameter transformation rule between the target business system and one of the other business systems, which is not limited herein. Optionally, 1 rule engine processes the parameter conversion rule between the target service system and the plurality of other service systems, and when the number of parameter conversion requests sent by the target service system or the other service systems is large, a large number of parameter conversion requests cannot be satisfied by one rule engine, so that a plurality of rule engines in a rule engine cluster need to be called to process the parameter conversion requests in parallel, so as to improve the efficiency of the parameter conversion requests.
Optionally, the parameter obtaining module further includes:
acquiring the data volume of the parameter conversion request;
determining the magnitude corresponding to the parameter conversion request according to the data volume;
the number of rule engines to invoke is determined based on the magnitude.
The data size of the parameter conversion request and the order of magnitude have a first mapping relation, the order of magnitude and the number of the rule engines to be called have a second mapping relation, the corresponding order of magnitude can be obtained by inquiring the first mapping relation according to the data size, and the corresponding number of the rule engines to be called can be obtained by inquiring the second mapping relation according to the order of magnitude.
Optionally, after determining the number of rule engines to be invoked based on the magnitude, the parameter obtaining module further includes:
acquiring the number of the rule engines to be called;
splitting the parameter conversion request according to a preset rule based on the number of the rule engines to obtain a target parameter conversion request of each rule engine;
and distributing the target parameter conversion request to the rule engine.
The rule engine is divided into a main rule engine and sub-rule engines, when the parameter conversion request is a request between the target business system and one of the other business systems, the main rule engine sends the target parameter conversion rule to the sub-rule engines, then splits a large number of parameter conversion requests, and each sub-rule engine respectively processes the target parameter conversion request. When the parameter conversion request is a request between the target service system and a plurality of other service systems, the general rule engine firstly determines information of the other service systems corresponding to the parameter conversion request, and respectively sends the parameter conversion rules corresponding to the other service systems to the sub-rule engines, and the sub-rule engines process the corresponding target parameter conversion request, so that the condition that the plurality of rule engines process the requests between different other service systems in a crossed manner is avoided, and the parameter conversion efficiency can be improved.
The link calling module 205 is configured to call the optimal execution link according to the target interface parameter, so as to obtain a parameter conversion result.
In at least one embodiment of the present application, an optimal execution link corresponding to the target interface parameter is obtained, and the target interface parameter is processed based on the optimal execution link to obtain a parameter conversion result.
Optionally, the invoking the optimal execution link according to the target interface parameter to obtain a parameter conversion result includes:
acquiring an optimal execution link corresponding to the target interface parameter;
determining execution logic in the optimal execution link and a priority of each of the execution logic;
and processing the target interface parameters through the execution logic according to the priority order to obtain a parameter conversion result.
The execution logic includes a filtering logic, a merging logic, a splitting logic, and the like, which is not limited herein.
In at least one embodiment of the present application, the link invoking module further includes: and visualizing the optimal execution link, and directly viewing the visualized link to obtain the execution state of the current link. Illustratively, when a certain link node of the optimal execution link operates abnormally, the abnormal parameter conversion can be highlighted in a marking mode, so that related personnel can visually find the abnormal parameter conversion and repair the problem in time.
The result output module 206 is configured to output the parameter conversion result.
In at least one embodiment of the present application, when a parameter conversion request is output as a target service system, outputting a parameter conversion result to a corresponding other service system; and when the output parameter conversion request is other service systems, outputting the parameter conversion result to the corresponding target service system.
Fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present application. In the preferred embodiment of the present application, the computer device 3 includes a memory 31, at least one processor 32, at least one communication bus 33, and a transceiver 34.
It will be appreciated by those skilled in the art that the configuration of the computer device shown in fig. 3 is not a limitation of the embodiments of the present application, and may be a bus-type configuration or a star-type configuration, and that the computer device 3 may include more or less hardware or software than those shown, or a different arrangement of components.
In some embodiments, the computer device 3 is a device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The computer device 3 may also include a client device, which includes, but is not limited to, any electronic product capable of interacting with a client through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a digital camera, etc.
It should be noted that the computer device 3 is only an example, and other existing or future electronic products, such as those that may be adapted to the present application, are also included in the scope of the present application and are incorporated herein by reference.
In some embodiments, the memory 31 has stored therein a computer program which, when executed by the at least one processor 32, implements all or part of the steps of the rule engine based parameter transformation method as described. The Memory 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable rewritable Read-Only Memory (Electrically-Erasable Programmable Read-Only Memory (EEPROM)), an optical Read-Only disk (CD-ROM) or other optical disk Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to use of the computer device, and the like.
The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
In some embodiments, the at least one processor 32 is a Control Unit (Control Unit) of the computer device 3, connects various components of the entire computer device 3 by using various interfaces and lines, and executes various functions and processes data of the computer device 3 by running or executing programs or modules stored in the memory 31 and calling data stored in the memory 31. For example, the at least one processor 32, when executing the computer program stored in the memory, implements all or part of the steps of the rule engine-based parameter transformation method described in the embodiments of the present application; or implement all or part of the functionality of the rule engine based parameter transformation means. The at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips.
In some embodiments, the at least one communication bus 33 is arranged to enable connection communication between the memory 31 and the at least one processor 32 or the like.
Although not shown, the computer device 3 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to implement functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The computer device 3 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a computer device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the specification may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present application without departing from the spirit and scope of the technical solutions of the present application.

Claims (10)

1. A parameter conversion method based on a rule engine is characterized by comprising the following steps:
when a rule issuing instruction is received, acquiring a target parameter conversion rule corresponding to the rule issuing instruction;
analyzing the target parameter conversion rule to obtain a target parameter set;
inputting the target parameter set into a link calculation model trained in advance to obtain an optimal execution link;
receiving and analyzing a parameter conversion request to obtain a target interface parameter corresponding to the parameter conversion request;
calling the optimal execution link according to the target interface parameter to obtain a parameter conversion result;
and outputting the parameter conversion result.
2. The rules engine based parameter transformation method of claim 1, wherein before said receiving a rule issuing instruction, the method further comprises:
acquiring first service data of a target interface in a target service system;
analyzing the first service data to obtain a first attention attribute corresponding to the target service system;
acquiring second service data corresponding to the target interface in other service systems;
analyzing the second service data to obtain a second attention attribute corresponding to the other service systems;
and acquiring the incidence relation between the first attention attribute and the second attention attribute, and determining a parameter conversion rule between the target service system and the other service systems according to the incidence relation.
3. The method of claim 1, wherein parsing the target parameter transformation rule to obtain a target parameter set comprises:
analyzing the target parameter conversion rule;
detecting whether the target parameter conversion rule contains a target rule parameter or not;
when the detection result is that the target parameter conversion rule contains target rule parameters, positioning all the target rule parameters and acquiring target attribute parameters connected with the target rule parameters;
and constructing a target parameter set according to the target rule parameters and the target attribute parameters.
4. The method of claim 1, wherein the inputting the target parameter set into a pre-trained link calculation model to obtain an optimal execution link comprises:
calling the link calculation model to obtain the logic relation of each target parameter in the target parameter set;
determining the priority order of the target parameters according to the logic relation;
and combining the target parameters based on the priority order to construct an optimal execution link.
5. The method of claim 1, wherein the receiving and parsing a parameter transformation request to obtain target interface parameters corresponding to the parameter transformation request comprises:
analyzing the parameter conversion request and detecting whether the parameter conversion request carries a preset identifier or not;
when the detection result is that the parameter conversion request carries a preset identifier, acquiring the preset identifier;
and determining target interface parameters corresponding to the preset identification.
6. The rules engine based parameter transformation method of claim 1, wherein the method further comprises:
acquiring the data volume of the parameter conversion request;
determining the magnitude corresponding to the parameter conversion request according to the data volume;
the number of rule engines to invoke is determined based on the magnitude.
7. The rule engine based parameter transformation method of claim 6, wherein after the determining the number of rule engines to invoke based on the magnitude, the method further comprises:
acquiring the number of the rule engines to be called;
splitting the parameter conversion request according to a preset rule based on the number of the rule engines to obtain a target parameter conversion request of each rule engine;
and distributing the target parameter conversion request to the rule engine.
8. A rules engine based parameter transformation apparatus, wherein the rules engine based parameter transformation apparatus comprises:
the rule obtaining module is used for obtaining a target parameter conversion rule corresponding to a rule issuing instruction when the rule issuing instruction is received;
the rule analysis module is used for analyzing the target parameter conversion rule to obtain a target parameter set;
the link acquisition module is used for inputting the target parameter set into a link calculation model trained in advance to obtain an optimal execution link;
the parameter acquisition module is used for receiving and analyzing a parameter conversion request to obtain a target interface parameter corresponding to the parameter conversion request;
the link calling module is used for calling the optimal execution link according to the target interface parameter to obtain a parameter conversion result;
and the result output module is used for outputting the parameter conversion result.
9. A computer device comprising a processor for implementing a rules engine based parameter transformation method as claimed in any one of claims 1 to 7 when executing a computer program stored in a memory.
10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the rules engine based parameter transformation method of any of claims 1 to 7.
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