Dynamic analysis method based on EL expression, server, product and medium

Abstract

The invention discloses a dynamic analysis method based on an EL expression, a server, a product and a medium, and relates to the field of electrical digital data processing.The method comprises the steps that a plurality of analysis rules are imported based on writing operation of a user, each analysis rule comprises a rule identifier and an execution configuration, and each execution configuration comprises an execution mode and a parameter definition; collecting a compiled text of the user to obtain original business data, reading the mark grammar in the original business data, and extracting the rule identifier and the actual parameter; selecting a corresponding analysis rule according to the service identifier, and performing type conversion on the actual parameter according to a parameter definition in the analysis rule to obtain a type conversion parameter; and integrating the type conversion parameters into executable contents based on the execution mode in the analysis rule, and executing the executable contents to obtain an analysis result. By implementing the method, the working efficiency of business addition or modification can be improved.

Description

Technical Field

[0001] The present application relates to the field of electronic digital data processing, and in particular to a dynamic parsing method, server, product and medium based on EL expression. Background Art

[0002] As the process of enterprise informatization continues to deepen, the demand for dynamic data processing in business systems is growing. Especially in enterprise application scenarios, the system needs to fill, convert and calculate data in real time according to actual business conditions to meet the flexibility requirements of business operations.

[0003] In related technologies, dynamic data processing is achieved by pre-defining data processing templates in the code. Such systems usually use hard-coding to set data processing logic and directly write data conversion rules in the code. During the processing, the system reads data according to the preset code logic, and then obtains the final result through string concatenation or fixed calculation formulas.

[0004] However, this implementation method of predefined templates has limitations in actual applications. When adding new business scenarios or modifying existing business rules, technicians need to directly modify the source code and redeploy the system, which makes it difficult for the system to quickly respond to changes in business needs. Since the processing logic is solidified in the program code, business personnel cannot independently adjust data processing rules according to actual needs, which reduces the work efficiency of adding or modifying business. Summary of the invention

[0005] The present application provides a dynamic parsing method, server, product and medium based on EL expression, which are used to improve the work efficiency of adding or modifying business.

[0006] In the first aspect, the present application provides a dynamic parsing method based on EL expression, which is applied to a server, and the method includes: based on the user's writing operation, importing multiple parsing rules, the parsing rules contain rule identifiers and execution configurations, and the execution configurations include execution methods and parameter definitions; collecting the user's written text to obtain original business data, the original business data contains markup syntax, the markup syntax is in the form of double curly braces, and the double curly braces are provided with business identifiers and actual parameters, and the business identifier corresponds to the rule identifier; reading the markup syntax in the original business data, extracting the rule identifier and the actual parameter; selecting a corresponding parsing rule according to the business identifier, and performing type conversion on the actual parameter according to the parameter definition in the parsing rule to obtain a type conversion parameter; integrating the type conversion parameter into executable content based on the execution method in the parsing rule, and executing the executable content to obtain a parsing result.

[0007] By adopting the above technical solution, users can import parsing rules containing rule identifiers and execution configurations through writing operations, collect the markup syntax in the text written by the user, extract the rule identifier and actual parameters, select the rule according to the business identifier and convert the parameter type, and finally integrate the execution according to the execution method to obtain the parsing results. The rule configuration is separated from the business data processing, and data can be flexibly processed according to different rules without modifying the source code. The system's response speed to changes in business needs is improved, the flexibility and scalability of business processing are enhanced, and at the same time, the development workload of technical personnel is reduced and development efficiency is improved.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of integrating the type conversion parameter into executable content based on the execution mode in the parsing rule, and executing the executable content to obtain the parsing result specifically includes: when the executable mode is SQL template parsing, obtaining the SQL template configured in the parsing rule, filling the type conversion parameter into the SQL template to obtain a SQL statement, and executing the SQL statement to obtain the parsing result; when the executable mode is Java class parsing, obtaining a preset Java class according to the parsing rule, passing the type conversion parameter into a specified method of the preset Java class to execute to obtain the parsing result; when the executable mode is process parsing, obtaining the process execution content configured in the parsing rule, passing the type conversion parameter into the process execution content, and executing the process execution content to obtain the parsing result.

[0009] By adopting the above technical solutions, different execution methods have their own advantages when processing data based on the parsing rule execution method. SQL template parsing can fill parameters into the template to generate SQL statements for execution. SQL is good at complex data queries and can accurately obtain data, which is suitable for data retrieval scenarios. Java class parsing can process complex business logic and meet customized needs by passing parameters to preset Java class methods. Process parsing operates according to the process execution content to ensure standardization. The three methods work together and can be flexibly selected according to the scenario, thereby efficiently integrating parameter execution.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the step of integrating the type conversion parameters into executable content based on the execution mode in the parsing rule, and executing the executable content to obtain the parsing result, the method also includes: when it is detected that the tag syntax is updated, extracting the updated rule identifier and actual parameters; reselecting the corresponding parsing rule according to the updated rule identifier, and performing type conversion on the updated actual parameter to obtain type conversion parameters; reintegrating the type conversion parameters into executable content based on the parsing rule and executing it to obtain an updated parsing result.

[0011] By adopting the above technical solution, when the markup syntax is updated, the updated rule identifier and actual parameters can be extracted in time, the parsing rules can be reselected and the parameter types can be converted, and the updated parsing results can be obtained by integrating and executing again. This mechanism ensures that when business rules change, it can quickly adapt to new requirements and always maintain the accuracy and timeliness of data processing.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of integrating the type conversion parameters into executable content based on the execution mode in the parsing rule, and executing the executable content to obtain the parsing result, the method also includes: assigning a version identifier to each parsing rule, the version identifier containing the modified content and effective time of each version of the parsing rule; deploying the parsing rule to a test environment, and using the original business data for verification; recording the execution status of the parsing rule in the test environment, the execution status including the execution results and exception information during the execution process; when the test verification passes, deploying the parsing rule to a production environment.

[0013] By adopting the above technical solution, version identification is provided for the parsing rules, the modification content and effective time are clearly defined, the evolution of the rules can be traced, and they are deployed in the test environment and verified with original business data. Isolation and comprehensive data guarantee the test effect, and the execution status is recorded to help locate the problem. After the test is passed, the production environment is put online, and strict processes are implemented to ensure its stability, performance and security, so as to achieve effective rule management and quality control, improve system reliability, and avoid failures caused by rule problems.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of integrating the type conversion parameters into executable content based on the execution mode in the parsing rule, and executing the executable content to obtain the parsing result, the method also includes: storing the parsing result in a result cache; when the usage frequency of the result cache is lower than a preset usage frequency threshold, deleting the result data corresponding to the parsing result from the result cache at a preset time point; and storing the result cache in a hierarchical manner according to the usage frequency.

[0015] By adopting the above technical solution, the analysis results are stored in the cache, and its key-value pair format and multi-information storage are easy to manage. Storage is hierarchical according to frequency, and dynamic migration balances cost and performance. Low-frequency data is cleaned according to thresholds to avoid space waste. It can efficiently utilize the cache, accelerate data processing, optimize resource utilization, and ensure the effectiveness and timeliness of cached data.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of integrating the type conversion parameters into executable content based on the execution mode in the parsing rule, and executing the executable content to obtain the parsing result, the method also includes: analyzing the calling frequency of each of the parsing rules in different time periods; when there is a target parsing rule whose calling frequency is greater than a preset calling frequency threshold, recording the intermediate calculation results generated by the target parsing rule during the execution process; setting the storage period of the intermediate calculation results to a preset storage period; when the preset storage period is reached, cleaning up the intermediate calculation results.

[0017] By adopting the above technical solutions, the frequency of rule calls is analyzed, and multi-dimensional statistics are used to assist optimization. When the threshold is exceeded, the intermediate results are recorded, and their rich information and indexes are easy to retrieve. The retention period is set to be cleared upon expiration, and data availability and space release are managed based on time. This can optimize resource utilization and server performance, adjust cache, allocate resources and predict load, improve system efficiency, and ensure stable operation.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of integrating the type conversion parameters into executable content based on the execution mode in the parsing rule, and executing the executable content to obtain the parsing result, the method also includes: setting an access level and a visible range for the parsing result; creating a user authority configuration table, which contains access rights of different users to the parsing results; when a parsing result access request from a requesting party is received, verifying the authority level of the requesting party; when the authority level does not comply with the user authority configuration table, desensitizing the parsing results that exceed the authority range of the authority level.

[0019] By adopting the above technical solutions, the access level and visible scope of the analysis results can be set, data access rights can be accurately controlled, data security can be protected, sensitive information leakage can be prevented, and the confidentiality and integrity of system data can be maintained.

[0020] In a second aspect, an embodiment of the present application provides a server, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the server to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when executed on a server, enables the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a server, enable the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0023] It is understandable that the server provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application allows users to import parsing rules containing rule identifiers and execution configurations through writing operations, collect the markup syntax in the text written by the user, extract the rule identifier and actual parameters, select the rule according to the business identifier and convert the parameter type, and finally integrate the execution according to the execution method to obtain the parsing results. It separates the rule configuration from the business data processing, and can flexibly process data according to different rules without modifying the source code. It improves the system's response speed to changes in business needs, enhances the flexibility and scalability of business processing, and also reduces the development workload of technical personnel and improves development efficiency.

[0025] 2. When this application processes data through the execution method based on parsing rules, different execution methods have their own advantages. SQL template parsing can fill parameters into the template to generate SQL statements for execution. SQL is good at complex data queries, can accurately obtain data, and is suitable for data retrieval scenarios. Java class parsing can process complex business logic and meet customized needs by passing parameters to preset Java class methods. Process parsing operates according to the process execution content to ensure standardization. The three methods work together and can be flexibly selected according to the scenario, thereby efficiently integrating parameter execution.

[0026] 3. When the markup syntax is updated, this application can timely extract the updated rule identifier and actual parameters, reselect the parsing rules and convert the parameter type, and integrate and execute again to obtain the updated parsing results. This mechanism ensures that the system can quickly adapt to new requirements when business rules change, and always maintain the accuracy and timeliness of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flowchart of a dynamic parsing method based on EL expression in an embodiment of the present application; Figure 2 is another flowchart of the dynamic parsing method based on EL expression in the embodiment of the present application; Figure 3It is a schematic diagram of a physical device structure of a server in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0031] The order system of a large e-commerce platform has to process the price calculation of millions of orders every day. The order amount not only needs to consider the original price of the product, but also involves multiple factors such as member discounts, promotional activities, coupons, points deductions, and tiered prices. For example, during the Double 11 period, an order may include multiple discounts such as 50 off for orders over 300, 95% off for VIP members, and 20 off for the user's first order. As the business develops, the discount rules are becoming more and more complicated. For example, some products participate in the full discount but cannot use coupons, some categories have different VIP discounts, and some activities are limited to specific user groups. The operation team often needs to quickly adjust the price strategy to respond to market changes, which brings huge challenges to the calculation of the order amount.

[0032] The commodity pricing rules in the related technologies are hard-coded in the code. For example, to calculate member discounts, it is hard-coded that VIP is 10% off and SVIP is 20% off; to calculate full-discount activities, it is hard-coded that 50 yuan off for purchases over 300 yuan; for first-order discounts, it is also hard-coded that 20 yuan is immediately discounted. In this way, every time a new promotion is added or the discount rules are modified, the code must be modified and republished. For example, to add a "95% discount for gold members", the code for member discounts must be modified; to change the VIP discount from 10% off to 15% off, the code must also be modified, and promotional activities often change. It may be 50 yuan off for purchases over 300 yuan this week, and 30 yuan off for purchases over 200 yuan next week. Developers have to modify the code every time, which seriously affects operational efficiency.

[0033] After adopting the new solution, the operator first configures multiple parsing rules in the background management system, such as configuring the "member discount" rule, specifying the rule identifier as "memberDiscount", the execution method as SQL query, and the parameter definition including the member type and product category. After that, the operator uses the double curly bracket tag syntax in the order description text, such as "{{memberDiscount|userType=VIP, categoryId=1}}" to reference the rule. After the system detects the tag, it automatically extracts the "memberDiscount" rule identifier and actual parameters, converts the parameters to the correct data type according to the rule definition, and finally executes the SQL query to calculate the discount amount. In this way, the operator only needs to write the tagged text, and the system can automatically calculate the order amount according to the configured rules without the intervention of developers.

[0034] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of a dynamic parsing method based on EL expressions in an embodiment of the present application.

[0035] S101. Based on the writing operation of the user, a plurality of parsing rules are imported. The parsing rules include rule identifiers and execution configurations. The execution configurations include execution modes and parameter definitions.

[0036] Among them, the rule identifier represents the identifier that uniquely identifies the parsing rule; the execution configuration represents the specific configuration information of the rule execution, including the execution mode and parameter definition; the execution mode is used to specify the parsing type of the rule, such as SQL template parsing, Java class parsing or process parsing; the parameter definition is used to specify the type, format and constraints of the parameters in the parsing process.

[0037] This step is performed when the server is initialized or the rules are updated. Specifically, the server first receives the parsing rules written by the user through the web interface or configuration file. Each rule contains a unique rule identifier for subsequent rule matching and calling. The execution configuration section defines in detail how the rules are executed, such as using SQL templates for data query, calling Java classes for business processing, or executing predefined processing flows. At the same time, the data type, format requirements, and valid range of the input and output parameters are clearly specified through parameter definition to ensure the accuracy and security of data processing.

[0038] In some embodiments, rule import and configuration can be implemented in a variety of ways: optionally, a graphical Web interface is provided, and the user fills in the rule information through a form, including selecting the execution method, writing an SQL template or specifying a Java class, and the server automatically verifies the integrity and correctness of the rules, and finally saves them to the rule library; optionally, support batch import of rules through configuration files, the server parses the configuration file content, extracts the rule information and formats it, and performs version management and status tracking on the rules. It is understandable that other methods can also be used to implement rule import and configuration, which are not limited here.

[0039] S102: Collect the text written by the user to obtain original business data, where the original business data contains markup syntax, and the markup syntax is in the form of double curly brackets. A business identifier and actual parameters are set in the double curly brackets, and the business identifier corresponds to the rule identifier.

[0040] Among them, the original business data refers to the text data containing the content to be parsed written by the user in the business server; the markup syntax refers to the special grammatical structure marked in the form of double curly brackets; the business identifier refers to the unique identifier used to identify the processing rules in the markup syntax; the actual parameters refer to the specific parameter values ​​that need to be passed into the rules for processing.

[0041] This step is performed when data parsing is required. Specifically, the server monitors and collects the text content written by the user in the business server and identifies the special markup syntax contained therein. The markup syntax uses double curly brackets (such as {{Rule A: parameter 1, parameter 2}}), where the part before the colon is the business identifier, which is used to match the corresponding parsing rule, and the part after the colon is the actual parameter, which contains the specific data value that needs to be passed into the rule for processing. The server ensures the integrity of the collected data and performs necessary preprocessing to prepare for subsequent parsing.

[0042] In some embodiments, data collection and tag recognition can be implemented in a variety of ways: Optionally, the server monitors the content changes in the text editing area in real time, and when a double curly bracket tag is detected, the tag content is immediately parsed, the business identifier and parameter information are extracted, and format verification is performed; Optionally, batch processing is performed when data is saved, and the server scans the entire text content, matches the tag syntax through regular expressions, parses all the tag content to be processed, and performs structured storage. It is understandable that other methods can also be used to implement data collection and tag recognition, which are not limited here.

[0043] S103: Read the markup syntax in the original business data, and extract the rule identifier and the actual parameter.

[0044] Among them, reading refers to the process of identifying and obtaining the content of the markup syntax from the original business data; extraction refers to the operation of separating the rule identifier and actual parameters from the markup syntax; the markup syntax refers to a special syntax structure wrapped in double curly braces, which is used to identify the content that needs to be parsed; the rule identifier refers to the unique identifier used to match the parsing rule; the actual parameter refers to the specific parameter value that needs to be passed to the parsing rule for processing, which can contain single or multiple parameter items, and the parameter items are separated by commas.

[0045] This step is performed after the server completes the collection of the original business data. Specifically, the server first scans the original business data and identifies the markup syntax content through regular expressions or specific parsers. For each identified markup syntax, the server parses its internal structure and divides the content in the double curly brackets into two parts: the rule identifier and the actual parameters. The server verifies the validity of the rule identifier to ensure that it complies with the naming specification and that the corresponding parsing rules exist in the rule base. For the actual parameter part, the server will perform parameter segmentation and preliminary formatting to ensure that the parameter format meets the requirements of the rule definition. At the same time, the server will record the location information of the markup syntax in the original text to prepare for subsequent result replacement.

[0046] In some embodiments, the reading and parsing of the marked grammar can be achieved in a variety of ways: Optionally, the server adopts a state machine parsing method, first identifies the starting position of the double curly brackets, then parses the content character by character, identifies the rule identifier and parameter separator, and finally extracts the content of each part. The whole process includes the steps of initializing the parser, scanning the text content, state conversion processing, extracting the target content, and verifying the parsing results; Optionally, the server uses a parsing method based on lexical analysis, regards the marked grammar as a special language structure, identifies the grammatical tags through the lexical analyzer, generates a sequence of lexical units, and then performs grammatical analysis, and finally constructs a parse tree. The specific steps include lexical analysis initialization, tag identification and classification, generation of lexical units, construction of grammar trees, extraction of target information, etc. It can be understood that other methods can also be used to implement the reading and parsing operations of the marked grammar, which are not limited here.

[0047] S104: Select a corresponding parsing rule according to the service identifier, and perform type conversion on the actual parameter according to the parameter definition in the parsing rule to obtain a type conversion parameter.

[0048] Among them, the business identifier represents a unique identifier used to locate a specific parsing rule in the rule base; the parsing rule refers to a pre-configured data processing rule, which contains information such as execution method and parameter definition; the parameter definition is used to specify the data type, format requirements and conversion rules of the parameter; type conversion represents the process of converting the actual parameter into a data type that meets the rule requirements; the type conversion parameter refers to the standard format parameter after the type conversion is completed.

[0049] This step is performed after the tag syntax parsing is completed. Specifically, the server first searches the rule base for matching parsing rules based on the extracted business identifier. After obtaining the rule, the server reads the parameter definition information in the rule, including the number, order, data type and other requirements of the parameters. Then, the server performs type conversion processing on the actual parameters and converts the parameter value to the target type defined by the rule, such as converting a string to a numeric type, a date type, etc. During the conversion process, the server will perform data validity verification to ensure that the converted parameter value is within the allowed range. For complex parameter types, the server will perform corresponding formatting processing to ensure that the parameter format meets the rule requirements.

[0050] In some embodiments, parameter rule matching and type conversion can be implemented in a variety of ways: Optionally, the server adopts a cache-accelerated matching method, first searches for the rule corresponding to the business identifier in the rule cache, and if not found, loads the rule from the rule library and updates the cache, and then performs the conversion operation according to the parameter type defined by the rule, and the specific steps include cache query, rule loading, cache update, parameter type identification, type conversion execution, etc. Optionally, the server uses a dynamic type conversion mechanism to obtain parameter type information through reflection or dynamic proxy, and then calls the corresponding type converter to perform parameter conversion, and the specific steps include parameter type analysis, converter selection, conversion rule matching, execution of conversion operations, result verification, etc. It can be understood that other methods can also be used to implement parameter rule matching and type conversion processing, which are not limited here.

[0051] S105: Integrate the type conversion parameters into executable content based on the execution mode in the parsing rule, and execute the executable content to obtain a parsing result.

[0052] This step specifically includes: When the executable mode is SQL template parsing, the SQL template configured in the parsing rule is obtained, the type conversion parameter is filled into the SQL template to obtain an SQL statement, and the SQL statement is executed to obtain the parsing result; When the executable mode is Java class parsing, a preset Java class is obtained according to the parsing rule, and the type conversion parameter is passed into a specified method of the preset Java class to execute and obtain the parsing result; When the executable mode is process parsing, the process execution content configured in the parsing rule is obtained, the type conversion parameter is passed into the process execution content, and the process execution content is executed to obtain the parsing result.

[0053] Among them, the execution mode indicates the type of data processing method defined in the parsing rule; the type conversion parameter refers to the standard format parameter after type conversion; the executable content indicates the specific instructions or codes generated according to the execution mode and executable by the server; the parsing result refers to the processing result data obtained after executing the executable content; the SQL template refers to the predefined SQL statement template containing parameter placeholders; the Java class parsing refers to the method of performing data processing by calling the preset Java class method; the process parsing refers to the method of executing multiple processing steps in the order of the predefined processing flow; the SQL statement refers to the complete SQL query statement generated after filling the parameters into the SQL template; the preset Java class indicates the Java class defined in the server for processing specific business logic; the process execution content refers to the process configuration information that defines the processing steps and execution order.

[0054] This step is performed after the parameter type conversion is completed. Specifically, the server selects the corresponding processing flow according to the execution mode configured in the parsing rule. For the SQL template parsing method, the server obtains the SQL template from the rule configuration, fills the converted parameters into the template according to the placeholder position, generates a complete SQL statement, and then executes the SQL query through the database connection and obtains the result. For the Java class parsing method, the server instantiates the preset Java class through the reflection mechanism, calls the specified method and passes in the converted parameters, executes the business processing logic and obtains the return result. For the process parsing method, the server loads the process configuration information, executes the processing operations in sequence according to the defined step order, passes the parameters to each processing node, and finally summarizes the processing results.

[0055] In some embodiments, parsing processing of different execution modes can be implemented in a variety of ways: Optionally, the server uses a template engine to process SQL template parsing, first loads the SQL template file, parses the parameter placeholders in the template, creates a parameter mapping relationship, performs parameter replacement operations, verifies the generated SQL statement, establishes a database connection, executes an SQL query, processes the query results, closes the database connection, and returns the processing results; Optionally, the server uses a dynamic proxy to process Java class parsing, including creating a class loader, loading the target class file, parsing the class definition information, creating a proxy object, preparing method parameters, executing method calls, capturing and processing exceptions, processing return results, releasing server resources, and returning execution results. It is understandable that other methods can also be used to implement different types of parsing processing operations, which are not limited here.

[0056] In the embodiment of process analysis, the execution control of the process can be realized in a variety of ways: optionally, the server adopts the workflow engine mode, first loads the process definition file, parses the process node configuration, creates a process instance, initializes the process variables, executes the start node, passes the processing parameters, sequentially executes the process nodes, processes the node return results, judges the process branch, executes the next node, until the process ends and returns the final result; optionally, the server uses the state machine mode, including initializing the state machine, loading the state definition, setting the initial state, receiving the processing parameters, executing the state transition, processing the state action, judging the transition condition, updating the state data, recording the state history, and returning the processing result after reaching the terminal state. It can be understood that other methods can also be used to realize the execution control of process analysis, which is not limited here.

[0057] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the dynamic parsing method based on EL expression in an embodiment of the present application.

[0058] S201. Integrate the type conversion parameters into executable content based on the execution mode in the parsing rule, and execute the executable content to obtain a parsing result.

[0059] During the execution process, the server first obtains the specified execution mode from the parsing rule configuration, including SQL template parsing, Java class parsing, or process parsing. For parameters that have completed type conversion, the server will adopt corresponding processing strategies according to different execution modes: in SQL template parsing mode, fill the parameters into the predefined SQL statement template; in Java class parsing mode, construct the parameter list required for method call; in process parsing mode, organize the parameters into the data structure required for process execution.

[0060] The server then executes the integrated content and obtains the execution results. Specifically, in SQL template parsing mode, the server executes the generated SQL statement and obtains the database query results; in Java class parsing mode, the server calls the specified Java class method and obtains the method return value; in process parsing mode, the server executes each step in sequence according to the predefined processing flow and collects the processing results of each step. Finally, the server performs format standardization on the execution results to ensure that the result format conforms to the server specification definition, and returns the processed results to the caller.

[0061] S202: When it is detected that the markup syntax is updated, extract the updated rule identifier and actual parameters.

[0062] The server continuously checks the tag syntax content in the original business data through a real-time monitoring mechanism. These tags are in the form of double curly braces (such as {{business identifier: parameter}}). When the server detects that the tag syntax content has changed, it immediately starts the extraction process. The extraction process first performs an integrity scan on the updated tag syntax to ensure that the tag syntax format is correct, and then separates it into two parts: the rule identifier and the actual parameter.

[0063] The server will perform a series of verifications on the extracted content. First, the validity of the rule identifier is verified to confirm that there is a corresponding parsing rule in the server rule library; then the format of the actual parameters is checked to see if it meets the requirements of the rule definition, including the number of parameters, parameter type, etc. Once the verification is passed, the server will normalize the extracted rule identifier and actual parameters, and pass the processed data to the subsequent parsing steps. If any exceptions are found during the verification process, the server will record detailed error information and handle it according to the preset exception handling process.

[0064] S203: reselect the corresponding parsing rule according to the updated rule identifier, and perform type conversion on the updated actual parameter to obtain a type conversion parameter.

[0065] After receiving the updated rule identifier and actual parameters, the server first performs an exact match in the rule library to find the corresponding parsing rule configuration. After finding the matching parsing rule, the server reads the parameter definition information preset in the rule, including the target type of the parameter, the value range and other constraints. Subsequently, the server starts the parameter conversion process and converts the updated actual parameters into the data types required in the rule definition, such as converting strings into integers, decimals, dates and other types.

[0066] During the parameter conversion process, the server will perform strict type checking and validity verification on each conversion operation. For example, for numeric type conversion, the server will verify whether the converted value is within the allowed range; for date type conversion, the server will check whether the date format complies with the specification. After completing the type conversion of all parameters, the server organizes the converted parameters into a standard data structure for use in subsequent execution steps. If any abnormal situation occurs during the conversion process, the server will immediately terminate the conversion operation, record detailed error information, and start the corresponding error handling process to ensure the stable operation of the server.

[0067] S204: Based on the parsing rule, the type conversion parameter is reintegrated into executable content and executed to obtain an updated parsing result.

[0068] In the dynamic parsing server based on EL expressions, the parameters that have completed type conversion need to be reintegrated and executed according to the definition of the parsing rules. The server obtains the specific execution method by reading the execution configuration of the parsing rules. For example, in SQL template parsing, the server fills the new parameter value into the preset SQL template to generate a complete SQL statement, such as replacing ${age} in "SELECT * FROM users WHERE age>${age}" with the actual parameter value after conversion; in Java class parsing, the server constructs a method call statement containing updated parameters, such as "userService.queryByCondition(age, name)"; in process parsing, the server organizes the updated parameters into a format that can be recognized by the process engine to drive the execution of the business process.

[0069] In the execution phase, the server performs actual operations based on the integrated content and collects the execution results. For SQL template parsing, the server executes SQL statements through the database connection pool and obtains the query result set; for Java class parsing, the server executes the specified Java method through reflection mechanism or direct call and obtains the return value; for process parsing, the server executes each processing step in the predefined process node sequence and collects the processing results of each node. Finally, the server performs unified formatting on the execution results to ensure that the returned data structure conforms to the standard format defined by the server, including field mapping, data type conversion and format normalization of the result set.

[0070] S205: Allocate a version identifier for each parsing rule, wherein the version identifier includes the modified content and effective time of each version of the parsing rule.

[0071] The version management mechanism is the core component of parsing rule maintenance. It tracks the evolution history of rules by assigning a unique version identifier to each parsing rule. The version identifier uses a standardized format, such as "rule identifier-year-month-day hour-minute-second-serial number", to ensure that a certain version of the rule can be uniquely identified in the server. The modification content records the specific items of the rule change, including the adjustment of the execution method, the change of parameter definition, the optimization of SQL templates, etc. The effective time defines the specific time point when the rule version begins to take effect, and is recorded in a standard timestamp format to facilitate version switching control on the server.

[0072] The complete version management process includes the recording, storage, and maintenance of version information. When a rule changes, the server automatically generates a new version identifier and creates a new version record in the version control table. The version record records the specific content of the modification in detail, including the modified configuration items, the values ​​before and after the modification, the reason for the modification, and other information. At the same time, the server establishes an association between versions and supports version tracing and rollback operations. In order to ensure the reliability of version management, the server uses a transaction mechanism to process the update operation of version information and protects the security of version data through a database backup mechanism. During the query and execution process, the server uses a version control mechanism to ensure that the correct version of the rules are used for parsing.

[0073] S206: deploy the parsing rule into a test environment, and use the original business data for verification.

[0074] Test environment deployment is a key step to ensure the correctness of parsing rules, including three main stages: environment preparation, rule deployment, and verification testing. The test environment is an isolated server independent of the production environment. It has the same hardware and software configuration and server architecture as the production environment, but uses independent database instances and configuration information. The original business data is the input data set used for test verification, which contains test cases in various business scenarios, covering normal scenarios, boundary scenarios, and abnormal scenarios. The verification process evaluates the correctness of the rules by comparing the test results with the expected results.

[0075] The specific process of deployment verification is first to prepare the environment, including configuration check of the test environment, database initialization, test data import, etc. Then the rules are deployed, and the latest version of the parsing rules is deployed to the test environment through the configuration management server. The deployment process includes the transmission of rule files, the update of configuration parameters, and the cleaning of caches. In the verification phase, the automated testing framework is used to execute test cases, collect test results, and generate test reports. The test report records the execution results of each test case in detail, including input parameters, actual output, expected output, execution time, and other information. For abnormal situations, the server records detailed error information and stack traces to facilitate developers to locate and repair problems.

[0076] S207: Record the execution status of the parsing rule in the test environment, where the execution status includes the execution result and abnormal information during the execution process.

[0077] The execution record is a comprehensive monitoring record of the parsing rules during the running process of the test environment. The execution results include quantitative indicators such as the output data, execution time, and resource consumption of the rule execution, which are stored in a structured format for subsequent analysis and comparison. The exception information records all abnormal conditions that occur during the execution process, including parameter verification failure, type conversion errors, database operation exceptions, etc. Each exception record contains detailed data such as exception type, occurrence time, error information, stack trace, etc.

[0078] The server uses a multi-level logging mechanism to achieve complete records of execution status. At the data level, the server establishes a special monitoring log table to record detailed information for each rule execution, including fields such as rule identification, version number, execution timestamp, input parameters, execution time, and output results. For abnormal situations, the server additionally records detailed information about the exception, including the time when the exception occurred, the identification code of the exception type, the specific description of the exception, and the parameter value that caused the exception. The server also records performance indicators such as CPU usage, memory usage, number of database connections, and other server resource indicators. These records use a hierarchical storage strategy, important exception information is permanently stored, and ordinary execution records are set with a reasonable retention period. Through these detailed records, the technical team can accurately evaluate the execution effect of the rules and promptly discover and resolve potential problems.

[0079] S208: After the test verification is passed, the parsing rule is deployed to the production environment.

[0080] Production environment deployment is a key step to put fully tested and verified parsing rules into actual business use. The production environment is the server environment that carries the actual business operation and directly serves the end users. Therefore, it has the highest requirements for server stability, performance and data security. The test verification pass criteria include comprehensive evaluation results in multiple dimensions such as successful functional verification, performance indicators met, and complete exception handling mechanism.

[0081] The deployment of the production environment adopts strict online process control. The first is the deployment preparation stage, which includes formulating a detailed deployment plan, preparing a rollback plan, and evaluating deployment risks. The deployment plan clearly stipulates the deployment time window, deployment steps, participants and their responsibilities. The deployment execution stage uses automated deployment tools to transfer rule files, update configurations, and restart services according to the preset deployment scripts. During the deployment process, the server status is monitored in real time, and the execution results of each deployment step are recorded. After the deployment is completed, the server is verified, including functional verification and performance verification. If an abnormality is found, the rollback plan is immediately executed to restore to the state before deployment. After the deployment is successful, the server operation status is continuously monitored to ensure the normal operation of the business.

[0082] S209: Store the parsing result in the result cache.

[0083] Result cache is a high-performance storage mechanism for storing the results of parsing rule execution. The parsing results contain information such as the data content generated by the rule execution, the execution timestamp, and the data version number. These data are organized in the form of key-value pairs, where the key is a unique identifier generated by the combination of the rule identifier and the parameter, and the value is the serialized execution result data. The cache server adopts a distributed cache architecture to support fast data reading, writing, and automatic synchronization.

[0084] The cache implementation adopts a multi-level cache architecture, including two levels: local memory cache and distributed cache. The local memory cache uses the LRU (least recently used) algorithm to manage cached data, providing the fastest access speed for the most frequently accessed data. The distributed cache uses the Redis cluster to store a large amount of cached data to ensure data reliability and consistency. When the execution result is written to the cache, the server first writes the data to the distributed cache and then updates the local cache. The cached data is set with an expiration time, and the space is automatically cleared after the expiration. The server uses the cache preheating mechanism to load high-frequency access data into the cache in advance when the server is started or the rules are updated. At the same time, the server implements version control of cached data. When the rules are updated, the relevant cached data will be marked as invalid to ensure data consistency.

[0085] S210: When the usage frequency of the result cache is lower than a preset usage frequency threshold, the result data corresponding to the analysis result is deleted from the result cache at a preset time point.

[0086] The usage frequency of the result cache refers to the number of times a specific cached data is accessed per unit time, which is counted by the access counter. The preset usage frequency threshold is the minimum access frequency standard predefined by the server. Data below this threshold is considered low-frequency data. The preset time point is a fixed time node for performing cleanup operations, which is usually set during a period of low server load, such as 3 a.m. every day. The result data is the specific content stored in the cache after the parsing rules are executed, including execution results, timestamps, version numbers, and other information.

[0087] The server uses a scheduled task mechanism to automatically clean up cached data. Each cached data item is associated with an access counter that records the number of accesses during the statistical period. At the end of each statistical period, the server calculates the access frequency of the data item and compares the access frequency with the preset threshold. For data items with an access frequency lower than the threshold, the server marks them as pending cleanup. When the preset cleanup time point is reached, the cleanup task scans all data items to be cleaned and performs the deletion operation. The deletion operation uses a batch processing method, with a fixed number of data items processed in each batch to avoid sudden increases in server performance. At the same time, the server records the execution status of each cleanup operation, including indicators such as the amount of data cleaned and the time consumed. These records are used for subsequent server optimization and capacity planning.

[0088] S211 . The result cache is stored in a hierarchical manner according to the usage frequency.

[0089] Hierarchical storage is a mechanism for hierarchical management of cached data based on usage frequency. The usage frequency is obtained by real-time statistical data access times, and the data is divided into different access frequency levels based on the statistical results. The result cache adopts a multi-level storage architecture. Different levels of storage media have different access speed and storage capacity characteristics, including memory, solid-state drives, mechanical hard drives and other storage devices.

[0090] The server implements a dynamic hierarchical storage management mechanism. First, multiple storage levels are established, such as the high-frequency layer (access frequency > 100 times / hour) uses memory storage, the medium-frequency layer (10-100 times / hour) uses SSD storage, and the low-frequency layer (<10 times / hour) uses ordinary hard disk storage. The server uses the access counter to count the access frequency of each data item in real time and runs the data migration task in the background. When the access frequency of a data item changes, the data migration operation is triggered to transfer the data item to the storage layer of the corresponding frequency level. The migration process is executed asynchronously to avoid affecting the normal operation of the server. At the same time, the server maintains a unified data index table to record the current storage level of each data item to ensure the accuracy of data access. Through this hierarchical storage mechanism, the server can achieve the optimal balance between storage cost and access performance.

[0091] S212: Analyze the calling frequency of each parsing rule in different time periods.

[0092] Call frequency analysis is a statistical analysis of the usage of parsing rules. Parsing rules are configuration units for data processing, and each rule has a unique identifier. Different time periods refer to multiple statistical periods predefined by the server, including multiple time dimensions such as hourly, daily, and weekly. The call frequency is the number of times a rule is executed within a specific time period, which is counted by the call counter.

[0093] The server implements a complete call frequency analysis function. At the data collection level, the server creates a call record table for each parsing rule, recording the timestamp, call parameters, execution results, and other information of each call. The analysis program aggregates and counts the call records according to the predefined time dimension to generate call frequency data for each time period. The statistical results include indicators in multiple dimensions, such as average call frequency, peak call frequency, call frequency change trend, etc. The server stores these statistical data in a special statistical information table and automatically updates the statistical results through scheduled tasks. The analysis results are used for server optimization, such as dynamically adjusting cache strategies, optimizing resource allocation, and predicting server load. Statistical data also serves as an important basis for server capacity planning and performance optimization, helping to determine the timing and optimization direction of server expansion.

[0094] S213: When there is a target parsing rule whose calling frequency is greater than a preset calling frequency threshold, record an intermediate calculation result generated during the execution of the target parsing rule.

[0095] The target parsing rule refers to a specific rule that is frequently called in the server and is uniquely identified by the rule identifier. The call frequency is obtained through counter statistics, which records the number of times the rule is executed per unit time. The preset call frequency threshold is the frequency standard value set by the server, which is used to identify rules with high frequency calls, such as more than 100 calls per minute. The intermediate calculation results are interim data generated during the rule execution process, including data conversion results, temporary statistical values, intermediate states and other information, which are generated during the calculation process before the final result is generated.

[0096] The server has established a special mechanism for recording intermediate results. First, the server uses the performance monitoring module to count the call frequency of each parsing rule in real time, and the counter updates the statistical value every minute. When it is detected that the call frequency of a rule exceeds the preset threshold, the server activates the intermediate result recording function of the rule. During the rule execution process, the server inserts recording points at key computing nodes to capture and save intermediate calculation data. These data are stored in a special intermediate result table, and each record contains fields such as rule ID, execution timestamp, calculation step ID, and data content. At the same time, the server indexes the intermediate results and supports fast retrieval by dimensions such as rule ID, time range, and calculation step. These recorded intermediate results are used for performance analysis, problem diagnosis, and optimization reference.

[0097] S214: Set the storage period of the intermediate calculation result to a preset storage period.

[0098] The storage management of intermediate calculation results adopts a time-based retention policy. The retention period is the length of time the data is retained, which is determined by the server based on the importance of the data and the storage resource status. The preset retention period is the data retention time pre-defined by the server, which can be a fixed value (such as 7 days, 30 days) or a time period dynamically set according to the data type. Each intermediate calculation result contains two time attributes: the data generation time and the expiration time.

[0099] The server implements a flexible shelf life management mechanism. In the intermediate result record table, each record contains an expiration time field, which is calculated by adding the data generation time to the preset shelf life when the data is written. The server also maintains a shelf life configuration table to store the preservation policies for different types of data, including basic shelf life, maximum shelf life, whether extension is allowed, and other configuration items. When the intermediate results are written to the storage, the server automatically calculates and sets the expiration time. For special cases, the server supports manual adjustment of the shelf life of single or batch data. Once the expiration time of the data is set, it will serve as the basis for subsequent cleanup operations. Through this mechanism, the server ensures that the data is available within the validity period, while providing a clear time standard for cleaning up expired data.

[0100] S215: When the preset storage period is reached, the intermediate calculation results are cleared.

[0101] The cleanup operation is the process of deleting expired intermediate calculation results. Expiration is determined based on the expiration time of the data record. When the server time exceeds the expiration time, the data is marked as expired. The cleanup process includes three stages: data marking, physical deletion, and storage space recovery to ensure the effective use of server storage resources. Data cleanup uses batch processing to avoid sudden increases in server performance.

[0102] The server implements the automatic cleanup function through scheduled tasks. The cleanup task runs according to the preset execution plan, such as starting at 2 a.m. every day. When executed, the task first scans the intermediate result table to find all expired data records. For the expired data found, the server first marks it as pending cleanup, and then deletes it in batches according to the batch size (such as 1,000 records per batch). The deletion operation is executed in a transactional manner to ensure data consistency. After the physical deletion is completed, the server triggers the storage space recovery operation to release the storage space occupied by the deleted data. The execution status of each cleanup operation, including the amount of data cleaned, execution time, storage space recovery, and other information, is recorded in the server log for subsequent statistical analysis and server optimization.

[0103] S216: Set the access level and visible range for the parsing result.

[0104] The access level is a hierarchical definition of the access rights to the analysis results, which are usually divided into multiple levels, such as public level (Level 1), internal level (Level 2), confidential level (Level 3), top secret level (Level 4), etc. Each level corresponds to different access restrictions. The visible scope defines the user groups with access rights, including user groups, departments, roles and other dimensions, such as server administrator groups, financial departments, operational roles, etc. The analysis results are the data content generated by the execution of rules, including different types of information such as basic data, statistical data, and analysis results.

[0105] The server implements the access control mechanism through the permission management module. First, add the access level field and the visible range field to the parsing result table to store the permission information of each result data. The access level is stored in a digital encoding format, such as 1 for public level, 2 for internal level, and so on. The visible range is stored in JSON format and contains information such as the user group ID, department ID, role ID, etc. that are allowed to access. When the parsing results are generated, the server automatically sets the initial access level and visible range based on the rule configuration and data characteristics. These permission settings are used for subsequent access control to ensure that the data can only be accessed by authorized users. At the same time, the server provides a permission configuration interface that allows administrators to adjust the access level and visible range of the data.

[0106] S217: Create a user authority configuration table, which contains access rights of different users to the analysis results.

[0107] The user permission configuration table is a data structure that stores user access permission information. The table contains basic fields such as user ID, permission level, permission range, effective time, and expiration time. Access permissions define the user's authorization to operate the parsed results, including read permissions, modify permissions, delete permissions, etc. Each permission has a corresponding permission code and description information. Permission configuration adopts the role-based access control (RBAC) model and supports multi-level mapping relationships between users, roles, and permissions.

[0108] The process of establishing a user permission configuration table includes two stages: table structure design and data initialization. The table structure design includes the following fields: user ID (primary key), user name, permission level (integer), permission range (JSON), permission type (string), effective time (timestamp), expiration time (timestamp), status identifier (integer), creation time (timestamp), update time (timestamp), etc. The server also creates a permission change log table to record all change histories of permission configuration. During the data initialization phase, the server sets the default permission configuration data based on the organizational structure and business requirements. Permission configuration supports batch import and export, which facilitates the management of large-scale permission data. The server ensures the consistency of permission data through a transaction mechanism and protects the security of permission data through a regular backup mechanism.

[0109] S218: When receiving a request for accessing the parsing result from the requesting party, verify the authority level of the requesting party.

[0110] The requester is the user or server that initiates the data access request. Each requester has a unique identifier. The parsed access request contains the requester information, target data identifier, access type, etc. The permission level is the level of access rights that the requester has, which is recorded in the user permission configuration table. Permission verification is the process of matching the requester's permission level with the target data access level.

[0111] The permission verification process adopts a multi-level verification mechanism. When the server receives an access request, it first extracts the user ID and access token in the request, and verifies the validity of the user identity through the identity authentication service. After the authentication is passed, the server queries the user permission configuration table to obtain the permission level and permission range of the requester. Subsequently, the server obtains the access level and visible range configuration of the target parsing result, and compares the permission information of the requester with the access requirements of the data. The verification process checks multiple dimensions: whether the permission level meets the requirements, whether it is within the visible range, whether the access time is within the validity period, etc. The server records detailed logs for each permission verification operation, including verification time, verification results, verification details, and other information. These records are used for security audits and problem tracking.

[0112] S219. When the permission level does not conform to the user permission configuration table, the parsing results that exceed the permission range of the permission level are desensitized.

[0113] Desensitization is a security processing mechanism that masks or deforms sensitive data. Permission level mismatch means that the requester's permission level is lower than the access level required for the data. The user permission configuration table stores the permission level definitions of different users. The permission scope specifies the data fields and content range that users can access. Exceeding the permission level means that the requester attempts to access data content that exceeds its permission scope.

[0114] The server implements a complete data desensitization process. When a permission mismatch is detected, the server first reads the data desensitization rule configuration to determine the fields and desensitization methods that need to be desensitized. The desensitization rule configuration defines desensitization strategies for different types of data, such as retaining the first and last 4 digits of the ID card number, only displaying the first 3 digits and the last 4 digits of the mobile phone number, and only displaying the last 4 digits of the bank account number. The server processes the original data according to the desensitization rules and generates a desensitized data version. The desensitization process uses an irreversible algorithm to ensure that the original information cannot be restored from the desensitized data. After the processing is completed, the server returns the desensitized data results and records the execution of the desensitization process in the access log. This mechanism ensures the security of data access while maintaining the flexibility of data use.

[0115] The following describes the server in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of a server in an embodiment of the present application.

[0116] It should be noted that Figure 3 The structure of the server shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0117] like Figure 3 As shown, the server includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0118] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0119] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0120] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0122] Specifically, the server of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the dynamic parsing method based on EL expression provided in the above embodiment is implemented.

[0123] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the server described in the above embodiment; or may exist independently without being assembled into the server. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the server, the server implements the dynamic parsing method based on EL expression provided in the above embodiment.

[0124] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0125] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0126] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A dynamic parsing method based on EL expression, characterized in that: Applied to a server, the method comprises: Based on the user's writing operation, multiple parsing rules are imported, wherein the parsing rules include rule identifiers and execution configurations, and the execution configurations include execution modes and parameter definitions; Collecting the user's written text to obtain original business data, the original business data contains a markup syntax, the markup syntax is in the form of double curly brackets, a business identifier and actual parameters are set in the double curly brackets, and the business identifier corresponds to the rule identifier; Read the markup syntax in the original business data, and extract the rule identifier and the actual parameter; Selecting a corresponding parsing rule according to the service identifier, and performing type conversion on the actual parameter according to the parameter definition in the parsing rule to obtain a type conversion parameter; The type conversion parameters are integrated into executable content based on the execution mode in the parsing rule, and the executable content is executed to obtain a parsing result.

2. The method according to claim 1, characterized in that The step of integrating the type conversion parameters into executable content based on the execution mode in the parsing rule, and executing the executable content to obtain the parsing result specifically includes: When the executable mode is SQL template parsing, the SQL template configured in the parsing rule is obtained, the type conversion parameter is filled into the SQL template to obtain an SQL statement, and the SQL statement is executed to obtain the parsing result; When the executable mode is Java class parsing, a preset Java class is obtained according to the parsing rule, and the type conversion parameter is passed into a specified method of the preset Java class to execute and obtain the parsing result; When the executable mode is process parsing, the process execution content configured in the parsing rule is obtained, the type conversion parameter is passed into the process execution content, and the process execution content is executed to obtain the parsing result.

3. The method according to claim 1 or 2, characterized in that: After the step of integrating the type conversion parameters into executable content based on the execution mode in the parsing rule, and executing the executable content to obtain a parsing result, the method further includes: When it is detected that the tag syntax is updated, extracting the updated rule identifier and actual parameters; Reselecting a corresponding parsing rule according to the updated rule identifier, and performing type conversion on the updated actual parameter to obtain a type conversion parameter; Based on the parsing rule, the type conversion parameters are reintegrated into executable content and executed to obtain an updated parsing result.

4. The method according to claim 1, characterized in that After the step of integrating the type conversion parameters into executable content based on the execution mode in the parsing rule, and executing the executable content to obtain a parsing result, the method further includes: Allocate a version identifier for each parsing rule, wherein the version identifier includes the modified content and effective time of each version of the parsing rule; Deploy the parsing rules into a test environment and verify them using the original business data; Recording the execution of the parsing rules in the test environment, wherein the execution status includes the execution result and abnormal information during the execution process; When the test is verified to be successful, the parsing rules are deployed to the production environment.

5. The method according to claim 1, characterized in that After the step of integrating the type conversion parameters into executable content based on the execution mode in the parsing rule, and executing the executable content to obtain a parsing result, the method further includes: Storing the parsing result in a result cache; When the usage frequency of the result cache is lower than a preset usage frequency threshold, deleting the result data corresponding to the parsing result from the result cache at a preset time point; The result cache is stored in a hierarchical manner according to the usage frequency.

6. The method according to claim 1, characterized in that After the step of integrating the type conversion parameters into executable content based on the execution mode in the parsing rule, and executing the executable content to obtain a parsing result, the method further includes: Analyze the calling frequency of each of the parsing rules in different time periods; When there is a target parsing rule whose calling frequency is greater than a preset calling frequency threshold, recording the intermediate calculation results generated during the execution of the target parsing rule; Setting the storage period of the intermediate calculation results to a preset storage period; When the preset preservation period is reached, the intermediate calculation results are cleared.

7. The method according to claim 1, characterized in that After the step of integrating the type conversion parameters into executable content based on the execution mode in the parsing rule, and executing the executable content to obtain a parsing result, the method further includes: Setting an access level and a visible range for the parsing result; Create a user rights configuration table, wherein the user rights configuration table contains access rights of different users to the parsing results; When receiving a request for access to the parsed result from a requesting party, verifying the permission level of the requesting party; When the authority level does not conform to the user authority configuration table, the parsing results that exceed the authority range of the authority level are anonymized.

8. A server, characterized in that: The server includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the server to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a server, the server is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a server, the server is caused to execute the method according to any one of claims 1 to 7.

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