CN117454278A - Method and system for realizing digital rule engine of standard enterprise - Google Patents

Abstract

The method comprises the steps of creating and configuring enterprise digital decision basic information, including basic database configuration, basic api configuration, basic decision tree configuration and contact decision tree parameter configuration; drag rule configuration, decision rule flow, setting condition nodes and rule nodes, and carrying out node assignment; setting a parameter, debugging and verifying results, executing through a data flow chart, and returning whether the node is successful or not. The standard enterprise digital rule engine method realizes automatic decision management, flexible decision flow configuration, personalized variable formula processing design and support of various business scenes by packaging unified enterprise data, providing stable technical architecture and simplifying a user operation interface, and can help enterprises to realize more efficient and accurate decision and business analysis.

Description

Method and system for realizing digital rule engine of standard enterprise

Technical Field

The invention relates to the technical field of digital decision making, in particular to a method and a system for realizing a digital rule engine of a standard enterprise.

Background

In the digital age, existing enterprise rule engine technology has some disadvantages. With the development of big data technology, conventional rule engines face challenges in handling massive amounts of data. If only the dependency database is used for offline analysis, a great deal of time is often required, and problems such as data abnormality and physical interruption easily occur, so that the process of analyzing the rule engine is interrupted, and the reliability, the accuracy and the timeliness are affected. In addition, conventional rule engines require manual code writing to dynamically modify rules and associated data sources, and the configuration generation rule engines cannot be dragged in real time, which can consume significant human costs for code writing, testing, and publishing, resulting in long time consumption. Meanwhile, the traditional rule engine cannot intuitively display the rules, the rules are required to be manually arranged, the resolution is low, and the understanding difficulty is increased. The traditional rule engine development status has the following status:

1: complex enterprise digital rule writing: conventional enterprise digital rule engines typically require the use of specialized rule languages or programming languages to write the rules. This requires specific technical knowledge and coding capabilities for rule developers, increasing learning curves and development costs. Whereby hard coding is often required.

2: an enterprise digital rules engine configuration interface that lacks visualization: conventional enterprise digital rule engines often do not provide intuitive visualization tools to expose and orchestrate rules. This makes understanding and modification of rules difficult and increases the risk of errors. And no online debugging function is provided, and the running and debugging are needed through the local code after the online debugging function is obtained in the past.

3: limited extensibility: conventional enterprise rule engines may face some limitations that may lead to performance problems when large numbers of rules, complex logic, or high concurrency need to be handled. Furthermore, adding new rules or modifying existing rules may require redeployment of the entire rule engine, which is a complex and time-consuming process for large systems. The lack of online custom code, or in the form of a supporting plug-in package, is integrated into the rules engine.

4: limitations on context awareness: conventional enterprise rule engines often lack awareness of business contexts. They cannot make intelligent decisions based on real-time data or environmental information, but can only rely on predefined rules for processing.

5: limitation of knowledge representation: conventional enterprise rule engines typically represent knowledge using a conditional statement and rule based approach. This approach may not flexibly handle complex knowledge structures, fuzzy logic, or uncertainties, thereby limiting the expressive power of the rule engine.

6: maintainability problem: since rules in conventional rule engines are typically scattered in code or configuration files, it is difficult to understand and modify the rules. This increases maintenance costs of the rules and easily results in conflicts, duplications, or inconsistencies between the rules.

7: there is no general rule set and rule package summarizing the enterprise digital rules engine.

The traditional enterprise digital rule engine has less summary on enterprise rule methods and cannot meet the industry accumulation of enterprise digital. In summary, conventional enterprise digital rule engines suffer from drawbacks in terms of complexity of rule writing, lack of visualization, limitations of extensibility, limitations of context awareness, limitations of knowledge representation, and maintainability issues. With the advent of the big data age, the conventional enterprise rule engine has failed to meet the requirement of large-scale analysis, so that the conventional enterprise digital rule engine technology has shortcomings in terms of processing massive data, dynamic configuration, rule display, cross-database operation and the like, and needs further improvement and innovation.

Disclosure of Invention

In order to solve a plurality of technical problems existing in the traditional enterprise digital rule engine in the prior art, the invention provides a method and a system for realizing the standard enterprise digital rule engine, so as to solve the technical problems.

According to a first aspect of the present invention, a method for implementing a standard enterprise digital rule engine is provided, including:

s1: establishing and configuring enterprise digital decision basic information, including basic database configuration, basic api configuration, basic decision tree configuration and contact decision tree parameter entering configuration;

s2: drag rule configuration, decision rule flow, setting condition nodes and rule nodes, and carrying out node assignment;

s3: setting a parameter, debugging and verifying results, executing through a data flow chart, and returning whether the node is successful or not.

In some specific embodiments, S1 specifically comprises:

s11: adding basic data, creating a database, configuring a table name, a table description, a table corresponding field set and a standard enterprise digital standard table, wherein the standard enterprise digital standard table comprises enterprise financial data, client data, supply chain data, market data, production data and network data;

s12: adding basic data api interface support, uploading a user-defined third-party api jar packet, analyzing request api parameters, and returning a next request name, an input parameter and an output parameter;

s13: adding decision base settings, a decision name, a decision description and a decision type;

S14: the request for entry includes entry name, field type.

In some specific embodiments, the decision types include a funnel decision and a coverage decision, the funnel decision is judged according to a rule sequence, the subsequent rule judgment is not continued after the result is output, and the coverage decision is complete to count the result output by the decision set after the judgment of all the decision sets.

In some specific embodiments, S2 specifically comprises:

s21: adding a start node;

s22: adding a conditional branch for filtering initial data, and if the condition is not satisfied, not executing downwards;

s23: adding rule nodes, adding a relation group, and judging whether the flow is executed downwards or not according to a combined relation formula of the relation group;

and S24, assigning nodes, wherein the common assignment comprises assignment types including fixed values, variable values and node values and conditional assignment, and the conditional assignment is to assign variable names and type assignments to original variables meeting the conditions.

In some specific embodiments, the interface out-parameter configuration of S2 is derived from the add-on base data and the add-on base data API interface support in S1 for configuring the base data source or API interface.

In some specific embodiments, an enterprise standard digitizing function is configured in S2, where the enterprise standard digitizing function specifically includes: validate_email: verifying the validity of the email address, generate_password: generating a random password with a specified length, wherein the password is format_phone_number: formatting the telephone number to conform to a particular international or regional format, encrypt: encrypting the data using a given key, decrypt: decrypting the data using the given key, calculate_profile: calculating profit of the enterprise, according to given income and expense, the overt_currency: converting a given amount from one currency to another, send_notification: sending a notification message to the designated recipient, calculate_revenue: calculating total revenue for the business based on the given sales, calculate_offers: calculating the total expenditure of the enterprise, based on the given cost, calculate_gross_margin: calculating the gross profit margin of the business, i.e. the ratio between net revenue and sales, calculate_net_income: calculating the net income of enterprises, including tax_flash_flow: calculating cash flow of the enterprise, based on given cash inflow and cash outflow, calculating_roi: calculating the return on investment of the enterprise, and calculating the value of the enterprise according to the initial investment and the final value: calculating the profit and loss balance point of the enterprise, namely, selling the amount to reach the level capable of covering fixed and variable cost, and calculating the value of the enterprise: calculating operational capital of the enterprise based on the current asset and the current liability, calculating_debt_to_equivalence_ratio: calculating the liability and stakeholder equity ratio of the enterprise, and measuring the use degree of the financial lever of the enterprise, add_customer: adding a new customer to the database, including name, email and phone number, get_customer_by_id: acquiring client information from a database according to the client ID, and updating_customer_info: updating information of a specific client, based on a given client ID and update data, delete_customer: deleting a particular client from the database, based on a given client ID, search_routers: searching clients in the client database according to the keywords, and returning a matching result, get_customer_orders: acquiring an order list of a specific customer, based on a given customer ID, calculating_total_event: calculating a total consumption amount for the particular customer, based on the given customer ID, get_customer_contacts: acquiring a contact list of a specific client, and based on a given client ID, adding_customer_contact: adding the contact of the particular customer to a database, including contact name, email and phone number, update_customer_contact: updating information of a specific client contact person, and according to a given contact person ID and updating data, creating_provider: create a new vendor and provide vendor name and contact information, get_provider_by_id: acquiring provider information from a database according to the provider ID, and updating_provider_info: updating information of a specific vendor, based on a given vendor ID and update data, delete_provider: deleting a particular vendor from the database, based on a given vendor ID, search_suppers: searching suppliers in a supplier database according to the keywords, and returning a matching result, wherein the matching result is create_search_order: creating a purchase order comprising a vendor ID and a ordered item list, get_purchase_orders: all purchase orders for a particular vendor are acquired, based on a given vendor ID, update_purchase_order: updating information of a specific purchase order, and according to a given order ID and update data, cancel_purchase_order: cancel a particular purchase order, calculate_inventory_level according to the given order ID: inventory levels are calculated based on the business's inventory and sales data to evaluate whether inventory needs to be replenished.

In some specific embodiments, S3 specifically comprises:

s31: ending node setting and parameter setting, wherein the type comprises one of parameter entering, node and variable, and field name, result and field interpretation;

s32: and executing debugging, filling in parameter entering data, and displaying whether each node is executed successfully or not.

According to a second aspect of the present invention, a computer-readable storage medium is presented, on which one or more computer programs are stored which, when executed by a computer processor, implement the above-described method.

According to a third aspect of the present invention, there is provided a system for implementing a standard enterprise digital rules engine, comprising:

basic information creation unit: the configuration is used for creating and configuring enterprise digital decision basic information, including basic database configuration, basic api configuration, basic decision tree configuration and contact decision tree parameter entering configuration;

decision unit: configuring a drag rule configuration, deciding a rule flow, setting a condition node and a rule node, and carrying out node assignment;

an output unit: the configuration is used for setting out parameters, debugging the verification result, executing through the data flow diagram, and returning whether the node is successful or not.

In some particular embodiments, the system is integrated with a web management platform that can parse standard SQL statements into target-side SQL statements.

The invention provides a method and a system for realizing a standard enterprise digital rule engine, which have the following technical effects:

comprehensively packaging enterprise digitization rules: the method can uniformly package various important data such as enterprise financial data, client data, supply chain data, market data, production data, network data and the like. This has the advantage that enterprises can manage and utilize these data more efficiently, playing a key role in decision making and business analysis.

Stability and commonality are ensured: the rule engine of the application has stability and universality because the application adopts pure Java development and is constructed based on a micro-service architecture. This means that it can operate in different enterprise environments and can handle large amounts of data, ensuring high reliability and good performance of the system.

Ease of use and good interface interaction experience: the present application focuses on providing a user-friendly interface interaction experience. Through the use mode similar to an Excel function, a user can easily process variables and judge rules. Such a design allows non-professionals to use the rules engine easily without excessive programming requirements.

Automated decision management: the rule engine has an automatic decision management function, and the need of manual intervention is reduced. The method can automatically make decisions and execute corresponding operations according to preset rules and conditions. This greatly improves the decision efficiency and accuracy of the enterprise.

Flexible decision flow configuration: the method of the application supports flexible configuration of decision processes. The user can define different decision flows according to specific service requirements, and adjust and optimize the decision flows when needed. This flexibility enables the enterprise to better accommodate market changes and business developments.

Individualized variable formula processing design: the design function of variable formula processing is provided, so that a user can customize the calculation mode of the variable according to own requirements and business logic. Thus, enterprises can perform personalized data processing according to specific conditions, and more accurate and targeted results are obtained.

Support of multiple business scenarios: the standard enterprise digital rule engine is suitable for various business scenes. Whether financial decision, marketing, supply chain management or customer relationship management, key operations such as rule filtering, behavior scoring and the like can be realized through the rule engine of the application, so that the decision effect and the competitiveness of enterprises are improved.

The method and the device realize automatic decision management, flexible decision flow configuration, personalized variable formula processing design and support of various service scenes by packaging unified enterprise data, providing stable technical architecture and simplifying a user operation interface. The technical effects together provide a powerful and easy-to-use digital decision platform for enterprises, help the enterprises to realize more efficient and accurate decision and business analysis, and promote the development and innovation of the enterprises. The application has wide application prospect and can play an important role in various industries and fields.

Drawings

The accompanying drawings are included to provide a further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and together with the description serve to explain the principles of the invention. Many of the intended advantages of other embodiments and embodiments will be readily appreciated as they become better understood by reference to the following detailed description. Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:

FIG. 1 is a flow chart of a method of implementing a standard enterprise digital rules engine in accordance with one embodiment of the present application;

FIG. 2 is a flow chart of basic information configuration of a particular embodiment of the present application;

FIG. 3 is a flow chart of decision rule configuration of a specific embodiment of the present application;

FIG. 4 is a flow chart of the out-referencing and debugging of a specific embodiment of the present application;

FIG. 5 is a framework diagram of an implementation system of a standard enterprise digital rules engine in accordance with one embodiment of the present application;

fig. 6 is a schematic diagram of a computer system suitable for use in implementing an electronic device of an embodiment of the present application.

Detailed Description

The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.

It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

FIG. 1 illustrates a flow chart of a method of implementing a standard enterprise digital rules engine in accordance with an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:

s1: establishing and configuring enterprise digital decision basic information, including basic database configuration, basic api configuration, basic decision tree configuration and contact decision tree parameter entering configuration;

in a specific embodiment, fig. 2 shows a flowchart of a basic information configuration of a specific embodiment of the present application, and as shown in fig. 2, the basic information configuration specifically includes:

s11: and (5) configuring a basic database. Adding basic data (skipping if the decision tree does not need a basic data source), creating a new database, and selecting OLTP, OLAP and a plurality of bins/data lakes. And selecting related information of databases such as a host ip, a port, a user name, a password and the like, and configuring a table name, a table description and a field set corresponding to the table.

Configuration table condition query, selecting a query mode: and (5) accurately searching and fuzzy searching. Entering into the ginseng: parameter name, default value, parameter description, etc. Facilitating subsequent rule engine variable invocations.

Configuring a standard enterprise digital standard table, for example: enterprise financial data, customer data, supply chain data, market data, production data, and network data. And defining standard universal tables, and facilitating enterprises to write the standard universal tables into the specified tables.

S12: basic api configuration. Adding basic data API interface support (skipping if the decision tree does not need a foundation), uploading a user-defined third-party API jar packet, analyzing request API parameters, returning API request names, and entering and exiting parameters.

S13: and (5) configuring a basic decision tree. Adding decision base settings, decision names, decision descriptions and decision types: funnel type decision (the decision mode can judge according to the rule sequence, once the result is output, the subsequent rule judgment can not be continued to save resources), overlay type decision (the decision mode can completely judge all decision sets, and finally the number of the results output by the decision sets is counted to obtain the final decision result, and the number of the decision results can be freely configured).

S14: the contact decision tree is configured as a parametrization. Request for entering into the parameters: the entry name, field name, type (string, value, boolean), field end, multiple sets of field types may be added. The final ingress parameters become JSON type.

S2: drag rule configuration, decision rule flow, setting condition nodes and rule nodes, and carrying out node assignment;

in a specific embodiment, fig. 3 shows a flowchart of a decision rule configuration of a specific embodiment of the present application, and as shown in fig. 3, the decision rule configuration specifically includes:

S21: a start node is added.

S22: conditional branching-for filtering initial data. And if the condition is not satisfied, not executing downwards. The condition node contains conditions of the entry, for example: name-not null, age-interval 25-35.

S7: rule nodes. A relationship group is added, and the node passes or fails when the first group of relationship groups is established. The combination relation is used for judging whether the flow is executed downwards or not. Wherein the variables are as follows:

the configuration of the current interface parameter comes from the foregoing S11: adding basic data; s12: add basic data API interface support. For configuring the underlying data source or API interface.

In a specific embodiment, an intermediate function list is also configured, based on the EXCEL function usage. Enterprise standard function methods, such as enterprise standard digitized functions, are added:

validate_email (email) verifies the validity of the email address.

generate password (length), generating random cipher with specified length.

format_phone_number (phone_number) the phone number is formatted to conform to a specific international or regional format.

encrypt (data, key) is that data is encrypted using a given key.

Decrypt (data, key) decrypts data using a given key.

calculate_profits (revenues) calculate the profit of the business, based on the given incomes and payouts.

The conversion of a given amount from one currency to another.

send_notification (messages) sends notification messages to the intended recipient, either by email, text message, or other suitable means.

calcate_ revenue (sales): the total revenue for the business is calculated based on the given sales.

calcate_ expenses (costs): the total expenditure of the enterprise is calculated, based on the given costs.

calcuiate_gross_margin (costs): the gross profit margin of the business, i.e., the ratio between net revenue and sales, is calculated.

calculate_net_income (revenue, extensions, taxes): the net income of the enterprises is calculated, and tax is considered.

calciulate_flash_flows (inflow): the cash flow of the enterprise is calculated based on the given cash inflow and cash outflow.

calculate_roi (initial_introduction, final_value): and calculating the return on investment of the enterprise, and according to the initial investment and the final value.

calculate_break_even (samples_price, variable_costs, fixed_costs): the balance point of the enterprise, that is, the sales reach a level that can cover both fixed and variable costs, is calculated.

calculating_working_potentials (current_tasks): operational capital of the enterprise is calculated based on the current assets and the current liabilities.

calculate_debt_to_equivalence_ratio (total_debt, total_equivalence): and calculating the liability and stakeholder equity ratio of the enterprise, and measuring the use degree of the financial lever of the enterprise.

add_customer (name, email, phone_number) adds the new customer to the database, including name, email, and phone number.

get_customer_by_id (ID) the customer information is obtained from the database based on the customer ID.

update_customer_info (ID, data) updates information of a specific customer based on a given customer ID and update data.

Delete_customer (ID) removes a particular customer from the database, based on a given customer ID.

search_ customers (keyword), searching clients in the client database according to the keywords, and returning matching results.

get_customer_ordinates (customer_id) gets a list of orders for a particular customer, based on a given customer ID.

calculate_total_event (customer_id) the total amount of consumption of a particular customer is calculated based on a given customer ID.

get_customer_contacts (customer_id) gets a contact list for a particular customer based on a given customer ID.

add_customer_contact (customer_id, contact_name, contact_phone) adds the contact of a particular customer to a database, including contact name, email, and phone number.

update_customer_contact (contact_id, data) updates information of a particular customer contact, based on a given contact ID and update data.

create_provider (name, contact_info) creates a new vendor and provides vendor name and contact information.

get_provider_by_id (provider_id) provider information is obtained from the database based on the provider ID.

update_provider_info (provider_id, data) updates information of a specific provider according to a given provider ID and update data.

delete_provider (provider_id) removes a particular vendor from the database, based on a given vendor ID.

search_ suppliers (keyword) searching the provider in the provider database according to the keyword and returning a matching result.

create_purchase_order (item), which creates a purchase order, includes a vendor ID and a list of ordered items.

get_purchase_orders (supplier_id) get all purchase orders for a particular vendor based on a given vendor ID.

update_purchase_order (order_id, data) updates information of a particular purchase order, based on a given order ID and update data.

cancel_purchase_order (order_id), cancel a particular purchase order, according to a given order ID.

Inventory levels are calculated based on the business's inventory and sales data to evaluate whether inventory needs to be replenished.

S24: and assigning nodes. The method comprises the following steps of common assignment: selecting variable names and assignment types: fixed values, variable values, node values (conditional branches, basic information judgment, rule nodes, etc.); the method also comprises the following steps of: and selecting the name and the assignment type of the assignment variable under the condition that the original variable is more than or equal to or less than a certain condition is met: fixed values, variable values, node values (conditional branches, basic information decisions, rule nodes, etc.).

S3: setting a parameter, debugging and verifying results, executing through a data flow chart, and returning whether the node is successful or not.

In a specific embodiment, fig. 4 shows a flowchart of the parameter-out and debugging of a specific embodiment of the present application, and as shown in fig. 4, the parameter-out and debugging specifically includes:

s31: and setting the parameter setting by the ending node. For example:

type one:

type (2): entry, field name, result (field name corresponding to entry), field interpretation.

Type two:

type (2): nodes, field names, results (conditional branches, rule nodes, compute nodes, assignment nodes), field interpretations.

Type three:

type (2): variables, field names, results (select corresponding variables), field interpretations.

S32: debugging is performed. Fill in the enrollment data, for example: the gender, name, occupation and time of arrival show whether each node is executed successfully or not, and the success or not of each node is returned through the execution of the data flow chart.

In one specific example:

starting node: a start node;

conditional branching: failing to pass, look at the detail;

and (3) basic information judgment: not performed;

assignment node: not performed;

rule node: not performed;

end node: and the ending node is not abnormal, and the detail is output.

The invention can solve the problem of scene rule calculation related to enterprise financial data, client data, supply chain data, market data, production data and network data in the prior art. The logic engine is simplified and interaction optimization is performed. The logic engine is focused on logic processing, including data processing, rule judgment and business assembly; the rule engine is focused on rule judgment and plays an important role in the scenes of enterprise digital decision, rule filtering, behavior scoring and the like. The invention has the following technical characteristics:

1. ) Unified packaging enterprise digitization rules: enterprise financial data, customer data, supply chain data, market data, production data, and network data are uniformly packaged for better management and application of such data.

2. ) Pure Java development and microservice architecture is used: pure Java development is adopted, and the architecture is built based on a micro-service architecture, so that the stability and the universality of the system are ensured.

3. ) Simple and easy-to-use interface interaction experience: providing a good interface interaction experience, the user can handle variables as easily and efficiently as using Excel functions.

4. ) Automated decision management: the rule engine can automatically perform decision management, and the need of manual intervention is reduced.

5. ) And (3) configuration of decision flow: the flexible configuration of the decision process is supported, so that a user can define the decision process according to specific requirements.

6. ) Variable formula processing design: the design function of variable formula processing is provided, so that a user can customize the calculation mode of the variables.

7. ) A variety of business scenarios: the method is suitable for various business scenes, including enterprise digital decision making, rule filtering, behavior scoring and the like.

8. ) On-line variable processing, interface dragging and on-line testing: and multiple functions of supporting online variable processing, interface dragging, online testing and the like are constructed so as to meet the requirements of users.

FIG. 5 illustrates a system architecture diagram for implementing a standard enterprise digital rules engine of one embodiment of the present application, which includes a basic information creation unit 501, a decision unit 502, and an output unit 503, where the basic information creation unit 501 is configured to create and configure enterprise digital decision basic information, including a basic database configuration, a basic api configuration, and a basic decision tree configuration, and a contact decision tree joining configuration, as illustrated in FIG. 5; the decision unit 502 is configured for dragging rule configuration, deciding rule flow, setting condition nodes and rule nodes, and carrying out node assignment; the output unit 503 is configured to set the parameter, debug the verification result, execute through the data flow chart, and return whether the node is successful or not.

In particular embodiments, the system is integrated with a web management platform that can parse standard SQL statements into target-side SQL statements. The system has the following effects:

first, the Web management platform provides standard SQL language support so that users can query and process different types of data sources using generic SQL syntax. This function is very practical in practical applications because it can improve operation efficiency, reduce learning cost, and has a wide range of applications.

And secondly, the Web management platform provides a function of analyzing the standard SQL statement into a target end SQL statement, so that a user can execute corresponding query and processing operations aiming at different types of databases. This function is very practical in practical applications because it can improve query efficiency, accuracy and flexibility, and has a wide range of applications.

Third, the Web management platform provides management platform support for analyzing the parsing of standard SQL and managing SQL lifecycle each time. This function is very practical in practical applications because it can improve maintainability, traceability and monitorability of SQL queries, and has a wide range of applications.

Fourth, the Web management platform provides optimization suggestions for standard SQL parsing statements of the corresponding database, thereby helping users optimize performance and accuracy of SQL queries. This function is very practical in practical applications because it can improve operation efficiency, reduce cost, and has a wide range of applications.

Fifth, the Web management platform provides index hit conditions of standard SQL parsing corresponding database sentences, so that users are helped to better understand the execution process and result of SQL queries. This function is very practical in practical applications because it can improve query efficiency, accuracy and controllability, and has a wide range of applications.

Sixth, the Web management platform provides a grammar for analyzing the standard SQL into a database of the target end, returns a result set queried by the target end and supports historical data query and storage records, so that a user can conveniently check and analyze the SQL query result, and trace back and analyze the historical data. This function is very practical in practical applications because it can improve query efficiency, accuracy and traceability, and has a wide range of applications.

Seventh, the Web management platform provides database configuration, configurable relational databases, non-relational databases, distributed databases, and the like.

Eighth, the Web management platform provides SQL generic UDF function extension support so that developers can customize the desired functionality and share code among multiple queries. This function is very practical in practical applications because it can simplify complicated operations, improve development efficiency and code reusability, and has a wide range of applications.

In summary, the Web management platform provides a series of functions, has very high technical practicality, and can help users to better inquire, process and analyze data, and improve operation efficiency and accuracy.

The system integrates and optimizes in the Web management platform, is more visual to use and is convenient for users to use. And secondly, the Web management platform provides a function of analyzing the standard SQL statement into the target end SQL statement, and the function is realized based on a Calcite tool, so that the method has higher technical innovation and practicability. The method can convert the general SQL grammar into the grammar required by various target databases, and improves the query efficiency and accuracy by optimizing suggestions, index hit conditions and the like. The Web management platform can help users to query, process and analyze data better, improves the operation efficiency and accuracy, and has wide application range. The following is a detailed description of some application prospects of this approach:

finance and insurance industry: in the fields of finance and insurance, the rule engine can help enterprises to perform key operations such as financial decision making, risk assessment, behavior scoring and the like. By performing rule judgment and analysis on a large amount of client data and market data, enterprises can more accurately predict market trends, identify potential risks and formulate corresponding strategies.

Retail and electronic commerce: in the retail and electronic commerce fields, the rule engine of the present application can be applied to price policy formulation, promotional campaign management, customer relationship management, and the like. Enterprises can adjust prices and make preferential strategies in real time through a rule engine according to different market conditions and consumer behaviors, and sales effects and customer satisfaction are improved.

Manufacturing and supply chain management: in manufacturing and supply chain management, the rules engine of the present application may be used in terms of production scheduling, inventory management, and supply chain optimization. Through carrying out rule calculation and analysis on production data, supply chain data and market data, enterprises can coordinate production planning and material purchasing better, inventory cost is reduced, and flexibility and response speed of a supply chain are improved.

Medical and health fields: in the medical and health fields, the rule engine of the application can be applied to the aspects of medical decision support, disease prediction, personalized treatment and the like. By performing rule judgment and analysis on clinical data, patient data, and medical knowledge, a medical institution can more accurately diagnose diseases, formulate treatment schemes, and perform personalized medical management on patients.

Thing networking and intelligent house: in the fields of the Internet of things and intelligent home, the rule engine can be applied to equipment control, energy management, intelligent decision making and the like. By carrying out rule judgment and analysis on the sensor data and the user behavior data, enterprises and families can realize automatic control of equipment, energy consumption optimization and intelligent decision execution.

Traffic and city management: in traffic and city management, the rule engine of the present application may be applied in traffic flow management, intelligent traffic systems, city planning, etc. Through carrying out rule calculation and analysis on traffic data, positioning data and environment data, the city manager can better optimize traffic flow, improve city safety and formulate reasonable city development planning.

In summary, the standard enterprise digital rule engine method of the application has wide application prospects in various fields of finance, retail, manufacturing, medical treatment, internet of things, transportation and the like. The method can help enterprises to realize more intelligent and efficient decision making and business operation, promote the competitiveness of the enterprises and promote the development and innovation of the industry. With the increasing amount of data and the continuing advancement of technology, this application believes that this approach will find wider application in the future and will bring more value to the enterprise.

Referring now to FIG. 6, a schematic diagram of a computer system suitable for use in implementing embodiments of the present application is shown. The electronic device shown in fig. 6 is only an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present application.

As shown in fig. 6, the computer system includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Liquid Crystal Display (LCD) or the like, a speaker or the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 601. It should be noted that the computer readable storage medium of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable storage medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations of the present application may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules involved in the embodiments described in the present application may be implemented by software, or may be implemented by hardware.

As another aspect, the present application also provides a computer-readable storage medium that may be included in the electronic device described in the above embodiments; or may exist alone without being incorporated into the electronic device. The computer-readable storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: establishing and configuring enterprise digital decision basic information, including basic database configuration, basic api configuration, basic decision tree configuration and contact decision tree parameter entering configuration; drag rule configuration, decision rule flow, setting condition nodes and rule nodes, and carrying out node assignment; setting a parameter, debugging and verifying results, executing through a data flow chart, and returning whether the node is successful or not.

The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the invention referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or equivalents thereof is possible without departing from the spirit of the invention. Such as the above-described features and technical features having similar functions (but not limited to) disclosed in the present application are replaced with each other.

Claims (10)

1. A method for implementing a standard enterprise digital rules engine, comprising:

s1: establishing and configuring enterprise digital decision basic information, including basic database configuration, basic api configuration, basic decision tree configuration and contact decision tree parameter entering configuration;

s2: drag rule configuration, decision rule flow, setting condition nodes and rule nodes, and carrying out node assignment;

s3: setting a parameter, debugging and verifying results, executing through a data flow chart, and returning whether the node is successful or not.

2. The method for implementing the standard enterprise digital rule engine according to claim 1, wherein S1 specifically comprises:

s11: adding basic data, creating a database, configuring a table name, a table description, a table corresponding field set and a standard enterprise digital standard table, wherein the standard enterprise digital standard table comprises enterprise financial data, client data, supply chain data, market data, production data and network data;

s12: adding basic data api interface support, uploading user-defined third-party api and jar packets, analyzing request api parameters, and returning a next request name, an input parameter and an output parameter;

s13: adding decision base settings, a decision name, a decision description and a decision type;

S14: the request for entry includes entry name, field type.

3. The method for implementing the digital rule engine of the standard enterprise according to claim 2, wherein the decision types include a funnel decision and a overlay decision, the funnel decision is judged according to a rule sequence, the subsequent rule judgment is not continued after the result is output, and the overlay decision is completed to make the result output by the statistical decision set after the judgment of all decision sets.

4. The method for implementing the standard enterprise digital rule engine according to claim 2, wherein S2 specifically comprises:

s21: adding a start node;

s22: adding a conditional branch for filtering initial data, and if the condition is not satisfied, not executing downwards;

s23: adding rule nodes, adding a relation group, and judging whether the flow is executed downwards or not according to a combined relation of the relation group;

and S24, assigning nodes, wherein the common assignment comprises assignment types including fixed values, variable values and node values and conditional assignment, and the conditional assignment is to assign variable names and types to original variables meeting the conditions.

5. The implementation method of the standard enterprise digital rule engine according to claim 4, wherein the interface out-parameter configuration of S2 is derived from the add-on base data and add-on base data API interface support in S1, and is used for configuring a base data source or an API interface.

6. The method for implementing the standard enterprise digital rule engine according to claim 4, wherein an enterprise standard digital function is configured in S2, and the enterprise standard digital function specifically includes: validate_email: verifying the validity of the email address, generate_password: generating a random password with a specified length, wherein the password is format_phone_number: formatting the telephone number to conform to a particular international or regional format, encrypt: encrypting the data using a given key, decrypt: decrypting the data using the given key, calculate_profile: calculating profit of the enterprise, according to given income and expense, the overt_currency: converting a given amount from one currency to another, send_notification: sending a notification message to the designated recipient, calculate_revenue: calculating total revenue for the business based on the given sales, calculate_offers: calculating the total expenditure of the enterprise, based on the given cost, calculate_gross_margin: calculating the gross profit margin of the business, i.e. the ratio between net revenue and sales, calculate_net_income: calculating the net income of enterprises, including tax_flash_flow: calculating cash flow of the enterprise, based on given cash inflow and cash outflow, calculating_roi: calculating the return on investment of the enterprise, and calculating the value of the enterprise according to the initial investment and the final value: calculating the profit and loss balance point of the enterprise, namely, selling the amount to reach the level capable of covering fixed and variable cost, and calculating the value of the enterprise: calculating operational capital of the enterprise based on the current asset and the current liability, calculating_debt_to_equivalence_ratio: calculating the liability and stakeholder equity ratio of the enterprise, and measuring the use degree of the financial lever of the enterprise, add_customer: adding a new customer to the database, including name, email and phone number, get_customer_by_id: acquiring client information from a database according to the client ID, and updating_customer_info: updating information of a specific client, based on a given client ID and update data, delete_customer: deleting a particular client from the database, based on a given client ID, search_routers: searching clients in the client database according to the keywords, and returning a matching result, get_customer_orders: acquiring an order list of a specific customer, based on a given customer ID, calculating_total_event: calculating a total consumption amount for the particular customer, based on the given customer ID, get_customer_contacts: acquiring a contact list of a specific client, and based on a given client ID, adding_customer_contact: adding the contact of the particular customer to a database, including contact name, email and phone number, update_customer_contact: updating information of a specific client contact person, and according to a given contact person ID and updating data, creating_provider: create a new vendor and provide vendor name and contact information, get_provider_by_id: acquiring provider information from a database according to the provider ID, and updating_provider_info: updating information of a specific vendor, based on a given vendor ID and update data, delete_provider: deleting a particular vendor from the database, based on a given vendor ID, search_suppers: searching suppliers in a supplier database according to the keywords, and returning a matching result, wherein the matching result is create_search_order: creating a purchase order comprising a vendor ID and a ordered item list, get_purchase_orders: all purchase orders for a particular vendor are acquired, based on a given vendor ID, update_purchase_order: updating information of a specific purchase order, and according to a given order ID and update data, cancel_purchase_order: cancel a particular purchase order, calculate_inventory_level according to the given order ID: inventory levels are calculated based on the business's inventory and sales data to evaluate whether inventory needs to be replenished.

7. The method for implementing the standard enterprise digital rule engine according to claim 1, wherein S3 specifically comprises:

s31: ending node setting and parameter setting, wherein the type comprises one of parameter entering, node and variable, and field name, result and field interpretation;

s32: and executing debugging, filling in parameter entering data, and displaying whether each node is executed successfully or not.

8. A computer readable storage medium having stored thereon one or more computer programs, which when executed by a computer processor implement the method of any of claims 1-7.

9. A system for implementing a standard enterprise digital rules engine, comprising:

basic information creation unit: the configuration is used for creating and configuring enterprise digital decision basic information, including basic database configuration, basic api configuration, basic decision tree configuration and contact decision tree parameter entering configuration;

decision unit: configuring a drag rule configuration, deciding a rule flow, setting a condition node and a rule node, and carrying out node assignment;

an output unit: the configuration is used for setting out parameters, debugging the verification result, executing through the data flow diagram, and returning whether the node is successful or not.

10. The system for implementing a standard enterprise digital rules engine of claim 9, wherein the system is integrated with a web management platform that can parse standard SQL statements into target-side SQL statements.