Platform management method and system based on low-code development

Abstract

The invention relates to a platform management method and system based on low code development, and the method comprises the steps: obtaining a plurality of heterogeneous data sources, extracting first data from the plurality of heterogeneous data sources, and carrying out the preprocessing of the first data, and obtaining standard data used for constructing a knowledge graph; and acquiring a plurality of different entities and entity relationships among the different entities from the standard data, and constructing the knowledge graph according to the plurality of different entities and the entity relationships among the different entities. And mapping entity nodes in the knowledge graph and entity relationships among different entity nodes to a low-dimensional space through a graph embedding model to perform vector representation so as to extract global knowledge including the entity relationships among different entity nodes and a network topology structure. And acquiring a predefined business process and a plurality of corresponding decision points when the business process is executed, and evaluating an execution result of the business process by executing global knowledge and a current business process variable to obtain a corresponding guidance decision.

Description

Technical Field

[0001] The present invention relates to the field of low-code development and data management technology, and in particular to a platform management method and system based on low-code development. Background Art

[0002] In today's rapidly developing digitalization and automation, enterprises are facing increasingly complex data management and business process optimization needs. Low-code development platforms have become increasingly popular in recent years due to their simplified programming requirements and improved development efficiency. These platforms allow non-technical users to quickly build applications through a graphical interface, greatly shortening the development cycle and reducing development costs. However, although low-code platforms provide the convenience of building applications, they often lack the ability to handle complex data integration, deep data analysis, and advanced business process automation.

[0003] With the rise of big data technology and artificial intelligence, enterprises are in urgent need of advanced tools that can integrate huge data sets from multiple data sources and extract useful information from them to support decision making. Most current low-code platforms find it difficult to provide effective tools to support this demand, which to some extent limits the application of low-code platforms in data-driven decision support systems. However, the future development trend may pay more attention to the integration of artificial intelligence and machine learning technologies, making low-code platforms not only tools for rapid application development, but also support systems for intelligent decision-making. This includes the use of advanced data processing and analysis technologies, such as natural language processing and predictive analysis, to improve the accuracy and timeliness of decision-making. In addition, with the popularization of cloud computing technology, more companies will choose cloud infrastructure to deploy their low-code development platforms, which can not only provide stronger data processing capabilities, but also bring higher scalability and flexibility. In this environment, data security and privacy protection will also become issues that need to be considered when developing these platforms.

[0004] Although low-code development platforms have significant advantages, they still face challenges in handling complex business logic and maintaining system flexibility and scalability. At present, existing low-code platforms often find it difficult to fully meet the needs of enterprises in data integration and automated advanced business processes. They are usually limited in their ability to automate large-scale data processing and provide in-depth data insights, which to some extent limits the application of low-code platforms in some highly specialized industries. The mainstream low-code platforms on the market currently provide a visual development environment that allows developers to design user interfaces, configure data models, and implement business logic by dragging and dropping components. These platforms usually integrate some basic data processing functions and third-party API support to facilitate the integration of systems with existing IT architectures and external services. However, these platforms have limited support for complex data operations. Although they can handle some basic data integration tasks, their functions are often insufficient to meet user needs when large-scale data integration, advanced data cleaning and transformation, and complex data relationship analysis are required. Therefore, these existing low-code platforms often lack sufficient flexibility and configuration depth when executing complex and non-standardized automated business processes. Summary of the invention

[0005] Based on this, it is necessary to provide a platform management method and system based on low-code development that has high flexibility and configuration depth when executing complex and non-standardized automated business processes to address the above-mentioned technical problems.

[0006] The present invention provides a platform management method based on low-code development, the method comprising:

[0007] Acquire multiple heterogeneous data sources, extract first data from the multiple heterogeneous data sources, and preprocess the first data to obtain standard data for constructing a knowledge graph, wherein the preprocessing includes data cleaning and data standardization;

[0008] Acquire a plurality of different entities and entity relationships between different entities from the standard data, and construct a knowledge graph according to the plurality of different entities and entity relationships between different entities;

[0009] Mapping the entity nodes in the knowledge graph and the entity relationships between different entity nodes to a low-dimensional space for vector representation through a graph embedding model to extract global knowledge including the entity relationships between the different entity nodes and the network topology structure;

[0010] Obtaining a predefined business process and a plurality of decision points corresponding to the execution of the business process, and evaluating the execution result of the business process by executing the global knowledge and current business process variables to obtain corresponding guiding decisions;

[0011] Among them, the first data is useful data or key data that meets the set conditions, the standard data is composed of multiple different entities and entity relationships between different entities, the current business process variables are business environment variables when the predefined business process is currently executed, and the guiding decision is used to optimize the management of the predefined business process.

[0012] In one embodiment, the step of acquiring multiple heterogeneous data sources, extracting first data from the multiple heterogeneous data sources, and preprocessing the first data to obtain standard data for constructing a knowledge graph includes:

[0013] Extracting a plurality of first data sources from the plurality of heterogeneous data sources according to the set condition, and extracting a plurality of data columns satisfying the set condition from the plurality of first data sources;

[0014] A fusion algorithm is called to integrate the multiple data columns to obtain a data set, and different data sets are merged according to a set key, and the data in the data set is the first data.

[0015] In one embodiment, the step of obtaining multiple heterogeneous data sources, extracting first data from the multiple heterogeneous data sources, and preprocessing the first data to obtain standard data for constructing a knowledge graph further includes:

[0016] The missing values ​​in the first data are replaced, and the abnormal data in each data column whose values ​​do not meet the set threshold are deleted to clean the first data, and the cleaned first data are standardized to obtain the standard data.

[0017] In one embodiment, acquiring a plurality of different entities and entity relationships between different entities from the standard data, and constructing a knowledge graph according to the plurality of different entities and entity relationships between different entities, includes:

[0018] Determine entity attributes, entity relationship types, and entity relationship weights according to the multiple different entities and entity relationships between the different entities;

[0019] Based on the entity attributes, entity relationship types and entity relationship weights, the knowledge graph is constructed with multiple different entities as nodes and entity relationships between the different entities as edges;

[0020] Among them, each entity corresponds to a node, and the entity relationship between each two different entities corresponds to an edge.

[0021] In one embodiment, mapping the entity nodes in the knowledge graph and the entity relationships between different entity nodes to a low-dimensional space for vector representation through a graph embedding model to extract global knowledge including the entity relationships between the different entity nodes and the network topology structure includes:

[0022] Selecting a first entity node and a second entity node from the knowledge graph, and obtaining an entity relationship weight between the first entity node and the second entity node, wherein the first entity node and the second entity node are directly connected neighbor nodes and have an entity relationship;

[0023] The graph embedding model is called to map the first entity node, the second entity node, and the entity relationship between the first entity node and the second entity node to a vector representation in a low-dimensional space according to the entity relationship weight.

[0024] In one embodiment, the obtaining of a predefined business process and a plurality of decision points corresponding to the execution of the business process, and evaluating the execution result of the business process by executing the global knowledge and the current business process variables to obtain a corresponding guiding decision, includes:

[0025] Executing the predefined business process according to the set business rules to obtain the multiple decision points and determine the business environment variables when the business process is currently executed;

[0026] Obtaining different execution states and execution results by executing the business process under the global knowledge and the business environment variables, and evaluating the multiple decision points based on the different execution states and execution results to generate the guiding decision;

[0027] The predefined business process is updated and optimized based on the guiding decision to generate a new business process.

[0028] In one embodiment, the method further comprises:

[0029] Obtaining the current resource configuration corresponding to the predefined business process and the constraints satisfied by the resource configuration optimization, and calling the minimization cost function to perform cost minimization processing on the current resource configuration;

[0030] The historical data of the business process execution corresponding to the current resource configuration is analyzed and evaluated according to the global knowledge, and the optimal resource configuration is generated by combining the constraint conditions and the minimization cost function.

[0031] The present invention also provides a platform management system based on low-code development, the system comprising:

[0032] A data preprocessing module, used to obtain multiple heterogeneous data sources, extract first data from the multiple heterogeneous data sources, and preprocess the first data to obtain standard data for constructing a knowledge graph, wherein the preprocessing includes data cleaning and data standardization;

[0033] A knowledge graph construction module, used to obtain a plurality of different entities and entity relationships between different entities from the standard data, and to construct a knowledge graph according to the plurality of different entities and entity relationships between different entities;

[0034] A global knowledge extraction module, used to map entity nodes and entity relationships between different entity nodes in the knowledge graph to a low-dimensional space for vector representation through a graph embedding model, so as to extract global knowledge including entity relationships between different entity nodes and network topology structure;

[0035] An evaluation and guidance module is used to obtain a predefined business process and a plurality of decision points corresponding to the execution of the business process, and evaluate the execution result of the business process by executing the global knowledge and the current business process variables to obtain a corresponding guidance decision;

[0036] Among them, the first data is useful data or key data that meets the set conditions, the standard data is composed of multiple different entities and entity relationships between different entities, the current business process variables are business environment variables when the predefined business process is currently executed, and the guiding decision is used to optimize the management of the predefined business process.

[0037] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the platform management method based on low-code development as described in any one of the above.

[0038] The present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, implements a platform management method based on low-code development as described in any one of the above.

[0039] The platform management method and system based on low-code development obtains multiple heterogeneous data sources, extracts data from multiple heterogeneous data sources, and pre-processes the extracted data for data cleaning and data standardization to obtain standard data for constructing a knowledge graph. Subsequently, multiple different entities and entity relationships between different entities are obtained from the obtained standard data, and a knowledge graph is constructed based on multiple different entities and entity relationships between different entities. The entity nodes in the knowledge graph and the entity relationships between different entity nodes are then mapped to a low-dimensional space for vector representation through a graph embedding model to extract global knowledge including entity relationships between different entity nodes and network topology structures. Finally, a predefined business process and multiple decision points corresponding to the execution of the business process are obtained, and the execution results of the business process are evaluated by executing global knowledge and current business process variables to obtain corresponding guiding decisions to complete the optimization management of the final predefined business process. The method integrates data processing functions, deep learning and intelligent algorithms, complex business process automation tools, and business process optimization functions through a low-code platform, achieving more efficient data integration, in-depth data analysis and intelligent decision-making, flexible business process automatic optimization, and cost-effective resource management. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 One of the flow charts of the platform management method based on low-code development provided by the present invention;

[0042] Figure 2 A schematic diagram of the overall platform management process of a platform management method based on low-code development in a specific embodiment provided by the present invention;

[0043] Figure 3 The second flowchart of the platform management method based on low-code development provided by the present invention;

[0044] Figure 4 The third flowchart of the platform management method based on low-code development provided by the present invention;

[0045] Figure 5 The fourth flowchart of the platform management method based on low-code development provided by the present invention;

[0046] Figure 6The fifth flow chart of the platform management method based on low-code development provided by the present invention;

[0047] Figure 7 The sixth flow chart of the platform management method based on low-code development provided by the present invention;

[0048] Figure 8 A schematic diagram of the structure of the platform management system based on low-code development provided by the present invention;

[0049] Fig. 9 This is a diagram of the internal structure of the computer device provided by the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] Combine the following Figure 1-Figure 9 Describe the platform management method and system based on low-code development of the present invention.

[0052] like Figure 1 As shown, in one embodiment, a platform management method based on low-code development includes the following steps:

[0053] Step S110, obtain multiple heterogeneous data sources, extract first data from the multiple heterogeneous data sources, and preprocess the first data to obtain standard data for constructing a knowledge graph, and the preprocessing includes data cleaning and data standardization.

[0054] Specifically, the server obtains multiple heterogeneous data sources, i.e., multi-source data, extracts useful data or key data that meets set conditions, i.e., first data, from the multiple heterogeneous data sources, and performs data cleaning and data standardization preprocessing on the extracted first data to obtain standard data for constructing a knowledge graph.

[0055] Among them, data standardization includes data fusion or integration and the merging or combining of data columns or data sets, and standard data is composed of multiple different entities and entity relationships between different entities.

[0056] Combination Figure 2As shown, in a specific embodiment, the platform management method based on low-code development provided by the present invention first needs to perform data integration and preprocessing. During this process, the system automatically extracts data from multiple data sources and optimizes data quality and consistency by applying complex cleaning and fusion algorithms. Data integration first involves extracting relevant data from various databases, file systems, or online interfaces, and then removing incomplete, erroneous, or irrelevant data through a data cleaning process to ensure the accuracy and availability of the data. Ultimately, data from different sources are integrated through data fusion technology to create a unified data view, which provides a basis for subsequent data analysis and decision support.

[0057] In this embodiment, data integration and preprocessing include three main steps: data extraction, data cleaning and data fusion. These steps need to deal with data inconsistencies from different sources, such as format differences, duplicate data, and missing values.

[0058] The algorithm formula for data extraction is:

[0059] data_extracted=CONCATENATE(F_extract(data_source_i,SELECT(c1,c2,...,cn)FROM conditions)).

[0060] Where data_source_i is the i-th data source, such as a database, CSV file, or API. SELECT(c1,c2,...,cn) is the specific column extracted from the data source, such as user ID, timestamp, sales, etc. conditions is the condition that needs to be met when extracting data, such as a specific date range or a specific data value. CONCATENATE is a function used to combine data extracted from different data sources to form a unified data set. data_extracted is the extracted and integrated data.

[0061] The algorithm formula for data cleaning is:

[0062] Cleaned_data=F_clean(data_extracted,REPLACE(Null,'Unknown'),DELETEFROM data_extracted WHERE c1>threshold).

[0063] Among them, REPLACE(Null,'Unknown') is used to replace null values ​​in the data with 'Unknown', which can avoid errors caused by missing values ​​in subsequent processing. DELETE FROM data_extracted WHERE c1>threshold means deleting records whose data values ​​in a column exceed the set threshold, which is used to remove abnormal data and ensure data quality. Cleaned_data is the data after replacing null values ​​and deleting abnormal values.

[0064] The algorithm formula for data fusion is:

[0065] Integrated_data=MERGE(Cleaned_data1,Cleaned_data2 ON key1=key2USINGmethod).

[0066] Among them, MERGE is to merge two data sets into one according to the specified key. ON key1=key2 indicates the key for merging two data sets, such as user ID or product code. USING method indicates the method used when merging, such as averaging, taking the maximum value, or taking the latest value. Integrated_data is a comprehensive view of the merged data set for in-depth data analysis and reporting.

[0067] In practical applications, such as in the retail industry, sales data from different stores may be stored in different database systems, and each store may have different data entry standards, such as inconsistent date formats and currency units. The data integration and preprocessing process first aggregates this data into a central warehouse through data extraction. The data cleaning process standardizes the date format, converts the currency unit, and cleans up illogical sales records (such as data with abnormally high sales). Finally, the cleaned data from all stores is merged through the data fusion process to form a unified data set that can be used to generate national sales reports. This process not only improves the accuracy and consistency of the data, but also greatly enhances the analyzability of the data, providing companies with timely and accurate business insights and supporting more effective decision making.

[0068] Step S120, obtaining multiple different entities and entity relationships between different entities from standard data, and constructing a knowledge graph based on the multiple different entities and entity relationships between different entities.

[0069] Specifically, the server obtains multiple different entities and entity relationships between different entities from the standard data obtained in step S110, and completes the construction of the knowledge graph based on the multiple different entities and entity relationships between different entities.

[0070] Combination Figure 2 As shown, in a specific embodiment, the platform management method based on low-code development provided by the present invention uses a knowledge graph as a powerful means of data organization and representation, which forms a graphical representation of entities and their relationships by structurally integrating information from different sources. This representation can not only reveal the complex relationships between data, but also support complex queries and data analysis, and is the basis of intelligent systems such as recommendation systems, automatic question-answering systems, and complex decision support systems.

[0071] The algorithm formula for constructing the knowledge graph is:

[0072] G=create_graph((entity1,attr1,attr2,...,attrN),(relationship(entity1,entity2),type_of_relationship,strength_of_relationship)).

[0073] Among them, G is the constructed knowledge graph, which is a graph composed of nodes (entities) and edges (relationships). entity1,entity2 are entities in the graph, which can be people, places, objects, concepts, etc. attr1,attr2,...,attrN are the attributes of the entity, which describe the characteristics of the entity, such as name, date, location, etc. relationship(entity1,entity2) represents the relationship between entities, such as "works at", "located in", "has", etc. type_of_relationship is the type of entity relationship, which indicates the nature of the relationship between entities. strength_of_relationship is the strength or weight of the entity relationship, which can represent the importance, frequency or confidence of the relationship.

[0074] For example, a knowledge graph is constructed within a large multinational company to optimize resource allocation and strengthen internal collaboration. The data sources within the company can include human resource databases, project management systems, and customer relationship management systems. Entities can include employees, departments, projects, and customers, and employee attributes may include name, position, skills, department, and project experience. Project attributes can include project name, related departments, responsible person, and timeline. The relationship between employees and projects can be "participation", with a relationship type of "participation" and strength determined by participation time or contribution. The relationship between employees and departments can be "belongs to", which simply indicates the affiliation between employees and departments. After using the above information to build a knowledge graph, the company can conduct human resource planning more effectively and optimize team composition by analyzing employee skills and project requirements. When an employee leaves or a new project is launched, quickly querying the knowledge graph can help users find potential employees with the required skills and experience. The knowledge graph can also support more complex queries, such as "find all employees who have participated in at least three projects and have data science skills", which is extremely critical for staffing new projects. In addition, by analyzing the strength of the relationship between employees and projects, management can evaluate employees' core skills and potential career paths, further supporting career development and training plans. In this way, knowledge graphs not only enhance the visualization and queryability of data, but also greatly improve the decision-making quality and operational efficiency of organizations.

[0075] Step S130, mapping the entity nodes in the knowledge graph and the entity relationships between different entity nodes to a low-dimensional space for vector representation through a graph embedding model, so as to extract global knowledge including the entity relationships between different entity nodes and the network topology structure.

[0076] Specifically, the server uses a graph embedding model to map the entity nodes in the knowledge graph and the entity relationships between different entity nodes to a low-dimensional space for vector representation, so as to extract global knowledge including the entity relationships between different entity nodes and the network topology structure, that is, the extracted global knowledge is the vector representation of the low-dimensional space.

[0077] Combination Figure 2As shown, in a specific embodiment, the platform management method based on low-code development provided by the present invention, after the knowledge graph is built, the system already has a rich entity and relationship network, which lays the foundation for extracting valuable information from the graph. The global knowledge extraction process converts complex graph structures into vector forms that can be used for machine learning through a graph embedding model. This process directly depends on the structure and quality of the knowledge graph, and the quality and effect of graph embedding are directly affected by the quality of the graph. Global knowledge extraction is the process of abstracting and extracting useful information from a knowledge graph using graph embedding technology. Graph embedding technology maps nodes (entities) and edges (relationships) in the graph to vector representations in a low-dimensional space, so that these vectors can capture and retain key information of the original graph structure, including relationships between nodes and network topology. Such vector representations can be used for various machine learning tasks, such as classification, prediction, and recommendation systems, thereby enhancing the performance of decision support systems.

[0078] The algorithm formula for global knowledge extraction is:

[0079] global_knowledge=embed_graph(G,model,SUM_OVER(neighbors(v)*weight(v,u)FOR u IN V)).

[0080] Where G is the constructed knowledge graph, represented as a graph consisting of nodes and edges. model is a graph embedding model, such as Node2Vec, GraphSAGE, etc. These models are used to convert graph data into points in vector space. neighbors(v) is the set of neighbor nodes of node v, which are other nodes directly connected to node v. weight(v,u) is the weight of the edge between nodes v and u, indicating the strength or importance of the relationship between the two nodes. V is the set of all nodes in the graph. global_knowledge is the global knowledge extracted by the graph embedding model, represented as a vector representation of the node.

[0081] Taking social network analysis as an example, a typical application scenario is to use graph embedding technology to identify influential users or communities. A social network can be modeled as a knowledge graph, where individual users are nodes and interactions between users such as comments, sharing or likes are edges. The weight of the edge can be based on the frequency or depth of the interaction. In a social network, each user is a node, and interactions between users, such as friendships or jointly participated discussions, constitute edges. The weight of the edge can be based on the quantity or quality of the interaction, such as the number of posts that are jointly liked. Using a graph embedding model such as GraphSAGE, the social network pattern of each user is converted into a vector, and a vector representing the user's social behavior is generated by aggregating the neighbor information of each user node (considering the weight of the edge). Based on the similarity in the vector space, the community structure in the social network is identified, and core users and influential nodes are identified, which are centrally located in the vector space or connected to multiple important communities. In addition, these vectors can also be used to predict user behavior, such as possible purchase decisions, acceptance of new products, etc. By analyzing the extracted global knowledge, marketing teams can design more effective advertising strategies to promote products to specific influential users and use their social influence to increase the visibility and attractiveness of products. It makes it feasible and effective to extract useful information from huge and complex social network data, supporting enterprises to make accurate data-based decisions in a highly competitive market environment.

[0082] Step S140, obtaining a predefined business process and a plurality of decision points corresponding to the execution of the business process, and evaluating the execution result of the business process by executing global knowledge and current business process variables to obtain corresponding guiding decisions.

[0083] Specifically, the server obtains the predefined business process and multiple decision points corresponding to the execution of the business process, and evaluates the execution results of the business process by executing global knowledge and the business environment variables of the predefined business process during the current execution (i.e., the current business process variables), and obtains corresponding guiding decisions. The guiding decisions are used to optimize the management of the predefined business process and update the business process in real time.

[0084] Combination Figure 2 As shown, in a specific embodiment, the platform management method based on low-code development provided by the present invention also includes a business process automation and optimization process, which improves various aspects of the business through intelligent tools, making the entire operation more efficient and accurate. In this process, the data and performance feedback generated by the system can be further analyzed to provide real-time business insights for resource management. These insights flow directly into the resource prediction and optimization configuration process. The system uses the knowledge obtained from the business process to predict resource requirements and formulate a more reasonable resource configuration, including the allocation of physical resources, as well as the optimal configuration of human and financial resources.

[0085] In this embodiment, the global knowledge extracted previously is used to guide or optimize the execution of the business process. The automated process can not only perform routine tasks, but also make intelligent decisions based on dynamic data and contextual environment in complex decision points.

[0086] The algorithm formula for business process automation and optimization is:

[0087] executed_process=execute_process(defined_process,IF condition THENaction ELSE alternative,EVALUATE(global_knowledge,context)).

[0088] Among them, defined_process is a predefined business process, which can be a flowchart or step description of any business operation, such as order processing, customer service response, etc. IF condition THEN action ELSE alternative describes the decision point encountered in the execution of the business process, and selects to execute action or alternative according to the condition. These conditions may be based on business rules or dynamic data analysis. EVALUATE(global_knowledge,context) represents a function that uses extracted global knowledge and current business environment variables (context) to evaluate and guide decisions. It can involve complex logic and machine learning model outputs, such as risk assessment, customer preference prediction, etc. Context is the current business environment variables. These variables include but are not limited to customer data, market conditions, resource status, etc., which are crucial for the evaluation of decision points. Executed_process is the process state after execution, which indicates the result of the process execution, which can be an updated database record, a delivered order, or other business output.

[0089] For example, in the order processing system of an online retailer, order processing is a complex business process involving multiple processes such as inventory management, payment processing, logistics, and customer communication. Using automation and optimization in this process can significantly improve efficiency and customer satisfaction. First, define a standard order processing process, including order receipt, inventory check, payment confirmation, shipment, and customer notification. During the inventory check stage, if the product is out of stock, automatically decide whether to recommend alternative products or notify the customer to delay shipment. Use the "IF condition THEN action ELSE alternative" structure. If the inventory quantity < the order quantity, recommend similar products, otherwise continue the regular process. "EVALUATE(global_knowledge,context)" here can evaluate the product substitution acceptance rate based on historical data or personalized recommendations based on customer purchase history. Global knowledge can include inventory trends, seasonal demand changes, etc. This knowledge helps the system make smarter decisions. The process is executed according to the results of the evaluation, and the execution results of each step are fed back to the system for monitoring and further optimization. In addition, the execution result "executed_process" can be a confirmation of successful order processing or a report on abnormal processing. Through this automation and optimization, retailers can process orders more efficiently, reduce customer churn due to out-of-stock situations, optimize inventory management, and improve the quality of customer service. In addition, the automatically recorded data can also be used for subsequent business strategy adjustments and system improvements.

[0090] In this embodiment, it also includes a resource prediction and optimization configuration process, which is a key component in enterprise resource management, especially in resource-constrained and cost-sensitive business environments. This process involves using global knowledge extracted from data analysis and knowledge graphs, combined with current resource conditions, to predict future demand and optimize resource configuration accordingly, with the goal of maximizing resource utilization efficiency and minimizing operating costs while ensuring that business needs and constraints are met.

[0091] The algorithm formula for resource prediction and optimal configuration is:

[0092] optimized_resources=optimize(global_knowledge,current_resources,MINIMIZE(cost_function)SUBJECT TO constraints).

[0093] Among them, global_knowledge is the extracted global knowledge, including historical data analysis, trend forecasting, demand patterns and other information, which is used to guide resource allocation decisions. Current_resources is the current available resource status, including inventory levels, human resources, financial resources, etc. MINIMIZE(cost_function) means that the goal is to minimize the cost function, which usually involves reducing resource waste, reducing excessive inventory, optimizing manpower allocation, etc. SUBJECT TO constraints are the constraints that need to be met in the optimization process, such as budget constraints, supply chain capabilities, market demand, regulatory requirements, etc. optimized_resources is the optimized resource allocation, and the results are intended to provide the most effective resource utilization strategy to support business operations.

[0094] For example, the main challenge facing a manufacturing company is how to effectively manage the operation of the production line when the supply of raw materials is unstable and the market demand fluctuates greatly. In this case, resource forecasting and optimal allocation become particularly important. Global knowledge includes analysis of market trends, review of historical sales data, consideration of seasonal changes, etc. This information can help companies predict product demand in the coming months. In addition, detailed records of current raw material inventory, machine utilization, and staffing are used to evaluate financial resources and determine the funds available for procurement and investment. Cost functions can include raw material costs, labor costs, storage costs, and opportunity costs (revenue lost due to production line stagnation), and constraints such as production capacity, delivery time, quality standards, and environmental impact. Use mathematical programming methods (such as linear programming and integer programming) to solve optimization problems, try different resource configurations through algorithms, calculate costs under various circumstances, and find the configuration with the lowest cost. The optimization results guide the adjustment of production plans, such as increasing production of high-demand products, reducing inventory of low-demand products, and rescheduling worker shifts. Implement the optimized resource allocation, monitor any deviations during execution, and adjust strategies based on actual operation conditions. Collect feedback to adjust the global knowledge base and improve subsequent prediction and optimization models. In this way, manufacturing companies can better cope with market uncertainties, optimize production efficiency, and reduce resource waste, thereby maintaining competitiveness in a highly competitive market, which not only improves economic benefits but also enhances the company's sustainable development capabilities.

[0095] The platform management method based on low-code development obtains multiple heterogeneous data sources, extracts data from multiple heterogeneous data sources, and pre-processes the extracted data for data cleaning and data standardization to obtain standard data for constructing a knowledge graph. Subsequently, multiple different entities and entity relationships between different entities are obtained from the obtained standard data, and a knowledge graph is constructed based on multiple different entities and entity relationships between different entities. The entity nodes in the knowledge graph and the entity relationships between different entity nodes are then mapped to a low-dimensional space for vector representation through a graph embedding model to extract global knowledge including entity relationships between different entity nodes and network topology structures. Finally, the predefined business process and multiple decision points corresponding to the execution of the business process are obtained, and the execution results of the business process are evaluated by executing global knowledge and current business process variables to obtain corresponding guiding decisions to complete the optimization management of the final predefined business process. This method integrates data processing functions, deep learning and intelligent algorithms, complex business process automation tools, and business process optimization functions through a low-code platform, achieving more efficient data integration, in-depth data analysis and intelligent decision-making, flexible business process automatic optimization, and cost-effective resource management.

[0096] like Figure 3 As shown, in one embodiment, the platform management method based on low-code development provided by the present invention obtains multiple heterogeneous data sources, extracts first data from the multiple heterogeneous data sources, and pre-processes the first data to obtain standard data for constructing a knowledge graph, specifically including the following steps:

[0097] Step S112: extracting a plurality of first data sources from a plurality of heterogeneous data sources according to a set condition, and extracting a plurality of data columns satisfying the set condition from the plurality of first data sources.

[0098] Step S114, calling a fusion algorithm to integrate multiple data columns to obtain a data set, and merging different data sets according to a set key, the data in the data set is the first data.

[0099] Step S116, replacing the missing values ​​in the first data, and deleting the abnormal data in each data column whose values ​​do not meet the set threshold, so as to clean the first data, and standardize the cleaned first data to obtain standard data.

[0100] like Figure 4 As shown, in one embodiment, the platform management method based on low-code development provided by the present invention obtains multiple different entities and entity relationships between different entities from standard data, and constructs a knowledge graph based on the multiple different entities and entity relationships between different entities, specifically including the following steps:

[0101] Step S122: determining entity attributes, entity relationship types, and entity relationship weights according to a plurality of different entities and entity relationships between different entities.

[0102] Step S124, based on entity attributes, entity relationship types and entity relationship weights, a knowledge graph is constructed with multiple different entities as nodes and entity relationships between different entities as edges.

[0103] Among them, each entity corresponds to a node, and the entity relationship between each two different entities corresponds to an edge.

[0104] like Figure 5 As shown, in one embodiment, the platform management method based on low-code development provided by the present invention maps entity nodes in the knowledge graph and entity relationships between different entity nodes to low-dimensional space for vector representation through a graph embedding model to extract global knowledge including entity relationships between different entity nodes and network topology structures, specifically including the following steps:

[0105] Step S132, select a first entity node and a second entity node from the knowledge graph, and obtain an entity relationship weight between the first entity node and the second entity node, the first entity node and the second entity node are directly connected neighbor nodes and have an entity relationship.

[0106] Step S134 , calling the graph embedding model to map the first entity node, the second entity node, and the entity relationship between the first entity node and the second entity node to a vector representation in a low-dimensional space according to the entity relationship weight.

[0107] like Figure 6 As shown, in one embodiment, the platform management method based on low-code development provided by the present invention obtains a predefined business process and multiple decision points corresponding to the execution of the business process, and evaluates the execution result of the business process by executing global knowledge and current business process variables to obtain corresponding guiding decisions, which specifically includes the following steps:

[0108] Step S142, executing the predefined business process according to the set business rules to obtain multiple decision points and determine the business environment variables when the business process is currently executed.

[0109] Step S144, obtaining different execution states and execution results by executing the business process under the global knowledge and business environment variables, and evaluating multiple decision points based on the different execution states and execution results to generate a guiding decision.

[0110] Step S146: updating and optimizing the predefined business process based on the guiding decision to generate a new business process.

[0111] like Figure 7 As shown, in one embodiment, the platform management method based on low-code development provided by the present invention further includes the following steps:

[0112] Step S710, obtaining the current resource configuration corresponding to the predefined business process and the constraints satisfied by the resource configuration optimization, and calling the minimization cost function to perform cost minimization processing on the current resource configuration.

[0113] Step S720 , analyzing and evaluating the historical data of the business process execution corresponding to the current resource configuration according to the global knowledge, and generating an optimized resource configuration in combination with the constraint conditions and the minimization cost function.

[0114] The platform management system based on low-code development provided by the present invention is described below. The platform management system based on low-code development described below and the platform management method based on low-code development described above can be referenced to each other.

[0115] like Figure 8 As shown, in one embodiment, a platform management system based on low-code development includes a data preprocessing module 810, a knowledge graph construction module 820, a global knowledge extraction module 830 and an evaluation guidance module 840.

[0116] The data preprocessing module 810 is used to obtain multiple heterogeneous data sources, extract first data from the multiple heterogeneous data sources, and preprocess the first data to obtain standard data for constructing a knowledge graph. The preprocessing includes data cleaning and data standardization.

[0117] The knowledge graph construction module 820 is used to obtain multiple different entities and entity relationships between different entities from standard data, and to construct a knowledge graph based on the multiple different entities and entity relationships between different entities.

[0118] The global knowledge extraction module 830 is used to map the entity nodes in the knowledge graph and the entity relationships between different entity nodes to a low-dimensional space for vector representation through a graph embedding model, so as to extract global knowledge including the entity relationships between different entity nodes and the network topology structure.

[0119] The evaluation and guidance module 840 is used to obtain predefined business processes and multiple decision points corresponding to the execution of the business processes, and evaluate the execution results of the business processes by executing global knowledge and current business process variables to obtain corresponding guidance decisions.

[0120] Among them, the first data is useful data or key data that meets the set conditions, the standard data is composed of multiple different entities and entity relationships between different entities, the current business process variables are the business environment variables when the predefined business process is currently executed, and the guiding decision is used to optimize the management of the predefined business process.

[0121] In this embodiment, the platform management system based on low-code development provided by the present invention, the data preprocessing module is specifically used for:

[0122] A plurality of first data sources are extracted from a plurality of heterogeneous data sources according to set conditions, and a plurality of data columns satisfying the set conditions are extracted from the plurality of first data sources.

[0123] The fusion algorithm is called to integrate multiple data columns to obtain a data set, and different data sets are merged according to a set key, and the data in the data set is the first data.

[0124] In this embodiment, the platform management system based on low-code development provided by the present invention, the data preprocessing module is specifically used for:

[0125] The missing values ​​in the first data are replaced, and the abnormal data in each data column whose value does not meet the set threshold is deleted to clean the first data, and the cleaned first data is standardized to obtain standard data.

[0126] In this embodiment, the platform management system based on low-code development provided by the present invention, the knowledge graph construction module is specifically used for:

[0127] Entity attributes, entity relationship types, and entity relationship weights are determined based on multiple different entities and entity relationships between different entities.

[0128] Based on entity attributes, entity relationship types and entity relationship weights, a knowledge graph is constructed with multiple different entities as nodes and entity relationships between different entities as edges.

[0129] Among them, each entity corresponds to a node, and the entity relationship between each two different entities corresponds to an edge.

[0130] In this embodiment, the platform management system based on low-code development provided by the present invention, the global knowledge extraction module is specifically used for:

[0131] A first entity node and a second entity node are selected from the knowledge graph, and an entity relationship weight between the first entity node and the second entity node is obtained. The first entity node and the second entity node are directly connected neighbor nodes and have an entity relationship.

[0132] The call graph embedding model maps the first entity node, the second entity node, and the entity relationship between the first entity node and the second entity node to a vector representation in a low-dimensional space according to the entity relationship weight.

[0133] In this embodiment, the platform management system based on low-code development provided by the present invention, the evaluation guidance module is specifically used for:

[0134] Execute predefined business processes according to set business rules to obtain multiple decision points and determine the business environment variables when the business process is currently executing.

[0135] By executing the business processes under global knowledge and business environment variables, different execution states and execution results are obtained, and multiple decision points are evaluated based on different execution states and execution results to generate guiding decisions.

[0136] Update and optimize predefined business processes based on guided decisions to generate new business processes.

[0137] In this embodiment, the platform management system based on low-code development provided by the present invention further includes a resource configuration optimization module for:

[0138] Get the current resource configuration corresponding to the predefined business process and the constraints satisfied by the resource configuration optimization, and call the minimization cost function to minimize the cost of the current resource configuration.

[0139] Based on global knowledge, historical data of the business process execution corresponding to the current resource configuration is analyzed and evaluated, and the optimal resource configuration is generated by combining constraints and minimizing cost functions.

[0140] Fig. 9 An example of a physical structure diagram of an electronic device is shown. The electronic device may be a smart terminal, and its internal structure diagram may be as follows: Fig. 9 As shown. The electronic device includes a processor, a memory and a network interface connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a platform management method based on low-code development is implemented, and the method includes:

[0141] Acquire multiple heterogeneous data sources, extract first data from the multiple heterogeneous data sources, and preprocess the first data to obtain standard data for constructing a knowledge graph, wherein the preprocessing includes data cleaning and data standardization;

[0142] Obtain multiple different entities and entity relationships between different entities from standard data, and construct a knowledge graph based on the multiple different entities and entity relationships between different entities;

[0143] The entity nodes in the knowledge graph and the entity relationships between different entity nodes are mapped to a low-dimensional space for vector representation through a graph embedding model to extract global knowledge including entity relationships between different entity nodes and network topology structures.

[0144] Obtain predefined business processes and multiple decision points corresponding to the execution of business processes, and evaluate the execution results of business processes by executing global knowledge and current business process variables to obtain corresponding guiding decisions;

[0145] Among them, the first data is useful data or key data that meets the set conditions, the standard data is composed of multiple different entities and entity relationships between different entities, the current business process variables are the business environment variables when the predefined business process is currently executed, and the guiding decision is used to optimize the management of the predefined business process.

[0146] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present invention, and does not constitute a limitation on the electronic device to which the scheme of the present invention is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0147] On the other hand, the present invention further provides a computer storage medium storing a computer program, which, when executed by a processor, implements a platform management method based on low-code development, the method comprising:

[0148] Acquire multiple heterogeneous data sources, extract first data from the multiple heterogeneous data sources, and preprocess the first data to obtain standard data for constructing a knowledge graph, wherein the preprocessing includes data cleaning and data standardization;

[0149] Obtain multiple different entities and entity relationships between different entities from standard data, and construct a knowledge graph based on the multiple different entities and entity relationships between different entities;

[0150] The entity nodes in the knowledge graph and the entity relationships between different entity nodes are mapped to a low-dimensional space for vector representation through a graph embedding model to extract global knowledge including entity relationships between different entity nodes and network topology structures.

[0151] Obtain predefined business processes and multiple decision points corresponding to the execution of business processes, and evaluate the execution results of business processes by executing global knowledge and current business process variables to obtain corresponding guiding decisions;

[0152] Among them, the first data is useful data or key data that meets the set conditions, the standard data is composed of multiple different entities and entity relationships between different entities, the current business process variables are the business environment variables when the predefined business process is currently executed, and the guiding decision is used to optimize the management of the predefined business process.

[0153] In another aspect, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and when the processor executes the computer instructions, a platform management method based on low-code development is implemented, the method comprising:

[0154] Acquire multiple heterogeneous data sources, extract first data from the multiple heterogeneous data sources, and preprocess the first data to obtain standard data for constructing a knowledge graph, wherein the preprocessing includes data cleaning and data standardization;

[0155] Obtain multiple different entities and entity relationships between different entities from standard data, and construct a knowledge graph based on the multiple different entities and entity relationships between different entities;

[0156] The entity nodes in the knowledge graph and the entity relationships between different entity nodes are mapped to a low-dimensional space for vector representation through a graph embedding model to extract global knowledge including entity relationships between different entity nodes and network topology structures.

[0157] Obtain predefined business processes and multiple decision points corresponding to the execution of business processes, and evaluate the execution results of business processes by executing global knowledge and current business process variables to obtain corresponding guiding decisions;

[0158] Among them, the first data is useful data or key data that meets the set conditions, the standard data is composed of multiple different entities and entity relationships between different entities, the current business process variables are the business environment variables when the predefined business process is currently executed, and the guiding decision is used to optimize the management of the predefined business process.

[0159] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0160] By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0161] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0162] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A platform management method based on low-code development, characterized in that: The method comprises: Acquire multiple heterogeneous data sources, extract first data from the multiple heterogeneous data sources, and preprocess the first data to obtain standard data for constructing a knowledge graph, wherein the preprocessing includes data cleaning and data standardization; Acquire a plurality of different entities and entity relationships between different entities from the standard data, and construct a knowledge graph according to the plurality of different entities and entity relationships between different entities; Mapping the entity nodes in the knowledge graph and the entity relationships between different entity nodes to a low-dimensional space for vector representation through a graph embedding model to extract global knowledge including the entity relationships between the different entity nodes and the network topology structure; Obtaining a predefined business process and a plurality of decision points corresponding to the execution of the business process, and evaluating the execution result of the business process by executing the global knowledge and current business process variables to obtain corresponding guiding decisions; Among them, the first data is useful data or key data that meets the set conditions, the standard data is composed of multiple different entities and entity relationships between different entities, the current business process variables are business environment variables when the predefined business process is currently executed, and the guiding decision is used to optimize the management of the predefined business process.

2. The platform management method based on low-code development according to claim 1 is characterized in that: The acquiring of multiple heterogeneous data sources, extracting first data from the multiple heterogeneous data sources, and preprocessing the first data to obtain standard data for constructing a knowledge graph includes: Extracting a plurality of first data sources from the plurality of heterogeneous data sources according to the set condition, and extracting a plurality of data columns satisfying the set condition from the plurality of first data sources; A fusion algorithm is called to integrate the multiple data columns to obtain a data set, and different data sets are merged according to a set key, and the data in the data set is the first data.

3. The platform management method based on low-code development according to claim 2 is characterized in that: The step of obtaining a plurality of heterogeneous data sources, extracting first data from the plurality of heterogeneous data sources, and preprocessing the first data to obtain standard data for constructing a knowledge graph further includes: The missing values ​​in the first data are replaced, and the abnormal data in each data column whose values ​​do not meet the set threshold are deleted to clean the first data, and the cleaned first data are standardized to obtain the standard data.

4. The platform management method based on low-code development according to claim 1 is characterized in that: The acquiring a plurality of different entities and entity relationships between different entities from the standard data, and constructing a knowledge graph according to the plurality of different entities and entity relationships between different entities, includes: Determine entity attributes, entity relationship types, and entity relationship weights according to the multiple different entities and entity relationships between the different entities; Based on the entity attributes, entity relationship types and entity relationship weights, the knowledge graph is constructed with multiple different entities as nodes and entity relationships between the different entities as edges; Among them, each entity corresponds to a node, and the entity relationship between each two different entities corresponds to an edge.

5. The platform management method based on low-code development according to claim 4 is characterized in that: The entity nodes in the knowledge graph and the entity relationships between different entity nodes are mapped to a low-dimensional space for vector representation through a graph embedding model to extract global knowledge including the entity relationships between different entity nodes and the network topology structure, including: Selecting a first entity node and a second entity node from the knowledge graph, and obtaining an entity relationship weight between the first entity node and the second entity node, wherein the first entity node and the second entity node are directly connected neighbor nodes and have an entity relationship; The graph embedding model is called to map the first entity node, the second entity node, and the entity relationship between the first entity node and the second entity node to a vector representation in a low-dimensional space according to the entity relationship weight.

6. The platform management method based on low-code development according to claim 1 is characterized in that: The obtaining of a predefined business process and a plurality of decision points corresponding to the execution of the business process, and evaluating the execution result of the business process by executing the global knowledge and the current business process variables to obtain a corresponding guiding decision, includes: Executing the predefined business process according to the set business rules to obtain the multiple decision points and determine the business environment variables when the business process is currently executed; Obtaining different execution states and execution results by executing the business process under the global knowledge and the business environment variables, and evaluating the multiple decision points based on the different execution states and execution results to generate the guiding decision; The predefined business process is updated and optimized based on the guiding decision to generate a new business process.

7. The platform management method based on low-code development according to any one of claims 1 to 6, characterized in that: The method further comprises: Obtaining the current resource configuration corresponding to the predefined business process and the constraints satisfied by the resource configuration optimization, and calling the minimization cost function to perform cost minimization processing on the current resource configuration; The historical data of the business process execution corresponding to the current resource configuration is analyzed and evaluated according to the global knowledge, and the optimal resource configuration is generated by combining the constraint conditions and the minimization cost function.

8. A platform management system based on low-code development, characterized in that: The system comprises: A data preprocessing module, used to obtain multiple heterogeneous data sources, extract first data from the multiple heterogeneous data sources, and preprocess the first data to obtain standard data for constructing a knowledge graph, wherein the preprocessing includes data cleaning and data standardization; A knowledge graph construction module, used to obtain a plurality of different entities and entity relationships between different entities from the standard data, and to construct a knowledge graph according to the plurality of different entities and entity relationships between different entities; A global knowledge extraction module, used to map entity nodes and entity relationships between different entity nodes in the knowledge graph to a low-dimensional space for vector representation through a graph embedding model, so as to extract global knowledge including entity relationships between different entity nodes and network topology structure; An evaluation and guidance module is used to obtain a predefined business process and a plurality of decision points corresponding to the execution of the business process, and evaluate the execution result of the business process by executing the global knowledge and the current business process variables to obtain a corresponding guidance decision; Among them, the first data is useful data or key data that meets the set conditions, the standard data is composed of multiple different entities and entity relationships between different entities, the current business process variables are business environment variables when the predefined business process is currently executed, and the guiding decision is used to optimize the management of the predefined business process.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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