Code-free platform development method based on closed-loop management

Abstract

The invention relates to the technical field of code-free data processing, and discloses a code-free platform development method based on closed-loop management, and the method comprises the steps: abstracting a business concept into a semantic placeholder, and building a semantic placeholder library; constructing an organizational structure tree, and constructing a ternary logic mapping table comprising context dimensions, semantic dimensions and interpreter dimensions based on the semantic placeholder library and the organizational structure tree; using the ternary logic mapping table to develop a runtime delay binding execution engine and construct a semantic definition and mapping version management mechanism; establishing a hierarchical semantic inheritance and difference mechanism; adding a semantic convergence controller and developing a semantic distance calculation engine; integrating a version management mechanism and a semantic distance measurement result, calculating a semantic health degree score, and establishing a semantic optimization closed-loop mechanism; according to the method, service intention and technology implementation are decoupled through semantic abstraction, semantic dynamic adaptation and convergence of multiple departments and multi-level organizations are achieved, and the cross-department cooperation efficiency of the closed-loop management process is improved.

Description

Technical Field

[0001] This invention relates to the field of no-code data processing technology, and more specifically, to a no-code platform development method based on closed-loop management. Background Technology

[0002] With the deepening of enterprise digital transformation, no-code / low-code development platforms, as important tools for lowering technical barriers and accelerating the digitalization of business processes, have been widely used in enterprise applications. These platforms, through visual interfaces and configurable methods, enable business personnel to independently develop business modules, effectively bridging the gap between business requirements and technical implementation in traditional development models.

[0003] In the prior art, Chinese patent application CN117648075A discloses a method for constructing a no-code data processing system. This method, through editor interaction and process design, compiler design, executor design, and visual editing operations, supports users in configuring data processing logic via drag-and-drop and selection. The configuration is compiled into SQL code and imported into the data processing engine for execution, reducing the data development threshold and alleviating the information gap between business personnel and data development engineers. Chinese patent CN115048090B discloses an online no-code development system and method. The system includes controls and connected entity modules. Through the collaboration of an open interface (including connected / unconnected states), trigger, processing, operation, and execution ends, the controls retrieve interface states and process operation information based on user startup commands, fully leveraging the flexible configuration advantages of the no-code development platform.

[0004] However, existing no-code development technologies still struggle to meet the semantic convergence requirements of closed-loop management in multi-department, multi-level organizations. Existing technologies CN117648075A focus on the visual configuration of data processing logic, while CN115048090B emphasizes the interaction and adaptation of controls and entity modules. Neither addresses the dynamic adaptation mechanism for "semantic differences in business operations across multiple departments under template reuse scenarios." When an enterprise standardizes its closed-loop management process (goal setting - task allocation - execution tracking - result evaluation) into configurable templates, different departments, such as the power generation department and the fuel management department, may use templates with the same parameters. This is because there are deep semantic differences in the definition of core indicators; for example, the power generation department defines it as the monthly power generation efficiency of the generating units, while the fuel management department defines it as the monthly power generation per ton of coal. The parameterization methods of existing technologies can only cover time. Shallow configurations such as cycles and thresholds cannot dynamically match the data sources and calculation logic of different departments. Moreover, existing platforms mostly adopt a "static binding" mechanism, which fixes the data source and calculation rules at the time of deployment. When business rules evolve, such as when the power generation department adds a new unit operation mode and needs to adjust the efficiency calculation logic, or when there are multiple levels of organizations, such as the company-department-team, and target decomposition, such as when the company-level coal consumption target is distributed to each power generation department according to different rules, the parameter dependencies and coverage rules become extremely complex. This causes the template to be unable to dynamically adapt to semantic variations, and ultimately the problem of "semantic divergence of execution results in different departments with the same template" occurs. This affects the uniformity and accuracy of the closed-loop management process and makes it difficult to support the refined management needs of multi-department organizations. Summary of the Invention

[0005] To overcome the aforementioned shortcomings of existing technologies, this invention provides a no-code platform development method based on closed-loop management. By abstracting business concepts into semantic placeholders and establishing a semantic placeholder library, a ternary logical mapping table containing context, semantic, and interpreter dimensions is constructed to achieve dynamic association between the runtime lazy-binding execution engine and semantic definitions. Simultaneously, through hierarchical semantic inheritance and differential mechanisms, a semantic convergence controller, and a semantic distance calculation engine, the interpreter differences of different organizational nodes for the same semantic placeholder are quantified. A semantic health scoring and optimization closed-loop mechanism is constructed, effectively solving the problem of semantic convergence difficulties in closed-loop processes caused by template parameter polymorphism under multi-departmental isolation, significantly improving cross-departmental collaboration efficiency and business process standardization.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A no-code platform development method based on closed-loop management includes:

[0008] Abstract business concepts into semantic placeholders, assign a unique identifier to each semantic placeholder and establish a semantic placeholder library; construct an organizational structure tree, and based on the semantic placeholder library and organizational structure tree, construct a ternary logical mapping table containing context dimension, semantic dimension and interpreter dimension; use the ternary logical mapping table to develop a runtime lazy binding execution engine, and based on the semantic placeholder library and ternary logical mapping table, construct a version management mechanism for semantic definition and mapping;

[0009] Based on the ternary logical mapping table and organizational structure tree, a hierarchical semantic inheritance and differential mechanism is established; a semantic convergence controller is added to the runtime delayed binding execution engine; a semantic distance calculation engine is developed based on the hierarchical semantic inheritance and differential mechanism and the semantic convergence controller; the semantic distance calculation engine is used to quantify the semantic distance between interpreters defined by different organizational nodes for the same semantic placeholder, and generate semantic distance measurement results.

[0010] Integrate the version management mechanism of semantic definition and mapping with the semantic distance measurement results, calculate the semantic health score and establish a semantic optimization closed-loop mechanism.

[0011] The method for constructing the organizational structure tree includes: obtaining organizational structure data, parsing the parent-child relationship between organizational nodes in the organizational structure data, and generating an organizational structure tree.

[0012] The method for constructing the ternary logical mapping table includes:

[0013] Traverse the organizational structure tree to extract each organizational node as a context instance, and perform a Cartesian product operation on the context instance and the semantic placeholders in the semantic placeholder library to form the combination to be mapped.

[0014] Configure an interpreter for each combination to be mapped, and construct a ternary logical mapping table by associating the context instance, semantic placeholder, and interpreter.

[0015] An interpreter inheritance mechanism is established in the ternary logical mapping table. The interpreter inheritance mechanism means that when a context instance lacks an interpreter definition, the interpreter of the parent node is searched upwards along the organizational structure tree.

[0016] The runtime lazy binding execution engine obtains the current execution context, searches for and executes the interpreter in the ternary logical mapping table based on the current execution context, and performs dynamic parsing and result replacement of semantic placeholders.

[0017] The method for establishing the hierarchical semantic inheritance and differential mechanism includes:

[0018] Traverse the organizational structure tree and create a semantic definition set for each organizational node. The semantic definition set contains two subsets: an inherited semantic set and a differential semantic set.

[0019] Automatically copy all semantic placeholders and their interpreter definitions in the ternary logic map from the parent node to the inherited semantic set of the current node;

[0020] In the differential semantic set, cover mode rules, transformation mode rules, and append mode rules are defined to implement three differential operation modes: cover, transformation, and append to the interpreter inherited from the parent node.

[0021] The semantic distance metric result is the global average semantic distance;

[0022] The method for calculating the global average semantic distance includes: calculating the data structure distance value in the data structure dimension, calculating the behavioral logic distance value in the behavioral logic dimension, calculating the business concept distance value in the business concept dimension, and then taking a weighted average of the data structure distance value, the behavioral logic distance value, and the business concept distance value to obtain the global average semantic distance.

[0023] The semantic convergence controller is used to identify the four stages of the closed-loop management process and apply different semantic convergence strategies to each stage. The four stages include the goal setting stage, the task allocation stage, the execution tracking stage, and the result evaluation stage.

[0024] The method for calculating the semantic health score includes:

[0025] The version management mechanism for semantic definition and mapping is extended into a semantic version evolution chain, and standardized templates are generated.

[0026] The semantic health score is calculated by combining the semantic distance metric, the semantic version evolution chain, and the standardized template.

[0027] The method for generating the semantic version evolution chain includes:

[0028] Extract each change record of semantic placeholders or interpreters from the version management mechanism of semantic definition and mapping;

[0029] Each change record is encapsulated as a semantic change block, and the semantic change blocks are linked in chronological order using hash pointers to construct an immutable semantic version evolution chain.

[0030] The method for generating the standardized template includes:

[0031] Identify high-performing departments and extract their semantic placeholders and interpreters as best practice candidates;

[0032] Break down the best practice candidates into common and unique components;

[0033] The common parts are solidified into the core logic of the template, and the unique parts are transformed into new semantic placeholders to generate a standardized template.

[0034] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0035] This invention enables dynamic adaptation and strategic convergence of business semantics in complex organizational environments, allowing the same template to maintain consistent underlying logic while automatically matching differentiated business semantics when running in different departments. This fundamentally eliminates the chaos of multi-level parameter inheritance and the risk of semantic divergence, ensuring that closed-loop processes have executable uniformity and measurable consistency across the entire organization. By abstracting business concepts into semantic placeholders and establishing a semantic placeholder library, a complete decoupling of business intent and technical implementation is achieved. This allows the same template to exhibit different behaviors in different business contexts, significantly reducing maintenance costs associated with business changes. A ternary logical mapping table containing context, semantic, and interpreter dimensions is constructed, establishing a dynamic association between the organizational structure tree and semantic placeholders, supporting runtime dynamic matching and execution logic based on organizational context. Through hierarchical semantic inheritance and differential mechanisms, the top-down transmission and bottom-up differentiated adjustment of semantic rules in multi-level organizations are realized, ensuring both the consistency of top-level strategy and meeting the flexibility needs of lower-level businesses. The combination of a semantic convergence controller and a semantic distance calculation engine quantitatively evaluates the interpreter differences of different organizational nodes for the same semantic placeholder, providing an objective basis for cross-departmental semantic collaboration. The establishment of a semantic health scoring and optimization closed-loop mechanism enables the semantic system to dynamically adapt to business development, continuously improving the standardization level of enterprise business language and the efficiency of cross-departmental collaboration. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 A flowchart illustrating a no-code platform development method based on closed-loop management, provided as an embodiment of the present invention;

[0038] Figure 2 A schematic diagram illustrating the relationship between context instances, semantic placeholders, and interpreters provided in embodiments of the present invention;

[0039] Figure 3 A flowchart illustrating the method for establishing a hierarchical semantic inheritance and differential mechanism provided in this embodiment of the invention;

[0040] Figure 4 A flowchart illustrating the principle of generating standardized templates provided in this embodiment of the invention;

[0041] Figure 5 This is a functional module diagram of a no-code platform development system based on closed-loop management, provided for an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example 1

[0044] Please see Figure 1 As shown, this embodiment provides a no-code platform development method based on closed-loop management, including:

[0045] Step S10: Abstract business concepts into semantic placeholders, assign a unique identifier to each semantic placeholder and establish a semantic placeholder library; construct an organizational structure tree, and based on the semantic placeholder library and organizational structure tree, construct a ternary logical mapping table containing context dimension, semantic dimension and interpreter dimension; based on the semantic placeholder library and ternary logical mapping table, construct a version management mechanism for semantic definition and mapping.

[0046] Further, step S10 includes:

[0047] Step S11: Abstract the business concept into semantic placeholders, assign a unique identifier to each semantic placeholder, and establish a semantic placeholder library.

[0048] Specifically, based on the online form or table building engine of the no-code platform, a dedicated semantic placeholder definition interface is created. This interface provides visual operation controls, including business concept input boxes, data type selectors, and drop-down menus for applicable organizational levels. Business administrators use this interface to perform semantic placeholder abstraction. Semantic placeholders refer to abstract identifiers in the form of business concept descriptions, used when building closed-loop management templates on the no-code platform, instead of directly specifying specific data table fields or calculation logic. This decouples business intent from specific technical implementations. During the abstraction process, administrators do not need to focus on specific technical implementation details, such as specifying that the data comes from the "number of equipment failures" field in the equipment management department's data table. Instead, they focus on the business itself, defining core business concepts as semantic placeholders in the format of "<business concept description>". For example, for business concepts related to enterprise unit operation efficiency assessment, it can be defined as "<unit monthly power generation efficiency>", for business concepts related to equipment maintenance quality tracking, it can be defined as "<equipment monthly failure rate>", and for business concepts related to fuel utilization efficiency, it can be defined as "<fuel monthly power generation per ton of coal>". After the administrator completes the semantic placeholder definition, a unique identifier is automatically assigned to each placeholder. This identifier is generated using the platform's internal preset encoding rules, ensuring that it is not repeated across the entire platform and can be used for unique indexing and association of subsequent semantic placeholders. Simultaneously, a corresponding record is created for each placeholder in the semantic placeholder library. This record contains complete metadata, specifically including the semantic placeholder's name, unique identifier, creation time, creator, business meaning description, applicable organizational level, and data type constraints. The name of a semantic placeholder is the "<Business Concept Description>" defined by the administrator. The creation time is a timestamp automatically recorded by the platform. The creator is the account information of the administrator who recorded the operation. The business meaning description is the business logic explanation represented by the placeholder, entered by the administrator. For example, "<Monthly Generating Efficiency of the Unit> means the ratio of the actual monthly generating output of each generating team to the rated monthly generating output," and "<Monthly Equipment Failure Rate> means the ratio of the number of equipment failures per month to the total operating hours of each maintenance team." The applicable organizational level refers to the organizational node level to which the placeholder can be applied, selected through the drop-down menu, such as company level, department level, or team level. Data type constraints refer to the data type of the semantic placeholder's value, such as numeric, character, or date. The semantic placeholder library is a structured database used to centrally store all semantic placeholders and their metadata, supporting management operations such as querying, modifying, and deleting placeholders.

[0049] When a user uses semantic placeholders in the formula editing area of ​​the no-code platform, such as using "<unit monthly power generation efficiency>" in the formula "If <unit monthly power generation efficiency> ≥ target value, then it is judged as qualified" in the "Result Evaluation" node, the formula parsing engine performs real-time syntax analysis on the input formula. By recognizing the enclosing format of "<" and ">", it accurately locates the semantic placeholders and marks them as pending parsing. These placeholders are also highlighted in a special color on the interface to prompt the user that this part of the content needs to be dynamically parsed at runtime. The formula parsing engine is the core component of the no-code platform used to identify semantic placeholders within formulas in process nodes and mark their pending parsing status. Abstracting business concepts into semantic placeholders achieves a complete separation between business intent and concrete implementation. This eliminates the need to bind specific data sources or computational logic during template design, allowing the same template to exhibit different behaviors in different business contexts. This lays the foundation for building a dynamic binding mechanism in step S12, solving the problem of strong binding between templates and concrete implementations in traditional no-code platforms, which leads to semantic incompatibility when reusing templates across departments. The platform automatically assigns unique identifiers and establishes a semantic placeholder library, enabling centralized and standardized management of semantic placeholders. This facilitates administrators in querying the metadata information of each placeholder and tracing the creation and modification records of placeholders. It also avoids different administrators repeatedly defining placeholders for the same business concepts, ensuring the consistency of semantic placeholders within the platform. The formula parsing engine identifies semantic placeholders and marks them as pending parsing, providing a clear target object for dynamic parsing at runtime. This avoids missing placeholders during runtime parsing, ensuring that process node formulas can be completely parsed. At the same time, the interface highlights prompts to improve users' awareness of placeholder status and reduce operational errors. When a business concept is adjusted, only the metadata (such as the business meaning description) of the corresponding placeholder needs to be modified in the semantic placeholder library. There is no need to modify the deployed template, which greatly reduces the maintenance cost caused by business changes and provides a clear management object for subsequent semantic version management.

[0050] Step S12: Synchronize organizational structure data from the enterprise human resource management system to construct an organizational structure tree. Based on the semantic placeholder library and the organizational structure tree, construct a ternary logical mapping table containing context dimension, semantic dimension and interpreter dimension.

[0051] Further, step S12 includes:

[0052] Step S121: Synchronize organizational structure data in real time from the enterprise human resource management system through the REST API interface, and parse the parent-child relationship between organizational nodes in the organizational structure data to generate an organizational structure tree;

[0053] Step S122: Traverse the organizational structure tree to extract each organizational node as a context instance, and perform a Cartesian product operation on the context instance and the semantic placeholders in the semantic placeholder library to form the combination to be mapped.

[0054] Step S123: Configure an interpreter for each combination to be mapped, and construct a ternary logical mapping table by associating the context instance, semantic placeholder, and interpreter.

[0055] Step S124: Establish an interpreter inheritance mechanism in the ternary logical mapping table. When a context instance lacks an interpreter definition, search upwards along the organizational structure tree for the interpreter of the parent node.

[0056] Each node in the organizational structure tree represents an organizational unit (such as a company, department, or work group), and the parent-child relationships between nodes reflect the hierarchical relationships of these units. The organizational structure tree is constructed using the enterprise human resource management system as the data source and a REST API interface as the data synchronization method. The REST API interface was chosen because it has a standardized request and response format, enables cross-system data interaction, and supports real-time data acquisition, avoiding errors and delays caused by manually entering organizational structure data. Using pre-configured REST API request parameters, including the access address of the human resource management system, authentication information, and data query conditions, a data retrieval request is sent to the human resource management system. This request specifies the name, number, parent unit number, and responsible person of the organizational unit to be retrieved. Upon receiving the request, the human resource management system verifies its validity. If the verification is successful, it returns JSON-formatted data containing the aforementioned organizational unit information. The received JSON data is parsed to extract the organizational unit number and parent unit number. The hierarchy of each organizational unit is determined based on the parent unit number. For example, organizational unit number "001" with parent unit number "0" is the root node (company level); organizational unit number "001001" with parent unit number "001" is a child node of node "001" (power generation department); organizational unit number "001001001" with parent unit number "001001" is a child node of node "001001" (power generation team 1), and so on, constructing a complete organizational structure tree. The organizational structure tree is traversed using a depth-first search algorithm, starting from the root node and visiting each child node sequentially until all nodes have been traversed. During the traversal, information such as the number and name of each organizational node is extracted, treating each organizational node as an independent context instance. Finally, all defined semantic placeholders are extracted from the semantic placeholder library, including the unique identifier and name of each placeholder. To ensure coverage of all possible "context instance-semantic placeholder" combinations and avoid the lack of a corresponding interpreter during subsequent semantic parsing due to missing combinations, a Cartesian product operation is performed on all context instances and semantic placeholders. For example, if there are two context instances, "Power Generation Department" and "Equipment Management Department," and two semantic placeholders, "<Unit Monthly Power Generation Efficiency>" and "<Equipment Monthly Failure Rate>," then the Cartesian product operation generates four combinations to be mapped: "Power Generation Department - <Unit Monthly Power Generation Efficiency>", "Power Generation Department - <Equipment Monthly Failure Rate>", "Equipment Management Department - <Unit Monthly Power Generation Efficiency>", and "Equipment Management Department - <Equipment Monthly Failure Rate>".

[0057] The platform provides a dedicated interpreter configuration interface, which displays all the mapping combinations generated in step S122. For each mapping combination, the corresponding interpreter type is selected and the specific content is configured according to actual business needs. The interpreter is a component used to implement the specific execution logic of semantic placeholders. It can be divided into four types: data query statements, calculation formulas, external API calls, and process references, each corresponding to different semantic parsing requirements. For the data query statement type interpreter, the data source is selected through the visual query builder, such as the power generation operation data table, equipment failure data table, and fuel consumption data table and fields in the enterprise data warehouse, such as the "actual power generation" field in the power generation operation data table, the "number of failures" field in the equipment failure data table, and the "total coal consumption" field in the fuel consumption data table. Query conditions are then set, and these configurations are automatically converted into corresponding SQL query statements. For the calculation formula type interpreter, the platform's formula editing component is used, employing the platform's preset syntax rules, such as support for addition, subtraction, multiplication, division, and logical judgments, to define calculations. For example, the calculation formula configured for "Power Generation Department - <Monthly Power Generation Efficiency of Units>" is "(Actual Power Generation / Rated Power Generation) × 100", and the calculation formula configured for "Fuel Management Department - <Monthly Power Generation per Ton of Coal>" is "Total Monthly Power Generation / Total Monthly Coal Consumption". For interpreters of the external API call type, configure the API address and request method of the external system, such as GET, POST, request parameters, authentication information, etc., to ensure that the system can obtain external data through the API, such as calling the power grid dispatch system API to obtain the planned power generation of the units. For interpreters of the process reference type, select the corresponding sub-process template from the platform's existing BPMN process template library, and configure the process triggering conditions and parameter passing rules, such as referencing the "Equipment Emergency Shutdown Handling" sub-process for "<Equipment Fault Handling Process>". After completing the configuration of all interpreters to be mapped, store the relationship between the context instance, semantic placeholder, and interpreter in the database to construct a ternary logical mapping table; the relationship between the context instance, semantic placeholder, and interpreter is as follows: Figure 2 As shown, context instances and semantic placeholders are the requirements for semantic parsing. The combination of the two clarifies the specific scenario of a certain business concept of a certain department. The ternary logic mapping table is the core matching hub, storing the association between the combination of context instances and semantic placeholders and the corresponding execution logic. The interpreter is the final execution component. Through the matching of the ternary table, it finds the specific logic that can be implemented for a specific scenario. The three together realize the precise binding from "scenario + concept" to "execution logic".

[0058] An "inheritance" flag is pre-set in the ternary logic mapping table for each combination of context instance and semantic placeholder, with the default state being "yes". When runtime needs to parse a specific semantic placeholder corresponding to a context instance, it first queries the ternary logic mapping table to determine if a dedicated interpreter has been configured for the combination of the context instance and semantic placeholder. If configured, the dedicated interpreter is used directly. If not configured (i.e., the interpreter field for the combination in the ternary logic mapping table is empty), it searches upwards according to the parent-child relationship of the organizational structure tree, looking up the parent node of the context instance to find the interpreter corresponding to the combination of the semantic placeholder. If the parent node is configured, the interpreter of the parent node is inherited and used. If the parent node is still not configured, it continues to search upwards for the interpreter of the grandparent node, and so on, until the root node of the organizational structure tree is reached. If the root node still does not have a corresponding interpreter configured, the platform uses the preset default interpreter, such as returning a "no data" prompt or a default value. For example, if "Power Generation Team 1" is a child node of "Power Generation Department" and no interpreter is configured for "<Unit Monthly Power Generation Efficiency>", then the interpreter for the combination of "Power Generation Department" and this placeholder is searched. If "Power Generation Department" has been configured (the calculation formula is "(Actual Power Generation / Rated Power Generation) × 100"), then "Power Generation Team 1" will inherit and use this interpreter.

[0059] Step S121 uses a REST API interface to synchronize organizational structure data, enabling real-time acquisition and automatic updates of organizational structure data. This ensures that the organizational structure tree accurately reflects the actual organizational structure changes of the enterprise, such as the addition of maintenance teams or adjustments to departmental affiliations. It avoids inaccurate context instances due to data lag, which could affect semantic parsing results. Parsing the parent-child relationships of organizational nodes generates the organizational structure tree, clarifying the hierarchical structure of organizational units and providing a clear hierarchical basis for the subsequent interpreter inheritance mechanism, ensuring the inheritance path is traceable. Step S122 performs a Cartesian product operation to form the combination to be mapped, covering all possible combinations of "context instance - semantic placeholder." This avoids situations where no interpreter is available due to missing combinations, ensuring the comprehensiveness of semantic parsing and solving the problem of process execution interruption caused by incomplete combination coverage in traditional platforms. Step S123 configures the interpreter to construct a ternary logic mapping table, establishing the association between context, semantics, and execution logic, forming a complete semantic parsing framework. This enables the platform to dynamically match execution logic according to different contexts, achieving dynamic adaptation of semantics across departments (such as the different interpretations of "<core operating indicators>" by the power generation department and the equipment management department). Simultaneously, different types of interpreter configurations meet diverse business needs, such as data querying, formula calculation, and external platform integration, improving the platform's applicability. Step S124 establishes an interpreter inheritance mechanism, reducing the configuration workload for business administrators. It eliminates the need to repeatedly configure interpreters for all semantic placeholders on each organizational node; for example, at the team level, there's no need to repeatedly configure existing interpreters at the department level. This lowers the platform's usage threshold and maintenance costs, while ensuring that even if lower-level organizational nodes do not have interpreters configured, the process can still execute normally through the inheritance mechanism, avoiding process interruptions and improving platform stability. When an enterprise adds a new organizational node, such as a new desulfurization and denitrification team, this node can directly inherit the interpreter from its parent node (Equipment Management Department). This eliminates the need to reconfigure interpreters for all semantic placeholders, significantly shortening the deployment cycle of new organizational nodes and facilitating rapid adaptation to semantic parsing requirements during organizational restructuring. Without step S12, the lack of an organizational structure tree would result in an indeterminate context dimension, preventing the platform from distinguishing between different departments and levels of business scenarios, such as the differences between the power generation department and the fuel management department, thus hindering dynamic cross-departmental semantic adaptation. The lack of a ternary logic mapping table would prevent the association between semantic placeholders and interpreters, making it impossible to determine the execution logic corresponding to the placeholders at runtime, thus preventing normal process execution. Furthermore, the lack of an interpreter inheritance mechanism would require administrators to repeatedly configure interpreters for all semantic placeholders of each organizational node, significantly increasing workload and hindering rapid deployment when adding new organizational nodes, severely impacting the platform's usability and scalability.

[0060] Step S13: Obtain the current execution context, develop a runtime delayed binding execution engine using a ternary logical mapping table, the runtime delayed binding execution engine searches for and executes the interpreter in the ternary logical mapping table according to the current execution context, and performs dynamic parsing and result replacement of semantic placeholders;

[0061] Further, step S13 includes:

[0062] Step S131: When the process node containing semantic placeholders is executed, the organizational unit to which the user belongs, the user role, the current time and business parameters are extracted from the session information of the current user, and the current execution context is constructed to establish an execution context cache.

[0063] Step S132: Parse all semantic placeholders in the process nodes and mark the identified semantic placeholders as semantic placeholders to be parsed;

[0064] Step S133: Take the organizational unit in the current execution context as the context dimension, and use the organizational unit to search for the interpreter corresponding to the semantic placeholder to be parsed in the ternary logical mapping table. If it is not found, the interpreter inheritance mechanism is triggered to search upward along the organizational structure tree.

[0065] Step S134: Execute the found interpreter to obtain the return result, replace the semantic placeholder to be parsed with the return result and update the execution context cache.

[0066] Specifically, the process is a business process definition built on the BPMN (Business Process Model and Notation) standard. It is created in a no-code platform through a visual process designer and contains multiple process nodes that can be executed sequentially or in parallel. A process node is the basic execution unit in the process and corresponds to a specific business activity. For example, a closed-loop management process includes goal setting nodes, task assignment nodes, execution tracking nodes, and result evaluation nodes. Each node can be configured with business rules such as execution conditions, calculation formulas, and judgment logic. Semantic placeholders can be used to replace specific data fields or calculation logic in these configurations.

[0067] When the BPMN process engine reaches a certain process node, the runtime delayed binding execution engine is automatically triggered through the listener pattern. The listener intercepts the node execution events of the process engine and inserts semantic parsing logic before the node is executed. The runtime delayed binding execution engine first extracts execution context data, including the user's organizational unit, user role, current time, and business parameters, from the user session information stored in the platform login authentication system. The user's organizational unit is obtained by querying the association between the user account and the organizational structure tree node. For example, if the user account "power001" is associated with the "Power Generation Department" node, then the extracted organizational unit is "Power Generation Department". The user role is obtained by parsing the permission identifier in the session, such as "Department Administrator", "Operation Shift Worker", "Equipment Maintenance Worker", etc. Different roles may correspond to different business parameter visibility ranges (e.g., maintenance workers can view fault data, and shift workers can view operation data). The current time is obtained through the platform's built-in time service, accurate to the millisecond level, and is used to match time-related business rules, such as querying "current month" data and calculating "unit runtime". Business parameters are extracted from the startup parameters of the process instance and the intermediate data generated during the process, such as the "unit number" parameter passed when the process starts (e.g., "#01 unit") and the "target value" parameter generated by the preceding node, such as "1 million kilowatt-hours". The completed execution context is written to the execution context cache. The key of the cache is a combination of the process instance ID and the node ID to ensure uniqueness.

[0068] The formula parsing engine performs syntactic analysis on the business logic expression of the current process node. First, the engine identifies the symbol sequence in the expression through lexical analysis, filtering out character segments that begin with "<" and end with ">", marking them as semantic placeholders to be parsed. Then, it verifies the syntactic validity of the placeholders in the expression through syntactic analysis. For example, it checks whether "<unit monthly power generation efficiency>" is in the correct operational position, such as to the left of "≥". If a syntactic error exists, such as the placeholder not being closed ("<unit monthly power generation efficiency"), an error message is returned and the process execution is paused. For instance, for the node logic "If <unit monthly power generation efficiency> ≥ <unit monthly power generation target value>, then it is considered qualified", the formula parsing engine identifies two semantic placeholders to be parsed: "<unit monthly power generation efficiency>" and "<unit monthly power generation target value>", highlighting them on the interface and recording their unique identifiers in the background for later interpreter lookup.

[0069] In step S133, when performing a query operation in the ternary logical mapping table using organizational units, a combination of "organizational unit - semantic placeholder" conditions is used. For example, using "organizational unit = power generation department" and "semantic placeholder = <unit monthly power generation efficiency>" as conditions, the corresponding interpreter record is searched in the ternary logical mapping table. If a matching interpreter is found, the interpreter is directly obtained; if no matching record is found, that is, the current organizational unit has not configured a dedicated interpreter for the semantic placeholder, the interpreter inheritance mechanism established in step S124 is triggered: based on the parent-child relationship of the organizational structure tree, the parent nodes of the current organizational unit are traversed upwards. For example, if the current organizational unit is "power generation team 1" and the parent node is "power generation department", then the query is performed again using "organizational unit = power generation department" and "semantic placeholder = <unit monthly power generation efficiency>". If a matching interpreter exists in the parent node, the interpreter is inherited; if the parent node still has no matching record, the traversal continues upwards to the "company level" node, until the root node is reached. If the root node still does not have a matching interpreter, the preset default interpreter will be automatically called. The logic of the default interpreter is to return a text message "No valid data" and record a warning log for the administrator to troubleshoot later.

[0070] Based on the type of interpreter found (data query statement, calculation formula, external API call, process reference), the corresponding parsing operation is performed: For the data query statement type, the parameters in the execution context are substituted into the placeholders of the query statement to generate an executable SQL statement, which is then executed via JDBC connection to the enterprise data warehouse to obtain the returned result; For the calculation formula type, the formula is parsed according to the platform's preset syntax rules, first parsing the semantic placeholders nested in the formula, and then performing arithmetic operations to obtain the calculation result; For the external API call type, an HTTP request is constructed according to the API address, request method, and request parameters configured in the interpreter, and after adding authentication information, it is sent to the external system, and the response data is received and parsed, such as calling the fuel procurement system API to obtain "monthly coal consumption" "8000 tons"; For the process reference type, the subprocess instance is started according to the subprocess template ID and parameter passing rules configured in the interpreter, and the execution result of the subprocess is obtained, such as "equipment fault handling completed".

[0071] After obtaining the interpreter's return result, the corresponding semantic placeholders in the process nodes are replaced with this result. For example, in the statement "If <unit monthly power generation completion rate> ≥ <unit monthly power generation target value>, then it is judged as qualified", "<unit monthly power generation completion rate>" is replaced with "110%", and "<unit monthly power generation target value>" is replaced with "100%", forming the complete executable logic "If 110% ≥ 100%, then it is judged as qualified". Simultaneously, the execution context cache is updated, and the replaced result is written to the cache as a new business parameter for use by subsequent process nodes. A parsing log is also recorded, containing the execution context, semantic placeholders, interpreter source (directly defined / inherited from parent node), execution result, and execution time. The log is stored in a distributed logging system for auditing and troubleshooting.

[0072] Step S131 extracts user session information to construct the execution context and establishes a cache, achieving accurate acquisition and efficient reuse of runtime environment information. This provides accurate dimensional basis for interpreter lookup and avoids efficiency losses caused by repeated extraction of session information. It ensures that the process execution of different users and departments can match their respective business contexts. At the same time, the caching mechanism reduces IO overhead, ensures the real-time performance of process execution, and solves the problem that traditional platforms cannot dynamically obtain the runtime environment, leading to a disconnect between semantic parsing and actual business scenarios. Step S132 calls the formula parsing engine to identify and mark the semantic placeholders to be parsed, achieving accurate location and status identification of semantic placeholders. This clarifies the target objects searched by the interpreter, avoiding omissions or misidentifications. It ensures that all placeholders that need to be parsed can be processed, and the interface highlighting improves the administrator's awareness of the placeholder status, reducing process execution errors caused by unparsed placeholders. Step S133 queries the interpreter based on organizational units and triggers the inheritance mechanism, realizing dynamic matching and hierarchical reuse of interpreters. It accurately selects execution logic based on business context, while avoiding redundant interpreter configuration for lower-level organizations. When using the same template across departments and levels, it can match interpreters that conform to its own business semantics, ensuring the accuracy of semantic parsing and reducing the configuration workload for administrators. This solves the problem of semantic incompatibility when reusing templates across departments due to static interpreter binding in traditional platforms. Step S134 executes the interpreter and completes result replacement and cache update, realizing the conversion of semantic placeholders into specific business data and the transfer of context data. It transforms abstract semantic placeholders into executable business logic and provides data support for subsequent nodes. Process nodes can automatically execute business logic adapted to their department without manual intervention, and the transfer of context data ensures the continuity of process execution. If step S13 is missing, the interpreter cannot be dynamically matched at runtime. The semantic framework built by steps S11 and S12 remains at the static definition level, and the template still needs to be bound to a fixed technical implementation. The problem of cross-departmental semantic adaptation cannot be solved. The semantic placeholders in the process nodes cannot be parsed, and the process execution will be interrupted at the node containing the placeholder. The closed-loop management process cannot be connected. Furthermore, subsequent steps such as S22 and S31, which depend on the runtime parsing results, will lose data support, and functions such as semantic convergence and intelligent attribution cannot be realized. As a result, the whole solution cannot solve the core problem of "difficulty in semantic convergence of closed-loop process caused by template parameter polymorphism under multi-department isolation".

[0073] Step S14: Based on the semantic placeholder library and the ternary logical mapping table, construct a version management mechanism for semantic definition and mapping.

[0074] The version management mechanism for semantic definitions and mappings is a management system used to record the change history of semantic placeholder metadata and interpreter definitions in the ternary logic mapping table. It supports version creation, querying, and rollback. This includes a change record collection mechanism and a version record generation mechanism. The change record collection mechanism collects change records, and its specific implementation process is as follows: An operation monitoring component is set up in the management interface of the semantic placeholder library and the ternary logic mapping table. When an administrator performs a modification to the semantic placeholder metadata, such as modifying the business meaning description of "<unit monthly power generation efficiency>", changing it from "the ratio of actual power generation to rated power generation" to "the ratio of actual effective power generation to rated power generation", where effective power generation = actual... When the power generation minus plant power loss, or when the interpreter definition is modified (e.g., the interpreter for the "<Unit Monthly Power Generation Efficiency>" in the power generation department is changed from "(Actual Power Generation / Rated Power Generation) × 100" to "(Actual Power Generation - Plant Power Loss) / Rated Power Generation × 100"), the monitoring component captures the operation type (add / modify / delete), operation object, content before change, content after change, and operator account in real time, and automatically pops up a "Modification Reason Input Box" to require the administrator to fill in the reason for the change. After all information is collected, a change record is generated and stored in the change record table of the version management database. The unique identifier of the change record is generated by a combination of the operation timestamp and a random sequence. Based on the collected change records, a version record generation mechanism is constructed: the version number adopts a three-part structure of "major version number - minor version number - revision number". The major version number is used to identify major changes in semantic definition, such as adding core semantic placeholders or adjusting the interpreter type system. The minor version number is used to identify functional changes, such as modifying the business meaning of multiple semantic placeholders or adjusting the execution logic of a certain type of interpreter. The revision number is used to identify minor fixes, such as correcting syntax errors in interpreter statements or supplementing missing metadata items for semantic placeholders. The increment rule of the version number is automatically determined based on the magnitude of the change or manually specified by the administrator. Each time a version record is generated, related change records within a specified time range or a specified business scenario are first filtered. Then, these change records are associated with the newly generated version number. The modification timestamp, modification personnel information, and modification reason explanation are automatically extracted from the change records. Using a text comparison algorithm, such as the Levenshtein distance algorithm, a version difference comparison report is generated, highlighting the changed content.

[0075] Subsequently, a version locking and rollback mechanism is constructed: When a process instance starts, it reads all semantic placeholders currently associated with the process template, such as "<Monthly Generating Efficiency of Units>", "<Monthly Generating Target Value of Units>", and "<Standardization Completion Rate>" associated with the "Monthly Unit Operation Assessment" template, as well as the interpreters of these semantic placeholders under the corresponding organizational units. It queries the version management database to obtain the current valid versions of these semantic definitions, i.e., the latest versions not marked as "obsolete," and records these version numbers in the context data of the process instance, thus achieving version locking. During process execution, even if the administrator modifies the version of the relevant semantic placeholders or interpreters, the process instance still uses the version locked at startup to ensure the consistency of execution logic. For example, if a power generation department's "Monthly Unit Operation Assessment" process instance locks the version of "<Monthly Generating Efficiency of Units>" to V1.2.0 at startup, and the administrator updates the interpreter of this placeholder to V1.3.0 during execution, the process instance still uses the V1.2.0 version interpreter to complete the parsing of subsequent nodes. The version locking mechanism ensures the stability of semantic definitions during process execution, preventing inconsistencies in execution logic caused by changes in semantic definitions during process execution. It ensures that all nodes of the same process instance use unified semantic definitions. For example, the "target setting" node and the "result evaluation" node use the same version of the "<unit monthly power generation efficiency>" interpreter. This avoids the problem of "mismatch between target and evaluation standards" caused by different statistical logic between target and evaluation values ​​due to version inconsistencies. It solves the problem that changes in semantic definitions in traditional platforms directly affect the running process, leading to abnormal execution results.

[0076] The construction of the change record collection mechanism enables comprehensive capture and structured storage of semantic definition change information, providing complete raw data for version records and ensuring that every change is traceable. It avoids omissions or errors in manually recording change information, providing a precise data foundation for subsequent version tracing and problem diagnosis, and solving the problem of traditional platforms lacking change records and being unable to trace the semantic definition evolution process. The construction of the version record generation mechanism enables the orderly encapsulation and versioned management of change records, associating scattered change records into identifiable and comparable version units, clarifying the evolution stage of semantic definitions. Administrators can quickly locate the semantic definition status at a certain stage and clearly understand the change content through difference comparison. For example, comparing versions V1.0.0 and V1.2.0, it can be determined in which version the interpreter of "<unit monthly power generation efficiency>" added the plant power loss deduction logic, and what the specific adjustments were, avoiding the problem of chaotic change content and inability to distinguish semantic definitions at different stages in traditional platforms. Without step S14, the entire solution would face the obstacle of semantic definition out of control. Changes to semantic placeholders and interpreters are undocumented. When business data anomalies occur, such as large fluctuations in monthly power generation efficiency, it's impossible to trace whether the change was caused by semantic definition changes, leading to a stalemate in problem investigation. More significantly, semantic definitions may change at any time during process instance execution, resulting in different nodes within the same process instance using different versions of semantic definitions. For example, the "target setting" node might use an older version of the interpreter to calculate the target value, while the "result evaluation" node uses a newer version to calculate the actual value. The different statistical logics directly distort the evaluation results and disrupt the logical consistency of the execution results. Simultaneously, the negotiation process in subsequent step S24 lacks implementation guarantees. The semantic definitions agreed upon through negotiation cannot be solidified through versioning, easily leading to the problem of "arbitrary modifications after negotiation." Furthermore, the semantic version evolution chain in step S31 loses its data foundation, making it impossible to achieve full lifecycle traceability of semantic evolution. Ultimately, the semantic governance capabilities of the entire solution are significantly weakened, failing to address the problem of "management chaos caused by the evolution of semantic definitions as business develops."

[0077] Step S10, through a progressive technical approach of "semantic abstraction - context association - dynamic execution - version control," constructs a basic framework for separating business intent from technical implementation, fundamentally solving the shortcomings of static template binding in traditional no-code platforms. Specifically, Step S11 abstracts business concepts into semantic placeholders in the format of "<business concept description>" and establishes a library containing metadata. Combined with a formula parsing engine marking placeholders to be parsed, this directly decouples business from technology, eliminating the need for templates to be bound to specific data sources or computational logic. This supports business personnel in designing templates independently and provides a unified semantic carrier for cross-departmental reuse. Unique IDs and metadata specifications ensure semantic consistency across the entire platform. Step S12, through REST API synchronization with HR system data, constructs an organizational structure tree. This tree is then covered by a Cartesian product of all combinations of "context instance - semantic placeholder," and multiple types of interpreters are configured to form a ternary logical mapping table. Overlaying an interpreter inheritance mechanism establishes a precise association between "department / level - semantics - execution logic," avoiding the need for combination... The omissions that cause process interruptions reduce the workload of repetitive configuration in lower-level organizations and adapt to the needs of enterprise organizational structure adjustments, such as the addition of new work teams; Step S13 extracts session information to build an execution context cache, parses placeholders and dynamically looks up the interpreter based on a ternary table, replaces the placeholders and updates the cache after execution, transforming the static semantic framework into runtime dynamic capabilities, so that changes in business rules only require updating the interpreter without redeploying the template, ensuring that process execution is coherent and efficient; Step S14 collects change records through the listening component, encapsulates them with a three-segment version number to form a traceable version management mechanism, and, together with version locking during execution, ensures that semantic evolution is auditable and free from chaos, providing traceability basis for problem investigation and solidifying the results of cross-departmental consultation, providing a data foundation for the subsequent semantic evolution chain.

[0078] Step S20: Based on the ternary logical mapping table and organizational structure tree, establish a hierarchical semantic inheritance and differentiation mechanism; develop a semantic distance calculation engine based on the hierarchical semantic inheritance and differentiation mechanism, the semantic distance calculation engine is used to quantify the semantic distance between interpreters defined by different organizational nodes for the same semantic placeholder, generate semantic distance measurement results and language distance warnings; trigger cross-departmental negotiation process based on semantic distance warnings;

[0079] Further, step S20 includes:

[0080] Step S21: Based on the ternary logical mapping table and organizational structure tree, establish a hierarchical semantic inheritance and differential mechanism that supports three modes: overlay, transformation, and append.

[0081] See Figure 3 Furthermore, step S21 includes:

[0082] Step S211: Traverse the organizational structure tree and create a semantic definition set for each organizational node. The semantic definition set includes two subsets: an inherited semantic set and a differential semantic set.

[0083] Step S212: Automatically copy all semantic placeholders and their interpreter definitions in the ternary logic mapping table from the parent node to the inherited semantic set of the current node;

[0084] Step S213: Define overlay mode rules, transformation mode rules and append mode rules in the differential semantic set to implement the three differential operation modes of overlay, transformation and append to the interpreter inherited from the parent node.

[0085] Specifically, the hierarchical semantic inheritance and differential mechanism is a management mechanism used to realize the "top-down inheritance and bottom-up differential adjustment" of semantic rules in multi-level organizations. Its core is to maintain an "inherited semantic set" and a "differential semantic set" for each organizational node, taking into account both the consistency of top-level strategy and the flexibility of lower-level business. In step S211, all nodes of the organizational architecture tree are traversed by a depth-first traversal algorithm. During the traversal, a semantic definition set is created for each node. The semantic definition set contains two subsets: the inherited semantic set and the differential semantic set. The inherited semantic set is used to store the semantic rules inherited from the parent node, and the differential semantic set is used to store the semantic rules that are personalized and adjusted by the current node. The purpose of dividing into two subsets is to clarify the source and priority of semantic rules and avoid confusion between inherited rules and custom rules.

[0086] After creating the semantic set, all records are automatically copied from the semantic definition set of the parent node to the inherited semantic set of the current node. During the copying process, the validity of the inherited semantic set of the parent node needs to be verified, such as whether the interpreter has syntax errors and whether the data source is reachable. If the semantic definition set of the parent node is empty, it is traced back to the grandparent node and then to the root node. If the root node still has no valid semantic rules, a default rule, such as "return no data prompt", is inserted into the inherited semantic set of the current node. This ensures that semantic rules are passed down in an orderly manner from top to bottom along the organizational structure, avoiding the duplication of common rules in lower-level organizations and greatly reducing the workload of semantic configuration in multi-level organizations. For example, "Power Generation Team 1" does not need to reconfigure the basic interpreter of "<Unit Monthly Power Generation Efficiency>" and can directly inherit the rules of "Power Generation Department". At the same time, it ensures that the top-level strategy can penetrate to the grassroots teams through the inheritance mechanism and avoid deviations in the transmission of semantic rules.

[0087] Coverage Mode Rule: The administrator enters a new interpreter content corresponding to the unique identifier of the same semantic placeholder in the inherited semantic set into the differential semantic set. Through the mechanism of "unique matching of semantic placeholder unique identifiers", the interpreter in the differential semantic set has higher priority than the interpreter in the inherited semantic set. For example, the interpreter content of the company-level "<unit annual power supply coal consumption reduction rate>" is "(previous year's power supply coal consumption - this year's power supply coal consumption) / previous year's power supply coal consumption × 100". The Equipment Management Department enters a new interpreter content of "<unit annual power supply coal consumption reduction rate>" into its own differential semantic set as "(previous year's power supply coal consumption - this year's power supply coal consumption - technical transformation consumption reduction) / previous year's power supply coal consumption × 100". When the platform runs and parses this semantic placeholder, it will prioritize using the interpreter in the Equipment Management Department's differential semantic set to cover the semantic rule of the parent node (company level). The coverage mode meets the special business needs of departments (such as the equipment management department needing to separately calculate the contribution of technological transformation to reduce energy consumption). Without disrupting the existing semantic rules of the parent node, it allows subordinate organizations to flexibly adjust the semantic logic according to their own business characteristics, avoiding the need to modify the global semantic rules due to changes in local business needs, and ensuring the stability of the global rules and the adaptability of local business.

[0088] Transformation mode rules: In the formula editing component, a preset identifier is used to reference the semantic rules of the parent node, which is described as the "parent node" reference identifier. When the administrator defines the content of the interpreter, he can use this identifier to reference the execution result of the corresponding semantic placeholder of the parent node, and then overlay the calculation logic that meets his own business needs. For example, after the interpreter for "<Monthly Power Generation Target of Units>" at the power generation department level executes, it returns the result "A", such as 1 million kilowatts. In its own differential semantic set, the interpreter content defined for "<Monthly Power Generation Target of Units>" by Power Generation Team 1 is "Parent Node <Monthly Power Generation Target of Units> × B", where B is an adjustment coefficient set by the team based on the unit capacity ratio or equipment health status, such as 0.6. Since Power Generation Team 1 is responsible for Unit #01, its capacity accounts for 60% of the total capacity of the power generation department. When the platform runs, it first executes the interpreter for "<Monthly Power Generation Target of Units>" of the parent node (power generation department) and obtains the result "A". Then, it multiplies "A" and "B" to obtain the power generation target result at the team level, which is 600,000 kilowatts. This mode realizes dynamic correlation calculation of semantic rules, ensuring that the semantic rules of lower-level organizations have a clear logical relationship with the semantic rules of higher-level organizations, avoiding semantic disconnect during target decomposition, and reducing errors from manual calculation and input of target values, thus improving the accuracy and efficiency of target decomposition.

[0089] Appendix Mode Rules: For interpreters that return set-type results, such as "<List of Key Inspection Equipment>", an "Add Entries" edit box is provided in the differential semantic set. After the administrator enters the new entries, the platform merges the set returned by the parent node interpreter with the differentially added entries into a new set. For example, the Equipment Management Department's "<List of Key Inspection Equipment>" interpreter returns "Boiler, Steam Turbine, Generator". The Equipment Maintenance Team adds "Desulfurization and Denitrification Device" to the differential set. Because the team has added desulfurization and denitrification equipment maintenance responsibilities, the platform returns "Boiler, Steam Turbine, Generator, Desulfurization and Denitrification Device" at runtime. This mode expands the content based on the parent node's set data, avoiding lower-level organizations from repeatedly entering existing set data from the parent node, while meeting the expansion needs of local business and improving data maintenance efficiency.

[0090] Step S21 addresses the shortcomings of traditional multi-level semantic management platforms, which either rely entirely on inheritance without flexibility or on full customization without uniformity: the inherited semantic set ensures the consistency of top-level strategy transmission, while the differential semantic set provides flexibility for lower-level business adjustments. The three differential modes cover different adjustment scenarios, enabling semantic rules to be transmitted in an orderly manner within a multi-level organization while also adapting to local needs.

[0091] Step S22: Add a semantic convergence controller to the runtime delayed binding execution engine. The semantic convergence controller is used to identify the four stages of the closed-loop management process and apply different semantic convergence strategies to each stage. The four stages include the goal setting stage, task allocation stage, execution tracking stage, and result evaluation stage.

[0092] The semantic convergence controller is integrated into the runtime lazy binding execution engine and achieves semantic convergence through a process of "stage identification - policy matching - rule execution". First, each node of the closed-loop management process is labeled with stage attributes in the BPMN process engine, such as "goal setting stage" and "task allocation stage". The semantic convergence controller identifies the current stage of the process by reading the stage attributes of the nodes and then matches the corresponding convergence strategy, as shown in Table 1.

[0093] Table 1. Semantic Convergence Strategies for Each Stage of the Closed-Loop Management Process

[0094] Process Phase Convergence strategy types Core objective Goal setting phase Allow semantic divergence Preserve departmental business differences and adapt to core needs. Task allocation phase Structural convergence Eliminate format differences and support cross-departmental monitoring Execution tracking phase Allow semantic divergence Adapt to the personalized tracking needs of different departments Results evaluation phase Forced complete convergence Normalize evaluation results to enable cross-departmental comparisons

[0095] Target Setting Phase: This phase allows for semantic divergence. The semantic convergence controller does not interfere with the semantic parsing process, allowing each department to use any semantic placeholder defined in step S11 to express its target. For example, the Power Generation Department uses "<Monthly Unit Power Generation Efficiency>" as the target placeholder, the Fuel Management Department uses "<Monthly Power Generation per Ton of Coal>" as the target placeholder, and the Equipment Management Department uses "<Monthly Equipment Failure Rate>" as the target placeholder. The controller only records the placeholders used by each department without imposing any format or logical restrictions. The convergence strategy in the target setting phase preserves the differences in departmental business operations and avoids a decrease in business adaptability due to a unified target placeholder. For example, it is not necessary to force the Fuel Management Department to use "Power Generation Efficiency" as the target indicator, as this aligns with the core business needs of each department. For instance, the Power Generation Department focuses on power generation, the Fuel Department focuses on fuel utilization, and the Equipment Department focuses on equipment stability.

[0096] Task Allocation Phase: This phase involves structural convergence. The semantic convergence controller invokes a pre-defined standardized task model. This model contains fixed fields, including task identifier, responsible person, target value, target unit, deadline, associated process instance ID, and corresponding unit number. The semantic convergence controller extracts or calculates the necessary data for each department's target semantic placeholders and populates the model. The convergence strategy in the task allocation phase transforms the targets of different departments into structured task data, eliminating management confusion caused by differences in task formats. For example, there's no need to design separate task management interfaces for the power generation and fuel departments; a unified task model supports cross-departmental task monitoring, allowing management to view the task progress of each department through a single interface.

[0097] Execution Tracking Phase: This phase again allows semantic divergence. The semantic convergence controller allows each department to configure interpreters for personalized tracking indicators in the ternary logic mapping table. The runtime execution engine parses the tracking data through these interpreters. For example, the power generation department configures an interpreter for "<Real-time Unit Load Rate>", the fuel management department configures an interpreter for "<Fuel Incoming Quality Inspection Pass Rate>", and the equipment management department configures an interpreter for "<Equipment Inspection Completion Rate>". The semantic convergence controller does not interfere with the parsing of these indicators, but only records the tracking data. The convergence strategy in the execution tracking phase meets the personalized needs of departmental execution tracking, making the tracking indicators more aligned with the actual business operations of the departments. For example, the power generation department needs to monitor the load in real time, and the fuel department needs to monitor coal quality, improving the accuracy and reference value of the tracking data and helping departments to promptly identify execution deviations, such as a load rate that is consistently lower than the target, resulting in substandard power generation efficiency.

[0098] Results Evaluation Phase: This phase forces full convergence. The semantic convergence controller invokes a pre-defined special semantic placeholder, "<Standardized Completion Rate>", whose interpreter formula is "Actual Value / Target Value × 100". During runtime, the semantic convergence controller first obtains the "Target Value" through the interpreter of each department's target placeholder, and then obtains the "Actual Value" through the interpreter of the execution tracking indicators, substituting them into the formula to calculate the completion rate. The convergence strategy in the results evaluation phase normalizes evaluation results of different dimensions, enabling cross-departmental performance comparisons and solving the problem of traditional platforms being unable to make horizontal comparisons due to different evaluation indicator dimensions. It also provides management with a unified data basis for making overall decisions.

[0099] The semantic convergence controller in step S22 balances business flexibility and management uniformity through a "phased differentiation strategy": the divergent strategy in the goal setting and execution tracking phase retains departmental characteristics, while the convergence strategy in the task allocation and result evaluation phase ensures management efficiency, solving the shortcomings of traditional platforms that are either "unmanageable with full divergence or inflexible with full convergence." Simultaneously, semantic convergence and the runtime latency binding execution engine in step S13 work in deep collaboration. The execution engine provides semantic parsing capabilities, and the controller provides convergence strategy guidance. The combination of these two ensures that the semantic parsing results are adapted to departmental business needs while also meeting the management requirements of each stage of the process.

[0100] Step S23: Develop a semantic distance calculation engine based on hierarchical semantic inheritance and differential mechanism and semantic convergence controller. The semantic distance calculation engine is used to quantify the semantic distance between interpreters defined by different organizational nodes for the same semantic placeholder from three dimensions: data structure, behavioral logic and business concept, and generate semantic distance measurement results and semantic distance warning.

[0101] Step S22 employs a phased semantic convergence strategy, allowing semantic divergence during the goal setting and execution tracking phases. It records the differentiated semantic placeholders and their interpreter definitions used by various departments, such as "<Monthly Unit Power Generation Efficiency>" for the Power Generation Department, "<Monthly Power Generation per Ton of Coal>" for the Fuel Management Department, and "<Monthly Equipment Failure Rate>" for the Equipment Management Department. While these divergent semantic definitions have clear business meanings within their respective departments, misunderstandings may arise during cross-departmental collaboration, affecting the final unified convergence of the "<Standardized Completion Rate>" in the result evaluation phase. Step S23's semantic distance calculation engine targets these divergent semantic placeholders identified in Step S22, particularly those with the same name but potentially different semantics used by different departments during the goal setting and execution tracking phases. By quantitatively analyzing the differences in their interpreters across three dimensions—data structure, behavioral logic, and business concepts—it assesses whether these semantic definitions can be effectively converged into a unified "<Standardized Completion Rate>" during the result evaluation phase. When the semantic distance calculation results show that the differences of certain key semantic placeholders are too large between different departments, it means that the semantic convergence controller in step S22 may face difficulties in converting these divergent business indicators into a unified completion rate. For example, the "monthly power generation efficiency of the generating unit" in the power generation department and the "monthly equipment failure rate" in the equipment management department are too different in terms of dimensions and calculation logic, making it difficult to achieve meaningful normalization comparison through a simple "actual value / target value" formula. At this time, the generated semantic distance warning will trigger subsequent cross-departmental consultation to ensure that the semantic convergence strategy can truly achieve semantic unification of the closed-loop process.

[0102] In the data structure dimension, the data structure distance value is calculated by analyzing the data type, measurement unit, and scale of the results returned by the interpreter. In the behavioral logic dimension, the behavioral logic distance value is calculated by analyzing the number of data sources referenced by the interpreter, the complexity of the calculation formula, and the data update frequency. In the business concept dimension, the business concept distance value is calculated. The data structure distance value, behavioral logic distance value, and business concept distance value are weighted and averaged to obtain the global average semantic distance. When the global average semantic distance is greater than the preset semantic distance threshold, a semantic distance warning is generated.

[0103] The semantic distance calculation engine is developed based on hierarchical semantic inheritance and differential mechanisms. Its core function is to transform the differences in interpreters for the same semantic placeholder across different organizational nodes into measurable indicators through multi-dimensional quantitative analysis, thereby enabling early identification of semantic divergence risks. The data structure dimension corresponds to the external manifestation of the interpreter's output, the behavioral logic dimension corresponds to the execution process characteristics of the interpreter, and the business concept dimension corresponds to the core business meaning of the interpreter. These three dimensions together cover all aspects of semantic differences, avoiding the omission of differences caused by traditional single-dimensional analysis. The analysis objects in the data structure dimension include the data type, unit of measurement, and dimension of the results returned by the interpreter. Data types include numeric, character, and set types; units of measurement include kilowatt-hours, times, and tons; and dimensions include energy, count, and efficiency. These parameters directly determine whether the output results of different interpreters can be directly compared. The analysis objects in the behavioral logic dimension include the number of data sources referenced by the interpreter, the complexity of the calculation formula, and the data update frequency. The number of data sources includes single data source and multiple data source associations; the complexity of the calculation formula includes simple arithmetic operations and multi-variable logical judgments; and the data update frequency includes real-time updates, daily updates, and monthly updates. These parameters reflect the consistency of the interpreter's execution process. The analysis objects in the business concept dimension include the business meaning description text of semantic placeholders, associated business process nodes, and applicable business scenarios. Associated business process nodes include target setting nodes and result evaluation nodes; and applicable business scenarios include power generation efficiency statistics and equipment failure statistics. These parameters reflect the consistency of the business intent behind the interpreter.

[0104] The data structure distance value is calculated by constructing a "feature matching degree matrix". For each analysis parameter—data type, unit, and dimension—a matching weight is assigned. For example, data types that are completely identical are assigned a weight of 1; partially identical data types (e.g., integers and floating-point numbers in numeric types) are assigned a weight of 0.5; completely different data types are assigned a weight of 0. Units that are completely identical are assigned a weight of 1, units that can be converted using a fixed coefficient are assigned a weight of 0.3, and units that cannot be converted are assigned a weight of 0. Dimensions that belong to the same category (e.g., electrical energy and thermal energy in energy dimensions) are assigned a weight of 0.4, and units that are cross-category are assigned a weight of 0. The matching weights of each parameter are weighted and summed according to a preset ratio (e.g., 0.4 for data type, 0.3 for unit, and 0.3 for dimension). This summation is then subtracted from 1 to obtain the data structure distance value. The distance value ranges from 0 to 1; a larger value indicates a greater difference in data structures.

[0105] The calculation method for behavioral logic distance values ​​includes: for the number of data sources, a value of 1 is assigned to the same number, 0.6 is assigned to a difference of 1, and 0 is assigned to a difference of ≥2; for the complexity of the calculation formula, an Abstract Syntax Tree (AST) comparison is used, with a value of 1 for completely identical AST structures, 0.5 for identical core operation logic but different number of parameters, and 0 for different core operation logic; for the data update frequency, a value of 1 is assigned to the same frequency, 0.4 is assigned to adjacent frequencies (e.g., daily and weekly), and 0 is assigned to non-adjacent frequencies. The matching results of the above parameters are weighted and summed according to a preset ratio, such as 0.3 for the number of data sources, 0.4 for the calculation formula complexity, and 0.3 for the update frequency. The summation result is then subtracted from 1 to obtain the behavioral logic distance value, which also ranges from 0 to 1. A larger value indicates a greater difference in behavioral logic.

[0106] The method for calculating the business concept distance value includes: First, building a dedicated corpus based on internal business documents such as business manuals, process specifications, and historical negotiation records, and training it using Word2Vec or BERT models to ensure that the model can accurately identify the semantic associations of specific business terms; then, converting the business meaning description texts of the two semantic placeholders to be compared into word vectors, and calculating the semantic similarity between the two using a cosine similarity algorithm; finally, subtracting the cosine similarity value from 1 to obtain the business concept distance value, which ranges from 0 to 1, with a larger value indicating a greater difference in business concepts.

[0107] The global average semantic distance is synthesized using a weighted average algorithm, with the formula: D = W1 × D1 + W2 × D2 + W3 × D3, where D is the global average semantic distance, D1 is the data structure distance value, D2 is the behavioral logic distance value, and D3 is the business concept distance value; W1, W2, and W3 are the weight coefficients of the three dimensions, and the weights are determined based on the degree of influence of each dimension on the semantic convergence of the closed-loop process: the business concept dimension directly determines whether the semantic essence is consistent, and has the greatest impact on the normalization comparison in the result evaluation stage, so W3 takes the highest value, for example, W3 = 0.4; the data structure dimension determines whether the result can be directly calculated, and has the second greatest impact, so W1 = 0.3; the behavioral logic dimension affects the consistency of the execution process, and has the lowest weight, so W2 = 0.3. The logical design of this formula is to enable the global average semantic distance to reflect the essential differences in business while taking into account the differences in data form and execution process, ensuring that the quantitative results match the actual semantic convergence requirements. When the differences in business concepts are large, even if the data structure and behavioral logic are consistent, the global distance will still be at a high level, accurately identifying semantic conflicts of "homographs".

[0108] The method for setting the semantic distance threshold includes: first, extracting cases from the platform's historical operations where semantic differences caused abnormal execution of closed-loop processes, and calculating the global average semantic distance of the corresponding semantic placeholder in each case; then, taking the minimum of these distance values ​​and lowering it by 10%-20% as the semantic distance threshold T. The purpose of this downward adjustment is to reserve a risk buffer space and avoid missed warnings due to data fluctuations. When the global average semantic distance D between two organizational nodes for a certain semantic placeholder is greater than T, the platform automatically generates a semantic distance warning. The warning information includes a difference analysis report and recommended measures: the difference analysis report must list the distance values ​​of the three dimensions, the difference points of each dimension, and the organizational node level corresponding to the difference; the recommended measures recommend subsequent operations based on the degree of difference.

[0109] Step S23 transforms the abstract phenomenon of "semantic divergence" into quantifiable and monitorable indicators, making previously implicit cross-departmental semantic differences explicit. Managers can promptly identify semantic inconsistencies before or during the execution of closed-loop processes, avoiding the exposure of contradictions only at critical stages such as result evaluation, thereby reducing increased management costs and decision-making delays caused by semantic disagreements. Simultaneously, multi-dimensional distance calculation ensures a comprehensive assessment of semantic differences, considering both formal consistency at the data level and substantive consistency in business logic and conceptual understanding, avoiding biases caused by single-dimensional judgments. Step S23 provides the triggering basis and specific targets for the cross-departmental negotiation process in Step S24, ensuring that negotiation is not initiated blindly but is conducted on semantic definitions with significant differences, improving the accuracy and efficiency of semantic governance. Without step S23, cross-departmental semantic differences would be difficult for the platform to automatically identify, potentially leading to the neglect of obvious semantic conflicts until abnormalities occur during process execution, passively addressing the issue and significantly increasing the difficulty and cost of problem-solving.

[0110] Step S24: Trigger cross-departmental consultation process based on semantic distance warning and update ternary logic mapping table.

[0111] Step S24 triggers a cross-departmental negotiation process based on semantic distance warnings, updating the ternary logical mapping table through conflict visualization, solution solicitation, impact assessment, and decision execution. Specifically, when a semantic distance warning is generated, the platform automatically creates a negotiation process instance in the BPMN process engine and determines the participating departments and responsible persons based on the applicable scope of the semantic placeholders. The conflict visualization stage utilizes the version management mechanism of semantic definition and mapping from step S14 to extract the definitions, interpreter implementations, historical change records, and sample execution results of the semantic placeholders from both sides of the conflict, displaying them in a side-by-side comparison view to make differences readily apparent. For example, the differences in data source and calculation formula between the interpreters for "<Monthly Energy Consumption Index>" from the Power Generation Department and the Fuel Management Department can be clearly presented. The solution solicitation stage uses the platform's task allocation function to push solution submission notifications to the responsible persons of participating departments, allowing for the submission of multiple solutions, such as unifying interpreter definitions, adding exclusive semantic placeholders, and adjusting inheritance relationships, ensuring that the business needs of each department are fully considered. The impact assessment phase automatically identifies the process templates, historical data volume, and cross-departmental collaboration nodes affected by the proposed solution. It quantifies the adjustment workload required for implementing the solution, such as the number of template modifications and data re-parsing entries, and analyzes the potential impact on existing business, such as whether it leads to a decrease in the comparability of historical assessment results. The decision-making and execution phase, based on pre-set governance rules such as hierarchical arbitration and voting, forms the final solution. The ternary logical mapping table update function adjusts the interpreter or semantic placeholders and synchronizes them to the version management mechanism for semantic definitions and mappings to generate a new version, ensuring that changes are traceable.

[0112] Step S24 standardizes and streamlines the resolution process of cross-departmental semantic conflicts, replacing the traditional offline communication model. The transparency and data-driven nature of the negotiation process reduces communication barriers and subjective disagreements between departments, making the consensus reached more aligned with actual business needs. Simultaneously, the impact assessment proactively predicts the costs and risks of implementing the solution, avoiding resource waste caused by decision-making errors. Step S24 proactively adjusts interpreters with excessive semantic distance to an acceptable range through negotiation, ensuring complete convergence of the semantic convergence controller in step S22 during the result evaluation phase. Furthermore, the updated ternary logic mapping table provides accurate semantic parsing basis for the runtime delayed binding execution engine in step S13. Without step S24, semantic distance warnings cannot be translated into actual semantic adjustment actions, leading to persistent semantic disagreements. The difficulty of semantic convergence in the closed-loop process cannot be fundamentally resolved, and the efficiency and accuracy of cross-departmental business collaboration will continue to be affected.

[0113] Step S20 addresses the semantic variation issues in goal decomposition and the varying semantic convergence requirements at different stages of the closed-loop process in multi-level organizations through hierarchical semantic inheritance and differential mechanisms, phased semantic convergence strategies, semantic distance quantification, and cross-departmental negotiation processes. The hierarchical semantic inheritance and differential mechanism ensures the orderly transmission of top-level strategies through inheritance relationships while allowing lower-level organizations to flexibly adjust through overriding, transformation, and addition modes, balancing semantic consistency with business flexibility. The semantic convergence controller dynamically adjusts the convergence strategy based on the characteristics of each stage of the closed-loop process—namely, the divergent requirements of goal setting and the unified requirements of result evaluation—ensuring that semantic parsing adapts to departmental business differences while meeting management uniformity requirements. The semantic distance calculation engine quantifies semantic differences, providing an objective standard for conflict identification. The cross-departmental negotiation process provides a standardized path for resolving conflicts. These technologies work together to enable the orderly transmission, dynamic adjustment, and strategic convergence of semantic definitions that were previously difficult to unify in a multi-departmental and isolated environment. This not only solves the semantic divergence problem caused by template parameter polymorphism but also promotes the standardization of business language within enterprises, reduces communication costs for cross-departmental collaboration, and provides quantifiable evaluation criteria and traceable change records for the subsequent semantic governance step S30. This drives semantic management from a passive response to proactive governance and lays a solid foundation for the large-scale application of no-code platforms in multi-departmental enterprises.

[0114] Step S30: Integrate the version management mechanism of semantic definition and mapping with the semantic distance measurement results, build a semantic governance dashboard, calculate the semantic health score, and establish a semantic optimization closed-loop mechanism.

[0115] Semantic definitions evolve with business development, and a lack of effective governance can lead to fragmentation and uncontrollability. At the same time, successful local practices are difficult to distill into organizational-level standard capabilities. Step S30 achieves the healthy evolution, knowledge accumulation, and continuous optimization of the semantic system by building a complete governance system.

[0116] Further, step S30 includes:

[0117] Step S31: Extend the version management mechanism of semantic definition and mapping into a semantic version evolution chain;

[0118] Further, step S31 includes:

[0119] Step S311: Extract each change record of the semantic placeholder or interpreter from the version management mechanism of semantic definition and mapping;

[0120] Step S312: Encapsulate each change record into a semantic change block, and link the semantic change blocks in chronological order using hash pointers to construct an immutable semantic version evolution chain.

[0121] The semantic placeholder or interpreter change record includes information such as operation type, operation object, content before and after the change, operator, and reason for change, all derived from the change record table of the version management database in step S14. In the semantic change block encapsulation stage, each extracted change record is encapsulated into an independent semantic change block. The block structure design must include the following fields: block header and block body. The block header ensures the immutability of the chain structure and includes the hash value of the previous block, the timestamp of the current block, and the block hash value. The hash value of the previous block is obtained by calculating the complete data of the previous block using the SHA-256 algorithm. The block hash value is calculated by calculating the SHA-256 value of all data in the current block body. The block body stores complete change record information, i.e., the extracted operation type, operation object, content before and after the change, and other full-dimensional data. SHA-256 is used because this algorithm has an "avalanche effect," meaning that any small data change within the block will cause a significant change in the block hash value, thus allowing tampering to be identified immediately. This step transforms discrete change records into structured block units, while ensuring the immutability of blocks through hash value association, providing a "trusted data foundation" for subsequent traceability. In the hash pointer linking step, semantically changed blocks are linked sequentially using hash pointers according to the timestamp order of the change records, forming a semantic version evolution chain. Specifically, the hash value of the block preceding the first block, i.e., the genesis block, is set to a preset fixed value, such as "00000000". The hash value of the preceding block is filled with the hash value of the previous block for each subsequent block, creating a "chain dependency" among all blocks. Modifying the content of any intermediate block will invalidate the hash values ​​of that block and all subsequent blocks. The platform can quickly detect tampering by periodically verifying the block hash value sequence. Step S31 uses hash pointers to link semantically changed blocks, making the integrity of each block dependent on the previous block, forming a chain structure. Any modification to the historical record will disrupt the consistency of the chain, ensuring that the semantic evolution process can be completely and truthfully traced, solving the problem of easily tampered records leading to traceability failure in traditional version management.

[0122] Step S32: Based on the semantic version evolution chain, generate a standardized template and release the standardized template through a cross-departmental consultation process;

[0123] See Figure 4 Furthermore, step S32 includes:

[0124] Step S321: Identify high-performing departments and extract semantic placeholders and interpreters for high-performing departments as best practice candidates;

[0125] Step S322: Decompose the best practice candidates into common and unique components;

[0126] Step S323: Solidify the common parts into the core logic of the template, convert the individual parts into new semantic placeholders, and generate a standardized template.

[0127] By quantifying multi-dimensional indicators, the semantic management and process execution levels of each organizational node are comprehensively evaluated to identify high-performing departments. These multi-dimensional indicators include process execution efficiency, target achievement rate, semantic stability, and reusability. Process execution efficiency is extracted from the runtime delay binding execution engine parsing logs in step S13, calculating the average execution time and parsing success rate of all processes within the department; shorter average time and higher success rate result in a higher score. Target achievement rate is extracted from the semantic convergence controller in step S22, calculating the department's average <standardized completion rate> over the past 3-6 months; a higher average value results in a higher score. Semantic stability is calculated from the semantic version evolution chain in step S31, calculating the frequency of changes to relevant semantic placeholders within the department; fewer changes and longer intervals between changes result in a higher score. Reusability is calculated from the inheritance records of the ternary logic mapping table in step S12, calculating the number of times the department's configured interpreter is referenced by subordinate or peer departments; more references result in a higher score. Weights are assigned to each indicator. For example, process execution efficiency is 0.3, target achievement rate is 0.3, semantic stability is 0.2, and reusability is 0.2. The comprehensive score of each department is calculated by weighted summation. Departments with a comprehensive score higher than the preset score threshold are high-performing departments. The score threshold is obtained by averaging the scores of all departments and adding 10%-15% above it, which serves as the high-performance threshold.

[0128] The common parts are reusable general logics across multiple departments. Methods for obtaining these common parts include collecting similar process templates from high-performing departments, extracting process node sequences, and using the Longest Common Subsequence (LCS) algorithm to identify common process steps. Syntax tree parsing is performed on the business rule expressions of each node to extract common operational structures. These common process steps and common operational structures are used as the common parts, such as the process steps in the "Unit Start-up and Shutdown Optimization Process" ("Equipment Inspection Before Start-up and Shutdown → Parameter Setting → Start-up Execution → Shutdown Review") and the core calculation formula for "<Unit Start-up and Shutdown Energy Saving Indicators>" (Energy Saving Rate = (Standard Start-up and Shutdown Energy Consumption - Actual Start-up and Shutdown Energy Consumption) / Standard Start-up and Shutdown Energy Consumption × 100). The individual parts are specific rules for particular departments, such as the specific values ​​in the "Parameter Setting" step and the specific values ​​for "Standard Start-up and Shutdown Energy Consumption." Step S323 solidifies the common parts into the core logic of the template, converts the individual parts into new semantic placeholders, generates a standardized template proposal, and submits it to the cross-departmental consultation process review in step S24. Once approved, it is released to the enterprise template marketplace. High-performing departments are identified through comprehensive indicators, ensuring that extracted best practices have practical business value and reusability, avoiding insufficient practice quality due to subjective selection. By breaking down common and unique aspects, the template retains general logic to guarantee reusability while adapting to the specific needs of different departments through new semantic placeholders. This solves the problem that traditional templates are either too rigid to adapt to differences or too flexible to maintain standardization. Standardized templates are released through a consultation process, ensuring their acceptance and applicability within the organization and promoting cross-departmental knowledge sharing. Without step S32, successful local practices are difficult to refine and promote, requiring departments to repeatedly design similar processes, leading to resource waste and inconsistent process quality. Simultaneously, organizational-level knowledge assets cannot be accumulated, impacting the overall efficiency of the enterprise's digital transformation.

[0129] Step S33: Construct a semantic governance dashboard by integrating semantic distance measurement results, semantic version evolution chain, and standardized template data; calculate semantic health score; and establish a semantic optimization closed-loop mechanism.

[0130] Further, step S33 includes:

[0131] Step S331: Calculate the semantic consistency score based on the global average semantic distance;

[0132] Step S332: Statistically analyze the change frequency in the semantic version evolution chain and calculate the semantic stability score;

[0133] Step S333: Analyze the number of times the standardized template is reused and calculate the semantic reuse rate score;

[0134] Step S334: Calculate the ratio of the number of business processes with defined semantic placeholders to the total number of processes, and then calculate the semantic coverage score.

[0135] Step S335: Combine the semantic consistency score, semantic stability score, semantic reuse rate score, and semantic coverage rate score into a semantic health score, which is then visualized on the semantic governance dashboard.

[0136] Step S331 calculates the semantic consistency score based on the global average semantic distance. The smaller the global average semantic distance, the higher the consistency score. The calculation formula is: Consistency Score = 1 - Global Average Semantic Distance, reflecting the degree of uniformity in semantic definitions across departments. Step S332 calculates the semantic stability score by statistically analyzing the change frequency in the semantic version evolution chain. The lower the change frequency, the higher the stability score. This is calculated by the ratio of the number of changes per unit time to the preset baseline change frequency. For example, Stability Score = 1 - (Actual Change Frequency / Baseline Change Frequency). The baseline change frequency is set based on the rate of change in the enterprise's business and reflects the stability of the semantic definition. For example, an energy company sets a baseline change frequency based on business iteration data from the past 3 years: Since the core business rule adjustments of the energy company are mostly concentrated in the annual budget preparation or equipment upgrade cycle, the rate of business change is relatively stable. Therefore, the baseline change frequency is set to once per quarter, that is, the baseline change frequency per unit time = 1 time / quarter. The setting is based on the fact that the semantic definition has been adjusted on average once every 3 months in the past 36 months of the company's history, taking into account both business flexibility and data stability requirements. Step S333 analyzes the number of times the standardized template is reused to calculate the semantic reuse rate score. The more times it is reused, the higher the reuse rate score. It is calculated by the ratio of the actual number of reuses to the potential number of reuses. For example, the reuse rate score = actual number of reuses / potential number of reuses. The potential number of reuses is the total number of departments that are applicable to the template, reflecting the promotion effect of the standardized template. For example, the standardized template is "<Monthly Core Indicator Evaluation of Units>". This template focuses on the evaluation logic of the core indicators of unit operation. The applicable scenarios cover all organizational units in the enterprise involved in unit operation management. After sorting, the departments that are applicable to this template include the power generation department, fuel management department, equipment management department, and desulfurization and denitrification department, a total of 4 departments. Therefore, the potential number of reuses = 4, which is the total number of departments that are applicable to this template.

[0137] Step S334 calculates the semantic coverage score by comparing the ratio of the number of business processes with defined semantic placeholders to the total number of processes. A higher ratio results in a higher coverage score, reflecting the scope of the semantic system's support for business processes. Step S335 combines the semantic consistency score, semantic stability score, semantic reuse rate score, and semantic coverage score into a semantic health score. The weights for each dimension are set according to the enterprise's business priorities. For example, semantic consistency has a significant impact on cross-departmental collaboration, so its weight can be set to 0.3; stability has a significant impact on data consistency, so its weight can be set to 0.2; reuse rate and coverage rate are each set to 0.25. The health score = consistency score × 0.3 + stability score × 0.2 + reuse rate score × 0.25 + coverage rate score × 0.25. This score is displayed on the semantic governance dashboard using visualization methods such as radar charts. The dashboard also provides trend analysis, such as changes in health over time and risk warnings, and establishes an optimization closed-loop mechanism: analyzing the correlation between business results and semantic definitions, evaluating the effectiveness of semantic definitions, automatically triggering optimization tasks for poorly performing definitions, and guiding administrators to adjust, negotiate, or redesign. Semantic health scoring quantifies the abstract state of semantic management into intuitive indicators, enabling managers to comprehensively grasp the health status of the semantic system and solving the problem of difficulty in quantifying and assessing semantic status in traditional management. The calculation of scores for each dimension is based on objective data from previous steps, such as semantic distance and change frequency, ensuring the objectivity and accuracy of the scoring. The dashboard's visualization and risk warning functions enable timely detection and handling of problems, preventing small issues from escalating into major conflicts. The optimized closed-loop mechanism, through continuous evaluation and adjustment, drives the semantic system to continuously adapt to business development, ensuring its long-term effectiveness. Without step S33, semantic management would lack a mechanism for global evaluation and continuous optimization, making it difficult to detect hidden problems in each stage. The semantic system may gradually deviate from requirements as business develops, ultimately affecting the effectiveness of the closed-loop management process and the progress of enterprise digital transformation.

[0138] Step S30 constructs a semantic version evolution chain, making the evolution of semantic definitions traceable and tamper-proof, ensuring clear and orderly records of semantic evolution. By refining standardized templates, it transforms successful local practices into organizational-level capabilities, promoting the accumulation and efficient reuse of knowledge assets. Through a semantic governance dashboard and optimization loop, it provides a systematic evaluation basis and continuous optimization path for semantic management, ensuring the semantic system dynamically adapts to business development. These technologies work synergistically to shift semantic management from passive response to proactive governance, not only ensuring the semantic convergence effect of the closed-loop process but also promoting the standardization of internal business language, reducing cross-departmental collaboration costs, laying a solid foundation for the large-scale application of no-code platforms, and helping enterprises achieve continuous accumulation of knowledge assets and steady improvement in management efficiency during digital transformation.

[0139] Example 2

[0140] This embodiment, based on Embodiment 1, provides a no-code platform development system based on closed-loop management, such as... Figure 5 As shown, it includes:

[0141] Semantic decoupling module: used to abstract business concepts into semantic placeholders, assign a unique identifier to each semantic placeholder and establish a semantic placeholder library; construct an organizational structure tree, and based on the semantic placeholder library and organizational structure tree, construct a ternary logical mapping table containing context dimension, semantic dimension and interpreter dimension; use the ternary logical mapping table to develop a runtime lazy binding execution engine, and based on the semantic placeholder library and ternary logical mapping table, construct a version management mechanism for semantic definition and mapping;

[0142] Semantic convergence module: It is used to establish a hierarchical semantic inheritance and differential mechanism based on the ternary logical mapping table and organizational structure tree; add a semantic convergence controller to the runtime delayed binding execution engine; and develop a semantic distance calculation engine based on the hierarchical semantic inheritance and differential mechanism and the semantic convergence controller. The semantic distance calculation engine is used to quantify the semantic distance between interpreters defined by different organizational nodes for the same semantic placeholder and generate semantic distance measurement results.

[0143] Semantic governance module: integrates the version management mechanism of semantic definition and mapping with the semantic distance measurement results, calculates the semantic health score and establishes a semantic optimization closed-loop mechanism.

[0144] Furthermore, in the semantic convergence module, the method for establishing a hierarchical semantic inheritance and differential mechanism includes:

[0145] Step S211: Traverse the organizational structure tree and create a semantic definition set for each organizational node. The semantic definition set includes two subsets: an inherited semantic set and a differential semantic set.

[0146] Step S212: Automatically copy all semantic placeholders and their interpreter definitions in the ternary logic mapping table from the parent node to the inherited semantic set of the current node;

[0147] Step S213: Define overlay mode rules, transformation mode rules and append mode rules in the differential semantic set to implement the three differential operation modes of overlay, transformation and append to the interpreter inherited from the parent node.

[0148] Furthermore, in the semantic governance module, the method for calculating the semantic health score includes:

[0149] Step S331: Calculate the semantic consistency score based on the global average semantic distance;

[0150] Step S332: Statistically analyze the change frequency in the semantic version evolution chain and calculate the semantic stability score;

[0151] Step S333: Analyze the number of times the standardized template is reused and calculate the semantic reuse rate score;

[0152] Step S334: Calculate the ratio of the number of business processes with defined semantic placeholders to the total number of processes, and then calculate the semantic coverage score.

[0153] Step S335: Combine the semantic consistency score, semantic stability score, semantic reuse rate score, and semantic coverage rate score into a semantic health score, which is then visualized on the semantic governance dashboard.

[0154] The methods and systems of this application may be implemented in many ways. For example, they may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order of steps for the method is for illustrative purposes only, and the steps of the method of this application are not limited to the order specifically described above, unless otherwise specifically stated.

[0155] In addition, the parts of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of the corresponding technical solutions in the prior art have not been described in detail, so as to avoid excessive elaboration.

[0156] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A no-code platform development method based on closed-loop management, characterized in that, The method comprises: Abstracting a business concept into a semantic placeholder, assigning a unique identifier to each semantic placeholder and establishing a semantic placeholder library; constructing an organizational architecture tree, constructing a ternary logical mapping table comprising context dimensions, semantic dimensions and interpreter dimensions based on the semantic placeholder library and the organizational architecture tree; developing a runtime delay binding execution engine using the ternary logical mapping table, constructing a version management mechanism for semantic definition and mapping based on the semantic placeholder library and the ternary logical mapping table; Based on the ternary logical mapping table and the organizational architecture tree, a hierarchical semantic inheritance and difference mechanism is established; a semantic convergence controller is added to the runtime delay binding execution engine, and a semantic distance calculation engine is developed based on the hierarchical semantic inheritance and difference mechanism and the semantic convergence controller, which is used to quantify the semantic distance between the interpreters defined by different organizational nodes for the same semantic placeholder, and generate a semantic distance measurement result; Integrating the version management mechanism for semantic definition and mapping and the semantic distance measurement result, calculating a semantic health score and establishing a semantic optimization closed-loop mechanism.

2. The no-code platform development method based on closed-loop management according to claim 1, wherein, The method for constructing the organizational architecture tree comprises: obtaining organizational architecture data, and parsing the parent-child relationship between organizational nodes in the organizational architecture data to generate the organizational architecture tree; The method for constructing the ternary logical mapping table comprises: Traversing the organizational architecture tree to extract each organizational node as a context instance, and performing Cartesian product operation on the context instance and the semantic placeholders in the semantic placeholder library to form a to-be-mapped combination; Configuring an interpreter for each to-be-mapped combination, and associating the context instance, the semantic placeholder and the interpreter to construct the ternary logical mapping table.

3. The no-code platform development method based on closed-loop management according to claim 2, characterized in that, An interpreter inheritance mechanism is established in the ternary logical mapping table, which refers to searching for the interpreter of the parent node along the organizational architecture tree when a certain context instance lacks interpreter definition.

4. The no-code platform development method based on closed-loop management according to claim 3, wherein, A current execution context is obtained, and the runtime delay binding execution engine searches for and executes the interpreter in the ternary logical mapping table according to the current execution context, and performs dynamic analysis and result replacement of the semantic placeholder.

5. The no-code platform development method based on closed-loop management according to claim 4, characterized in that, The method for establishing the hierarchical semantic inheritance and difference mechanism comprises: Traversing the organizational architecture tree to create a semantic definition set for each organizational node, wherein the semantic definition set comprises two subsets, namely an inherited semantic set and a differential semantic set; All semantic placeholders and their interpreter definitions in the ternary logical mapping table are automatically copied from the parent node to the inherited semantic set of the current node; Defining an override mode rule, a transformation mode rule and an addition mode rule in the differential semantic set to realize three differential operation modes, namely, overriding, transforming and adding the interpreter inherited from the parent node.

6. The no-code platform development method based on closed-loop management according to claim 5, wherein, The semantic distance measurement result is a global average semantic distance; The method for calculating the global average semantic distance comprises: calculating a data structure distance value in the data structure dimension, calculating a behavior logic distance value in the behavior logic dimension, and calculating a business concept distance value in the business concept dimension, and then performing weighted average on the data structure distance value, the behavior logic distance value and the business concept distance value to obtain the global average semantic distance.

7. The no-code platform development method based on closed-loop management according to claim 6, wherein, The semantic convergence controller is used to identify four stages of a closed-loop management process and apply different semantic convergence strategies to each stage, the four stages including a goal setting stage, a task allocation stage, an execution tracking stage and a result evaluation stage.

8. The no-code platform development method based on closed-loop management according to claim 7, wherein, The method for calculating the semantic health score comprises: Extending the version management mechanism of semantic definition and mapping to a semantic version evolution chain, and generating a standardized template; Integrating semantic distance measurement results, the semantic version evolution chain and the standardized template to calculate the semantic health score.

9. The no-code platform development method based on closed-loop management according to claim 8, wherein, The method for generating the semantic version evolution chain comprises: Extracting each change record of a semantic placeholder or interpreter from the version management mechanism of semantic definition and mapping; Encapsulating each change record as a semantic change block, linking the semantic change blocks in chronological order through a hash pointer, and constructing an unalterable semantic version evolution chain.

10. The no-code platform development method based on closed-loop management according to claim 9, wherein, The method for generating the standardized template comprises: Identifying a high-performance department, and extracting semantic placeholders and interpreters of the high-performance department as best practice candidates; Decomposing the best practice candidates into common parts and individual parts; Solidifying the common parts as template core logic, and converting the individual parts into new semantic placeholders to generate a standardized template.

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