Wetland analysis adaptive scheduling method and system based on knowledge graph traceability

Abstract

The invention belongs to the field of computer science, and discloses a wetland analysis adaptive scheduling method and system based on knowledge graph traceability, and the method comprises the steps: firstly constructing a wetland calculation ontology fusing OGC API-Processes, a wetland ontology and traceability information, so as to describe the semantic and traceability relationship among wetland calculation services, tasks and data; constructing an executable knowledge graph of integrated service and wetland data, executing a calculation task under a knowledge graph framework, tracking and tracing, and storing execution information to form a dynamic traceability graph; when it is detected that wetland data is updated, deducing affected tasks and data by utilizing depth-first search reverse traversal based on the traceability graph, and obtaining a local sub-graph; then, a self-adaptive scheduling algorithm based on a Kahn algorithm is applied, and a task set needing to be re-executed is judged and marked according to the timeliness of the tasks and upstream data / tasks; and finally, scheduling and executing the marked tasks according to the determined adaptive sequence, updating data, and realizing dynamic adaptive scheduling of wetland analysis.

Description

Technical Field

[0001] The present invention relates to the fields of computer science, geographic information systems, and knowledge engineering, and particularly to a wetland analysis adaptive scheduling method and system based on knowledge graph traceability. Background Art

[0002] In recent years, scholars have begun to explore using graph structures to represent and manage computational entities capable of performing specific operations and the relationships between them, enabling traditional knowledge graphs to evolve from static information repositories into dynamic, executable systems. Currently, the methods for integrating computational entities into knowledge graphs mainly include two ideas: embedded coding and external services. Embedded coding directly embeds computational logic into the graph. Although automation is achieved, there are complex maintenance and security risks. External services connect independent services through semantic Web service descriptions. Although reusability is improved, traditional descriptions only support syntactic-level integration. Semantic Web services have significantly improved service interoperability by introducing semantic information such as functions, I / O parameters, and execution conditions.

[0003] Wetland computing faces the challenge that a single functional entity is difficult to handle complex problems and requires coordinating multiple computational entities through business logic for collaborative solution. Traditional methods rely on expert experience for fixed choreography (such as icon-guided modeling), while semantic technologies can achieve automated process composition. Although existing research has constructed a service integration knowledge graph for intelligent composition, applications in the field of geographic information are still limited to remote sensing image analysis with fixed processes.

[0004] The dynamic characteristics of wetland ecosystems and their sensitivity to natural and human disturbances pose significant challenges to their monitoring and management. Integrating data entities and computational entities into a knowledge graph, although supporting dynamic updates, can trigger a cascade effect of data streams. When the source data changes, key issues such as the synchronous coordination of upstream and downstream computational entities, the matching of data availability and execution timing, and context-adaptive solution must be addressed, which poses new technical requirements for ensuring the timeliness and accuracy of wetland computing. For example, the sampling period of wetland monitoring data may not be consistent with the execution time of wetland computational entities, and the data linked when executing computational entities may be unavailable or invalid. Therefore, the same wetland problem-solving process will be different in different context backgrounds, requiring the solution environment of the wetland virtual geographic environment to have the ability to adapt to dynamic context changes. Determining which affected computational entities need to be reactivated for execution to ensure the synchronous update of all derived information and dependencies, thereby maintaining the timeliness and accuracy of wetland computing, is the current challenge faced by wetland computing.

[0005] The current wetland computing services have the following problems: First, existing semantic Web service ontologies (such as OWL-S and WSMO) are difficult to adapt to the particularity of wetland space processing services and lack runtime semantic expression; Second, the traditional Web service architecture only supports simple parameter passing and cannot achieve direct interaction between computing entities and wetland monitoring data entities in the knowledge graph; Third, the current system lacks an adaptive computing mechanism based on data cascade effects, and a traceability-driven dynamic execution framework needs to be established to ensure information consistency and computing efficiency.

[0006] Therefore, there is an urgent need for a method in current wetland computing that can effectively integrate wetland monitoring data and computing entities into a unified framework, and through tracking the evolution and traceability of data and tasks, achieve dynamic adaptation and intelligent scheduling of wetland computing processes. Summary of the Invention

[0007] Based on this, the present invention proposes a wetland analysis adaptive scheduling method and system based on knowledge graph traceability, which can effectively integrate wetland monitoring data and computing entities into a unified framework, and through tracking the evolution and traceability of data and tasks, achieve dynamic adaptation and intelligent scheduling of wetland computing processes.

[0008] In the first aspect, a wetland analysis adaptive scheduling method based on knowledge graph traceability provided by the present invention, its technical solution includes the following steps: (a) Construct a wetland computing ontology, extend and integrate the wetland monitoring ontology and the W3C traceability model based on the OGC API-Processes standard, and define the semantic and traceability relationships between wetland computing services, tasks, and data entities through parameter description classes, input / output description classes, and attribute mapping classes; (b) Package the wetland analysis process as an executable wetland computing service that can be remotely called based on the OGC API-Processes; (c) Integrate wetland computing entities and wetland monitoring data to construct an executable knowledge graph, and describe entity relationships through the ontology; (d) Bind computing services and data entities in the knowledge graph to trigger tasks, and track the execution process to obtain the traceability associations of input, output, task, and service details; (e) Store the traceability information to form a directed acyclic wetland computing traceability graph, with nodes being data or task entities and edges representing entity relationships; (f) When it is detected that a data entity is updated, reverse traverse the computing traceability graph to deduce the affected entities and task entities, and generate a local traceability subgraph; (g) Topologically sort the task entities in the subgraph. If the last execution time of a task is earlier than the latest time of the upstream task entity or input data entity, mark it as needing to be re-executed; (h) Schedule and execute the task entities that need to be re-executed according to the execution order determined by the adaptive scheduling algorithm to update the affected wetland data entities.

[0009] As an alternative implementation of the first aspect of the present application, step (a) further includes: defining the wetland computing service class corresponding to the OGC API-Processes processing concept and reusing its data attributes; defining the wetland computing task class as an active entity, and associating the executable wetland computing service and the input and output entities through attributes, and the entities point to the wetland data entities in the knowledge graph.

[0010] As an alternative implementation of the first aspect of the present application, in step (c), it further includes constructing a knowledge graph framework: constructing a knowledge graph interaction mediator for OGC API-Processes, and the knowledge graph interaction mediator is responsible for data interaction between the executable wetland computing service and the knowledge graph, and the data interaction is realized through an input mediation algorithm and an output mediation algorithm; the knowledge graph framework further includes a knowledge graph job manager, which is responsible for persisting the job information of the OGC API-Processes into the knowledge graph.

[0011] As an alternative implementation of the first aspect of the present application, the input mediation algorithm receives the request parameter list of the OGC API-Processes as input, traverses the parameter list, and for the complex object parameters identified as pointing to the entities in the knowledge graph, according to the attribute mapping defined in the wetland computing ontology, retrieves the wetland data entity attribute values corresponding to the complex object parameters from the knowledge graph, and assigns the wetland data entity attribute values to the corresponding attributes of the complex object parameters to generate an input parameter list adapted to the execution of the OGC API-Processes.

[0012] As an alternative implementation of the first aspect of the present application, the output mediation algorithm receives the execution result list of the OGC API-Processes as input, traverses the output parameters in the result list, and for the output parameters identified in the form of complex objects, queries the output description and its corresponding attribute mapping in the associated wetland computing ontology, creates a new wetland data entity instance in the knowledge graph and sets its type, takes the attribute values of the complex object parameters as the attribute values of the new entity instance according to the attribute mapping, and inserts the triple of the new entity instance and the attribute values into the knowledge graph.

[0013] As an alternative implementation of the first aspect of the present application, the knowledge graph job manager inherits from the BaseManager class of the Pygeoapi framework and sends SPARQL statements to the endpoints of the knowledge graph by overriding functions, including add_job, update_job, delete_job, get_jobs, and get_job_result, to achieve persistent management of the OGC API-Processes jobs in the knowledge graph.

[0014] As an alternative implementation of the first aspect of the present application, the SPARQL statements sent by the knowledge graph job manager to the endpoints of the knowledge graph include: using the INSERT statement to create a new job instance; using the DELETE statement to delete the original job instance and using the INSERT statement to add the updated job instance; using the DELETE statement to delete the job instance; using the SELECT statement to request a list of job instances; and requesting the execution result of the job instance.

[0015] In a second aspect, an embodiment of the present application provides a wetland analysis adaptive scheduling system based on knowledge graph traceability, including: A wetland calculation semantics and service construction module, configured to: construct a wetland calculation ontology, extend and integrate the wetland monitoring ontology and the W3C traceability model based on the OGC API-Processes standard, and define the semantics and traceability relationships among wetland calculation services, tasks, and data entities through parameter description classes, input / output description classes, and attribute mapping classes; encapsulate the wetland analysis process as an executable wetland calculation service that can be remotely called based on the OGC API-Processes. An executable knowledge graph and traceability record module, connected to the wetland calculation semantics and service construction module, configured to: integrate wetland calculation entities and wetland monitoring data to construct an executable knowledge graph, and describe entity relationships through an ontology; bind calculation services and data entities in the knowledge graph to trigger tasks, and track the execution process to obtain the traceability associations of input, output, tasks, and service details; store the traceability information to form a directed acyclic wetland calculation traceability graph, with nodes being data or task entities and edges representing entity relationships. An adaptive update and scheduling execution module, connected to the executable knowledge graph and traceability record module, configured to: when detecting an update of a data entity, reverse traverse the calculation traceability graph to deduce the affected entities and task entities, and generate a local traceability subgraph; perform a topological sort on the task entities in the subgraph, and if the last execution time of a task is earlier than the latest time of the upstream task entity or input data entity, mark it as needing to be re-executed; schedule and execute the task entities that need to be re-executed according to the execution order determined by the adaptive scheduling algorithm to update the affected wetland data entities.

[0016] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0017] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0018] Compared with the prior art, the present invention has the following beneficial effects: (1) Innovatively constructed a traceability-aware wetland computing ontology. By integrating the OGC API-P standard, the PROV model, and the wetland monitoring ontology, a unified semantic description framework was established, realizing a human-comprehensible service interface description and efficient integration of computing entities.

[0019] (2) Based on the OGC API-Processes framework, innovatively realized the service encapsulation of wetland computing entities. Through the self-developed knowledge graph interaction mediator, the technical bottleneck of traditional Web services was broken through, and an executable knowledge graph architecture supporting two-way data interaction between API-P services and knowledge graphs was constructed.

[0020] (3) Proposed a traceability-driven adaptive computing method. By dynamically tracking data dependency relationships and timeliness analysis, it can intelligently identify and only execute affected computing tasks, effectively solving the data cascade problem in the wetland environment, and significantly improving the computing efficiency while ensuring the consistency of computing results. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a sequence diagram of synchronous and asynchronous execution of OGC API - Processes in an embodiment of the present invention; Figure 2 It is an executable knowledge graph framework in an embodiment of the present invention; Figure 3 It is a diagram showing entities involved in a traceability graph in an embodiment of the present invention; Figure 4 It is a schematic structural diagram of a wetland analysis adaptive scheduling system based on knowledge graph traceability provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without making creative efforts shall fall within the protection scope of the present application.

[0023] The terms "first", "second", etc. in the description and claims of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein. In addition, "and / or" in the description and claims means at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the associated objects before and after. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0024] Embodiment 1 The present invention provides a wetland analysis adaptive scheduling method based on knowledge graph traceability, including constructing a traceability-aware wetland computing ontology, constructing an executable knowledge graph framework based on API-P, and implementing a traceability-driven knowledge graph adaptive computing method.

[0025] (1) Traceability-aware wetland computing ontology Constructing a unified semantic description model for wetland computing is the primary problem for realizing an executable knowledge graph of wetlands. To optimize the semantic expression of wetland computing, so that both humans and machines can more intuitively understand the service interface functions, facilitate the integration of computing entities, and subsequent traceability-based adaptive execution, the present invention designs a traceability-aware wetland computing ontology. This ontology is based on the concepts of the OGC API-Processes (API-P) standard and is extended, integrating the wetland monitoring ontology and the W3C data traceability (PROV) model.

[0026] It should be noted that Process (processing), Job (job), and Result (result) are three important concepts in the API-P architecture. Process defines the remotely callable processing service and its metadata; Job records the status information of specific execution instances; Result stores the output result in JSON format. These three elements constitute the execution framework of the wetland computing service.

[0027] In terms of the semantic description of provenance information, the present invention reuses the PROV model introduced by the W3C. The PROV model mainly includes three core concepts: activity (prov:Activity), entity (prov:Entity), and agent (prov:Agent), as well as seven relationships of mutual dependence between the concepts, and can describe provenance information from three perspectives: data flow, processing flow, and responsibility flow. On the basis of reusing the PROV model, the present invention optimizes its concepts and relationships, reduces information redundancy while completely recording the provenance semantics, so as to meet the specific requirements of the wetland computing field.

[0028] The present invention proposes an ontology specifically for the executable knowledge graph of wetland computing. This ontology not only incorporates the OGC API-Processes standard and integrates with the wetland monitoring ontology to promote the effective association of computing entities and monitoring data. At the same time, the wetland computing ontology particularly emphasizes the importance of provenance information and ensures the transparency and traceability of data entities and computing entities during the wetland computing process by reusing the PROV ontology.

[0029] The wetland computing service (wco:Process) represents a web service that can process, analyze, and simulate wetland data and can dynamically update the knowledge graph, corresponding to the Process in API-P. The data attributes of wco:Process directly reuse the attributes of Process in API-P. The Parameter Description (wco:ParameterDescription) class has two direct subclasses: the Input Description class (wco:InputDescription) and the Output Description class (wco:OutputDescription). The Parameter Description class is associated with the classes in the wetland monitoring ontology through the attribute wco:kg_uri, and this attribute indicates the type of the wetland data entity corresponding to the parameter in the knowledge graph. The Property Map class (wco:PropertyMap) specifies the mapping rules between the input and output parameter names (wco:prop_name) of API-P and the wetland monitoring ontology property URIs (wco:prop_uri), that is, wco:prop_uri corresponds to the property of wco:kg_uri, and wco:prop_name corresponds to the parameters that the wetland computing service needs to directly use.

[0030] The wetland calculation service provides the ability to perform wetland analysis and knowledge graph update, and is an abstract computational entity in the knowledge graph. The wetland calculation task (wco:Task) is an activity (prov:Activity) that utilizes this ability and is a specific computational entity. By using (prov:used) a wco:ProcessExecute entity to bind to specific data entities in the knowledge graph, the task can be directly executed. The wco:ProcessExecute entity can define multiple specific inputs (wco:Input), each input (wco:Input) entity is described by a corresponding wco:InputDescription entity, and points to a specific wetland data entity in the knowledge graph through the wco:kg_uri attribute. The type of this entity is the wetland monitoring ontology class pointed to by the wco:kg_uri attribute of the corresponding input description entity. The wco:Job class describes the runtime semantics of the task entity, is generated by (prov:wasGeneratedBy) the task entity, corresponds to the job of API-P, and thus also reuses the data attributes of the job. At the same time, the job class can connect multiple outputs (wco:Output). Similar to the input, the output points to a specific wetland data entity in the knowledge graph through the wco:kg_uri attribute.

[0031] (2) API-P-based Executable Knowledge Graph Framework The wetland computational entity referred to in the present invention is different from the general network services in the computer field. On the one hand, the wetland computational entity is directly executed in the knowledge graph rather than a static service. On the other hand, the wetland computational entity is not only a tool for processing and executing specific data, but also has the ability to transform the knowledge graph.

[0032] As Figure 1 shown, API-P supports two execution modes: asynchronous execution and synchronous execution. The former is for simple calculation processes with short running times, and the latter is applicable to complex calculation processes that take longer and are resource-intensive. The synchronous mode establishes a persistent connection and directly returns the calculation result, which is suitable for complex calculations; the asynchronous mode immediately returns the ID of the Job and disconnects, supporting status query and result acquisition, which is suitable for lightweight calculations. Both modes implement the management of calculation tasks through the Job object.

[0033] The present invention designs a knowledge graph interaction mediator to solve the data compatibility problem between API-P (JSON) and the knowledge graph (RDF), as Figure 2As shown in the figure, its core includes: 1) A mediator built based on Pygeoapi to achieve two-way conversion of data formats; 2) The developed Knowledge Graph Job Manager (KGManager) realizes the graph persistence management of jobs by rewriting five key functions (add_job / update_job / delete_job / get_jobs / get_job_result) and using SPARQL statements. Specifically, the job creation, status update, and result query are realized through INSERT / DELETE / SELECT statements respectively, completing the full life cycle management of API-P jobs to the knowledge graph.

[0034] The wetland computing entity of the present invention needs to use the wetland data entity in the knowledge graph for calculation, and neither the inline nor the reference mode can directly process the knowledge graph entity. Therefore, when describing APIP-P, each input description (wco:InputDescription) and output description of wco:Process define the wetland data entity concept pointed to by this parameter (wco:kg_uri), as well as the mapping wco:propertyMap between the wetland data entity attributes and the input attributes of the API-P complex object. The present invention designs an input mediation algorithm and an output mediation algorithm to achieve data interaction between API-P and the knowledge graph. The input mediation algorithm receives an API-P and its associated list of request parameters as input, identifies those parameters that are complex structure objects and associated with entities in the knowledge graph, accesses the knowledge graph according to the attribute mapping in the input description, retrieves the actual values associated with the data attribute URIs, and assigns these values to the corresponding input object attributes of API-P, returning a list of input parameters adapted for API-P execution. The output mediation algorithm integrates the execution result of API-P into the knowledge graph and traverses the output parameters in the execution result.

[0035] (3)Traceability-driven Adaptive Computing Method for Knowledge Graph As Figure 3 shown, based on the wetland computing ontology and the executable graph framework constructed above, the wetland knowledge graph forms an evolving dynamic knowledge graph. Within this dynamic knowledge graph, data entities follow a specific sequence, flowing from one processing task to the next, thus constituting an ordered data stream. According to the direction of the data stream, it can be divided into upstream and downstream. When the upstream data entity is updated, the downstream entity should also be recalculated. For example, when , it indicates that the data entity d2 is outdated and the task needs to be re-executed . Correspondingly, when it is necessary to obtain the updated downstream data entity, it is necessary to trace back the evolution path graph of the data entity, and this process is the traceability calculation.

[0036] The goal of provenance calculation is to construct a provenance graph (PG). The provenance graph can be regarded as a quadruple , which records the source, evolution process, and mutual relationship of information in the knowledge graph. Among them represents the set of data entities in the knowledge graph, represents the set of task entities in the knowledge graph; is a computing unit, representing a mapping of ordered pairs of elements from the set of task entities to the set of data entities , represents the original data entity of the task , represents the derived data entity; is a provenance unit, representing a mapping of ordered pairs of elements from the set of data entities to the set of task entities , represents the upstream task of the data entity d, represents the downstream task. By tracing back the computing unit and provenance unit in the dynamic knowledge graph, the topological structure of the provenance graph can be constructed. The provenance graph is in the form of a directed acyclic graph (DAG), whose nodes represent wetland computing task entities or wetland data entities, and the edges represent the derivative relationship or dependency relationship between entities. The present invention proposes a provenance calculation method based on the depth-first search (DFS) algorithm, which performs reverse traversal on the knowledge graph, starts from a specified data entity, searches for the derivative path of the data entity, and deduces the evolution process of the data entity to construct a local provenance subgraph of the provenance graph, so as to display the topological structures of three different generalization levels of the provenance graph, including from the simplest linear chain to multi-way trees and then to graph structures.

[0037] After obtaining the provenance graph of the data entity, the task execution can be adaptively adjusted according to the timeliness of the task entity to obtain accurate derivative data. The present invention designs a method for adaptive task execution based on the Kahn algorithm. The improved Kahn algorithm first calculates the in-degree of all task entities in the provenance graph, and puts the task entities with in-degree 0 into the list S. Loop to take a task entity without an upstream task from S_ , remove it, and find its downstream tasks. If remove_ If the in-degree of adjacent downstream tasks becomes 0 after the relevant edges, then they are added to S. The key lies in comparing the last execution time of the downstream task entity and the upstream task entity directly connected to it (or the latest time of the input data). If the time of the downstream task entity is earlier than any of the directly upstream task entities or the latest time of the input data, then the downstream task entity is marked as needing to be re-executed. Repeat the above process until the list S is empty and all tasks that need to be re-executed are completed. According to the provenance graph of the data entity d7 obtained by the provenance algorithm, the numbers above the task entities represent the last execution time, and the smaller the number, the earlier. According to the adaptive execution algorithm, the topological sorting of the provenance graph can be obtained. By comparing the last execution times of upstream and downstream tasks, the algorithm can determine which tasks need to be re-executed due to outdated upstream data or tasks.

[0038] Embodiment 2 Please refer to Figure 4 FIG., which shows a schematic structural diagram of a wetland analysis adaptive scheduling system based on knowledge graph provenance proposed in the second embodiment of the present application. The system includes the following key modules: Wetland calculation semantics and service construction module 100, configured to: construct a wetland calculation ontology, extend and integrate the wetland monitoring ontology and the W3C provenance model based on the OGC API-Processes standard, and define the semantics and provenance relationships among wetland calculation services, tasks, and data entities through parameter description classes, input / output description classes, and attribute mapping classes; encapsulate the wetland analysis process as an executable wetland calculation service that can be remotely called based on the OGC API-Processes; Executable knowledge graph and provenance record module 200, connected to the wetland calculation semantics and service construction module 100, configured to: integrate wetland calculation entities and wetland monitoring data to construct an executable knowledge graph, and describe entity relationships through an ontology; bind calculation services and data entities in the knowledge graph to trigger tasks, track the execution process to obtain the provenance associations of input, output, tasks, and service details; store the provenance information to form a directed acyclic wetland calculation provenance graph, where the nodes are data or task entities, and the edges represent entity relationships; Adaptive update and scheduling execution module 300, connected to the executable knowledge graph and provenance record module 200, configured to: when detecting an update of a data entity, reverse traverse the calculation provenance graph to deduce the affected entities and task entities, and generate a local provenance subgraph; perform topological sorting on the task entities in the subgraph. If the last execution time of a task is earlier than the latest time of the upstream task entity or the input data entity, then it is marked as needing to be re-executed; schedule and execute the task entities that need to be re-executed according to the execution order determined by the adaptive scheduling algorithm to update the affected wetland data entities.

[0039] An adaptive scheduling system for wetland analysis based on knowledge graph traceability in the embodiments of the present application may be a device, or a component, an integrated circuit, or a chip in a terminal. The device may be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device may be a mobile phone, a tablet computer, a laptop computer, a palmtop computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc., and the non-mobile electronic device may be a server, a Network Attached Storage (NAS), a personal computer (PC), etc. The embodiments of the present application do not make specific limitations.

[0040] An adaptive scheduling system for wetland analysis based on knowledge graph traceability in the embodiments of the present application may be a device with an operating system. The operating system may be the Android operating system, the iOS operating system, or other possible operating systems. The embodiments of the present application do not make specific limitations.

[0041] An adaptive scheduling system for wetland analysis based on knowledge graph traceability provided in the embodiments of the present application can implement each process implemented by an adaptive scheduling method for wetland analysis based on knowledge graph traceability in the method embodiments. To avoid repetition, it will not be elaborated here.

[0042] Optionally, the embodiments of the present application further provide an electronic device, including a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, it implements each process of the above-mentioned embodiments of the adaptive scheduling method for wetland analysis based on knowledge graph traceability and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0043] The embodiments of the present application further provide a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by the processor, it implements each process of the above-mentioned embodiments of the adaptive scheduling method for wetland analysis based on knowledge graph traceability and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0044] Wherein, the processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as a computer Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disc, etc.

[0045] It should be noted that, in this document, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the process, method, article or device comprising such element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, the features described with reference to certain examples may be combined in other examples.

[0046] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present application.

[0047] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Those of ordinary skill in the art, under the inspiration of the present application and without departing from the spirit and scope protected by the claims of the present application, can also make many forms, all of which fall within the protection scope of the present application.

Claims

1. An adaptive scheduling method for wetland analysis based on knowledge graph traceability, characterized in that Including the following steps: (a) Construct a wetland calculation ontology, extend and integrate the wetland monitoring ontology and the W3C provenance model based on the OGC API-Processes standard, and define the semantic and provenance relationships among wetland calculation services, tasks, and data entities through parameter description classes, input / output description classes, and attribute mapping classes; (b) Encapsulate the wetland analysis process based on OGC API-Processes into an executable wetland calculation service that can be remotely called; (c) Integrate wetland calculation entities and wetland monitoring data to construct an executable knowledge graph, and describe entity relationships through the ontology; (d) Bind calculation services and data entities in the knowledge graph to trigger tasks, and track the execution process to obtain the provenance associations of input, output, task, and service details; (e) Store the provenance information to form a directed acyclic wetland calculation provenance graph, with nodes being data or task entities and edges representing entity relationships; (f) When a data entity update is detected, reverse traverse the calculation provenance graph to deduce the affected entities and task entities, and generate a local provenance subgraph; (g) Topologically sort the task entities in the subgraph. If the last execution time of a task is earlier than the latest time of the upstream task entity or input data entity, mark it as needing to be re-executed; (h) Schedule and execute the task entities that need to be re-executed according to the execution order determined by the adaptive scheduling algorithm to update the affected wetland data entities.

2. The method according to claim 1, characterized in that, Step (a) further includes: Define the wetland calculation service class corresponding to the OGC API-Processes processing concept and reuse its data attributes; Define the wetland calculation task class as an active entity, and associate the executable wetland calculation service and input / output entities through attributes. The entities point to the wetland data entities in the knowledge graph.

3. The adaptive scheduling method for wetland analysis based on knowledge graph traceability according to claim 1, characterized in that In step (c), it further includes constructing a knowledge graph framework: Construct a knowledge graph interaction mediator for OGC API-Processes. The knowledge graph interaction mediator is responsible for data interaction between the executable wetland calculation service and the knowledge graph, and the data interaction is implemented through an input mediation algorithm and an output mediation algorithm; The knowledge graph framework further includes a knowledge graph job manager, which is responsible for persisting the job information of OGC API-Processes into the knowledge graph.

4. The adaptive scheduling method for wetland analysis based on knowledge graph traceability according to claim 3, characterized in that The input mediation algorithm receives the request parameter list of OGC API-Processes as input, traverses the parameter list, and for complex object parameters identified as pointing to entities in the knowledge graph, retrieves the wetland data entity attribute values corresponding to the complex object parameters from the knowledge graph according to the attribute mapping defined in the wetland calculation ontology, and assigns the wetland data entity attribute values to the corresponding attributes of the complex object parameters to generate an input parameter list adapted for the execution of OGC API-Processes.

5. The adaptive scheduling method for wetland analysis based on knowledge graph traceability according to claim 3, wherein The output mediation algorithm receives a list of execution results of the OGC API-Processes as input, traverses the output parameters in the result list, and for the output parameters identified in the form of complex objects, queries the output descriptions and their corresponding property mappings in the wetland calculation ontology associated therewith, creates a new wetland data entity instance in the knowledge graph and sets its type, takes the property values of the complex object parameters as the property values of the new entity instance according to the property mapping, and inserts the triple of the new entity instance and the property values into the knowledge graph.

6. The adaptive scheduling method for wetland analysis based on knowledge graph traceability according to claim 3, wherein The knowledge graph job manager inherits from the BaseManager class of the Pygeoapi framework and sends SPARQL statements to the endpoints of the knowledge graph by overriding functions. The overriding functions include add_job, update_job, delete_job, get_jobs, and get_job_result, to achieve the persistent management of the OGC API-Processes jobs in the knowledge graph.

7. The adaptive scheduling method for wetland analysis based on knowledge graph traceability according to claim 6, wherein The SPARQL statements sent by the knowledge graph job manager to the endpoints of the knowledge graph include: Using the INSERT statement to create a new job instance; Using the DELETE statement to delete the original job instance and using the INSERT statement to add the updated job instance; Using the DELETE statement to delete the job instance; Using the SELECT statement to request a list of job instances; and Requesting the execution results of the job instance.

8. An adaptive scheduling system for wetland analysis based on knowledge graph traceability, characterized in that, Including: The wetland calculation semantics and service construction module, configured to: construct the wetland calculation ontology, extend and integrate the wetland monitoring ontology and the W3C provenance model based on the OGC API-Processes standard, and define the semantics and provenance relationships among wetland calculation services, tasks, and data entities through parameter description classes, input / output description classes, and property mapping classes; encapsulate the wetland analysis process into an executable wetland calculation service that can be remotely called based on the OGC API-Processes. The executable knowledge graph and provenance record module, connected to the wetland calculation semantics and service construction module, is configured to: integrate wetland calculation entities and wetland monitoring data to construct an executable knowledge graph, and describe entity relationships through the ontology; bind calculation services and data entities in the knowledge graph to trigger tasks, track the execution process to obtain the provenance associations of input, output, tasks, and service details; store the provenance information to form a directed acyclic wetland calculation provenance graph, with nodes being data or task entities and edges representing entity relationships. An adaptive update and scheduling execution module, connected to the executable knowledge graph and traceability record module, is configured to: when detecting data entity updates, reverse traverse the computational traceability graph to derive affected entities and task entities, and generate a local traceability subgraph; perform topological sorting on the task entities in the subgraph, and if the last execution time of a task is earlier than the latest time of the upstream task entity or input data entity, mark it as needing to be re-executed; schedule and execute the task entities that need to be re-executed according to the execution order determined by the adaptive scheduling algorithm to update the affected wetland data entities.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, it implements the steps of a wetland analysis adaptive scheduling method based on knowledge graph traceability as described in any one of claims 1-7.

10. A readable storage medium, characterized in that, A program or instruction is stored on the readable storage medium. When the program or instruction is executed by the processor, it implements the steps of a wetland analysis adaptive scheduling method based on knowledge graph traceability as described in any one of claims 1-7.

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