Project knowledge base-based work order service tracking method, platform and medium

Abstract

The application provides a project knowledge base-based work order service tracking method, platform and medium, relates to the technical field of data processing, and the method comprises the following steps: constructing a project knowledge base in a project operation process; reading work order information, performing semantic analysis processing, and mapping the analysis result to the project knowledge base to generate a knowledge mapping result; extracting a historical processing path and a standard implementation path associated with the work order and meeting a preset condition, and constructing a service processing baseline track corresponding to the work order; in the work order execution process, real-time collection of work order processing behavior information is performed, continuous mapping to the project knowledge base is performed, and a real-time knowledge evolution track is generated; and work order service tracking analysis is performed, and a service tracking result is established. The application solves the technical problem that work order management in the prior art mostly depends on manual input and processing, lacks an automatic knowledge mining and application mechanism, and work order processing efficiency is low, error rate is high, and project progress and quality are affected.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a work order service tracking method, platform, and medium based on a project knowledge base. Background Technology

[0002] In modern project management and operation, the management and tracking of work orders are key to ensuring the smooth execution of projects. Work orders typically involve multiple aspects such as problem handling, task execution, and resource allocation. How to efficiently and intelligently track and manage these work orders, ensure that they can be executed along the optimal path, and promptly identify and address potential problems has become a pressing technical challenge.

[0003] Traditional work order management systems mostly rely on manual input and processing. Although they can record work order information and perform basic task allocation, they usually lack automated knowledge mining and application mechanisms. Manual input and processing often leads to data subjectivity and inaccuracy, thus missing opportunities to optimize work order processing, resulting in longer work order processing time, increased errors, and ultimately affecting project progress and quality. Summary of the Invention

[0004] This application provides a work order service tracking method, platform, and medium based on a project knowledge base, aiming to solve the technical problem that existing work order management technologies mostly rely on manual input and processing, lacking automated knowledge mining and application mechanisms, resulting in low work order processing efficiency, high error rates, and consequently affecting project progress and quality.

[0005] The first aspect disclosed in this application provides a work order service tracking method based on a project knowledge base. The method includes: during project operation and maintenance, constructing a project knowledge base, which is organized around projects and structurally models the technical solutions, implementation steps, historical issues, processing results, and risk associations formed throughout the project's entire lifecycle, forming multiple computable project knowledge units; when a work order is received, reading the work order information, which includes work order description information, associated project identifiers, and service context information, performing semantic parsing on the work order information, and mapping the parsing results to the project knowledge base to generate a knowledge mapping result representing the association between the work order and multiple project knowledge units; using the knowledge mapping result, extracting historical processing paths and standard implementation paths from the project knowledge base that meet preset conditions for work order association, and constructing a service processing baseline trajectory corresponding to the work order; during work order execution, collecting work order processing behavior information in real time, continuously mapping the processing behavior information to the project knowledge base to generate a real-time knowledge evolution trajectory; and using the matching relationship between the service processing baseline trajectory and the real-time knowledge evolution trajectory to perform work order service tracking analysis and establish service tracking results.

[0006] The second aspect of this application discloses a work order service tracking platform based on a project knowledge base. This platform is used in the aforementioned work order service tracking method based on a project knowledge base. The platform includes: a structured modeling module, used to construct a project knowledge base during project operation and maintenance. The project knowledge base is organized around projects, and performs structured modeling of technical solutions, implementation steps, historical issues, processing results, and risk associations formed throughout the project's entire lifecycle, forming multiple computable project knowledge units; and a knowledge mapping result generation module, used to read work order information after a work order is received. The work order information includes work order description information, associated project identifiers, and service context information. The module performs semantic parsing processing on the work order information and generates the parsing result. The results are mapped to the project knowledge base to generate a knowledge mapping result representing the association between the work order and multiple project knowledge units; the service processing baseline trajectory construction module is used to extract historical processing paths and standard implementation paths that meet preset conditions of association with the work order from the project knowledge base using the knowledge mapping result, and construct the service processing baseline trajectory corresponding to the work order; the knowledge evolution trajectory generation module is used to collect the processing behavior information of the work order in real time during the execution of the work order, and continuously map the processing behavior information to the project knowledge base to generate a real-time knowledge evolution trajectory; the service tracking result generation module is used to perform work order service tracking analysis and establish service tracking results by using the matching relationship between the service processing baseline trajectory and the real-time knowledge evolution trajectory.

[0007] The third aspect disclosed in this application provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the work order service tracking method based on a project knowledge base in the first aspect.

[0008] One or more technical solutions provided in this application have at least the following beneficial effects:

[0009] By building a structured project knowledge base during project operation and maintenance, technical solutions, implementation steps, historical issues, processing results, and risk information throughout the project lifecycle are integrated and modeled. This allows for the systematic storage of various knowledge units within the project and enables retrieval and computation as needed, improving the efficiency of project management and operation and maintenance. Semantic parsing of work order information extracts key information from each work order and associates it with relevant knowledge units in the project knowledge base, thereby improving the accuracy and relevance of work order processing and reducing errors and resource waste. Furthermore, by extracting historical processing paths and standard implementation paths highly relevant to work orders from the project knowledge base, a service processing baseline trajectory is constructed for each work order. This trajectory represents the ideal processing path and serves as a standard reference path for work order execution, ensuring that work order processing follows best practices and existing successful experiences, thus improving the efficiency of work order processing. The system improves efficiency and success rate. During work order execution, it collects and records work order processing behavior information in real time and continuously maps this information to the project knowledge base to generate a real-time knowledge evolution trajectory. This process ensures dynamic monitoring and real-time updates of the work order execution process. Real-time behavior data collection and mapping enhance the transparency and controllability of the work order execution process, and can immediately reflect any changes or deviations in work order execution, providing real-time data support for subsequent analysis and adjustments. By analyzing the matching relationship between the service processing baseline trajectory and the real-time knowledge evolution trajectory, it can assess the consistency and deviations in work order execution and generate service tracking results. This process helps monitor the progress, quality, and potential risks of work order execution, providing the project team with real-time feedback on the work order execution status, enabling the team to promptly identify and respond to problems, and reducing risks caused by delays or improper handling.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0011] Figure 1 A schematic diagram of the work order service tracking method based on project knowledge base provided in this application embodiment.

[0012] Figure 2 This is a schematic diagram of the work order service tracking platform based on a project knowledge base provided in an embodiment of this application.

[0013] Figure labeling: Structured modeling module 10, knowledge mapping result generation module 20, service processing baseline trajectory construction module 30, knowledge evolution trajectory generation module 40, service tracking result generation module 50. Detailed Implementation

[0014] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0015] Example 1, as Figure 1 As shown in the embodiment of this application, a work order service tracking method based on a project knowledge base is provided, the method including:

[0016] During project operation and maintenance, a project knowledge base is constructed. The project knowledge base takes the project as the organizational object and performs structured modeling of the technical solutions, implementation steps, historical problems, handling results and risk associations formed throughout the project life cycle, forming multiple computable project knowledge units.

[0017] During the project operation and maintenance phase, a project knowledge base is built by collecting and organizing various information throughout the project's entire lifecycle. The project lifecycle refers to the entire process from project initiation to completion, including planning, design, implementation, operation, and maintenance. At each stage, different technical solutions, implementation steps, historical issues, resolutions, and risks are generated. Technical solutions refer to the various technical solutions, design schemes, or strategies proposed during project execution; implementation steps refer to the steps or task arrangements required to complete the project tasks at each stage, typically procedural and standardized; historical issues refer to problems encountered during project operation and maintenance, such as technical difficulties, operational problems, or service failures; resolutions refer to the results or feedback on solutions obtained after resolving historical issues; and risk associations refer to various risks faced during project implementation, such as technical risks and management risks, which are closely related to all components of the project.

[0018] This information is integrated into the knowledge base through structured modeling. Structured modeling refers to storing this information in a standardized and computable manner and forming multiple project knowledge units. Each knowledge unit represents a knowledge fragment with a certain degree of independence, which can be called upon for subsequent processing or analysis.

[0019] Once a work order is received, the work order information is read. The work order information includes work order description information, associated project identifier, and service context information. The work order information is semantically parsed, and the parsing results are mapped to the project knowledge base to generate a knowledge mapping result that represents the association between the work order and multiple project knowledge units.

[0020] When a new work order is received, the system first reads the specific information of the work order, including: work order description information, which is a detailed description of the work order, including the task requirements, processing objectives, and source of the work order; associated project identifier, which is the identifier of the specific project associated with the work order, which can help determine which specific project or task the work order belongs to; and service context information, which is the specific service environment information related to the work order, including the priority of the work order, business environment, and related service processes.

[0021] Semantic parsing refers to natural language processing and semantic understanding of work order information. Through semantic parsing, useful information can be extracted from the work order description and its semantics understood. For example, it can identify key technical elements of the task, involved personnel or departments, service type, and objectives. After semantic parsing, the key information of the work order is transformed into a structured format for further processing. These parsing results are mapped to the previously built project knowledge base, meaning that the information of the work order is associated with relevant knowledge units in the knowledge base. The mapping result is structured data representing the relationships between the work order and multiple project knowledge units. These relationships can help optimize solutions based on historical knowledge base and experience when processing work orders later.

[0022] Using the knowledge mapping results, historical processing paths and standard implementation paths that meet preset conditions in relation to work orders are extracted from the project knowledge base, and a service processing baseline trajectory corresponding to the work order is constructed.

[0023] Based on the knowledge mapping results, historical processing paths related to the current work order are extracted from the project knowledge base. These historical processing paths refer to the steps and methods taken when executing work orders in similar situations in the past, including successful cases and solutions to historical problems. Standard implementation paths refer to the processing steps defined according to the project's standard processes or proven best practices. These processes are developed based on project experience, industry standards, and technical solutions, and can serve as ideal operational templates. The service processing baseline trajectory is a reference path created for the current work order. It combines historical processing paths and standard implementation paths, integrating information from each path to form a standard trajectory that can guide work order processing.

[0024] During the execution of a work order, the processing behavior information of the work order is collected in real time, and the processing behavior information is continuously mapped to the project knowledge base to generate a real-time knowledge evolution trajectory.

[0025] During the execution of a work order, the processing behavior of the work order is tracked in real time, including operation steps, processing results, operators, time nodes, work order status updates, and other information. This real-time data comes from multiple sources, such as the work order system, the operation records of the executors, and log files. By collecting the behavioral information of the work order, detailed data of each stage and operation is recorded to provide a basis for subsequent analysis.

[0026] As work order processing progresses, the real-time collected behavioral information is continuously mapped into the project knowledge base. This means that relevant information in the knowledge base is updated and improved in a timely manner based on the work order's execution status and processing steps. By combining this real-time data with the updated information in the project knowledge base, a real-time knowledge evolution trajectory is generated. This trajectory demonstrates the evolution of knowledge and information during work order processing and reflects the impact of work order execution on the knowledge base.

[0027] By utilizing the matching relationship between the service processing baseline trajectory and the real-time knowledge evolution trajectory, work order service tracking analysis is performed to establish service tracking results.

[0028] Matching analysis between the service processing baseline trajectory and the real-time knowledge evolution trajectory aims to determine whether the actual processing steps conform to the predetermined baseline trajectory. This matching process is achieved by comparing the steps, order, dependencies, etc., in the trajectories, calculating the matching status of each step, and the degree of matching between them. The results of the step-by-step matching are constructed into a matching matrix, which is a two-dimensional structure. The rows of the matrix represent the processing steps in the baseline trajectory, the columns represent the processing steps in the real-time trajectory, and each element in the matrix represents the matching status and degree of matching for the corresponding step.

[0029] Based on the matching matrix, further work order service tracking analysis is conducted, including processing path consistency analysis, knowledge unit call sufficiency analysis, and risk node exposure analysis. Based on the above analysis, the final service tracking results are generated. The service tracking results include important information such as success and failure in work order execution, risk warnings, and path deviations, which are used to provide feedback to relevant personnel or decision support to help with subsequent work order management and optimization.

[0030] Furthermore, constructing a service processing baseline trajectory corresponding to the work order includes:

[0031] Based on the knowledge mapping results, a set of project knowledge units whose relevance to work orders meets a set threshold is identified. For each project knowledge unit in the set, historical processing paths and standard implementation paths are extracted, and weights are assigned according to the work order priority and service context information. Based on the weight assignment results, the historical processing paths and standard implementation paths are weighted and integrated to form an initial service processing trajectory. The initial service processing trajectory is sorted according to the order of processing steps, dependencies, and technical constraints. The initial service processing trajectory is subjected to path optimization analysis based on historical processing success rate, processing time statistics, and risk impact indicators to establish a service processing baseline trajectory.

[0032] Utilizing the previous knowledge mapping results—the relationship mapping between work order information and project knowledge units—we can identify which project knowledge units are relevant to the processing of the current work order. To extract the most relevant knowledge units from the project knowledge base, a relevance threshold is set. This threshold determines which project knowledge units are sufficiently relevant to the work order and can be used as a processing basis. Relevance can be calculated based on multiple factors, such as the work order description, technical solutions, service context, and historical issues. Only project knowledge units whose relevance to the work order reaches or exceeds the set threshold are selected as valid candidate sets. Based on the threshold setting, a set of project knowledge units with sufficiently high relevance to the work order is identified. These unit sets serve as references in subsequent processing.

[0033] For each project knowledge unit selected from the project knowledge base, the relevant historical processing path is extracted. The historical processing path contains information such as steps, methods, and solutions used in handling similar problems in the past, which can provide experience reference for the processing of the current work order. In addition to the historical path, the standard implementation path corresponding to the project knowledge unit is also extracted. The standard implementation path is established based on the project's established standards or industry best practices and can serve as the ideal path for work order processing.

[0034] Each project knowledge unit is closely related to the work order processing. Weights are assigned to these knowledge units based on the work order's priority and service context information. Priority and context information influence the importance of each knowledge unit in work order processing. Priority is set based on factors such as the urgency and scope of impact of the work order, determining which historical or standard paths should be prioritized. Service context information includes the specific environment, technical requirements, and risk factors of the service, which influence the selection of paths or the processing order.

[0035] Based on the weight allocation results, the extracted historical processing paths and standard implementation paths are weighted and integrated. This means that information from different paths is merged into a whole according to the weight of each path. Through weighted integration, an initial service processing trajectory that meets the actual needs of the work order is generated. This trajectory is based on historical experience and standardized processes, combined with the specific background and requirements of the work order. The paths are sorted according to the order of processing steps, dependencies, and technical constraints. The order of processing steps and dependencies determine the logical flow during execution, while technical constraints restrict the execution order or method of certain steps. The goal of sorting is to ensure that the generated service processing trajectory is reasonable, feasible, and can be executed smoothly.

[0036] The initial service processing trajectory is optimized through path analysis to ensure it is optimal. This optimization analysis is based on several factors: historical success rate (analyzing the success rate of similar past work orders, adjusting the order of steps or processing methods in the trajectory, and prioritizing paths with higher success rates); processing time statistics (evaluating the execution time of each processing step, optimizing time waste in the process, and ensuring greater efficiency); and risk impact indicators (analyzing the risk exposure and impact of different paths, avoiding high-risk paths, and optimizing steps with higher risks). After optimization analysis, a final service processing baseline trajectory is formed. This trajectory will serve as the benchmark for work order processing, ensuring that work orders are executed as close to the optimal path and steps as possible to achieve efficient and accurate service delivery.

[0037] Furthermore, by utilizing the matching relationship between the service processing baseline trajectory and the real-time knowledge evolution trajectory, work order service tracking analysis is performed to establish service tracking results, including:

[0038] The processing steps of the real-time knowledge evolution trajectory are progressively matched with the knowledge units in the service processing baseline trajectory to establish progressive matching results. A matching matrix is ​​constructed based on the progressive matching results, where the rows of the matching matrix represent the baseline trajectory steps, the lists represent the real-time trajectory steps, and the matrix elements represent the matching status and matching degree. The matching matrix is ​​used to analyze the work order service status, including processing path consistency analysis, knowledge unit call sufficiency analysis, and risk node exposure analysis. Service tracking results are established based on the analysis results.

[0039] During work order execution, a real-time knowledge evolution trajectory is continuously collected and updated. This trajectory records the operation and status of each step in the actual processing of the work order. Each processing step in the real-time knowledge evolution trajectory is progressively matched with the knowledge units in the service processing baseline trajectory. The purpose of this matching is to determine whether the actual processing of the work order is consistent with the predetermined baseline trajectory. Progressive matching is performed in various ways, such as comparing information such as step names, processing content, and execution order. If the two steps match, the match is considered successful. The established progressive matching results show the matching status of each real-time processing step with the corresponding step in the baseline trajectory.

[0040] Based on the step-by-step matching results, the matching status of each baseline trajectory step and its corresponding real-time trajectory step is integrated into a matching matrix. The rows of the matching matrix represent the processing steps in the baseline trajectory, listed sequentially to illustrate the ideal processing flow of the work order. The columns of the matching matrix represent the processing steps in the real-time trajectory, reflecting the operational sequence experienced during actual execution. Each element in the matrix represents the matching status and degree of matching between the baseline trajectory step and the real-time trajectory step. The matching status can be complete matching, partial matching, or no matching, and the degree of matching quantifies the consistency between the two, for example, as a percentage. The matching matrix provides a clear structure for subsequent analysis, intuitively showing the deviation from the expected path during work order processing.

[0041] Using a matching matrix, a series of analyses are performed to evaluate the status of work order services, including: path consistency analysis, which analyzes the consistency between the actual execution path and the expected path of the work order by comparing the matching degree between the real-time trajectory and the baseline trajectory. This helps to identify whether the processing was completed according to the predetermined steps or whether it deviated from the planned path; knowledge unit sufficiency analysis, which checks whether relevant knowledge units in the project knowledge base are fully utilized during the work order processing. If certain key knowledge units are not utilized, it indicates that there is information omission in the work order processing, leading to inefficiency or improper processing; and risk node exposure analysis, which identifies potential risk nodes in the processing path by analyzing the matching matrix. If certain steps are not executed as expected or deviate from the baseline trajectory, risks will be triggered. This part of the analysis can help to identify potential risk sources in a timely manner and prevent the spread of errors or failures in work order processing.

[0042] Based on the aforementioned analysis results, service tracking results are generated. These results are not only an evaluation report, but also provide optimization suggestions for subsequent work order processing. For example, if the matching degree of certain steps is low, it is recommended to re-evaluate the execution method of those steps; if risk nodes are exposed, it is suggested that risk mitigation measures need to be taken.

[0043] Furthermore, utilizing the matching relationship between the service processing baseline trajectory and the real-time knowledge evolution trajectory to perform work order service tracking and analysis also includes:

[0044] Based on the service processing baseline trajectory, a knowledge expectation evolution interval for the corresponding work order is constructed. The knowledge expectation evolution interval is used to characterize the permissible changes in the order sequence, alternative processing methods, and risk avoidance paths in the work order processing process under project knowledge constraints. The real-time knowledge evolution trajectory is projected into the knowledge expectation evolution interval to determine whether the real-time processing steps fall into the corresponding knowledge expectation evolution interval. Based on the location, duration, and associated project knowledge units of the knowledge evolution boundary violation, work order service tracking analysis is performed to identify the current service deviation stage and potential risk diffusion direction of the work order.

[0045] The knowledge expectation evolution range refers to the allowed changes in the sequence of steps, alternative processing methods, and risk avoidance paths defined during the work order processing, based on the project's knowledge base and established standards. This range provides flexibility for work order execution while ensuring that the execution process does not deviate from the project's basic framework and expectations. Specifically, the knowledge expectation evolution range includes: changes in the sequence of steps: Under certain constraints, the execution steps of a work order can have a certain degree of flexibility. For example, the order of some steps can be adjusted according to the actual situation, but such adjustments must follow certain rules or constraints; alternative processing methods: If a step cannot be executed as originally planned, alternative solutions or processing methods are allowed to complete it. For example, in the event of equipment failure, a backup solution can be selected; risk avoidance paths: If a step carries a high risk during execution, alternative paths can be defined to avoid the risk and ensure the safety and stability of work order processing.

[0046] Projection refers to comparing each processing step in the real-time knowledge evolution trajectory with a predefined expected knowledge evolution range to determine whether each step is still within the allowable range of change. If the processing step falls within the expected knowledge evolution range, it means that the execution of the work order is within an acceptable range and conforms to the project knowledge constraints; if it falls outside the range, it means that the execution of the work order has deviated and there is a problem.

[0047] Analyzing knowledge evolution out-of-bounds events, which refer to work order execution steps exceeding the predetermined expected knowledge evolution range, indicates deviations or anomalies in the work order processing. First, the location and duration of the out-of-bounds event are determined. Location refers to the specific step in which the deviation occurred, and duration refers to the length of time the deviation occurred; these two factors help assess the severity of the deviation. Furthermore, the project knowledge units related to the deviating steps are analyzed. These project knowledge units include historical processing paths, technical solutions, risk management strategies, etc. By linking these project knowledge units, it can be determined whether the deviation has a significant impact on the overall work order execution or whether it can be corrected through adjustments.

[0048] Based on the time, location, and relevant knowledge units of the boundary violation, the current service deviation stage of the work order is identified. This indicates whether the work order has entered an abnormal state or is still within the allowable adjustment range. Analyzing whether this deviation may lead to the spread of more serious problems or risks—for example, whether a deviation in one step will cause the failure of subsequent steps, or whether a risk point will amplify as execution progresses—provides early warnings and helps in taking preventative measures by analyzing these potential risk propagation directions.

[0049] Furthermore, based on the service processing baseline trajectory, the expected evolution range of knowledge for the corresponding work order is constructed, including:

[0050] Based on the processing steps in the service processing baseline trajectory, the project knowledge unit corresponding to each processing step and the predefined pre-dependent relationships, post-influence relationships, and risk constraints in the project knowledge base are read. For each project knowledge unit, alternative knowledge units within the functional equivalence or first risk cost range of the project knowledge unit are extracted based on the historical processing paths recorded in the project knowledge base, and a knowledge unit substitution set is established. The allowed execution order interval and allowed position offset range of each project knowledge unit in the service processing are determined using the pre-dependent relationships and post-influence relationships. The knowledge unit substitution set, allowed execution order interval, allowed position offset range, and risk constraints are integrated to expand the service processing baseline trajectory, generating a knowledge evolution candidate set containing multiple allowed processing paths. The knowledge evolution candidate set is integrated into the expected knowledge evolution interval of the corresponding work order.

[0051] When constructing the expected evolution range of knowledge, we first take each processing step in the service processing baseline trajectory as the benchmark for subsequent analysis. For each processing step, we find and read the corresponding project knowledge unit. The project knowledge unit provides specific execution details and background knowledge for work order processing.

[0052] Pre-dependencies refer to other steps or tasks that must be completed before each processing step can begin. Identifying which steps must be completed before the current step ensures the rationality of the processing flow. For example, an operation may depend on the completion of a data preparation step or require the provision of a certain tool. Post-impact relationships refer to the subsequent steps that will be affected or triggered after the completion of the current processing step. Analyzing the consequences and impacts of each step ensures that operations are executed in sequence, avoiding situations where errors or deviations in previous steps affect subsequent processing. For example, the successful completion of a step may provide necessary data support for subsequent quality inspection steps. Each step is accompanied by different risk constraints, which may be risk factors related to technology, personnel, resources, time, etc. Reading and analyzing these conditions ensures that potential risks are avoided during the processing.

[0053] Based on historical processing paths, analyze alternative solutions and methods encountered in similar work orders or projects. For each project knowledge unit, extract its functionally equivalent alternative knowledge units. Functional equivalence refers to different processing methods or technical means that can achieve the same or similar effects. Through functional equivalence analysis, different paths or methods can be flexibly selected when problems arise. Alternative knowledge units within the first risk cost range refer to acceptable alternative solutions within a certain risk tolerance range. This ensures that alternative solutions can meet the processing objectives without causing excessive risks. Based on the above analysis, generate a knowledge unit alternative set for each processing step. This set contains different processing solutions and paths, and each alternative solution has a clear risk assessment, ensuring that when certain steps cannot be executed as originally planned, appropriate alternative solutions can be selected according to the actual situation.

[0054] Based on pre-dependencies, the execution sequence range for each project knowledge unit is determined. This range defines the time window or position range within which each step can be executed, ensuring that the project workflow order is not violated. The position offset range defines the allowable range within which the processing steps can be rearranged to some extent. Due to different project environments or execution conditions, the order of some steps may need to be adjusted appropriately. The allowable offset range for each step is evaluated based on subsequent influence relationships.

[0055] By integrating the knowledge unit substitution set, the allowed execution sequence range, the allowed location offset range, and risk constraints, these elements collectively determine the permissible range of step variations and feasible alternative paths during work order processing. Based on the above integration results, a knowledge evolution candidate set is generated. These candidate sets represent multiple paths that can be taken for work order execution under project knowledge constraints. These paths include different step sequences, different alternatives, and paths after location offsets. Each path undergoes a risk assessment to ensure its feasibility.

[0056] The generated knowledge evolution candidate set is integrated, and the feasibility, risk, and efficiency of all candidate paths are evaluated. Finally, the expected knowledge evolution range is generated. The expected knowledge evolution range provides multiple path options for the execution of the work order, ensuring that even if unexpected challenges or deviations are encountered, the work order can still be processed smoothly according to the predetermined project goals.

[0057] Furthermore, once service tracking results are established, they include:

[0058] Based on the service offset stage and potential risk propagation direction represented in the service tracking results, the service risk level of the work order is determined; if the service risk level reaches the preset warning conditions, the work order service warning is triggered, and warning information corresponding to the risk propagation direction is output to indicate the project operation risk or service failure risk that the current work order may cause.

[0059] The service tracking results mark the service deviation stage and the potential risk propagation direction, both of which are important indicators for assessing the risk level. The service deviation stage refers to the stage of deviation that occurs during the execution of the work order, that is, the degree of deviation between the actual execution path and the expected baseline trajectory. If the work order execution deviates, it will lead to reduced efficiency, quality problems, or service failures. Therefore, the degree of deviation directly affects the risk level of the work order. The potential risk propagation direction describes how the risk spreads or propagates to other parts during the execution of the work order. For example, the failure of one step will affect subsequent operations, causing problems in subsequent steps, and even affecting the success of the entire project.

[0060] Based on the above two indicators, the risk assessment is compared with the preset risk level standards to finally determine a service risk level for the work order. The risk levels are as follows: low risk, the work order is executed relatively smoothly, with small deviations and no obvious risk spread; medium risk, the work order is executed with some deviations, and the risk may spread, but major problems can be avoided by adjusting the processing path; high risk, the work order is executed with large deviations, and the risk spread may affect the progress or quality of the entire project, requiring immediate handling and intervention.

[0061] The service risk level of a work order is compared with preset warning conditions. These conditions can be set according to the actual situation of the project; for example, a warning is triggered when the work order's risk level is medium or high, allowing relevant personnel to take appropriate measures. Work order service warnings can be system-issued notifications, warnings, or alarms, designed to alert the project team or relevant personnel to the current work order's risk issues. When a warning is triggered, warning information corresponding to the direction of risk propagation is generated, helping the team better understand the specific manifestations and scope of impact of the risk. Based on the warning information, potential project operation risks or service failure risks are identified. Project operation risks may lead to delays, increased costs, or wasted resources, while service failure risks may lead to service interruptions, decreased quality, or reduced customer satisfaction. This means that the risk of a work order can affect not only the completion of a single task but also the progress, quality, and cost of the entire project.

[0062] Furthermore, based on the frequency, duration, and key project knowledge units involved in the knowledge evolution out-of-bounds in the continuous service tracking results, a dynamic service tracking strategy is configured, and the work order service tracking management is performed using the dynamic service tracking strategy.

[0063] Knowledge evolution boundary violation refers to a situation where, during work order execution, the actual execution steps exceed the expected knowledge evolution range. Based on continuous service tracking results, the frequency and duration of knowledge evolution boundary violations are recorded. Frequency indicates the number of times a boundary violation occurs within a certain period, and duration indicates the length of time the boundary violation event lasts. Key project knowledge units related to boundary violation events are also identified. These key project knowledge units include key technical steps, key decision points, major resources, or personnel.

[0064] Based on the analysis of the frequency, duration, and key knowledge units of boundary violations, a dynamic service tracing strategy is configured. This strategy automatically adjusts service tracing rules according to the current work order execution status, aiming to optimize the work order execution path and improve the real-time performance and responsiveness of service tracing. Dynamic service tracing strategies include enhanced monitoring, priority adjustment, and path reassessment. After configuring the dynamic service tracing strategy, new service tracing management is implemented. This means that during work order processing, the tracing strategy is adjusted in real time and dynamically based on changing execution conditions to ensure the work order is completed smoothly as expected.

[0065] Furthermore, the service tracking results are processed by time-series recording to construct a time-series tracking data sequence, and the time-series tracking data sequence is then stored and managed in a distributed and encrypted manner.

[0066] Time-series logging refers to arranging all service trace data in chronological order of execution time, integrating these records into a complete time-series trace data sequence. This data sequence is the timeline of the entire work order execution process, including the status and processing status of the work order at each moment during execution. Distributed encrypted storage is used for management. Distributed storage means that data is stored across different nodes, improving data reliability and accessibility. Encrypted storage refers to encrypting the time-series trace data to ensure that the data is not accessed or tampered with during storage. Distributed encrypted storage ensures data security, scalability, and high availability, while supporting efficient data retrieval and analysis.

[0067] Example 2 is based on the same inventive concept as the project knowledge base-based work order service tracking method in the previous examples, such as... Figure 2 As shown in the embodiment of this application, a work order service tracking platform based on a project knowledge base is provided, the platform including:

[0068] The structured modeling module 10 is used to build a project knowledge base during project operation and maintenance. This project knowledge base is organized around the project, and it performs structured modeling of technical solutions, implementation steps, historical issues, processing results, and risk associations formed throughout the project's entire lifecycle, forming multiple computable project knowledge units. The knowledge mapping result generation module 20 is used to read the work order information after a work order is received. This work order information includes work order description information, associated project identifiers, and service context information. The module performs semantic parsing on the work order information and maps the parsing results to the project knowledge base, generating a representation of the association between the work order and multiple project knowledge units. The system includes: a knowledge mapping result; a service processing baseline trajectory construction module 30, which uses the knowledge mapping result to extract historical processing paths and standard implementation paths from the project knowledge base that meet preset conditions for relevance to the work order, and constructs a service processing baseline trajectory corresponding to the work order; a knowledge evolution trajectory generation module 40, which collects the processing behavior information of the work order in real time during the execution of the work order, continuously maps the processing behavior information to the project knowledge base, and generates a real-time knowledge evolution trajectory; and a service tracking result generation module 50, which uses the matching relationship between the service processing baseline trajectory and the real-time knowledge evolution trajectory to perform work order service tracking analysis and establish service tracking results.

[0069] Furthermore, the service processing baseline trajectory construction module 30 is used to perform the following operation steps:

[0070] Based on the knowledge mapping results, a set of project knowledge units whose relevance to work orders meets a set threshold is identified. For each project knowledge unit in the set, historical processing paths and standard implementation paths are extracted, and weights are assigned according to the work order priority and service context information. Based on the weight assignment results, the historical processing paths and standard implementation paths are weighted and integrated to form an initial service processing trajectory. The initial service processing trajectory is sorted according to the order of processing steps, dependencies, and technical constraints. The initial service processing trajectory is subjected to path optimization analysis based on historical processing success rate, processing time statistics, and risk impact indicators to establish a service processing baseline trajectory.

[0071] Furthermore, the service tracking result generation module 50 is used to perform the following steps:

[0072] The processing steps of the real-time knowledge evolution trajectory are progressively matched with the knowledge units in the service processing baseline trajectory to establish progressive matching results. A matching matrix is ​​constructed based on the progressive matching results, where the rows of the matching matrix represent the baseline trajectory steps, the lists represent the real-time trajectory steps, and the matrix elements represent the matching status and matching degree. The matching matrix is ​​used to analyze the work order service status, including processing path consistency analysis, knowledge unit call sufficiency analysis, and risk node exposure analysis. Service tracking results are established based on the analysis results.

[0073] Furthermore, the service tracking result generation module 50 is used to perform the following steps:

[0074] Based on the service processing baseline trajectory, a knowledge expectation evolution interval for the corresponding work order is constructed. The knowledge expectation evolution interval is used to characterize the permissible changes in the order sequence, alternative processing methods, and risk avoidance paths in the work order processing process under project knowledge constraints. The real-time knowledge evolution trajectory is projected into the knowledge expectation evolution interval to determine whether the real-time processing steps fall into the corresponding knowledge expectation evolution interval. Based on the location, duration, and associated project knowledge units of the knowledge evolution boundary violation, work order service tracking analysis is performed to identify the current service deviation stage and potential risk diffusion direction of the work order.

[0075] Furthermore, the service tracking result generation module 50 is used to perform the following steps:

[0076] Based on the processing steps in the service processing baseline trajectory, the project knowledge unit corresponding to each processing step and the predefined pre-dependent relationships, post-influence relationships, and risk constraints in the project knowledge base are read. For each project knowledge unit, alternative knowledge units within the functional equivalence or first risk cost range of the project knowledge unit are extracted based on the historical processing paths recorded in the project knowledge base, and a knowledge unit substitution set is established. The allowed execution order interval and allowed position offset range of each project knowledge unit in the service processing are determined using the pre-dependent relationships and post-influence relationships. The knowledge unit substitution set, allowed execution order interval, allowed position offset range, and risk constraints are integrated to expand the service processing baseline trajectory, generating a knowledge evolution candidate set containing multiple allowed processing paths. The knowledge evolution candidate set is integrated into the expected knowledge evolution interval of the corresponding work order.

[0077] Furthermore, the service tracking result generation module 50 is used to perform the following steps:

[0078] Based on the service offset stage and potential risk propagation direction represented in the service tracking results, the service risk level of the work order is determined; if the service risk level reaches the preset warning conditions, the work order service warning is triggered, and warning information corresponding to the risk propagation direction is output to indicate the project operation risk or service failure risk that the current work order may cause.

[0079] Furthermore, based on the frequency, duration, and key project knowledge units involved in the knowledge evolution out-of-bounds in the continuous service tracking results, a dynamic service tracking strategy is configured, and the work order service tracking management is performed using the dynamic service tracking strategy.

[0080] Furthermore, the service tracking results are processed by time-series recording to construct a time-series tracking data sequence, and the time-series tracking data sequence is then stored and managed in a distributed and encrypted manner.

[0081] Through the foregoing detailed description of the work order service tracking method based on project knowledge base, those skilled in the art can clearly understand the work order service tracking platform based on project knowledge base in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the method section.

[0082] Example 3 provides a storage medium on which a computer program is stored, which, when executed by a processor, implements any step of Example 1.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A work order service tracking method based on a project knowledge base, characterized in that, The method includes: During project operation and maintenance, a project knowledge base is constructed. The project knowledge base takes the project as the organizational object and performs structured modeling of the technical solutions, implementation steps, historical problems, handling results and risk associations formed throughout the project life cycle, forming multiple computable project knowledge units. Once a work order is received, the work order information is read. The work order information includes work order description information, associated project identifier, and service context information. The work order information is semantically parsed, and the parsing results are mapped to the project knowledge base to generate a knowledge mapping result that represents the association between the work order and multiple project knowledge units. Using the knowledge mapping results, historical processing paths and standard implementation paths that meet preset conditions in relation to work orders are extracted from the project knowledge base, and a service processing baseline trajectory corresponding to the work order is constructed. During the execution of a work order, the processing behavior information of the work order is collected in real time, and the processing behavior information is continuously mapped to the project knowledge base to generate a real-time knowledge evolution trajectory. By utilizing the matching relationship between the service processing baseline trajectory and the real-time knowledge evolution trajectory, work order service tracking analysis is performed to establish service tracking results; Utilizing the matching relationship between the service processing baseline trajectory and the real-time knowledge evolution trajectory, the work order service tracking and analysis is performed, which also includes: Based on the service processing baseline trajectory, the knowledge expectation evolution interval of the corresponding work order is constructed. The knowledge expectation evolution interval is used to characterize the possible changes in the order of steps, the substitution of processing methods, and the risk avoidance path in the work order processing process under the constraints of project knowledge. The real-time knowledge evolution trajectory is projected into the expected knowledge evolution range, and it is determined whether the real-time processing steps fall into the corresponding expected knowledge evolution range. Based on the location, duration, and associated project knowledge units of the knowledge evolution boundary violation, perform work order service tracking analysis to identify the current service deviation stage of the work order and the potential risk propagation direction. Based on the service processing baseline trajectory, the expected evolution range of knowledge for the corresponding work order is constructed, including: Based on the processing steps in the service processing baseline trajectory, read the project knowledge unit corresponding to each processing step and the predefined pre-dependent relationships, post-influence relationships and risk constraints in the project knowledge base; For each project knowledge unit, based on the historical processing paths recorded in the project knowledge base, extract alternative knowledge units that are functionally equivalent to the project knowledge unit or within the first risk cost, and establish a knowledge unit substitution set; By utilizing pre-dependencies and post-influence relationships, the allowed execution order range and allowed position offset range of each project knowledge unit during service processing can be determined. By integrating the knowledge unit substitution set, allowed execution order interval, allowed position offset range and risk constraints, the service processing baseline trajectory is extended to generate a knowledge evolution candidate set containing multiple allowed processing paths. The knowledge evolution candidate set is integrated into the knowledge expected evolution interval of the corresponding work order.

2. The work order service tracking method based on project knowledge base as described in claim 1, characterized in that, Construct a service processing baseline trajectory corresponding to the work order, including: Based on the knowledge mapping results, identify a set of project knowledge units whose correlation with work orders meets a set threshold. For each project knowledge unit in the project knowledge unit set, historical processing path and standard implementation path information are extracted, and weights are assigned according to the priority of the work order and the service context information. Based on the weight allocation results, the historical processing paths and standard implementation paths are weighted and integrated to form an initial service processing trajectory. The initial service processing trajectory is sorted according to the order of processing steps, dependencies, and technical constraints. The initial service processing trajectory is analyzed by performing path optimization based on historical processing success rate, processing time statistics, and risk impact indicators to establish a service processing baseline trajectory.

3. The work order service tracking method based on a project knowledge base as described in claim 1, characterized in that, By utilizing the matching relationship between the service processing baseline trajectory and the real-time knowledge evolution trajectory, a work order service tracking analysis is performed to establish service tracking results, including: The processing steps of the real-time knowledge evolution trajectory are matched step by step with the knowledge units in the service processing baseline trajectory to establish a step-by-step matching result; A matching matrix is ​​constructed based on the step-by-step matching results. The rows of the matching matrix represent the baseline trajectory steps, the lists represent the real-time trajectory steps, and the matrix elements represent the matching status and matching degree. The matching matrix is ​​used to analyze the service status of work orders, including processing path consistency analysis, knowledge unit call sufficiency analysis, and risk node exposure analysis. Service tracking results are established based on the analysis findings.

4. The work order service tracking method based on a project knowledge base as described in claim 1, characterized in that, After establishing service tracking results, the following will be included: Based on the service offset stage and potential risk propagation direction represented in the service tracking results, the service risk level of the work order is determined. If the service risk level reaches the preset warning condition, a work order service warning is triggered, and warning information corresponding to the risk diffusion direction is output to indicate the project operation risk or service failure risk that the current work order may cause.

5. The work order service tracking method based on project knowledge base as described in claim 4, characterized in that, Based on the frequency, duration, and key project knowledge units involved in the knowledge evolution out-of-bounds in the continuous service tracking results, a dynamic service tracking strategy is configured, and the work order service tracking management is performed using the dynamic service tracking strategy.

6. The work order service tracking method based on project knowledge base as described in claim 1, characterized in that, The service tracking results are processed by time-series recording to construct a time-series tracking data sequence, which is then stored and managed in a distributed, encrypted manner.

7. A work order service tracking platform based on a project knowledge base, characterized in that: For implementing the work order service tracking method based on a project knowledge base as described in any one of claims 1-6, the platform comprises: The structured modeling module is used to build a project knowledge base during project operation and maintenance. The project knowledge base takes the project as the organizational object and performs structured modeling on the technical solutions, implementation steps, historical problems, handling results and risk associations formed throughout the project life cycle, forming multiple computable project knowledge units. The knowledge mapping result generation module is used to read the work order information after a work order is received. The work order information includes work order description information, associated project identifier, and service context information. The module performs semantic parsing processing on the work order information and maps the parsing results to the project knowledge base to generate a knowledge mapping result that represents the association between the work order and multiple project knowledge units. The service processing baseline trajectory construction module is used to extract historical processing paths and standard implementation paths that meet preset conditions in relation to work orders from the project knowledge base using the knowledge mapping results, and construct the service processing baseline trajectory corresponding to the work order. The knowledge evolution trajectory generation module is used to collect the processing behavior information of the work order in real time during the execution of the work order, continuously map the processing behavior information to the project knowledge base, and generate a real-time knowledge evolution trajectory. The service tracking result generation module is used to perform work order service tracking analysis and establish service tracking results by utilizing the matching relationship between the service processing baseline trajectory and the real-time knowledge evolution trajectory.

8. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the work order service tracking method based on the project knowledge base as described in any one of claims 1 to 6.

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