A power data evaluation process control method based on task scheduling

Abstract

This invention relates to the field of power data processing technology, specifically to a power data evaluation process control method based on task scheduling. The method includes the following steps: decomposing the power data evaluation process into independent task units; automatically resolving dependencies based on the correlation of input and output parameters of each task unit to construct a dynamic task dependency graph that supports real-time addition, deletion, and dependency updates of task nodes; setting core parameters such as task execution timeout thresholds and retry count limits; the scheduling engine automatically arranging the execution order based on the topological sorting results of the task dependency graph, combined with task priority and real-time system resource usage, triggering the start of dependency-free pre-tasks; the scheduling engine collecting task execution status data in real time; and displaying the task dependency graph status and the execution progress information of each task. This achieves precise task scheduling and coordinated advancement, reduces task execution delays and resource waste, and efficiently advances the power data processing process.

Description

Technical Field

[0001] This invention relates to the field of power data processing technology, and in particular to a power data evaluation process control method based on task scheduling. Background Technology

[0002] In the field of power data processing, data extraction, verification, analysis, and report generation need to be coordinated to form a complete power data processing chain. Efficient connection between each stage is crucial to the overall data processing efficiency and accuracy. With the continuous expansion of power data volume and the increasing complexity of data types, traditional power data processing workflows face numerous technical bottlenecks.

[0003] In existing technologies, traditional solutions for task scheduling lack an intelligent scheduling core and cannot dynamically arrange the execution order according to task characteristics and system resource status. In most cases, a fixed execution sequence is used, ignoring the differences in priority and resource requirements of different tasks. This results in high-priority tasks being delayed due to insufficient resource preemption, while low-priority tasks consume too many resources, leading to waste and low overall execution efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a power data evaluation process control method based on task scheduling, which aims to solve the technical problems in the existing technology where traditional solutions lack an intelligent scheduling core, cannot dynamically arrange the execution order according to task characteristics and system resource status, and mostly adopt a fixed execution sequence, ignoring the priority differences and resource requirements of different tasks. This results in high-priority tasks being delayed due to insufficient resource preemption, and low-priority tasks consuming too many resources and causing waste, leading to low overall execution efficiency.

[0005] To achieve the above objectives, the present invention employs a power data evaluation process control method based on task scheduling, comprising the following steps: Define the evaluation objects of power operation data and construct a power data evaluation process. The evaluation objects include data quality evaluation, business indicator evaluation and data compliance evaluation. The power data evaluation process includes data extraction, verification, analysis and report generation. The data extraction, verification, analysis and report generation stages in the power data assessment process are broken down into independent task units, and each task unit is configured with a unique identifier, input and output parameters, execution priority and resource requirement threshold. Based on the correlation between the input and output parameters of each task unit, the dependency relationship is automatically resolved, and a dynamic task dependency graph that supports real-time addition, deletion and dependency update of task nodes is constructed. Set the core parameters for task execution timeout threshold and retry limit to complete the scheduling engine configuration; The scheduling engine automatically orchestrates the execution order based on the topological sorting result of the task dependency graph, combined with task priority and real-time system resource usage, triggers the start of dependent pre-tasks, and allocates dedicated execution resources to each task. The scheduling engine collects task execution status data in real time and triggers a preset timeout handling mechanism for timeout tasks. The scheduling engine's monitoring function displays the status of the task dependency graph and the execution progress information of each task in real time, and supports real-time alarms for abnormal states.

[0006] In the process of breaking down the data extraction, verification, analysis, and report generation stages in the power data assessment process into independent task units, and configuring each task unit with a unique identifier, input and output parameters, execution priority, and resource requirement threshold: Clearly define the core functional boundaries of data extraction, verification, analysis, and report generation in the power data assessment process, and break them down into independent task units according to the principle of functional independence; Assign a unique identifier to each split task unit; Summarize the input data requirements and output data results for each task unit, and clarify the specific content and format standards of the input and output parameters; Execution priorities are assigned based on the importance and urgency of task units, and resource requirement thresholds are set for each task unit in conjunction with the computing and storage resources required for task execution.

[0007] Among them, in the step of automatically resolving dependencies based on the correlation of input and output parameters of each task unit and constructing a dynamic task dependency graph that supports real-time addition, deletion and dependency updates of task nodes: Extract the input parameter identifiers and output parameter identifiers of all task units and establish a parameter information index library; Compare the input and output parameter identifiers of each task unit; A graphical structure is used to present task units and dependencies, with each task unit as a node and dependencies as the connection links between nodes, thus initially constructing a task dependency graph. A dynamic dependency graph adjustment mechanism is set up to support the addition of new task nodes, removal of useless task nodes, and modification of dependency relationships between tasks in real time according to business needs.

[0008] In the step of comparing the input and output parameter identifiers of each task unit: Identify the correlation between the input parameters of one task unit and the output parameters of another task unit, and determine the direct dependencies between tasks.

[0009] Among the steps involved in configuring the scheduling engine, specifically setting the core parameters such as the task execution timeout threshold and the maximum number of retries: Based on the execution characteristics and reasonable time consumption expectations of various power data processing task types, execution timeout thresholds are set for different task units. Based on the temporary anomalies that may occur during task execution, set a maximum limit on the number of times the task can be retried; Enter the set timeout threshold and retry limit into the scheduling engine system to complete the parameter initialization configuration; Verify the parameter configuration results of the scheduling engine.

[0010] Among them, the process of the scheduling engine automatically orchestrating the execution order based on the topological sorting result of the task dependency graph, combined with task priority and real-time system resource usage, triggering the start of dependency-free pre-tasks, and allocating dedicated execution resources to each task includes: The scheduling engine performs topological sorting on the task dependency graph to generate an initial task execution sequence that conforms to the dependency constraints; By collecting real-time data on CPU, memory, and storage resource usage in the system; The initial sequence generated by topological sorting is combined with task priority and real-time system resource status to dynamically adjust the task execution order; The system identifies tasks without prerequisites, triggers their execution, and allocates independent dedicated resources to each executing task based on its resource requirement threshold.

[0011] Among them, in the step of the scheduling engine collecting task execution status data in real time and triggering the preset timeout handling mechanism for timeout tasks: The scheduling engine collects the execution status information of each task unit at a fixed frequency. The execution status information includes data on execution progress, elapsed time, and current running status. The actual execution time of each task is compared with the preset timeout threshold in real time to determine whether the task has timed out. When the execution time of a task is detected to have reached the timeout threshold, a preset timeout handling mechanism is triggered, which includes marking the timeout status and reclaiming the resources occupied by the task. Record relevant information about timeout tasks, including task identifier, timeout time, and execution context.

[0012] Among them, the step of using the scheduling engine's monitoring function to display the task dependency graph status and the execution progress information of each task in real time, and supporting real-time alarms for abnormal states: The scheduling engine continuously collects monitoring data on the current structural state of the task dependency graph, the execution progress of each task unit, and details of resource usage. The collected monitoring data is displayed in real time through a visual interface, presenting the overall picture of task execution and the current status of dependencies; Set rules for judging abnormal states, and trigger a real-time alarm mechanism when abnormal situations such as task timeout and execution failure are detected; Alarm information is delivered to staff via pop-up notifications and sound alerts.

[0013] This invention discloses a power data evaluation process control method based on task scheduling, comprising the following steps: breaking down the data extraction, verification, analysis, and report generation stages in the power data evaluation process into independent task units, and configuring each task unit with a unique identifier, input / output parameters, execution priority, and resource requirement threshold; automatically resolving dependencies based on the correlation of input / output parameters of each task unit, constructing a dynamic task dependency graph that supports real-time addition, deletion, and dependency updates of task nodes; setting core parameters such as task execution timeout threshold and retry limit, and configuring the scheduling engine, which has the functions of task status identification and conflict resolution; and the scheduling engine, based on the topological sorting result of the task dependency graph and combined with the task... The system automatically orchestrates the execution order based on priority and real-time system resource usage, triggering the startup of dependency-free pre-tasks and allocating dedicated execution resources to each task. The scheduling engine collects task execution status data in real time, triggers a preset timeout handling mechanism for timed-out tasks, and determines whether failed tasks meet retry conditions based on failure type; if so, it initiates retry according to an exponential backoff strategy. Through the scheduling engine's monitoring function, the system displays the task dependency graph status and execution progress information of each task in real time, and supports real-time alarms for abnormal states. Through these methods, precise task scheduling and collaborative advancement are achieved, effectively solving the problems of rigid task scheduling and unbalanced resource allocation in traditional solutions, reducing task execution delays and resource waste, and efficiently advancing the power data processing workflow. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart of the steps of the power data evaluation process control method based on task scheduling of the present invention.

[0016] Figure 2 This is a flowchart of steps S200 of the present invention.

[0017] Figure 3 This is a flowchart of steps S300 of the present invention.

[0018] Figure 4 This is a flowchart of steps S400 of the present invention.

[0019] Figure 5 This is a flowchart of steps S500 of the present invention.

[0020] Figure 6 This is a flowchart of steps S600 of the present invention.

[0021] Figure 7 This is a flowchart of steps S700 of the present invention. Detailed Implementation

[0022] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0023] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0024] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0025] Please see Figures 1-7 This invention provides a power data evaluation process control method based on task scheduling, comprising the following steps: S100: Define the evaluation objects of power operation data and construct a power data evaluation process. The evaluation objects include data quality evaluation, business indicator evaluation and data compliance evaluation. The power data evaluation process includes data extraction, verification, analysis and report generation.

[0026] In this implementation, data quality assessment includes: evaluating the completeness (e.g., whether key fields are missing), accuracy (e.g., whether metering data matches reality), consistency (e.g., whether the same indicator from different data sources conflicts), and timeliness (e.g., whether data collection and transmission are delayed). Business indicator assessment includes: power grid operation status assessment (e.g., the rationality and safety threshold matching of data such as line load rate, transformer utilization rate, and voltage stability); electricity consumption assessment (e.g., user electricity consumption statistics, peak-valley load distribution, and identification of abnormal electricity consumption behavior); and equipment health assessment (e.g., whether the operating parameters (temperature, pressure, energy consumption) of transmission and distribution equipment meet health standards). Data compliance assessment includes: evaluating whether the collection, storage, and transmission of power data comply with industry standards (e.g., power data security standards) and privacy protection requirements (e.g., compliance with user electricity data anonymization).

[0027] S200: The data extraction, verification, analysis and report generation stages in the power data assessment process are broken down into independent task units, and each task unit is configured with a unique identifier, input and output parameters, execution priority and resource requirement threshold.

[0028] In this embodiment, the data extraction, verification, analysis, and report generation stages in the power data evaluation process are broken down into independent task units, and each task unit is configured with a unique identifier, input and output parameters, execution priority, and resource requirement threshold. The specific process is as follows: S201: Clearly define the core functional boundaries of data extraction, verification, analysis, and report generation in the power data assessment process, and break them down into independent task units according to the principle of functional independence; S202: Assign a unique identifier to each split task unit; S203: Review the input data requirements and output data results for each task unit, and clarify the specific content and format standards of the input and output parameters; S204: Execution priorities are divided according to the importance and urgency of task units, and resource requirement thresholds for each task unit are set in combination with the computing and storage resources required for task execution.

[0029] In the aforementioned process, the first step is to prepare by deconstructing the power data processing workflow. This involves clearly defining the core functional boundaries of the four main stages: data extraction, verification, analysis, and report generation. For example, the data extraction stage focuses on the collection and initial screening of raw power data; the verification stage emphasizes data integrity and accuracy checks; the analysis stage focuses on in-depth data processing and pattern discovery; and the report generation stage is responsible for the structured presentation of results. Based on the principle of functional independence, each stage is broken down into independent, uncoupled executable task units. Subsequently, each decomposed task unit is assigned a unique identifier, which uses a combination of "task type-serial number" to ensure... The task is accurately identified throughout the entire process; then, the input data requirements for each task unit are comprehensively reviewed, including data sources, format standards, and precision thresholds, while clarifying the type, storage path, and presentation format of the output data results, forming a standardized input and output parameter specification document; finally, based on the business importance of the task unit (e.g., tasks involving core data verification have higher priority than ordinary data statistics tasks) and the urgency of execution, high, medium, and low execution priorities are divided, and then resource requirement thresholds for each task unit are set according to the resource consumption such as CPU computing power, memory usage, and storage space required for task execution, to avoid resource allocation imbalance.

[0030] S300: Automatically resolves dependencies based on the correlation of input and output parameters of each task unit, and constructs a dynamic task dependency graph that supports real-time addition, deletion and dependency updates of task nodes.

[0031] In this embodiment, dependencies are automatically resolved based on the correlation between the input and output parameters of each task unit, and a dynamic task dependency graph that supports real-time addition, deletion, and dependency updates of task nodes is constructed. The specific process is as follows: S301: Extract the input parameter identifiers and output parameter identifiers of all task units and establish a parameter information index library; S302: Compare the input and output parameter identifiers of each task unit; S303: A graphical structure is used to present task units and dependencies, with each task unit as a node and dependencies as the connection links between nodes, thus initially constructing a task dependency graph; S304: Set up a dynamic adjustment mechanism for the dependency graph, supporting the addition of new task nodes, removal of useless task nodes, and modification of dependency relationships between tasks in real time according to business needs.

[0032] In the above process, the input and output parameter identifiers of all task units are first extracted. The parameter identifiers contain key information such as parameter name, data type, and data encoding. Based on this information, a structured parameter information index is established, which supports quick retrieval of corresponding task units by parameter identifier. Then, by traversing the index, the input and output parameter identifiers of each task unit are compared. When the input parameter identifier of one task unit completely matches the output parameter identifier of another task unit, it is determined that there is a direct dependency relationship between the two, that is, the former is the predecessor of the latter, and the system automatically records this dependency relationship. Next, a graphical modeling method is used to abstract each task unit into an independent node. The dependency relationship between tasks is represented by directed line segments as the connection links between nodes. A task dependency graph is initially constructed according to the dependency logic order, which intuitively presents the association path between tasks. Finally, a dynamic adjustment mechanism for the dependency graph is set up, providing a visual operation entry point. This allows staff to add new task nodes in real time, remove useless task nodes, and adjust the dependency relationships between tasks by dragging links or modifying parameter matching relationships, ensuring that the dependency graph always remains consistent with the actual business process.

[0033] S400: Sets the core parameters for task execution timeout thresholds and retry limit, completing the configuration of the scheduling engine.

[0034] In this embodiment, the core parameters of setting the task execution timeout threshold and the maximum number of retries are used to complete the configuration of the scheduling engine. The specific process is as follows: S401: Based on the execution characteristics and reasonable time consumption expectations of each task type in power data processing, set execution timeout thresholds for different task units respectively; S402: Based on the temporary abnormal situations that may occur during task execution, set the maximum number of times the task can be retried; S403: Enter the set timeout threshold and retry limit into the scheduling engine system to complete the parameter initialization configuration; S404: Verify the parameter configuration results of the scheduling engine.

[0035] In the above process, considering the execution characteristics of different task types in power data processing, such as the data extraction task being greatly affected by the data source response speed and the high computational complexity of the analysis task, and referring to historical execution data and industry standards, a differentiated execution timeout threshold is set for each task unit to avoid misjudgment caused by a uniform threshold. For temporary anomalies that may occur during task execution, such as network fluctuations, temporary database disconnections, and temporary resource occupation, a maximum number of retries is set, generally defaulting to 3, but customizable according to business needs, taking into account the probability of anomaly repair and the cost of repeated execution. The set timeout threshold, retry limit, and other core parameters are entered into the scheduling engine system in batches or individually according to the scheduling engine's parameter entry specifications to complete the parameter initialization configuration, and the system automatically generates a parameter configuration list. Finally, the scheduling engine parameter verification program is started to perform logical verification (e.g., the timeout threshold must be greater than the minimum execution time of the task), numerical validity verification (e.g., the retry limit must be a non-negative integer), and parameter conflict verification (e.g., the sum of resource thresholds for different tasks does not exceed the total system resources). Once the verification passes, the parameters officially take effect; otherwise, an error message is returned with guidance on modification.

[0036] S500: The scheduling engine automatically arranges the execution order based on the topological sorting result of the task dependency graph, combined with task priority and real-time system resource usage, triggers the start of dependent pre-tasks, and allocates dedicated execution resources to each task.

[0037] In this implementation, the scheduling engine automatically orchestrates the execution order based on the topological sorting result of the task dependency graph, combined with task priority and real-time system resource usage, triggers the start of dependency-free pre-tasks, and allocates dedicated execution resources to each task. The specific process is as follows: S501: The scheduling engine performs topological sorting on the task dependency graph to generate an initial task execution sequence that conforms to dependency constraints; S502: Collects real-time data on CPU, memory, and storage resource usage in the system; S503: Combine the initial sequence generated by topological sorting with task priority and real-time system resource status to dynamically adjust the task execution order; S504: Identify tasks without prerequisite dependencies, trigger their execution, and allocate independent dedicated resources to each executing task based on its resource requirement threshold.

[0038] In the above process, the scheduling engine calls the topology sorting algorithm to analyze the constructed task dependency graph, traverse all task nodes and dependency links, eliminate the risk of circular dependencies, and generate an initial task execution sequence that meets the constraint that "the subsequent task can only be executed after the preceding task is completed." Through the system's built-in resource monitoring component, resource usage data such as CPU utilization, memory usage, and remaining storage space are collected in real time at a fixed frequency (e.g., once per second), and the resource status database is dynamically updated. The scheduling engine comprehensively analyzes the initial sequence generated by the topology sorting, the execution priority of each task unit, and the real-time resource status of the system. When the resources required by high-priority tasks are sufficient... When resources are sufficient, tasks are started according to the initial sequence. When high-priority tasks do not have enough resources, the resource pre-release mechanism of low-priority tasks is triggered, pausing non-core processes of low-priority tasks or releasing some cached resources to free up resources for high-priority tasks and dynamically adjusting the task execution order. Finally, the scheduling engine automatically scans the initial execution sequence, identifies tasks without any pre-dependent conditions, sends a start command to the system to trigger their execution, and allocates independent dedicated resources for each executing task from the system's idle resources according to the resource requirement threshold of each task, including specifying CPU cores, allocating independent memory space, and allocating dedicated storage areas to avoid resource competition between tasks.

[0039] S600: The scheduling engine collects task execution status data in real time and triggers a preset timeout handling mechanism for timed-out tasks.

[0040] In this embodiment, the scheduling engine collects task execution status data in real time and triggers a preset timeout handling mechanism for timeout tasks. The specific process is as follows: S601: The scheduling engine collects the execution status information of each task unit at a fixed frequency. The execution status information includes data on execution progress, elapsed time, and current running status. S602: Compare the actual execution time of each task with the preset timeout threshold in real time to determine whether the task has timed out; S603: When the execution time of a task is detected to have reached the timeout threshold, a preset timeout handling mechanism is triggered, which includes marking the timeout status and reclaiming the resources occupied by the task. S604: Records information related to timeout tasks, including task identifier, timeout time, and execution context.

[0041] During the above process, the scheduling engine sends status collection commands to each task unit at a preset fixed frequency (e.g., once every 10 seconds) to collect status information such as task execution progress (e.g., percentage of completed data), time consumed, and current running status (e.g., "pending execution," "in execution," "paused," etc.), and stores the collected data in the task monitoring database in real time. The system background process continuously compares the actual execution time of each task with the preset timeout threshold in real time. When the actual execution time does not exceed the timeout threshold, the task continues to execute normally. When the actual execution time of a task is detected to reach or exceed the preset timeout threshold, the preset timeout handling mechanism is immediately triggered. On the one hand, the status of the task is marked as "timeout" and synchronized to the monitoring interface. On the other hand, the resource reclamation program is started to release the system resources such as CPU, memory, and network connection occupied by the task, avoiding long-term idle resources. At the same time, the system automatically records relevant information of the timed-out task, including the unique identifier of the task, the specific time of the timeout, the execution context (e.g., the processed data fragments, the executed program modules, the currently called interfaces, etc.), and generates a timeout task log to provide data support for subsequent troubleshooting and optimization.

[0042] S600: Through the monitoring function of the scheduling engine, it can display the status of the task dependency graph and the execution progress information of each task in real time, and supports real-time alarms for abnormal status.

[0043] In this implementation, the scheduling engine's monitoring function displays the task dependency graph status and the execution progress information of each task in real time, and supports real-time alarms for abnormal states. The specific process is as follows: S701: The scheduling engine continuously collects monitoring data on the current structural state of the task dependency graph, the execution progress of each task unit, and details of resource usage. S702: The collected monitoring data is displayed in real time through a visual interface, presenting the overall picture of task execution and the current status of dependencies; S703: Set the rules for judging abnormal states. When abnormal situations such as task timeout and execution failure are detected, a real-time alarm mechanism is triggered. S704: Output alarm information to staff through pop-up prompts and sound alerts.

[0044] During the above process, the scheduling engine activates full-process monitoring mode, continuously collecting monitoring data such as the current structural status of the task dependency graph (e.g., node additions / removals, link changes), the execution progress of each task unit, and resource usage details (e.g., CPU utilization, memory usage), ensuring the comprehensiveness and real-time nature of data collection. Through various forms such as web-based monitoring panels and client-side visual interfaces, the collected monitoring data is displayed in real-time in intuitive formats such as charts (e.g., line charts showing execution progress, pie charts showing resource usage ratios), progress bars, and status labels. Staff can operate the interface by zooming, locating nodes, and tracing links. The system provides a clear understanding of the overall task execution and current dependencies; it pre-defines abnormal status judgment rules, clearly defining the judgment conditions and level classification standards for abnormal situations such as task timeout (actual time ≥ timeout threshold) and execution failure (returning error code, program crash). When the scheduling engine detects a situation that meets the abnormal judgment rules, it immediately triggers a real-time alarm mechanism; alarm information is simultaneously output through multiple methods such as system pop-up prompts, desktop sound reminders, and staff mobile APP message pushes. The alarm information includes abnormal task identifier, abnormal type, occurrence time, and preliminary investigation suggestions, ensuring that staff are aware of the situation in a timely manner and can respond quickly.

[0045] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0046] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A power data evaluation process control method based on task scheduling, characterized in that, Includes the following steps: Define the evaluation objects of power operation data and construct a power data evaluation process. The evaluation objects include data quality evaluation, business indicator evaluation and data compliance evaluation. The power data evaluation process includes data extraction, verification, analysis and report generation. The data extraction, verification, analysis and report generation stages in the power data assessment process are broken down into independent task units, and each task unit is configured with a unique identifier, input and output parameters, execution priority and resource requirement threshold. Based on the correlation between the input and output parameters of each task unit, the dependency relationship is automatically resolved, and a dynamic task dependency graph that supports real-time addition, deletion and dependency update of task nodes is constructed. Set the core parameters for task execution timeout threshold and retry limit to complete the scheduling engine configuration; The scheduling engine automatically orchestrates the execution order based on the topological sorting result of the task dependency graph, combined with task priority and real-time system resource usage, triggers the start of dependent pre-tasks, and allocates dedicated execution resources to each task. The scheduling engine collects task execution status data in real time and triggers a preset timeout handling mechanism for timeout tasks. The scheduling engine's monitoring function displays the status of the task dependency graph and the execution progress information of each task in real time, and supports real-time alarms for abnormal states.

2. The power data evaluation process control method based on task scheduling as described in claim 1, characterized in that, In the process of breaking down the data extraction, verification, analysis, and report generation stages in the power data assessment workflow into independent task units, and configuring each task unit with a unique identifier, input and output parameters, execution priority, and resource requirement threshold: Clearly define the core functional boundaries of data extraction, verification, analysis, and report generation in the power data assessment process, and break them down into independent task units according to the principle of functional independence; Assign a unique identifier to each split task unit; Summarize the input data requirements and output data results for each task unit, and clarify the specific content and format standards of the input and output parameters; Execution priorities are assigned based on the importance and urgency of task units, and resource requirement thresholds are set for each task unit in conjunction with the computing and storage resources required for task execution.

3. The power data evaluation process control method based on task scheduling as described in claim 1, characterized in that, In the steps of automatically resolving dependencies based on the correlation of input and output parameters of each task unit and constructing a dynamic task dependency graph that supports real-time addition, deletion, and dependency updates of task nodes: Extract the input parameter identifiers and output parameter identifiers of all task units and establish a parameter information index library; Compare the input and output parameter identifiers of each task unit; A graphical structure is used to present task units and dependencies, with each task unit as a node and dependencies as the connection links between nodes, thus initially constructing a task dependency graph. A dynamic dependency graph adjustment mechanism is set up to support the addition of new task nodes, removal of useless task nodes, and modification of dependency relationships between tasks in real time according to business needs.

4. The power data evaluation process control method based on task scheduling as described in claim 3, characterized in that, In the step of comparing the input and output parameter identifiers of each task unit: Identify the correlation between the input parameters of one task unit and the output parameters of another task unit, and determine the direct dependencies between tasks.

5. The power data evaluation process control method based on task scheduling as described in claim 1, characterized in that, In the process of configuring the scheduling engine, the core parameters of setting the task execution timeout threshold and the maximum number of retries are: Based on the execution characteristics and reasonable time consumption expectations of various power data processing task types, execution timeout thresholds are set for different task units. Based on the temporary anomalies that may occur during task execution, set a maximum limit on the number of times the task can be retried; Enter the set timeout threshold and retry limit into the scheduling engine system to complete the parameter initialization configuration; Verify the parameter configuration results of the scheduling engine.

6. The power data evaluation process control method based on task scheduling as described in claim 1, characterized in that, In the process where the scheduling engine automatically orchestrates the execution order based on the topological sorting result of the task dependency graph, combined with task priority and real-time system resource usage, triggers the start of dependency-free pre-tasks, and allocates dedicated execution resources to each task: The scheduling engine performs topological sorting on the task dependency graph to generate an initial task execution sequence that conforms to the dependency constraints; By collecting real-time data on CPU, memory, and storage resource usage in the system; The initial sequence generated by topological sorting is combined with task priority and real-time system resource status to dynamically adjust the task execution order; The system identifies tasks without prerequisites, triggers their execution, and allocates independent dedicated resources to each executing task based on its resource requirement threshold.

7. The power data evaluation process control method based on task scheduling as described in claim 1, characterized in that, In the process of the scheduling engine collecting task execution status data in real time and triggering a preset timeout handling mechanism for timeout tasks: The scheduling engine collects the execution status information of each task unit at a fixed frequency. The execution status information includes data on execution progress, elapsed time, and current running status. The actual execution time of each task is compared with the preset timeout threshold in real time to determine whether the task has timed out. When the execution time of a task is detected to have reached the timeout threshold, a preset timeout handling mechanism is triggered, which includes marking the timeout status and reclaiming the resources occupied by the task. Record relevant information about timeout tasks, including task identifier, timeout time, and execution context.

8. The power data evaluation process control method based on task scheduling as described in claim 1, characterized in that, In the process of using the scheduling engine's monitoring function to display the task dependency graph status and the execution progress information of each task in real time, and supporting real-time alerts for abnormal states: The scheduling engine continuously collects monitoring data on the current structural state of the task dependency graph, the execution progress of each task unit, and details of resource usage. The collected monitoring data is displayed in real time through a visual interface, presenting the overall picture of task execution and the current status of dependencies; Set rules for judging abnormal states, and trigger a real-time alarm mechanism when abnormal situations such as task timeout and execution failure are detected; Alarm information is delivered to staff via pop-up notifications and sound alerts.

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