Object operation execution state determination method, apparatus, and electronic device

Abstract

This invention discloses a method, apparatus, and electronic device for determining the operation execution state of an object. The method includes: acquiring multi-source operation data corresponding to a target object; determining the current operation task and the predetermined operation task corresponding to the target object within a target time period; determining a time difference parameter between the current operation task and the predetermined operation task, wherein the time difference parameter characterizes the operation time difference feature between the current operation task and the predetermined operation task; determining a target time constraint corresponding to the target object based on the time difference parameter; and determining the operation execution state corresponding to the target object based on the target time constraint and the multi-source operation data. This invention solves the technical problem in related technologies where the determination of the operation execution state of an object is inaccurate.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more specifically, to a method, apparatus, and electronic device for determining the operation execution status of an object. Background Technology

[0002] In related technologies, the operational execution status of an object during material production reflects the progress of production tasks. To ensure the orderly advancement of the production process, it is necessary to determine the operational execution status of the object. However, in related technologies, there is a technical problem of inaccurate determination of the operational execution status.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, and electronic device for determining the operation execution state of an object, in order to at least solve the technical problem in the related art where the determination of the operation execution state of an object is inaccurate.

[0005] According to one aspect of the present invention, a method for determining the operation execution state of an object is provided, comprising: acquiring multi-source operation data corresponding to a target object, wherein the multi-source operation data is data generated by the target object performing target operations based on different operating devices within a target time period; determining a current operation task and a predetermined operation task corresponding to the target object within the target time period; determining a time difference parameter between the current operation task and the predetermined operation task, wherein the time difference parameter is used to characterize the operation time difference feature between the current operation task and the predetermined operation task; determining a target time constraint corresponding to the target object based on the time difference parameter; and determining the operation execution state corresponding to the target object based on the target time constraint and the multi-source operation data.

[0006] Optionally, determining the operation execution state corresponding to the target object based on the target time constraint and the multi-source operation data includes: determining the operation time corresponding to each of the multiple initial operation data when the multi-source operation data includes multiple initial operation data; determining the operation time trajectory of the target object performing the target operation on different operating devices based on the multiple initial operation data and the operation time corresponding to each of the multiple initial operation data; and determining the operation execution state corresponding to the target object based on the target time constraint and the operation time trajectory.

[0007] Optionally, determining the operation time trajectory of the target object performing the target operation on different operating devices based on the plurality of initial operation data and the operation time corresponding to each of the plurality of initial operation data includes: determining, according to the execution order corresponding to each of the plurality of operation times, whether there are one or more candidate operation data among the plurality of initial operation data to obtain an operation determination result, wherein the plurality of initial operation data corresponds one-to-one with the plurality of operation times; if the operation determination result is that there are multiple candidate operation data among the plurality of initial operation data, determining the data priority corresponding to each of the multiple candidate operation data; determining the target operation data from the multiple candidate operation data based on the data priority corresponding to each of the multiple candidate operation data; until the plurality of operation times are completed, obtaining the target operation data corresponding to each of the plurality of operation times; and determining the operation time trajectory corresponding to the target object based on the target operation data corresponding to each of the plurality of operation times.

[0008] Optionally, determining the current operation task corresponding to the target object within the target time period includes: determining multiple candidate operation tasks for the target object within the target time period; determining the current task configuration corresponding to each of the multiple candidate operation tasks; and determining the current operation task from the multiple candidate operation tasks based on the current task configuration corresponding to each of the multiple candidate operation tasks, wherein the current operation task is a candidate operation task whose current task configuration is a predetermined task configuration among the multiple candidate operation tasks.

[0009] Optionally, determining the current task configuration corresponding to each of the plurality of candidate operation tasks includes: for any one of the plurality of candidate operation tasks, determining the current task configuration corresponding to that candidate operation task in the following manner: determining the current task level corresponding to that candidate operation task; determining the associated task level corresponding to the current task level, wherein the associated task level includes a parent task level and a child task level; and determining the current task configuration corresponding to that candidate operation task based on the current configuration set corresponding to the current task level and the associated configuration set corresponding to the associated task level.

[0010] Optionally, determining the current task configuration corresponding to any one of the candidate operation tasks based on the current configuration set corresponding to the current task level and the associated configuration set corresponding to the associated task level includes: determining the configuration filtering constraints corresponding to any one of the candidate operation tasks; and determining the current task configuration corresponding to any one of the candidate operation tasks based on the configuration filtering constraints, the current configuration set corresponding to the current task level, and the associated configuration set corresponding to the associated task level.

[0011] Optionally, determining the target time constraint corresponding to the target object based on the time difference parameter includes: determining multiple task feature data based on the time difference parameter, wherein the multiple task feature data correspond to different task features; and determining the target time constraint corresponding to the target object based on the multiple task feature data.

[0012] According to one aspect of the present invention, an apparatus for determining the operation execution state of an object is provided, comprising: an acquisition module, configured to acquire multi-source operation data corresponding to a target object, wherein the multi-source operation data is data generated by the target object performing target operations based on different operating devices within a target time period; a first determination module, configured to determine a current operation task and a predetermined operation task corresponding to the target object within the target time period; a second determination module, configured to determine a time difference parameter between the current operation task and the predetermined operation task, wherein the time difference parameter is used to characterize the operation time difference feature between the current operation task and the predetermined operation task; a third determination module, configured to determine a target time constraint corresponding to the target object based on the time difference parameter; and a fourth determination module, configured to determine the operation execution state corresponding to the target object based on the target time constraint and the multi-source operation data.

[0013] According to one aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the operation execution state determination method of any of the preceding claims.

[0014] According to one aspect of the present invention, a computer-readable storage medium is provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the operation execution state determination method of the object described in any of the preceding claims.

[0015] In this embodiment of the invention, by acquiring multi-source operation data corresponding to the target object, it is possible to comprehensively collect various execution information generated by the target object performing target operations based on different operating devices within the target time period. By determining the current operation task and the predetermined operation task corresponding to the target object within the target time period, it is possible to clarify the actual task and the preset task of the target object within the corresponding time period. By determining the time difference parameter between the current operation task and the predetermined operation task, it is possible to accurately reflect the difference characteristics of the two types of tasks in terms of operation time. By determining the target time constraint corresponding to the target object based on the time difference parameter, it is possible to form a time reference basis that is adapted to the actual task situation of the target object. By combining the target time constraint with multi-source operation data to determine the operation execution status, it is ensured that the determination of the operation execution status is more in line with the actual execution process, so as to effectively solve the problem of inaccurate determination of operation execution status in related technologies, and thus solve the technical problem of inaccurate determination of operation execution status in related technologies. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart of a method for determining the operation execution status of an object according to an embodiment of the present invention;

[0018] Figure 2 This is a schematic diagram of the system architecture for determining the operation execution status in an optional embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram of the multi-source heterogeneous data acquisition and fusion mechanism in an optional embodiment of the present invention;

[0020] Figure 4 This is a schematic diagram of the task definition and matching process in an optional embodiment of the present invention;

[0021] Figure 5 This is a schematic diagram of the multi-task hierarchical architecture and cross-task hierarchical statistical process in an optional embodiment of the present invention;

[0022] Figure 6 This is a structural block diagram of an object operation execution state determination device according to an embodiment of the present invention. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] First, some nouns or terms that appear in the description of the embodiments of this application shall be interpreted as follows:

[0026] CSV: CSV is a data format that uses commas as delimiters to store structured data in plain text format. It can be used to achieve lightweight storage and transmission of multi-source data.

[0027] JSON: JSON is a lightweight text-based data interchange format that uses a key-value pair structure to carry data and is suitable for parsing, transmitting, and integrating data from multiple sources.

[0028] XML: XML is an extensible markup language format that uses a hierarchical tag structure to describe data content and can be used for structured storage and transmission of multi-source operational data.

[0029] Example 1

[0030] According to an embodiment of the present invention, an embodiment of a method for determining the operation execution state of an object is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] Figure 1 This is a flowchart of a method for determining the operation execution state of an object according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0032] S102, acquire multi-source operation data corresponding to the target object, wherein the multi-source operation data is the data generated by the target object performing target operations based on different operating devices within the target time period;

[0033] This involves the target object, which is the object whose target operation execution status needs to be detected and evaluated. For example, in a material production scenario, the target object could be an operator on the production line.

[0034] This involves multi-source operational data, which originates from different operating devices and records various information about the target object during the execution of a target operation, including the operation time, operation content, and operation result. For example, in a material production scenario, multi-source operational data could specifically be data generated by operators determining operations based on different operating devices.

[0035] This involves a target time period, which is a predetermined time range for evaluating the operational execution status of the target object. Specifically, the target time period can be set according to actual needs, such as one hour, one day, one week, or one month.

[0036] This involves operating equipment, which is the device used by the target object to perform the target operation. In material production, the operating equipment can include fixed operating equipment and mobile operating equipment. For example, in a material production scenario, taking the target operation as a task determination operation as an example, the operating equipment is a device that can be used to perform the task determination operation. That is, the target object determines the task based on the operating equipment (e.g., confirming the execution of the current operation task).

[0037] This involves target operations, which are operations used to characterize the execution of target object job nodes and the confirmation of task status, to ensure that the target object meets the configuration requirements of the task being executed during the execution of the task. For example, in a material production scenario, the target operation can be a task determination operation.

[0038] By acquiring multi-source operation data generated by the target object performing target operations based on different operating devices within the target time period, it is possible to comprehensively record various information of the target object during the task execution process, ensure accurate assessment of the operation execution status, avoid assessment errors caused by missing or incomplete data, and achieve accuracy in determining the operation execution status.

[0039] S104, determine the current operation task and the scheduled operation task corresponding to the target object within the target time period;

[0040] This involves the current operation task, which is the operation task that the target object actually needs to perform within the target time period. For example, in a material production scenario, the current operation task can be a production task, maintenance task, inspection task, material delivery task, etc.

[0041] This involves pre-determined operational tasks, which are pre-determined operational tasks that target objects need to perform before the start of the target time period.

[0042] By identifying the current and planned operational tasks corresponding to the target object within the target time period, it is possible to clarify the actual tasks that the target object should perform within that time period and the pre-planned tasks. This ensures that there is a clear benchmark for the comparative analysis of operational execution status, avoids evaluation bias caused by unclear tasks, and achieves accuracy and relevance in the evaluation of operational execution status.

[0043] S106, determine the time difference parameter between the current operation task and the predetermined operation task, wherein the time difference parameter is used to characterize the operation time difference feature between the current operation task and the predetermined operation task;

[0044] This involves a time difference parameter, which quantifies the difference between the current and planned tasks in terms of time. It reflects the degree of deviation between the actual task (current task) and the planned task (scheduled task) in time, including but not limited to delays in start time, earlier or later end time, and increases or decreases in overall task execution time. For example, in a material production scenario, the scheduled task is to execute production task T1 on production line L1 from 9:00 to 17:00; the current task is to execute production task T2 on production line L2 from 10:00 to 18:00. Therefore, this time difference parameter includes differences in start time, end time, and execution duration.

[0045] Determining the time difference parameter between the current operation task and the scheduled operation task can accurately capture the inconsistency between the actual execution and the planned task in terms of time, thereby accurately reflecting the deviation between the operation execution status and the expectation, ensuring that the assessment of the operation execution status is timely and accurate, and avoiding misjudgment of status due to missing or ambiguous time information.

[0046] S108, Based on the time difference parameter, determine the target time constraint corresponding to the target object;

[0047] This involves a target time constraint, which is used to constrain the execution time of a target object's target operation to ensure that the operation's execution status meets the expected time requirements of the current task. For example, in a material production scenario, the predetermined task is for an operator to perform a production task on production line L1, requiring two target operations, with the two operations occurring between 8:55-9:05 and 16:55-17:05 respectively. The current task is for the operator to be reassigned to production line L2 to perform a new production task, requiring two target operations, with the two operations occurring between 8:00-11:00 and 17:00-19:00 respectively, and the time interval between the two target operations being no less than 8 hours. Therefore, the target time constraint is: perform the target operation twice, with the two operations occurring between 8:00-11:00 and 17:00-19:00 respectively, and the time interval between the two target operations being no less than 8 hours.

[0048] The time difference parameter can accurately reflect the time-dimensional adaptability difference between the current operation task and the predetermined operation task. By determining the target time constraint corresponding to the target object based on the time difference parameter, it can adapt to the actual time execution requirements after the operation task of the target object is switched, and replace the original unsuitable predetermined time requirements. This helps to accurately evaluate the operation execution status of the target object based on the time standard that fits the current task.

[0049] S110, based on the target time constraint and multi-source operation data, determines the operation execution status corresponding to the target object.

[0050] This includes the operation execution status, which is a comprehensive result of the target object's actual completion of the target operation, time compliance, etc. It can intuitively reflect whether the target object has completed the target operation according to the time requirements of the current operation task. For example, in the material production scenario, the operation execution status can be the target object's task operation being completed within the target time constraint, completed beyond the target time constraint, or not completed.

[0051] The target time constraint provides a time dimension benchmark that adapts to the current operation task for determining the operation execution status of the target object. Multi-source operation data provides comprehensive and authentic original data support for reconstructing the actual process of the target object performing the target operation. Thus, by combining the target time constraint with multi-source operation data to determine the operation execution status, it is ensured that the determination of the operation execution status is more in line with the actual execution process, thereby effectively solving the problem of inaccurate determination of the operation execution status in related technologies.

[0052] Through the above steps S102-S110, by acquiring multi-source operation data corresponding to the target object, it is possible to comprehensively collect various execution information generated by the target object performing target operations based on different operating devices within the target time period. By determining the current operation task and the predetermined operation task corresponding to the target object within the target time period, it is possible to clarify the actual task and the preset task of the target object within the corresponding time period. By determining the time difference parameter between the current operation task and the predetermined operation task, it is possible to accurately reflect the difference characteristics of the two types of tasks in operation time. By determining the target time constraint corresponding to the target object based on the time difference parameter, it is possible to form a time reference basis that adapts to the actual task situation of the target object. By combining the target time constraint with multi-source operation data to determine the operation execution status, it is ensured that the determination of the operation execution status is more in line with the actual execution process, so as to effectively solve the problem of inaccurate determination of operation execution status in related technologies, and thus solve the technical problem of inaccurate determination of operation execution status in related technologies.

[0053] As an optional embodiment, determining the operation execution state corresponding to the target object based on the target time constraint and multi-source operation data includes: determining the operation time corresponding to each of the multiple initial operation data when the multi-source operation data includes multiple initial operation data; determining the operation time trajectory of the target object performing the target operation on different operation devices based on the multiple initial operation data and the operation time corresponding to each of the multiple initial operation data; and determining the operation execution state corresponding to the target object based on the target time constraint and the operation time trajectory.

[0054] This involves multiple initial operation data, which are data collected from different operating devices when the target object performs the target operation within the target time period. Each of the multiple initial operation data corresponds to a different operating device.

[0055] This involves operation time, which is the specific time when the target object performs each specific target operation through various operating devices. It is time information that corresponds one-to-one with each initial operation data, and can accurately mark the execution time of a single target operation.

[0056] This involves operation time trajectories, which are formed by combining multiple initial operation data and their corresponding operation times, sorting and integrating them in chronological order, to create a complete timeline of the target object performing the target operation on different operating devices. This clearly presents the time sequence of the target object performing the target operation, the switching of operating devices, and the time distribution characteristics of each operation.

[0057] By matching the corresponding operation time to multiple initial operation data in multi-source operation data, each piece of original operation data can have a precise time identifier. Then, by combining the initial operation data and the corresponding operation time in sequence, the operation time trajectory of the target object performing the target operation on different operation devices can be sorted and integrated. This can completely restore the actual process of the target object's execution of the target operation, including the time sequence, device switching, and time distribution. Finally, based on the target time constraint, the operation time trajectory can be compared and analyzed. This allows for a comprehensive verification of the time compliance and actual completion of the target object's execution of the target operation, based on the time requirements of the current operation task. This ensures that the judgment of the operation execution status is based on a complete restoration of the actual operation process, avoiding judgment bias caused by single data or data analysis without time context. This achieves a more accurate and realistic determination of the target object's operation execution status.

[0058] As an optional embodiment, based on multiple initial operation data and the operation time corresponding to each of the multiple initial operation data, the operation time trajectory of the target object performing the target operation on different operation devices is determined, including: according to the execution order corresponding to each of the multiple operation times, for each corresponding operation time in the multiple operation times, determining whether there are one or more candidate operation data among the multiple initial operation data, and obtaining an operation determination result, wherein the multiple initial operation data corresponds one-to-one with the multiple operation times; if the operation determination result is that there are multiple candidate operation data among the multiple initial operation data, determining the data priority corresponding to each of the multiple candidate operation data; based on the data priority corresponding to each of the multiple candidate operation data, determining the target operation data from the multiple candidate operation data; until the multiple operation times are completed, obtaining the target operation data corresponding to each of the multiple operation times; and based on the target operation data corresponding to each of the multiple operation times, determining the operation time trajectory corresponding to the target object.

[0059] This involves the execution order corresponding to multiple operation times. The execution order corresponding to these multiple operation times can be the natural order formed by arranging the multiple operation times in chronological order, which can reflect the sequential process of the target object performing the target operation on different operating devices.

[0060] At the corresponding operation time, determine whether there are one or more candidate operation data among multiple initial operation data to obtain the operation determination result. The corresponding operation determination result includes: at the corresponding operation time, there is one candidate operation data among multiple initial operation data, or at the corresponding operation time, there are multiple candidate operation data among multiple initial operation data.

[0061] This involves multiple candidate operation data, which are operation data with the same operation time in the corresponding operation time (i.e., the same operation time).

[0062] This involves data prioritization, which is used to distinguish the importance or reliability of different candidate operation data. It is used to select the more suitable operation data for generating a time trajectory when multiple data points exist for the same operation time. Specifically, data priority can include:

[0063] Data source priority: This specifically includes data source attribute priority and data source quality priority;

[0064] Timestamp freshness: Data with the most recent timestamp takes precedence;

[0065] Manual confirmation mechanism: For conflicts that cannot be resolved automatically, a conflict report is generated for manual confirmation.

[0066] The priority of data source attributes is determined based on the characteristics of the data source attributes. Taking fixed operating devices and mobile operating devices as examples, the data source attributes of fixed operating devices include: stable deployment, reliable collection environment, and fixed data transmission link. On the other hand, the data source attributes of mobile operating devices include: susceptible to interference from location, signal, and environment, and relatively greater data fluctuation. Therefore, to ensure data reliability, data from fixed operating devices is set as a high-priority trusted source, and data from mobile operating devices is set as a secondary-priority reference source. That is, data from fixed operating devices takes precedence over data from mobile operating devices (configurable).

[0067] The data source quality priority is determined based on the data quality scores of each data source, including: dynamically determining the fusion weight of each data source in the data fusion process according to its data quality score in the multi-source operation data. The weight calculation formula is as follows:

[0068] Weight of data source i = Data quality score of data source i / Sum of data quality scores of all data sources;

[0069] This involves target operation data, which is representative operation data selected from multiple candidate operation data based on data priority, and used to construct the operation time trajectory. For example, the candidate operation data with the highest priority is used as the target operation data.

[0070] The system filters and judges the initial operation data for each operation time according to the execution order of multiple operation times. It can match the corresponding candidate operation data for each operation time. When multiple candidate operation data appear at the same time, it sets data priority based on data source attribute characteristics, data quality, timestamp freshness and other dimensions. It can reliably select the more credible and representative target operation data from multiple conflicting data. After traversing all operation times and completing data filtering, it integrates them to form an operation time trajectory. It can completely restore the real time context of the target object performing the target operation on different devices, ensuring the accuracy and consistency of the time trajectory, avoiding trajectory distortion caused by duplication or conflict of multi-source data, and providing a reliable and clear data foundation for subsequent determination of operation execution status based on target time constraints.

[0071] As an optional embodiment, determining the current operation task corresponding to the target object within the target time period includes: determining multiple candidate operation tasks for the target object within the target time period; determining the current task configuration corresponding to each of the multiple candidate operation tasks; and determining the current operation task from the multiple candidate operation tasks based on the current task configuration corresponding to each of the multiple candidate operation tasks, wherein the current operation task is a candidate operation task whose current task configuration is a predetermined task configuration among the multiple candidate operation tasks.

[0072] This involves multiple candidate operation tasks. These candidate operation tasks are multiple operation tasks that the target object may execute within the target time period and can be filtered and matched by the system. They form a candidate set for determining the final current operation task. For example, production tasks of production line L1 and production tasks of production line L2; or daytime single-period operation tasks, fixed double-period operation tasks, fixed triple-period operation tasks, daytime long-period operation tasks, and nighttime long-period operation tasks. Among them, daytime single-period: 08:00-17:00; fixed double-period: 8:00-08:00 the next day, with 24 hours of downtime; fixed triple-period: 8:00-20:00, 20:00-08:00 the next day, with 24 hours of downtime; daytime long-period: 08:00-20:00; nighttime long-period: 20:00-08:00 the next day, etc.

[0073] This includes the current task configuration, which represents the task status information corresponding to each candidate operation task, indicating whether the corresponding candidate operation task meets the execution conditions. For example, the current configurations for task active, inactive, pending confirmation, enabled, and disabled are respectively.

[0074] This involves pre-defined task configurations, which are standard status information preset to determine whether a task is active. These configurations serve as the criteria for selecting the current task from multiple candidate tasks. For example, the standard configurations correspond to "task active," "task inactive," "task pending confirmation," "task enabled," and "task disabled."

[0075] By first identifying multiple candidate operation tasks for the target object within the target time period, all matching candidate tasks within that time period can be comprehensively covered. Then, the corresponding current task configuration is determined for each candidate operation task, which can accurately obtain information such as the effective status and execution conditions of each task. Finally, the current operation task is selected from the candidate operation tasks based on the predetermined task configuration, which can accurately match operation tasks that meet the effective conditions and are suitable for actual execution. This ensures that the determined current operation task fits the actual working scenario of the target object within the target time period, avoiding matching deviations caused by incomplete task scope or inaccurate status judgment, and providing a real and reliable task basis for subsequent analysis of task time differences and determination of time constraints.

[0076] As an optional embodiment, determining the current task configuration corresponding to each of the multiple candidate operation tasks includes: for any one of the multiple candidate operation tasks, determining the current task configuration corresponding to that candidate operation task in the following manner: determining the current task level corresponding to that candidate operation task; determining the associated task level corresponding to the current task level, wherein the associated task level includes a parent task level and a child task level; and determining the current task configuration corresponding to that candidate operation task based on the current configuration set corresponding to the current task level and the associated configuration set corresponding to the associated task level.

[0077] This involves the current task level, which is the hierarchy to which the candidate operation task belongs, used to distinguish the hierarchical relationships of different tasks.

[0078] This involves associated task levels, which are other task levels that correspond to or are subordinate to the current task level, including the parent task level and child task levels corresponding to the current task level.

[0079] This involves the parent task level, which is the level above the current task level in the hierarchical structure; that is, the current task level belongs to the parent task level.

[0080] This involves subtask levels, which are the next level below the current task level in the hierarchical structure; that is, the subtask level belongs to the current task level.

[0081] This involves the current configuration set, which is a set of configuration information corresponding to the current task level, used to determine the effective status of tasks at this level.

[0082] This involves an associated configuration set, which is a set of configuration information corresponding to the associated task level, used together with the current configuration set to determine the final effective state of the task.

[0083] By determining the corresponding current task level for each candidate operation task, the structural relationship of the task can be clarified. Then, the parent and child task levels that are subordinate to the current task level can be located, and all related hierarchical information of the task can be obtained completely. Combining the current configuration set of the current task level with the related configuration sets of the associated task levels, the current task configuration is determined. The effective status of the task can be comprehensively judged from a multi-level structure, ensuring that the obtained current task configuration is more comprehensive and fits the actual belonging structure. This avoids configuration judgment bias caused by relying on only a single level of information, and provides a reliable judgment basis for accurately selecting the current operation task from multiple candidate operation tasks.

[0084] As an optional embodiment, the current task configuration corresponding to any candidate operation task is determined based on the current configuration set corresponding to the current task level and the associated configuration set corresponding to the associated task level. This includes: determining the configuration filtering constraints corresponding to any candidate operation task; and determining the current task configuration corresponding to any candidate operation task based on the configuration filtering constraints, the current configuration set corresponding to the current task level, and the associated configuration set corresponding to the associated task level.

[0085] This involves configuration filtering constraints, which are the filtering and retrieval constraints followed when selecting, matching, and updating configuration information in a multi-level structure. These constraints are used to determine the final effective configuration content from the configuration sets of the current task level and related task levels, including constraints such as configuration inheritance, overriding, query order, and change propagation.

[0086] By defining corresponding configuration filtering constraints for candidate operation tasks, the selection, inheritance, and transmission methods between multi-level configurations can be clarified. Then, based on these constraints and the configuration sets of the current task level and related task levels, the current task configuration can be determined. This allows for accurate matching of the final effective configuration content in a multi-level structure, ensuring that the determined current task configuration conforms to the configuration association between levels. This avoids inaccurate determination of the effective status due to multi-level configuration conflicts or query chaos, and provides a stable and reasonable configuration basis for accurately determining the effective status of candidate operation tasks in the future.

[0087] As an optional embodiment, determining the target time constraint corresponding to the target object based on the time difference parameter includes: determining multiple task feature data based on the time difference parameter, wherein the multiple task feature data correspond to different task features; and determining the target time constraint corresponding to the target object based on the multiple task feature data.

[0088] This involves multiple task feature data items, which are used to describe task attributes and correspond to different task feature dimensions, such as the time, scenario, process, and type of the task.

[0089] By determining multiple task feature items corresponding to different task feature dimensions through time difference parameters, the task attributes can be fully characterized from multiple dimensions. Then, the target time constraint can be determined based on these task feature items, so that the time constraint matches the actual task characteristics of the target object. This ensures that the target time constraint is more in line with the actual operation scenario and avoids unreasonable constraint settings due to a single dimension. It provides a more reasonable and suitable time benchmark for accurately determining the operation execution status in the future.

[0090] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0091] In related technologies, the operational execution status of an object during material production reflects the progress of production tasks. To ensure the orderly advancement of the production process, it is necessary to determine the operational execution status of the object. However, in related technologies, there is a technical problem of inaccurate determination of the operational execution status.

[0092] There is currently no effective solution to the above problems.

[0093] Therefore, an optional embodiment of the present invention provides a method for determining the operation execution state of an object, which can effectively solve the above-mentioned technical problems. Specifically, it can be implemented through an operation execution state determination system. Figure 2 This is a schematic diagram of the system architecture for determining the operation execution status in an optional embodiment of the present invention. Figure 3 This is a schematic diagram of the multi-source heterogeneous data acquisition and fusion mechanism in an optional embodiment of the present invention, such as... Figure 2 and Figure 3 As shown below, the system comprises: a multi-source data acquisition layer, a data fusion and preprocessing layer, a multi-task hierarchical architecture management layer, a task management layer, an application service layer, and a user interaction layer.

[0094] (1) Multi-source data acquisition layer:

[0095] The multi-source data acquisition layer is used to acquire multi-source operational data (i.e., multi-source data) corresponding to the target object, specifically including:

[0096] 1) Heterogeneous data adapter module: Adopting a plug-in architecture, it provides dedicated adapters for different data source types, including: mobile operating devices, fixed operating devices, etc.

[0097] 2) Relational Database Adapter: Supports multiple types of relational databases;

[0098] 3) No-relational database adapter: Supports multiple types of No-relational databases;

[0099] 4) Application Programming Interface (API) adapter: supports multiple types of interfaces;

[0100] 5) File system adapter: Supports reading CSV, JSON, and XML files;

[0101] 6) Message Queue Adapter: Supports various distributed message queue middleware, etc.

[0102] 7) Data source management unit: A relational database management system is used to store data source configurations, and the data source status is checked periodically through a pre-defined operating system service;

[0103] 8) Data Standardization Unit: For the collected multi-source heterogeneous data, the data format parsing component performs format unification, timestamp alignment, and data cleaning, converting it into a unified JSON format.

[0104] (2) Data fusion and preprocessing layer:

[0105] The data fusion and preprocessing layer is used to further fuse and preprocess the acquired multi-source operational data, specifically including:

[0106] 1) Data Quality Assessment Unit: Establish a data quality assessment index system (completeness, accuracy, timeliness), and use a weighted scoring model to assess the quality of data corresponding to each data source;

[0107] 2) Data fusion engine: Based on timestamps and the identifier (ID) of the target object (e.g., operator), it associates and merges multi-source data, dynamically allocates weights according to data quality scores, and generates complete operation trajectory data (i.e., operation time trajectory).

[0108] After dynamically assigning weights based on the data quality score, complete operation trajectory data is generated, as shown in the example below:

[0109]

[0110] Specifically, The data for data source i after weighting includes operation timestamps. Operation type Data source Data source weight Operation results For example, ; This represents the total number of data points in the data source.

[0111] By forming operation trajectory data from various data sources, the sequence of operation trajectory data allows for a complete traversal and retrieval of all data sources, ensuring the integrity and accuracy of subsequent conflict resolution. It also facilitates the rapid location and selection of the highest-weighted data source as the preferred trusted source based on the data source quality priority (weight from high to low), thereby efficiently and accurately resolving conflicts and ensuring the uniqueness and reliability of the final trajectory data.

[0112] Finally, a log of the conflict resolution results is generated, supporting traceability.

[0113] 3) Data verification module: Through a multi-source data cross-validation mechanism, it detects data anomalies and conflicts, and resolves conflicts using a priority strategy or manual confirmation mechanism;

[0114] 4) Data caching unit: Employs caching technologies such as distributed memory databases to improve data query efficiency.

[0115] (3) Multi-task hierarchical architecture management layer:

[0116] 1) Task hierarchy management unit: It adopts a tree architecture model and supports multi-level task hierarchy definition (factory task hierarchy → workshop task hierarchy → production line task hierarchy). Each task hierarchy node can be configured independently.

[0117] 2) Data isolation mechanism: Use database sharding or data tagging technology to achieve physical / logical isolation of data between different task levels;

[0118] 3) Configuration inheritance unit: Supports inheritance and overriding relationships of global configuration (such as factory task level) and local configuration (workshop task level). The sub-task level can selectively override the task configuration of the parent task level.

[0119] 4) Access Control Unit: Based on the Access Control Model (RBAC), it implements cross-task-level data access control.

[0120] (4) Task Management Layer:

[0121] Figure 4 This is a schematic diagram of the task definition and matching process in an optional embodiment of the present invention, such as... Figure 4As shown, the task management layer is used to define multiple candidate operation tasks corresponding to the target object (taking operation work as an example, such as production operator, inspection operator, etc.) before determining the current operation task and the scheduled operation tasks corresponding to the target object within a defined target time period, and to determine the current operation task from the multiple candidate operation tasks, including:

[0122] 1) Task Type Dimension Definition Module: This module uses a domain-specific language (DSL) to describe task type rules and supports custom multi-dimensional task types. These dimensions can include the following:

[0123] Time dimension: Supports dynamic adjustment of time range. For example, task types can be divided according to time dimension, including: single-period tasks, dual-period consecutive tasks, multi-period rotating tasks, long-period daytime tasks, long-period nighttime tasks, temporary scheduling tasks, etc.

[0124] Object type dimension: production operators, inspection operators, etc.;

[0125] Production line dimension: such as first production line, second production line, third production line, etc.;

[0126] Production task dimensions: material production, equipment maintenance, cleaning and disinfection, etc.;

[0127] In addition, the task type dimension definition module also supports custom dimension extensions.

[0128] 2) Task type matching algorithm unit: Based on the operator's actual activity data and preset task configuration rules, it automatically matches the operator's current operation task using a weight allocation mechanism and fuzzy matching algorithm;

[0129] 3) Dynamic statistics engine: It adopts a streaming computing engine (such as Azure Stream Analytics) to realize real-time statistical calculation, and supports the combination of multiple dimensions such as time, object type, production line, production task to statistically analyze the operation data corresponding to the target operation. The statistical results are updated in real time.

[0130] 4) Anomaly Identification and Compensation Module: Configure anomaly identification rules through the rule engine, and combine machine learning algorithms to automatically identify anomalies in the operation data corresponding to the target operation (such as time record anomalies) caused by non-operator reasons (such as operation anomalies, equipment anomalies or production scheduling anomalies), and automatically trigger the compensation mechanism.

[0131] 5) Statistical Dimension Extension Interface: Provides an API interface to support adding new statistical dimensions as needed, automatically identifying and generating corresponding statistical reports.

[0132] (5) Application service layer:

[0133] 1) Operation statistics service corresponding to the target operation: Provides an interface for the front end to call, supporting real-time query and historical statistics;

[0134] 2) Report generation module: Automatically generates operation reports corresponding to multi-dimensional target operations, and supports exporting to multiple file formats;

[0135] 3) Early warning notification service: When an operational anomaly or data conflict is detected corresponding to the target operation, an early warning notification (such as a software pop-up, SMS, email, etc.) is automatically sent.

[0136] 4) Cross-task level statistical aggregation service: It adopts a distributed query engine to perform parallel query and aggregation of data at multiple task levels, and supports cross-task level summary statistics.

[0137] (6) User interaction layer:

[0138] 1) Client management backend: Allows for system configuration, data query, report viewing, and permission management;

[0139] 2) Mobile application: Operators can view the operation records of the corresponding target operation, apply for task changes, apply for changes to the exceptions of the target operation, and receive notifications.

[0140] Based on the above, the following technical effects can be achieved:

[0141] 1) Improved data fusion capabilities: Through heterogeneous data adapter plug-in architecture and data quality assessment algorithm, it supports efficient fusion of data from multiple heterogeneous data sources, improving data integration efficiency by more than 50%;

[0142] 2) Improved data accuracy: Through cross-validation of multi-source data and dynamic weight allocation, the statistical error of the target operation caused by the error of a single data source is reduced, and the accuracy of the operation data corresponding to the target operation is improved to over 98%.

[0143] 3) Refined task type statistics: Supports hot updating of task type definition rules, real-time streaming computing, and dynamic expansion of dimensions. Task type statistics are upgraded from static configuration to dynamic configuration to adapt to changes in the production environment. Supports multi-dimensional task type definition and statistics to meet the complex task time-series scheduling needs of material production. The statistical dimensions are expanded from a single time dimension to multiple (more than 10).

[0144] 4) Improved data analysis efficiency: Reduces manual processing workload by more than 95%, automatically identifies anomalies through the rule engine, and shortens anomaly handling time from an average of 2 hours to 5 minutes;

[0145] 5) Improved efficiency of multi-task hierarchical management: Through a tree-structured architecture model and configuration inheritance mechanism, the cost of unified management of multi-task hierarchies is reduced by 60%, and the efficiency of cross-task hierarchical statistics is improved by 70%;

[0146] 6) Strong system integration: Standardized interface design allows for quick integration with existing resource management systems, production execution control systems, etc., reducing implementation costs;

[0147] 7) Strong system scalability: The plug-in architecture supports rapid integration with new data sources, and the dimensional expansion interface supports changes in business requirements, extending the system lifecycle;

[0148] 8) Production scheduling support: Multi-dimensional statistical reports can provide more comprehensive operational data analysis corresponding to target operations, assisting in cost optimization and task scheduling.

[0149] Optionally, for the acquired multi-source operational data, the following multi-source heterogeneous data fusion mechanism can be adopted to further fuse the data, so as to achieve unified access, quality assessment, dynamic weight allocation and conflict resolution of data from different sources and with different structures, wherein:

[0150] For the heterogeneous data adapter module, a heterogeneous data adapter plug-in architecture is adopted, including:

[0151] 1) Adopting a plug-in design, each data source type corresponds to an independent adapter plug-in;

[0152] 2) The adapter implements a unified interface specification: connect(), disconnect(), extractData(), and getStatus(). Connect() represents the connection establishment interface, used to establish a communication connection between the adapter and the corresponding data source, completing authentication and link initialization. Disconnect() represents the connection termination interface, used to actively disconnect the communication connection between the adapter and the data source, releasing system resources. ExtractData() represents the data extraction interface, used to extract structured or unstructured business data from the target data source according to rules, returning a standard format dataset. GetStatus() represents the status detection interface, used to obtain the real-time running status, connection status, and data acquisition status of the data source and adapter, returning status identifiers and health information.

[0153] 3) Supports dynamic loading and unloading of adapters, allowing new data source types to be added without system restart;

[0154] 4) Adapter plugins can be developed, tested, and deployed independently, improving system scalability;

[0155] For the data quality assessment unit, the following data quality assessment algorithms can be configured to achieve data quality assessment, including:

[0156] 1) Based on the data quality assessment index system, determine the scores for multiple types of data, including:

[0157] Completeness score: Field missing rate = Number of missing fields / Total number of fields, Completeness score = 1 - Missing rate;

[0158] For example, if there are 100 fields in total and 2 are missing, the missing rate is 2 / 100 = 0.02. Therefore, the completeness score is 1 - 0.02 = 0.98.

[0159] Accuracy score: Error rate = Number of error records / Total number of records; Accuracy score = 1 - Error rate.

[0160] For example, if there are 1000 records in total and 5 errors, the error rate is 5 / 1000 = 0.005. Therefore, the accuracy score is 1 - 0.005 = 0.995.

[0161] Timeliness score: Data delay time (seconds), Timeliness score = max(0, 1 - delay time / delay threshold);

[0162] For example, with a 10-second delay and a delay threshold of 100, the timeliness score = max(0, 1-10 / 100) = 0.90.

[0163] 2) A weighted scoring model is used to summarize the scores of multiple data categories to obtain a quality score:

[0164] Quality score = w1 × Completeness score + w2 × Accuracy score + w3 × Timeliness score;

[0165] Among them, w1, w2, and w3 are weighting coefficients, which can be configured according to business scenarios (default 0.4, 0.3, and 0.3).

[0166] For data fusion engines, the following dynamic weight allocation strategies can be adopted to dynamically assign weights based on data quality scores:

[0167] Based on the data quality scores of each data source in the multi-source operational data, their fusion weights in the data fusion process are dynamically determined. The weight calculation formula is as follows:

[0168] Data weight of data source i = Data quality score of data source i / Sum of data quality scores of all data sources;

[0169] Specifically, when the data quality score of a certain data source is lower than the threshold (such as 0.6), its weight will be automatically reduced or the data from the backup data source will be switched. In addition, manual intervention to adjust the weight is supported, such as setting the priority of production system data to the highest level manually.

[0170] For the data validation module, a data conflict resolution mechanism can be configured, including:

[0171] Perform conflict detection: When multiple inconsistent clock-in / out records appear at the same time point for the same operator, they are marked as data conflicts;

[0172] Execute conflict resolution strategies based on data priority, where data priority includes:

[0173] Data source priority: This specifically includes data source attribute priority and data source quality priority;

[0174] Timestamp freshness: Data with the most recent timestamp takes precedence;

[0175] Manual confirmation mechanism: For conflicts that cannot be resolved automatically, a conflict report is generated for manual confirmation.

[0176] The priority of data source attributes is determined based on the characteristics of the data source attributes. Taking fixed operating devices and mobile operating devices as examples, the data source attributes of fixed operating devices include: stable deployment, reliable collection environment, and fixed data transmission link. On the other hand, the data source attributes of mobile operating devices include: susceptible to interference from location, signal, and environment, and relatively greater data fluctuation. Therefore, to ensure data reliability, data from fixed operating devices is set as a high-priority trusted source, and data from mobile operating devices is set as a secondary-priority reference source. That is, data from fixed operating devices takes precedence over data from mobile operating devices (configurable).

[0177] The data source quality priority is determined based on the data quality scores of each data source, including: dynamically determining the fusion weight of each data source in the data fusion process according to its data quality score in the multi-source operation data. The weight calculation formula is as follows:

[0178] Weight of data source i = Data quality score of data source i / Sum of data quality scores of all data sources;

[0179] Finally, a log of the conflict resolution results is generated, supporting traceability.

[0180] Optionally, for the multi-task hierarchical architecture management layer, a multi-task hierarchical architecture support mechanism can be configured, including:

[0181] 1) Tree-structured model:

[0182] Task hierarchy data structure: An adjacency list model is used to store the hierarchical relationship of the task hierarchy;

[0183] For example, the table structure may include: task level unique identifier sOrg, task level unique identifier org_name, parent task level unique identifier parent_id, current task level ranking level, task level path, etc.

[0184] For task-level paths (such as Workshop 1 / Production Line 1), it is convenient to quickly query related task levels.

[0185] Supports an unlimited hierarchical task architecture, such as: Factory → Workshop → Production Line → Team → ...

[0186] Each task-level node can be configured independently: data source connection parameters, task type rules, statistical rules, and permission settings.

[0187] 2) Configure inheritance mechanism:

[0188] Configuration items are stored using key-value pairs: configuration item key name config_key, configuration item value config_value, task level identifier sOrg, and whether to inherit from the parent configuration is_inherited.

[0189] The configuration inheritance rules may include:

[0190] By default, child task levels inherit all configurations from their parent task levels.

[0191] Subtask levels can override configuration options of parent task levels (such as is_inherited=false);

[0192] When querying configurations, the system first searches for subtask-level configurations; if a configuration does not exist, it searches upwards to the parent task level.

[0193] Regarding configuration change propagation: When the configuration of the parent task level changes, the child task level is automatically notified (optional: whether to update synchronously).

[0194] 3) Data isolation strategy:

[0195] Physical isolation: Each task level uses an independent database instance or database, and the data is completely isolated;

[0196] Logical isolation: All task-level data is stored in the same database, and data isolation is achieved through the sOrg field;

[0197] Isolation strategies are configurable: higher-level task levels (e.g., the highest task level) can be configured with physical isolation, while lower-level task levels (e.g., other task levels besides the highest task level) can be configured with logical isolation.

[0198] Data access control: All data queries must include the sOrg header to ensure data isolation.

[0199] 4) Cross-task level statistical aggregation algorithms, including:

[0200] Cross-task level queries employ a distributed query engine to implement query flow and access control:

[0201] The query process includes:

[0202] Analyze the query conditions to determine the scope of the task hierarchy to be queried;

[0203] Based on the data isolation strategy, determine the query method (cross-database query or single-database query).

[0204] Parallel querying of data at each task level;

[0205] Perform aggregation calculations on the query results (sum, average, maximum, etc.);

[0206] Returns the aggregated results.

[0207] Access control, specifically, allows only authorized users to perform cross-task level queries. This can be implemented using access control models, including Role-Based Access Control (RBAC). Permission granularity includes task level, module level, and operation level. Permission inheritance is supported, meaning that permissions at a sub-task level inherit some permissions from the parent task level by default. Furthermore, permission verification can be configured: all API requests must verify whether the user has permission to access the corresponding task level data.

[0208] The following description, with specific examples, will further illustrate this point.

[0209] (I) Deployment and implementation of a multi-task hierarchical architecture:

[0210] Figure 5 This is a schematic diagram of the multi-task hierarchical architecture and cross-task hierarchical statistical process in an optional embodiment of the present invention, such as... Figure 5 As shown, a material production system comprises three subsystems (using plants A, B, and C as examples), and the system deployment is as follows:

[0211] (1) Task hierarchy architecture configuration:

[0212] 1) Create a task-level node for the material production system (sOrg=1);

[0213] 2) Create a task hierarchy node for factory A (sOrg=2, parent_id=1);

[0214] 3) Create a task hierarchy node for factory B (sOrg=3, parent_id=1);

[0215] 4) Create the C factory task hierarchy node (sOrg=4, parent_id=1);

[0216] 5) For each of the factories A, B, and C, you can continue to create sub-task levels such as workshops and work groups.

[0217] (2) Data isolation is implemented using a hybrid isolation strategy:

[0218] 1) Material production system-level data: Independent database instance db_group;

[0219] 2) Factory data: Logical isolation is used, and all factory data is stored in the same database db_factories, distinguished by the sOrg field;

[0220] 3) Data access control: All Structured Query Language (SCL) queries must include the condition "WHERE sOrg = ?", where "WHERE sOrg = EX" indicates that only data belonging to the EX task level is queried, where EX represents a specific task level among all task levels.

[0221] (3) Example of configuration inheritance:

[0222] For material production system-level configuration: Define unified basic rules for task types (i.e., time constraints), including daytime single-period: 08:00-17:00; fixed double-period: 8:00-08:00 the next day, with 24 hours of downtime; fixed triple-period: 8:00-20:00, 20:00-08:00 the next day, with 24 hours of downtime; daytime long-period: 08:00-20:00; nighttime long-period: 20:00-08:00 the next day, etc.

[0223] 1) Plant A coverage configuration: Adjust the daytime single-period time to 07:30-16:30;

[0224] 2) New configuration for Plant B: Added "Equipment Maintenance" task type (only for Plant B);

[0225] 3) Plant C inherited configuration: Use the default configuration of the material production system (daytime single time period 08:00-17:00).

[0226] (4) Implementation of access control:

[0227] 1) The administrator of the material production system (user1): can access data at all task levels;

[0228] 2) A factory administrator (user2): Can only access A factory data (sOrg=2 and its subtask levels);

[0229] 3) Permission verification: The API interface " / api / attendance" verifies whether user2 has permission to access the target sOrg before querying. " / api / attendance" is used to represent the task operation data access interface.

[0230] (II) Implementation of Multi-Source Heterogeneous Data Fusion:

[0231] Taking factory A (sOrg=2) as an example, the data source includes:

[0232] Production management system corresponding to the target operation: database, table structure (fixed);

[0233] The operation execution system corresponding to the target operation returns JSON format data through the application programming interface (REST API) representing state transitions;

[0234] The target operation corresponds to the operating device: data is returned in JSON format via a REST API;

[0235] The resource management system corresponding to the target operation returns XML format data through standardized, cross-system remote service call technology.

[0236] The process automation system corresponding to the target operation: a relational database management system database with a fixed table structure;

[0237] The specific process of data fusion:

[0238] (1) Adapter loading:

[0239] 1) The relational database adapter connects to the production management system database;

[0240] 2) Automated database connection process using relational database management system adapters;

[0241] 3) Represent the state transition (REST) ​​adapter calling the operation execution system of the target operation and the operation device API corresponding to the target operation;

[0242] 4) Standardized remote service invocation protocol adapter: The standardized, cross-system remote service invocation technology of the resource management system.

[0243] (2) Data format conversion:

[0244] Relational databases manage table data: data is retrieved through queries using the standard data manipulation language of relational databases and converted into a unified structured data table (DataTable).

[0245] Relational database table data: retrieved through the standard data manipulation language of relational databases and converted into a DataTable;

[0246] REST API data: JSON is returned directly, and the system internally deserializes it into a standard data model (or DataTable structure) for further processing;

[0247] Standardized remote service invocation protocols correspond to standardized, cross-system remote service invocation technologies: parsing XML responses, converting them into a unified JSON format, and internally deserializing them into a standard data model (or DataTable structure) for subsequent processing;

[0248] All data is presented in a unified example as follows:

[0249] {"sUserID": "xxx", "sUserName": "xxx","sRecordTime": "2026-01-01 08:00:00", "sOrg": 2, ...};

[0250] Wherein, "sUserID": "xxx" represents the unique identifier of the operation subject; "sUserName": "xxx" represents the user name; "sRecordTime": "2026-01-01 08:00:00" represents the specific time point when the data was generated, collected, or recorded; and "sOrg": 2 represents the task level identifier.

[0251] (3) Data quality assessment (taking a certain time period as an example):

[0252] Production Management System: Completeness 95% (5% of fields missing), Accuracy 98%, Timeliness 1 second delay → Quality Score = 0.4 × 0.95 + 0.3 × 0.98 + 0.3 × 0.99 = 0.971;

[0253] Operation execution system for the target operation: Completeness 90%, Accuracy 95%, Timeliness 5-second delay → Quality Score = 0.4 × 0.9 + 0.3 × 0.95 + 0.3 × 0.95 = 0.93;

[0254] The operating equipment corresponding to the target operation: integrity 85%, accuracy 90%, timeliness 10-second delay → quality score = 0.4×0.85+0.3×0.9+0.3×0.9=0.88;

[0255] Resource Management System: Completeness 98%, Accuracy 99%, Timeliness 60-second delay → Quality Score = 0.4 × 0.98 + 0.3 × 0.99 + 0.3 × 0.4 = 0.809 (Low Timeliness Score);

[0256] (4) Data fusion weight allocation:

[0257] Data source priority specifically includes data source attribute priority and data source quality priority;

[0258] The priority of data source attributes is determined based on the characteristics of the data source attributes. Taking fixed operating devices and mobile operating devices as examples, the data source attributes of fixed operating devices include: stable deployment, reliable collection environment, and fixed data transmission link. On the other hand, the data source attributes of mobile operating devices include: susceptible to interference from location, signal, and environment, and relatively greater data fluctuation. Therefore, to ensure data reliability, data from fixed operating devices is set as a high-priority trusted source, and data from mobile operating devices is set as a secondary-priority reference source. That is, data from fixed operating devices takes precedence over data from mobile operating devices (configurable).

[0259] The data source quality priority is determined based on the data quality scores of each data source, including: dynamically determining the fusion weight of each data source in the data fusion process according to its data quality score in the multi-source operation data. The weight calculation formula is as follows:

[0260] Weight of data source i = Data quality score of data source i / Sum of data quality scores of all data sources;

[0261] The specific examples of data source quality priorities are as follows:

[0262] Weight of the production management system: 0.971 / (0.971+0.93+0.88+0.809)≈0.27;

[0263] The system weight for executing the target operation is approximately 0.93 / 3.59 ≈ 0.26.

[0264] The weight of the operating device corresponding to the target operation is approximately 0.88 / 3.59 ≈ 0.245.

[0265] Resource management system weight: 0.809 / 3.59≈0.225;

[0266] After dynamically assigning weights based on the data quality score, complete operation trajectory data is generated, as shown in the example below:

[0267]

[0268] Specifically, The data for data source i after weighting includes operation timestamps. Operation type Data source Data source weight Operation results For example, ; This represents the total number of data points in the data source.

[0269] like:

[0270] List1 = [2026-03-26 08:05:30, Production task start confirmation, Production Management System, 0.27, Success];

[0271] List2 = [2026-03-26 08:05:35, Production task start confirmation, Operation execution system, 0.26, Success];

[0272] List3 = [2026-03-26 10:10:20, Production task node inspection confirmation, target operation corresponding operating equipment, 0.245, success];

[0273] List4=[2026-03-26 10:10:25, Production task node inspection confirmation, Resource Management System, 0.225, Success].

[0274] It should be noted that, due to the poor timeliness of resource management systems, their weight can be manually reduced or threshold filtering can be set in practical applications.

[0275] The above data source priorities are used to resolve subsequent data conflicts. Taking data source quality priority as an example, the data source priorities are determined according to the data source weight from high to low, such as the weight of the production management system > the weight of the operation execution system of the target operation > the weight of the operation equipment corresponding to the target operation > the weight of the resource management system. The data source with the highest weight is the priority trusted source.

[0276] (5) Data conflict resolution:

[0277] 1) Scenario: The production management system shows that the target object U has performed the target operation (e.g., task execution confirmation operation) at 08:00, but the operation execution system shows that the target operation was not executed until 08:05;

[0278] 2) Conflict detection: Inconsistent timestamps (08:00 and 08:05);

[0279] 3) Automatic Resolution: Based on data source priority, the highest priority data source is the preferred trusted source. Taking data source quality priority as an example for data conflict resolution, the specific data source priority can be determined according to the data source weight from high to low, that is, the data source with the highest weight is the preferred trusted source. For example, if the weight of the production management system is > the weight of the operation execution system of the target operation > the weight of the operation equipment corresponding to the target operation > the weight of the resource management system, then the data from the production management system will be used first.

[0280] 4) Record conflict logs for later review.

[0281] (III) Implementation of dynamic statistics:

[0282] If Factory A needs to temporarily adjust the task type due to equipment failure:

[0283] (1) Hot update of task type rules:

[0284] 1) Modify task type rules through the interactive interface: Adjust the execution time of affected production line tasks from 8 hours to 6 hours (08:30-14:30).

[0285] 2) The system saves the new rules to the configuration center;

[0286] 3) The task type matching module listens for configuration changes and automatically reloads the rules into memory;

[0287] 4) Rule effective time: The delay from configuration change to effective date is <1 second;

[0288] (2) Real-time statistics updates:

[0289] The streaming engine (Flink) receives the operation data stream corresponding to the target operation in real time;

[0290] Recalculate task type matching according to the new task type rules;

[0291] Statistical results (such as the total number of operators for fixed daytime tasks on production line L) are updated in real time to the statistical results table;

[0292] The front-end obtains statistical results through long-connection communication technology, and the interface refreshes automatically.

[0293] (3) Example of dimensional expansion:

[0294] The factory has added a "product batch" dimension requirement (to calculate the production operation hours for different product batches).

[0295] Add a new dimension through the dimension management interface, as shown in the example below:

[0296] Dimension name: product;

[0297] Data type: String;

[0298] Is it scalable? Yes.

[0299] Then, the system will automatically execute:

[0300] Add a "product" field (where "product" represents the dimension name) to the statistics table;

[0301] Update the statistical query logic;

[0302] Regenerate the statistical report template.

[0303] The operation data corresponding to the subsequent target operation will automatically include product information, and statistics can be compiled according to this dimension.

[0304] (iv) Implementation of cross-task level statistics:

[0305] The material production system needs to track the execution status of monthly target operations across all factories:

[0306] (1) Cross-task level query request:

[0307] 1) Receive API requests: Query multi-source operation data for task level 1, task level 2, and task level;

[0308] 2) Permission verification: Verify whether the current user has permission to access multi-source operation data at task level 1, task level 2, and task level 2.

[0309] (2) Distributed query execution:

[0310] 1) Based on the query engine, determine which factories, A, B, and C, need to be queried;

[0311] 2) Based on the data isolation strategy:

[0312] If the data from factories A, B, and C are stored in the same database (logically isolated), execute the pre-defined query statement;

[0313] If physical isolation is used (each factory has its own independent database), cross-database queries are required: execute parallel queries and aggregate the query results;

[0314] Based on the data retrieved from the query, the results are aggregated: the multi-source operation data corresponding to the target operations of the three factories are summarized and calculated (summation, average, etc.), and the aggregated results are returned to the front end.

[0315] The above optional implementation methods can achieve at least the following beneficial effects:

[0316] (1) Compared with related technologies, the present invention can comprehensively collect various execution information generated by the target object in the target time period based on different operating devices by acquiring multi-source operation data corresponding to the target object. By determining the current operation task and the predetermined operation task corresponding to the target object in the target time period, the actual task and the preset task of the target object in the corresponding time period can be clearly identified. By determining the time difference parameter between the current operation task and the predetermined operation task, the difference characteristics of the two types of tasks in operation time can be accurately reflected. By determining the target time constraint corresponding to the target object based on the time difference parameter, a time reference basis that adapts to the actual task situation of the target object can be formed. By combining the target time constraint with multi-source operation data to determine the operation execution status, the determination of the operation execution status is more in line with the actual execution process, so as to effectively solve the problem of inaccurate determination of operation execution status in related technologies, and thus solve the technical problem of inaccurate determination of operation execution status in related technologies when determining the operation execution status of the object.

[0317] (2) Compared with related technologies, this invention matches the corresponding operation time of each initial operation data in the multi-source operation data, so that each original operation data has a precise time identifier. Then, by combining the initial operation data and the corresponding operation time, the operation time trajectory of the target object performing the target operation on different operation devices can be sorted and integrated in sequence. This can completely restore the actual process of the target object performing the target operation, the time sequence, device switching and time distribution. Finally, the operation time trajectory is compared and analyzed based on the target time constraint. It can comprehensively check the time compliance and actual completion of the target object's target operation based on the time requirements of the current operation task, and ensure that the judgment of the operation execution status is based on the complete restoration of the actual operation process. This avoids the judgment bias caused by single data or data analysis without time context, and achieves a more accurate and realistic determination of the target object's operation execution status.

[0318] (3) Compared with related technologies, the present invention filters and judges the initial operation data under each operation time according to the execution order of multiple operation times. It can match the corresponding candidate operation data for each operation time. When multiple candidate operation data appear at the same time, the data priority is set by combining the data source attribute characteristics, data quality, timestamp freshness and other dimensions. It can stably select the target operation data with higher credibility and more representativeness from multiple conflicting data. After traversing all operation times and completing data filtering, the data is integrated to form the operation time trajectory. It can completely restore the real time context of the target object performing the target operation on different devices, ensure the accuracy and consistency of the time trajectory, and avoid trajectory distortion caused by the duplication or conflict of multiple source data. It provides a reliable and clear data foundation for subsequent determination of the operation execution status based on the target time constraint.

[0319] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0320] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0321] Example 2

[0322] According to embodiments of the present invention, an apparatus for implementing the above-described method for determining the operation execution state of an object is also provided. Figure 6 This is a structural block diagram of an object operation execution state determination device according to an embodiment of the present invention, such as... Figure 6 As shown, the device includes: an acquisition module 602, a first determination module 604, a second determination module 606, a third determination module 608, and a fourth determination module 610. The device will be described in detail below.

[0323] The acquisition module 602 is used to acquire multi-source operation data corresponding to the target object. The multi-source operation data is the data generated by the target object performing target operations based on different operating devices within the target time period.

[0324] The first determining module 604 is connected to the above-mentioned obtaining module 602 and is used to determine the current operation task and the predetermined operation task corresponding to the target object within the target time period.

[0325] The second determining module 606 is connected to the first determining module 604 and is used to determine the time difference parameter between the current operation task and the predetermined operation task, wherein the time difference parameter is used to characterize the operation time difference feature between the current operation task and the predetermined operation task.

[0326] The third determining module 608, connected to the second determining module 606, is used to determine the target time constraint corresponding to the target object based on the time difference parameter.

[0327] The fourth determining module 610, connected to the third determining module 608, is used to determine the operation execution status corresponding to the target object based on the target time constraint and multi-source operation data.

[0328] It should be noted here that the above-mentioned acquisition module 602, first determination module 604, second determination module 606, third determination module 608, and fourth determination module 610 correspond to steps S102 to S110 in the method for determining the operation execution status of the implementation object. The multiple modules and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0329] Example 3

[0330] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute instructions to implement the operation execution state determination method for any of the above-described objects.

[0331] Example 4

[0332] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the operation execution state determination method of any of the above-described objects.

[0333] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0334] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0335] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0336] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0337] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0338] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0339] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of determining an operation execution state of an object, characterized by, The method comprises: obtaining multi-source operation data corresponding to a target object, wherein the multi-source operation data is data generated by the target object based on different operation devices respectively performing target operations within a target period; determining a current operation task and a predetermined operation task corresponding to the target object within the target period; determining a time difference parameter between the current operation task and the predetermined operation task, wherein the time difference parameter is used to represent an operation time difference feature between the current operation task and the predetermined operation task; based on the time difference parameter, determining a target time constraint corresponding to the target object; based on the target time constraint and the multi-source operation data, determining an operation execution state corresponding to the target object.

2. The method of claim 1, wherein, The method comprises: in the case that the multi-source operation data comprises a plurality of initial operation data, determining operation times corresponding to the plurality of initial operation data respectively; based on the plurality of initial operation data and the operation times corresponding to the plurality of initial operation data respectively, determining an operation time trajectory of the target object performing the target operation on different operation devices; based on the target time constraint and the operation time trajectory, determining an operation execution state corresponding to the target object.

3. The method of claim 2, wherein, The method comprises: determining, according to an execution order corresponding to a plurality of operation times respectively, for a corresponding operation time in the plurality of operation times, whether there is one or more candidate operation data in the plurality of initial operation data, to obtain an operation determination result, wherein the plurality of initial operation data and the plurality of operation times correspond one by one; in the case that the operation determination result is that there are a plurality of candidate operation data in the plurality of initial operation data, determining data priorities corresponding to the plurality of candidate operation data respectively; based on the data priorities corresponding to the plurality of candidate operation data respectively, determining target operation data from the plurality of candidate operation data; until the plurality of operation times are executed, target operation data corresponding to the plurality of operation times respectively is obtained; based on the target operation data corresponding to the plurality of operation times respectively, determining an operation time trajectory corresponding to the target object.

4. The method of claim 1, wherein, The method comprises: determining a plurality of candidate operation tasks of the target object within the target period; determining current task configurations corresponding to the plurality of candidate operation tasks respectively; based on the current task configurations corresponding to the plurality of candidate operation tasks respectively, determining the current operation task from the plurality of candidate operation tasks, wherein the current operation task is a candidate operation task in the plurality of candidate operation tasks, and the corresponding current task configuration is a predetermined task configuration.

5. The method of claim 4, wherein, The determining the current task configuration corresponding to each of the plurality of candidate operation tasks comprises: For any one of the plurality of candidate operation tasks, the current task configuration corresponding to the any one of the plurality of candidate operation tasks is determined in the following manner: determining a current task level corresponding to the any one of the plurality of candidate operation tasks; determining an associated task level corresponding to the current task level, wherein the associated task level comprises a parent task level and a child task level; determining the current task configuration corresponding to the any one of the plurality of candidate operation tasks based on a current configuration set corresponding to the current task level and an associated configuration set corresponding to the associated task level.

6. The method of claim 5, wherein, The determining the current task configuration corresponding to the any one of the plurality of candidate operation tasks based on the current configuration set corresponding to the current task level and the associated configuration set corresponding to the associated task level comprises: determining a configuration filtering constraint corresponding to the any one of the plurality of candidate operation tasks; determining the current task configuration corresponding to the any one of the plurality of candidate operation tasks based on the configuration filtering constraint, the current configuration set corresponding to the current task level, and the associated configuration set corresponding to the associated task level.

7. The method according to any one of claims 1 to 6, characterized in that, The determining the target time constraint corresponding to the target object based on the time difference parameter comprises: determining a plurality of task feature item data based on the time difference parameter, wherein the plurality of task feature item data respectively correspond to different task features; determining the target time constraint corresponding to the target object based on the plurality of task feature item data.

8. An operation execution state determination apparatus of an object, characterized by comprising: Comprise: an acquisition module configured to acquire a plurality of source operation data corresponding to a target object, wherein the plurality of source operation data is data generated by the target object performing a target operation based on different operation devices within a target period; a first determination module configured to determine a current operation task and a predetermined operation task corresponding to the target object within the target period; a second determination module configured to determine a time difference parameter between the current operation task and the predetermined operation task, wherein the time difference parameter is used to represent an operation time difference feature between the current operation task and the predetermined operation task; a third determination module configured to determine a target time constraint corresponding to the target object based on the time difference parameter; a fourth determination module configured to determine an operation execution state corresponding to the target object based on the target time constraint and the plurality of source operation data.

9. An electronic device, comprising: Comprise: a processor; a memory for storing instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the object operation execution state determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer readable storage medium are executed by the processor of the electronic device, the electronic device can execute the object operation execution state determination method according to any one of claims 1 to 7.

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