Task supervision method and system for progress and risk assessment

Abstract

The invention relates to the related field of project progress risk supervision, and discloses a task supervision method and system for progress and risk assessment, which perform early warning and cumulative influence analysis on task deviation through real-time supervision and simulation assessment, and perform integral correction guidance. The situation that processing cannot be conducted in the middle and later periods due to error accumulation is effectively avoided, the situation that a large number of completed task content needs to be reworked during later reworking correction is avoided, and long-term smoothness and stability of the task progress are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of project schedule risk monitoring, specifically to a task monitoring method and system for schedule and risk assessment. Background Technology

[0002] Project schedule and risk management are crucial aspects of project management, working together to ensure the project is successfully completed within the predetermined timeframe, budget, and quality objectives. This includes, but is not limited to, the allocation and management of project schedule and time, as well as ongoing project completion quality assessments and risk predictions. It also involves the timely identification of potential risk events and the implementation of planned adjustments to mitigate them.

[0003] Existing technologies for progress and risk management lack the effectiveness of comprehensive risk assessment based on progress stages. Specifically, if a deviation or error occurs in a certain process and is not addressed or adjusted in a timely manner, it will have a chain reaction on the later stages of the project. The accumulation of errors within the allowable range may lead to rework, parameter adjustments, and redoing, resulting in a significant waste of time and impacting the overall project progress and quality. Summary of the Invention

[0004] The purpose of this invention is to provide a task monitoring method and system for progress and risk assessment, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A task monitoring method for schedule and risk assessment, comprising:

[0007] Data synchronization is performed in a loop according to a preset time period, and the task-related data of the time period nodes is updated in real time. The task-related data includes task process-related record data and task node result-related characterization data.

[0008] Based on the task planning objectives, the periodic indicator parameters of the current task stage are obtained. Based on the periodic indicator parameters, the task-related data is evaluated for difference to obtain the periodic micro-deviation. The task planning objectives are used to characterize the final result requirements of multiple stages of the task.

[0009] A chain-cumulative simulation with multiple bias tendencies is performed on the periodic micro-biases to obtain the fitting task results. The chain-cumulative simulation is used to characterize the superposition process of the final task results under the current existing error and the subsequent tasks under multiple bias conditions.

[0010] The results of various fitted tasks are evaluated by pre-set target specifications in the task planning objectives. The optimal result is selected and the results are divided into cycles. Based on the target specifications under the corresponding cycles, multiple cycles of correction guidance data are established. The correction guidance data is used to characterize the correction of the task objectives in subsequent cycles based on the current accumulated cycle micro deviations.

[0011] As a further aspect of the present invention: the step of obtaining the periodic indicator parameters of the current task stage based on the task planning objective, and performing difference evaluation on the task-related data based on the periodic indicator parameters to obtain the periodic micro-deviation includes:

[0012] Obtain the task planning objective, match the current task stage based on the task planning objective, obtain the currently ongoing task stage and the basic indicator parameters of the current task stage, the basic indicator parameters are used to characterize the total allowable error of the target after the completion of the current task stage;

[0013] The completion percentage of the task phase is evaluated based on the task completion status at the current time period node, and the basic indicator parameters are proportionally valued based on the completion percentage to generate periodic indicator parameters.

[0014] The error between the task-related data and the task completion status of the current time period node is calculated to obtain the actual error of the period. The difference is evaluated with the period indicator parameter. If it exceeds the period indicator parameter, it is judged as unqualified. The unqualified is used to indicate that the task completion content of the current time period needs to be reworked.

[0015] If the difference assessment result indicates that it exceeds the periodic index parameter, then the actual periodic error is set as a periodic micro-deviation.

[0016] As a further aspect of the present invention, it also includes the following steps:

[0017] Based on the task planning objectives, the historical database is matched for similarity to obtain historical data records of multiple similar task contents.

[0018] The historical data records are evaluated for time distribution to obtain the time occupancy distribution corresponding to the percentage of task completion.

[0019] Based on the time occupancy distribution, the number of cycles and the completion status of the task in the current time period are evaluated, and the total required time under the current efficiency is predicted.

[0020] The pre-allocated time in the task planning objectives is judged based on the total required time. If the total required time is exceeded, the task content completion requirements for the time period are incremented based on the proportion of the total required time, and the corresponding task correction feedback is updated and output.

[0021] As a further aspect of the present invention: in the step of real-time synchronization and updating of task-related data for time period nodes, the task-related data includes:

[0022] Measurement data is used to characterize the type of data that can be autonomously measured by the monitoring equipment through machine vision, remote sensing, laser or sound waves. The measurement data is acquired autonomously by the monitoring equipment controlling the sensing unit.

[0023] Non-measurable data is used to characterize intermediate task data that cannot be directly acquired and recorded by the sensing unit controlled by the monitoring equipment. This non-measurable data is uploaded periodically by the task personnel.

[0024] As a further aspect of the present invention, the step of performing a chain-cumulative simulation of the multi-deviation tendency of the said periodic micro-deviation includes:

[0025] Calculate the unit schedule deviation of the current cycle micro-deviation to establish the unit deviation rate;

[0026] Based on the unit deviation rate, left and right intervals are assigned to generate deviation fluctuation intervals. According to the task completion status of the current time period, a chain-like cumulative simulation of multiple subsequent cycles is performed to calculate the cumulative error range of the cycles in the subsequent multiple cycles to generate a fitted task result. The fitted task result includes a cycle chain connected end to end of the array. The integral of the error interval of each cycle chain along the task cycle direction corresponds to the error range area of ​​the task planning target and includes the error excess ratio. The error excess ratio is used to characterize the error safety of the corresponding cycle chain.

[0027] This invention aims to provide a task monitoring system for progress and risk assessment, comprising:

[0028] The cyclic synchronization module is used to perform data synchronization cyclically according to a preset time period, and to update the task-related data of the time period nodes in real time. The task-related data includes task process-related record data and task node result-related characterization data.

[0029] The cycle evaluation module is used to obtain cycle indicator parameters of the current task stage based on the task planning objectives, and to perform difference evaluation on task-related data based on the cycle indicator parameters to obtain cycle micro deviations. The task planning objectives are used to characterize the final result requirements of multiple stages of the task.

[0030] The chain fitting module is used to perform a chain cumulative simulation of the periodic micro-deviations to obtain the fitting task results. The chain cumulative simulation is used to characterize the superposition process of the final task results under the current existing error and the subsequent tasks under multiple deviations.

[0031] The risk guidance module is used to evaluate the results of various fitted tasks based on the pre-set target specifications in the task planning objectives, select the optimal result and split it into cycles, so as to establish multiple cycles of correction guidance data based on the target specifications under the corresponding cycles. The correction guidance data is used to characterize the correction of the task objectives in subsequent cycles based on the current accumulated cycle micro deviations.

[0032] As a further aspect of the present invention: the periodic evaluation module includes:

[0033] The stage standard acquisition unit is used to acquire the task planning goal, match the current task stage based on the task planning goal, and acquire the currently ongoing task stage and the basic indicator parameters of the current task stage. The basic indicator parameters are used to characterize the total allowable error of the target after the completion of the current task stage.

[0034] The periodic allowable matching unit is used to evaluate the completion percentage of the task phase results based on the task completion status of the current time period node, and to proportionally select the basic indicator parameters based on the completion percentage to generate periodic indicator parameters.

[0035] The periodic error assessment unit is used to calculate the error between the task-related data and the task completion status of the current time period node, obtain the actual periodic error, and evaluate the difference with the periodic index parameter. If it exceeds the periodic index parameter, it is judged as unqualified. The unqualified is used to indicate that the task completion content of the current time period needs to be reworked.

[0036] A periodic error feedback unit is used to set the actual periodic error as a periodic micro-deviation if the difference evaluation result is characterized as exceeding the periodic index parameter.

[0037] As a further embodiment of the present invention, it also includes a cycle efficiency management module, specifically comprising:

[0038] The type matching unit is used to perform similarity matching on the historical database based on multiple task stages of the task planning objectives, and to obtain historical data records of multiple similar task contents;

[0039] An efficiency distribution unit is used to evaluate the time distribution of the historical data records and obtain the time occupancy distribution corresponding to the percentage of task completion.

[0040] The demand assessment unit is used to assess the number of cycles and the completion status of the task content in the current time period based on the time occupancy distribution, and predict the total demand time under the current efficiency.

[0041] The cycle adjustment unit is used to judge the pre-allocated time in the task planning target based on the total required time. If the total required time is exceeded, the task content completion requirements of the time period are incremented based on the proportion of the total required time, and the corresponding task correction feedback is updated and output.

[0042] As a further embodiment of the present invention: the task-related data includes:

[0043] Measurement data is used to characterize the type of data that can be autonomously measured by the monitoring equipment through machine vision, remote sensing, laser or sound waves. The measurement data is acquired autonomously by the monitoring equipment controlling the sensing unit.

[0044] Non-measurable data is used to characterize intermediate task data that cannot be directly acquired and recorded by the sensing unit controlled by the monitoring equipment. This non-measurable data is uploaded periodically by the task personnel.

[0045] As a further embodiment of the present invention: the chain fitting module includes:

[0046] The deviation assessment unit is used to calculate the unit schedule deviation of the current cycle micro-deviation in order to establish the unit deviation rate;

[0047] The deviation fitting unit is used to assign values ​​to the left and right intervals based on the unit deviation rate to generate a deviation fluctuation interval. It performs chain-like cumulative simulation for subsequent multiple cycles based on the task completion status of the current time cycle, calculates the cumulative error range of the cycles in the subsequent multiple cycles, and generates a fitted task result. The fitted task result includes a cycle chain connected end to end of the array. The integral of the error interval of each cycle chain along the task cycle direction corresponds to the error range area of ​​the task planning target and includes the error excess ratio. The error excess ratio is used to characterize the error safety of the corresponding cycle chain.

[0048] Compared with the prior art, the beneficial effects of the present invention are: through real-time monitoring and simulation evaluation, early warning and cumulative impact analysis of task deviations are performed, and overall correction guidance is provided, effectively avoiding the situation that cannot be handled in the middle and later stages due to error accumulation, avoiding the need to rework a large amount of completed task content during the later rework correction, and ensuring the long-term smoothness and stability of task progress. Attached Figure Description

[0049] Figure 1 A flowchart for a task monitoring method used for progress and risk assessment.

[0050] Figure 2 This is a flowchart for phase time management in a task monitoring methodology used for progress and risk assessment.

[0051] Figure 3This is a diagram showing the components of a task monitoring system used for progress and risk assessment. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0053] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0054] like Figure 1 The task monitoring method for progress and risk assessment, as described in one embodiment of the present invention, includes the following steps:

[0055] S10, perform data synchronization loop according to preset time period, and update the task-related data of the time period node in real time. The task-related data includes task process-related record data and task node result-related characterization data.

[0056] S20, based on the task planning objective, obtain the periodic indicator parameters of the current task stage, and perform difference evaluation on the task-related data based on the periodic indicator parameters to obtain the periodic micro-deviation. The task planning objective is used to characterize the final result requirements of multiple stages of the task.

[0057] S30, perform a chain-cumulative simulation of the periodic micro-deviation with multiple deviation tendencies to obtain the fitting task results. The chain-cumulative simulation is used to characterize the superposition process of the final task results under the current existing error and the subsequent tasks under multiple deviation conditions.

[0058] S40, evaluate the results of various fitted tasks by using the target specifications set in the task planning objectives, select the optimal result and split it into cycles, so as to establish multiple cycles of correction guidance data based on the target specifications under the corresponding cycles. The correction guidance data is used to characterize the correction of the task objectives in subsequent cycles based on the current accumulated cycle micro deviations.

[0059] In this embodiment, real-time monitoring and simulation evaluation are used to provide early warnings and analyze the cumulative impact of task deviations, and to guide overall corrections. This effectively avoids situations where errors accumulate and become unmanageable in the later stages, preventing the need for extensive rework of completed tasks during later adjustments, and ensuring the long-term smoothness and stability of the task progress. In existing technologies, before a project begins, requirements are set for different stages, including time requirements, content requirements, and specific standards and specifications for each stage. During task progress, phased progress monitoring and risk assessments are typically conducted. However, in practice, due to the large amount of data and the complexity of overall assessments, periodic and phased error risk assessments are usually performed infrequently. Furthermore, when conducting periodic assessments, they are often limited to the acceptable error range, i.e., whether the current error is still within the specified range. This simplistic approach is prone to overlooking certain parameters or allowing long assessment intervals, resulting in accumulated errors that cannot be easily recovered through simple subsequent corrections, necessitating extensive content rework. Rework is a concern. Furthermore, evaluating based on the total error range of the standard results in poor smoothness of the parameters after project completion. Extensive back-and-forth corrections can lead to the accumulation of errors in subsequent cycles, potentially reaching a small, unmanageable range (e.g., stacking blocks with significant misalignment). At the top, ensuring the blocks don't collapse while maintaining the horizontal projection of the misaligned blocks within the error range can leave only a tiny area unusable. Within this tiny area, hand tremors during placement can exceed this range, making further progress impossible. At this point, the available precision of the equipment and processes cannot guarantee the continuation of subsequent tasks, leading to large-scale project redoing and impacting project timelines and financial costs. Therefore, this approach involves real-time synchronization and monitoring of the work process and content, using high-frequency periodic simulation assessments to evaluate the potential accumulation of small errors in the current work, guiding timely corrections in subsequent project operations and preventing uncontrollable errors.Specifically, the first step is to set a time period for cyclical evaluation, such as a workday, half a day, or even two hours (depending on project requirements). At the beginning or end of each time period, the project's task-related data is synchronized. Then, a project task monitoring program trained with big data performs modeling based on task planning objectives and conducts periodic monitoring and evaluation. This identifies the errors in the completed project tasks for each time period and assesses the cumulative error over multiple future time periods under different execution correction scenarios. This avoids situations where multiple periods overlap and progress cannot be made, allowing for the selection of appropriate simulation results as guiding parameters for corrective actions in the next project task execution period.

[0060] In another preferred embodiment of the present invention, the step of obtaining the periodic indicator parameters of the current task stage based on the task planning objective, and performing difference evaluation on the task-related data based on the periodic indicator parameters to obtain the periodic micro-deviation includes:

[0061] Obtain the task planning objective, match the current task stage based on the task planning objective, obtain the currently ongoing task stage and the basic indicator parameters of the current task stage, the basic indicator parameters are used to characterize the total allowable error of the target after the completion of the current task stage;

[0062] The completion percentage of the task phase is evaluated based on the task completion status at the current time period node, and the basic indicator parameters are proportionally valued based on the completion percentage to generate periodic indicator parameters.

[0063] The error between the task-related data and the task completion status of the current time period node is calculated to obtain the actual error of the period. The difference is evaluated with the period indicator parameter. If it exceeds the period indicator parameter, it is judged as unqualified. The unqualified is used to indicate that the task completion content of the current time period needs to be reworked.

[0064] If the difference assessment result indicates that it exceeds the periodic index parameter, then the actual periodic error is set as a periodic micro-deviation.

[0065] In this embodiment, the steps for obtaining the periodic micro-deviation are explained in detail. The main focus is on breaking down the total allowable error for the current task stage based on the progress percentage, refining the stage-specific error allowable value (i.e., the periodic index parameter), and then evaluating the tasks completed within the time period based on the periodic index parameter to determine whether they meet the long-term cumulative standard, and then screening for rework. The reason for using the total allowable error breakdown based on the progress percentage is to ensure the smoothness of the numerical transition after the completion of the stage task. Directly adjusting large parameters repeatedly within the total allowable error range would reduce the smoothness of the transition. Furthermore, based on the previous embodiment, such a correction method would gradually shrink the stable area where the "building blocks" can be assembled within the allowable range, increasing the probability of subsequent cumulative deviations exceeding the total allowable error.

[0066] like Figure 2 As shown, in another preferred embodiment of the present invention, the method further includes the following steps:

[0067] S51, based on multiple task stages of the task planning objectives, perform similarity matching on the historical database to obtain historical data records of multiple similar task contents;

[0068] S52, evaluate the time distribution of the historical data records to obtain the time occupancy distribution corresponding to the completion percentage of the task content;

[0069] S53, Based on the time occupancy distribution, evaluate the number of cycles and the completion status of the task content in the current time period, and predict the total required time under the current efficiency.

[0070] S54, based on the total required time, determine the pre-allocated time in the task planning target. If the total required time is exceeded, increment the task content completion requirements of the time period based on the proportion of the total required time, and update the task correction feedback accordingly for output.

[0071] In this embodiment, additional steps for project time planning and management are added. Specifically, the time distribution of historical similar projects is used to determine the time required. For example, when a completed task phase is divided into ten parts, the proportion of time required to complete each part according to the progress order is considered. Therefore, based on this proportion and the current task time and completion rate, the total amount of time still needed is determined, and the amount of tasks to be completed within the time period is planned to ensure that the current task phase does not delay the progress of the overall task planning goals.

[0072] In another preferred embodiment of the present invention, in the step of real-time synchronization and updating of task-related data for time period nodes, the task-related data includes:

[0073] Measurement data is used to characterize the type of data that can be autonomously measured by the monitoring equipment through machine vision, remote sensing, laser or sound waves. The measurement data is acquired autonomously by the monitoring equipment controlling the sensing unit.

[0074] Non-measurable data is used to characterize intermediate task data that cannot be directly acquired and recorded by the sensing unit controlled by the monitoring equipment. This non-measurable data is uploaded periodically by the task personnel.

[0075] In this embodiment, the data types for synchronization are divided into two categories. One category is measurement data that the monitoring system can directly acquire through external sensors in conjunction with relevant systems, such as the surface flatness and structural dimensions of the project's production equipment. The other category is non-measurement data, which needs to be recorded by personnel during the task's progress and uploaded and synchronized during time cycles, such as log data and specific method data of the execution plan.

[0076] As another preferred embodiment of the present invention, the step of performing a chain-cumulative simulation of the multi-deviation tendency of the periodic micro-deviation includes:

[0077] Calculate the unit schedule deviation of the current cycle micro-deviation to establish the unit deviation rate;

[0078] Based on the unit deviation rate, left and right intervals are assigned to generate deviation fluctuation intervals. According to the task completion status of the current time period, a chain-like cumulative simulation of multiple subsequent cycles is performed to calculate the cumulative error range of the cycles in the subsequent multiple cycles to generate a fitted task result. The fitted task result includes a cycle chain connected end to end of the array. The integral of the error interval of each cycle chain along the task cycle direction corresponds to the error range area of ​​the task planning target and includes the error excess ratio. The error excess ratio is used to characterize the error safety of the corresponding cycle chain.

[0079] In this embodiment, the errors within multiple completed time periods are representative and can be used to represent the current working status of the personnel and related equipment. Therefore, a unit deviation rate of the current task system (personnel and related equipment) is established based on the periodic micro-deviation, and a range is assigned based on this unit deviation rate to perform chain-like cumulative simulation and cyclic cumulative error superposition. The periodic chain is the constituent line generated by superposition within the error range, that is, the error trend in the actual task execution. When each periodic chain is used as a subsequent correction guide, it will correspond to an error coverage area based on the unit deviation rate. The sum of all error coverage areas is equal to the error range of the cyclic cumulative error superposition. Therefore, only a part of the periodic chains can be used as correction guides. In order to ensure the safety of the cumulative superposition of the periodic chains used for correction guidance, an error excess ratio is introduced here, that is, the proportion that may exceed the error range required by the task planning target after cumulative superposition. The smaller the ratio, the higher the error safety of the periodic chain.

[0080] like Figure 3 As shown, the present invention also provides a task monitoring system for progress and risk assessment, which includes:

[0081] The cyclic synchronization module 100 is used to perform data synchronization cyclically according to a preset time period, and to update the task-related data of the time period node in real time. The task-related data includes task process-related record data and task node result-related characterization data.

[0082] The cycle evaluation module 200 is used to obtain cycle indicator parameters of the current task stage based on the task planning objectives, perform difference evaluation on task-related data based on the cycle indicator parameters, and obtain cycle micro deviations. The task planning objectives are used to characterize the final result requirements of multiple stages of the task.

[0083] The chain fitting module 300 is used to perform a chain cumulative simulation of the periodic micro-deviation with multiple deviation tendencies to obtain the fitting task results. The chain cumulative simulation is used to characterize the superposition process of the final task results under the current existing error and the subsequent tasks under multiple deviation conditions.

[0084] The risk guidance module 400 is used to evaluate the results of various fitted tasks based on the target specifications set in the task planning objectives, select the optimal result and split it into cycles, so as to establish multiple cycles of correction guidance data based on the target specifications under the corresponding cycles. The correction guidance data is used to characterize the correction of the task objectives in subsequent cycles based on the current accumulated cycle micro deviations.

[0085] In another preferred embodiment of the present invention, the periodic evaluation module includes:

[0086] The stage standard acquisition unit is used to acquire the task planning goal, match the current task stage based on the task planning goal, and acquire the currently ongoing task stage and the basic indicator parameters of the current task stage. The basic indicator parameters are used to characterize the total allowable error of the target after the completion of the current task stage.

[0087] The periodic allowable matching unit is used to evaluate the completion percentage of the task phase results based on the task completion status of the current time period node, and to proportionally select the basic indicator parameters based on the completion percentage to generate periodic indicator parameters.

[0088] The periodic error assessment unit is used to calculate the error between the task-related data and the task completion status of the current time period node, obtain the actual periodic error, and evaluate the difference with the periodic index parameter. If it exceeds the periodic index parameter, it is judged as unqualified. The unqualified is used to indicate that the task completion content of the current time period needs to be reworked.

[0089] A periodic error feedback unit is used to set the actual periodic error as a periodic micro-deviation if the difference evaluation result is characterized as exceeding the periodic index parameter.

[0090] In another preferred embodiment of the present invention, a cycle efficiency management module is also included, specifically comprising:

[0091] The type matching unit is used to perform similarity matching on the historical database based on multiple task stages of the task planning objectives, and to obtain historical data records of multiple similar task contents;

[0092] An efficiency distribution unit is used to evaluate the time distribution of the historical data records and obtain the time occupancy distribution corresponding to the percentage of task completion.

[0093] The demand assessment unit is used to assess the number of cycles and the completion status of the task content in the current time period based on the time occupancy distribution, and predict the total demand time under the current efficiency.

[0094] The cycle adjustment unit is used to judge the pre-allocated time in the task planning target based on the total required time. If the total required time is exceeded, the task content completion requirements of the time period are incremented based on the proportion of the total required time, and the corresponding task correction feedback is updated and output.

[0095] In another preferred embodiment of the present invention, the task-related data includes:

[0096] Measurement data is used to characterize the type of data that can be autonomously measured by the monitoring equipment through machine vision, remote sensing, laser or sound waves. The measurement data is acquired autonomously by the monitoring equipment controlling the sensing unit.

[0097] Non-measurable data is used to characterize intermediate task data that cannot be directly acquired and recorded by the sensing unit controlled by the monitoring equipment. This non-measurable data is uploaded periodically by the task personnel.

[0098] In another preferred embodiment of the present invention, the chain fitting module includes:

[0099] The deviation assessment unit is used to calculate the unit schedule deviation of the current cycle micro-deviation in order to establish the unit deviation rate;

[0100] The deviation fitting unit is used to assign values ​​to the left and right intervals based on the unit deviation rate to generate a deviation fluctuation interval. It performs chain-like cumulative simulation for subsequent multiple cycles based on the task completion status of the current time cycle, calculates the cumulative error range of the cycles in the subsequent multiple cycles, and generates a fitted task result. The fitted task result includes a cycle chain connected end to end of the array. The integral of the error interval of each cycle chain along the task cycle direction corresponds to the error range area of ​​the task planning target and includes the error excess ratio. The error excess ratio is used to characterize the error safety of the corresponding cycle chain.

[0101] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0102] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the disclosure in the specification and embodiments. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0103] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A task monitoring method for progress and risk assessment, characterized in that, Include: Data synchronization is performed in a loop according to a preset time period, and the task-related data of the time period nodes is updated in real time. The task-related data includes task process-related record data and task node result-related characterization data. Based on the task planning objectives, the periodic indicator parameters of the current task stage are obtained. Based on the periodic indicator parameters, the task-related data is evaluated for difference to obtain the periodic micro-deviation. The task planning objectives are used to characterize the final result requirements of multiple stages of the task. A chain-cumulative simulation with multiple bias tendencies is performed on the periodic micro-biases to obtain the fitting task results. The chain-cumulative simulation is used to characterize the superposition process of the final task results under the current existing error and the subsequent tasks under multiple bias conditions. The results of various fitted tasks are evaluated by pre-set target specifications in the task planning objectives. The optimal result is selected and the results are divided into cycles. Based on the target specifications under the corresponding cycles, multiple cycles of correction guidance data are established. The correction guidance data is used to characterize the correction of the task objectives in subsequent cycles based on the current accumulated cycle micro deviations.

2. The task monitoring method for progress and risk assessment according to claim 1, characterized in that, The steps of obtaining the periodic indicator parameters of the current task stage based on the task planning objectives, and performing difference evaluation on task-related data based on the periodic indicator parameters to obtain the periodic micro-deviation include: Obtain the task planning objective, match the current task stage based on the task planning objective, obtain the currently ongoing task stage and the basic indicator parameters of the current task stage, the basic indicator parameters are used to characterize the total allowable error of the target after the completion of the current task stage; The completion percentage of the task phase is evaluated based on the task completion status at the current time period node, and the basic indicator parameters are proportionally valued based on the completion percentage to generate periodic indicator parameters. The error between the task-related data and the task completion status of the current time period node is calculated to obtain the actual error of the period. The difference is evaluated with the period indicator parameter. If it exceeds the period indicator parameter, it is judged as unqualified. The unqualified is used to indicate that the task completion content of the current time period needs to be reworked. If the difference assessment result indicates that it exceeds the periodic index parameter, then the actual periodic error is set as a periodic micro-deviation.

3. The task monitoring method for progress and risk assessment according to claim 2, characterized in that, It also includes the following steps: Based on the task planning objectives, the historical database is matched for similarity to obtain historical data records of multiple similar task contents. The historical data records are evaluated for time distribution to obtain the time occupancy distribution corresponding to the percentage of task completion. Based on the time occupancy distribution, the number of cycles and the completion status of the task in the current time period are evaluated, and the total required time under the current efficiency is predicted. The pre-allocated time in the task planning objectives is judged based on the total required time. If the total required time is exceeded, the task content completion requirements for the time period are incremented based on the proportion of the total required time, and the corresponding task correction feedback is updated and output.

4. The task monitoring method for progress and risk assessment according to claim 3, characterized in that, In the step of real-time synchronization and updating of task-related data for time period nodes, the task-related data includes: Measurement data is used to characterize the type of data that can be autonomously measured by the monitoring equipment through machine vision, remote sensing, laser or sound waves. The measurement data is acquired autonomously by the monitoring equipment controlling the sensing unit. Non-measurable data is used to characterize intermediate task data that cannot be directly acquired and recorded by the sensing unit controlled by the monitoring equipment. This non-measurable data is uploaded periodically by the task personnel.

5. The task monitoring method for progress and risk assessment according to claim 4, characterized in that, The steps for performing a chain-cumulative simulation of the multi-deviation tendency of the periodic micro-deviations include: Calculate the unit schedule deviation of the current cycle micro-deviation to establish the unit deviation rate; Based on the unit deviation rate, left and right intervals are assigned to generate deviation fluctuation intervals. According to the task completion status of the current time period, a chain-like cumulative simulation of multiple subsequent cycles is performed to calculate the cumulative error range of the cycles in the subsequent multiple cycles to generate a fitted task result. The fitted task result includes a cycle chain connected end to end of the array. The integral of the error interval of each cycle chain along the task cycle direction corresponds to the error range area of ​​the task planning target and includes the error excess ratio. The error excess ratio is used to characterize the error safety of the corresponding cycle chain.

6. A task monitoring system for progress and risk assessment, characterized in that, Include: The cyclic synchronization module is used to perform data synchronization cyclically according to a preset time period, and to update the task-related data of the time period nodes in real time. The task-related data includes task process-related record data and task node result-related characterization data. The cycle evaluation module is used to obtain cycle indicator parameters of the current task stage based on the task planning objectives, and to perform difference evaluation on task-related data based on the cycle indicator parameters to obtain cycle micro deviations. The task planning objectives are used to characterize the final result requirements of multiple stages of the task. The chain fitting module is used to perform a chain cumulative simulation of the periodic micro-deviations to obtain the fitting task results. The chain cumulative simulation is used to characterize the superposition process of the final task results under the current existing error and the subsequent tasks under multiple deviations. The risk guidance module is used to evaluate the results of various fitted tasks based on the pre-set target specifications in the task planning objectives, select the optimal result and split it into cycles, so as to establish multiple cycles of correction guidance data based on the target specifications under the corresponding cycles. The correction guidance data is used to characterize the correction of the task objectives in subsequent cycles based on the current accumulated cycle micro deviations.

7. The task monitoring system for progress and risk assessment according to claim 6, characterized in that, The periodic assessment module includes: The stage standard acquisition unit is used to acquire the task planning goal, match the current task stage based on the task planning goal, and acquire the currently ongoing task stage and the basic indicator parameters of the current task stage. The basic indicator parameters are used to characterize the total allowable error of the target after the completion of the current task stage. The periodic allowable matching unit is used to evaluate the completion percentage of the task phase results based on the task completion status of the current time period node, and to proportionally select the basic indicator parameters based on the completion percentage to generate periodic indicator parameters. The periodic error assessment unit is used to calculate the error between the task-related data and the task completion status of the current time period node, obtain the actual periodic error, and evaluate the difference with the periodic index parameter. If it exceeds the periodic index parameter, it is judged as unqualified. The unqualified is used to indicate that the task completion content of the current time period needs to be reworked. A periodic error feedback unit is used to set the actual periodic error as a periodic micro-deviation if the difference evaluation result is characterized as exceeding the periodic index parameter.

8. The task monitoring system for progress and risk assessment according to claim 7, characterized in that, It also includes a cycle efficiency management module, specifically including: The type matching unit is used to perform similarity matching on the historical database based on multiple task stages of the task planning objectives, and to obtain historical data records of multiple similar task contents; An efficiency distribution unit is used to evaluate the time distribution of the historical data records and obtain the time occupancy distribution corresponding to the percentage of task completion. The demand assessment unit is used to assess the number of cycles and the completion status of the task content in the current time period based on the time occupancy distribution, and predict the total demand time under the current efficiency. The cycle adjustment unit is used to judge the pre-allocated time in the task planning target based on the total required time. If the total required time is exceeded, the task content completion requirements of the time period are incremented based on the proportion of the total required time, and the corresponding task correction feedback is updated and output.

9. The task monitoring system for progress and risk assessment according to claim 8, characterized in that, The task-related data includes: Measurement data is used to characterize the type of data that can be autonomously measured by the monitoring equipment through machine vision, remote sensing, laser or sound waves. The measurement data is acquired autonomously by the monitoring equipment controlling the sensing unit. Non-measurable data is used to characterize intermediate task data that cannot be directly acquired and recorded by the sensing unit controlled by the monitoring equipment. This non-measurable data is uploaded periodically by the task personnel.

10. The task monitoring system for progress and risk assessment according to claim 9, characterized in that, The chain fitting module includes: The deviation assessment unit is used to calculate the unit schedule deviation of the current cycle micro-deviation in order to establish the unit deviation rate; The deviation fitting unit is used to assign values ​​to the left and right intervals based on the unit deviation rate to generate a deviation fluctuation interval. It performs chain-like cumulative simulation for subsequent multiple cycles based on the task completion status of the current time cycle, calculates the cumulative error range of the cycles in the subsequent multiple cycles, and generates a fitted task result. The fitted task result includes a cycle chain connected end to end of the array. The integral of the error interval of each cycle chain along the task cycle direction corresponds to the error range area of ​​the task planning target and includes the error excess ratio. The error excess ratio is used to characterize the error safety of the corresponding cycle chain.

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