A Cost Data Optimization Method and System Based on Flexible Task Order Mechanism

Abstract

This invention discloses a cost data optimization method and system based on a flexible task work order mechanism, belonging to the field of work order allocation technology. The system includes a dynamic sensing module, an efficiency analysis module, a cost optimization module, and a data storage module. The dynamic sensing module receives task work orders from clients and collects task records from all service objects within the park. The efficiency analysis module analyzes all work order records completed by each service object in each cycle based on the task records, calculates the cost index, and fits the efficiency index relationship by combining the interval duration of the work order records. The cost optimization module calculates the cost index of each task work order, sorts them, and performs the first round of allocation to service objects. Based on the efficiency index relationship, it predicts the efficiency index changes of each service object, determines the interval duration, and iteratively allocates task work orders. The data storage module generates a task list for each service object, records the process information of executing task work orders, and saves it to the task record.

Description

Technical Field

[0001] This invention relates to the field of work order allocation technology, specifically to a method and system for optimizing cost data based on a flexible task work order mechanism. Background Technology

[0002] In current enterprise operations management, integrated park services, and distributed team collaboration, efficient and low-cost task allocation is a core challenge for improving overall operational efficiency and resource utilization. Traditional job scheduling systems mostly achieve task flow and recording at the digital level, but with the increasing complexity of business and the growing demand for humanized and refined management, the limitations of existing methods are becoming increasingly apparent.

[0003] Currently, existing technologies generally suffer from several key shortcomings. First, task cost assessment is limited to a single dimension, focusing only on explicit indicators such as work time and resources, completely ignoring implicit factors like negative emotions embedded in the task description. This leads to severely distorted estimates of actual execution complexity and communication costs. Second, scheduling logic is based on static capacity or simple load matching, failing to scientifically model the efficiency decay patterns of individual workers under fatigue and the nonlinear recovery characteristics after rest, easily leading to local efficiency bottlenecks or resource idleness. Finally, allocation strategies are often intuitive or immediate one-off matching, lacking the global iterative optimization capability to simulate the impact of current allocation on the future efficiency of service recipients, causing overall scheduling to fall into local optima at the expense of long-term sustainability. Therefore, a new cost optimization mechanism that deeply integrates data intelligence and behavioral science is urgently needed to achieve dynamic optimization and cost control throughout the entire task execution cycle. Summary of the Invention

[0004] The purpose of this invention is to provide a cost data optimization method and system based on a flexible task work order mechanism to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides a cost data optimization method based on a flexible task work order mechanism, comprising:

[0006] S100 receives task orders from clients and collects task records from all service objects within the park.

[0007] A task order is a service order generated by the requester through the client according to their needs, which includes task requirements and task description.

[0008] The requester refers to the person who initiates the task order. The task requirements include the set values ​​of various requirements indicators filled in by the requester according to the fixed format of the preset template. The set values ​​refer to the minimum standards that the execution results of the corresponding task order must meet.

[0009] The task description should be supplemented by the requester according to their individual needs.

[0010] The service recipient refers to the person who performs the task order, and the task record includes the service recipient's work order records at different times. The work order record refers to the completed task orders, specifically including the task requirements, task description, and execution time period.

[0011] It enables dynamic sensing and aggregation of task requests and historical data. This not only ensures the timely entry of new task requirements but also builds a database containing the complete work history of each service recipient for subsequent analysis.

[0012] S200. Analyze the task records of each service object and parse all work order records completed within each cycle. Calculate the cost index for each work order record and fit an efficiency index relationship by combining the time intervals between work order records. Specifically, this includes:

[0013] S201, Preset sampling period Obtaining service objects In historical periods Within the task record, extract all work order records. According to the sampling period... After grouping, the cost index for each work order record is calculated separately. Specifically, this includes:

[0014] S2011, Parsing Work Order Records The task requirements and description are analyzed, and the set values ​​of each requirement indicator in the task requirements are examined. And count the total number of all required indicators. Each requirement indicator has a preset upper limit for its value. and lower limit .

[0015] S2012. Keywords in the task description are identified and extracted using an attention mechanism. Each keyword is then mapped using the Sentiment Dictionary SenticNet to obtain the negative sentiment intensity value for each keyword within the range [0,1]. A larger value indicates a higher negative sentiment intensity.

[0016] S2013, the average of the negative sentiment intensity values ​​of all keywords was calculated. Substitute the values ​​into the formula to calculate the work order record. Cost Index Calculate the cost index for each work order record. The formula for calculating the cost index is as follows:

[0017] ;

[0018] ;

[0019] Cost Index It is a dimensionless scalar value used to comprehensively quantify the expected execution complexity and resource consumption level of a task work order.

[0020] Basic execution difficulty Multiply by an amplification factor consisting of a negative emotion moderating coefficient.

[0021] In real-world business scenarios, negative emotions from the requester can significantly increase communication costs and reduce the margin for error, thereby non-linearly raising the overall task execution cost.

[0022] In the formula, The emotional impact intensity coefficient is an empirical constant. It determines the maximum possible amplification ratio of negative emotions on the overall cost index.

[0023] This is the emotional nonlinear amplification index, representing the nonlinear amplification effect of negative emotions on the difficulty level. When... At that time, the effect is linear. When At that time, with the emotional score The closer it gets to 1, the more rapidly the difficulty increase will accelerate.

[0024] For the first The weighting coefficients of the required indicators must meet the following requirements. The value is determined by domain experts or historical data, representing the relative importance of this indicator in the overall task difficulty assessment.

[0025] For the first The difficulty sensitivity index of the requirement indicator. It is an adjustment parameter used to characterize the non-linear characteristics of the indicator's difficulty increase. When... At that time, the difficulty and deviation showed a linear relationship.

[0026] when As the deviation term gets closer to 1, the calculated difficulty increases rapidly, indicating that the closer certain indicators are to the limit requirements, the more exponentially the difficulty of achieving them increases. It is a constant.

[0027] S202. Analyze the execution time period of each work order record within the same group, as well as the interval between work order records. Based on the cost index and interval of each work order record within the group, fit the service target. The efficiency index relationship. Specifically, it includes:

[0028] S2021. All work order records within the same group are arranged chronologically according to their execution time period, and each pair of adjacent work order records is analyzed by sorting. and The interval between them is recorded as the duration of the subsequent work order. The length of rest.

[0029] S2022. The duration of the execution period is used as the execution duration of the corresponding work order record. The cost index is divided by the execution duration to obtain the efficiency index of the corresponding work order record.

[0030] S2023, Calculate work order records by sorting Previously, the sum of the execution times of all work order records within the group was used as the work order record. The duration of the impact is calculated by summing the cost indices of all work order records within the group. The impact index.

[0031] S2024. Analyze the rest duration, efficiency index, impact duration, and impact index of each work order record, and then... Rest duration as the dependent variable Duration of impact and impact index They are used as independent variables respectively.

[0032] S2025. For each work order record, the dependent variable and all independent variables are packaged into a sample, and the total number of all work order records in all groups is counted. According to this The service objects are obtained by fitting the sample. The efficiency index relationship is as follows:

[0033] Preset reference efficiency constant and influence intensity coefficient , will this The independent variables in each sample are used as input values, and the difference between the output value and the dependent variable for each sample is used as the deviation value. The relationship is as follows:

[0034] ;

[0035] This study quantifies and predicts the dynamic work efficiency of service objects using a learnable, complex nonlinear model. (Logarithmic function) Its widespread application ensures that the influence of all factors conforms to the universal law of diminishing marginal returns. Whether it's the benefits of rest or the losses from accumulated fatigue, the initial changes are significant, but they tend to level off later.

[0036] In the formula, This is the rest duration elasticity coefficient, used to adjust the sensitivity of rest duration to efficiency recovery. The higher the value, the more significant the recovery in efficiency can be even with a shorter rest.

[0037] This is the fatigue time constant, used to determine the duration of influence. After normalization, it represents the standard working hours required for the service recipient to experience significant fatigue.

[0038] This is the workload constant; used to evaluate the influence index. The standard is normalized to represent the workload capacity of the service recipient.

[0039] The efficiency index relationship is used to predict the change in the efficiency index of each service object when it takes on the next task. This allows for the consideration of current costs and the prediction of the impact on the future efficiency of service objects when allocating tasks, thus achieving truly dynamic and forward-looking cost data optimization.

[0040] The deviations of all samples are summed to obtain the deviation coefficient. The minimum deviation coefficient is obtained by adjusting the baseline efficiency constant and the influence intensity coefficients, and the values ​​of the baseline efficiency constant and the influence intensity coefficients in the formula are fixed.

[0041] S203. Obtain the task records of each service object, calculate the cost index, and fit the respective efficiency index relationship.

[0042] Historical behavioral data is transformed into quantifiable personal capability models. This leap from recording to insight creates a dynamic and predictable digital model for each service recipient by establishing a relationship between cost and efficiency indices.

[0043] S300: Calculate and sort the cost index of each task order, and perform the first round of allocation based on the number of all service objects. Predict the efficiency index changes of each service object based on the efficiency index relationship, and iteratively allocate task orders after determining the interval. Specifically, this includes:

[0044] S301. Obtain all task work orders and calculate the cost index for each task work order; arrange all task work orders in descending order of cost index and use them as the task dispatch sequence.

[0045] S302. Extract the baseline efficiency constant from the efficiency index relationship of all service objects, arrange all service objects in descending order of the baseline efficiency constant, and use it as the first round of receiving sequence.

[0046] Task orders are sorted in descending order of cost index, and service objects are sorted in descending order of baseline efficiency constant. The initialization using a greedy algorithm aims to quickly match high-difficulty tasks with high-efficiency objects, forming a better initial solution.

[0047] S303. Assign each task order in the task dispatch sequence to the service object with the same ordinal number in the first round of receiving sequence in sequence, and count the number of all task orders in the task dispatch sequence. and the total number of all service recipients .

[0048] S304, when At that time, The result after rounding up As the round number. Iteratively built based on all service objects. The task allocation is completed by receiving the task in a round-robin fashion and then sequentially assigning all task orders in the task dispatch sequence. Specifically, this includes:

[0049] S3041. Obtain the task records of each service object, analyze the cost index and execution time of each work order record, and fit the time relationship between execution time and cost index for each service object.

[0050] S3042. Substitute the cost index of the assigned task work order for each service object into the time relationship to calculate the expected execution time. Use the expected execution time as the impact duration and the cost index as the impact index.

[0051] S3043. Substituting the duration of impact and the impact index into the efficiency index formula, we obtain the relationship between the efficiency index and the rest duration. According to the relation Analysis preset duration The changes in the internal efficiency index are plotted as a line graph.

[0052] S3044, Setting the line chart For each sampling point, calculate the ratio of the efficiency index to the duration, mark the sampling point with the largest ratio, and mark the duration corresponding to the sampling point as the rest duration.

[0053] Substituting the predicted duration of impact and the impact index into the efficiency index formula, we obtain a formula relating the efficiency index and rest duration. .

[0054] By plotting its line graph and finding the point with the largest ratio, we are essentially solving a local optimum problem for each service object: given a state of fatigue, how long should the rest period be to optimize the recovery of efficiency in the next stage? The duration of this marked sampling point is the data-driven scientific rest interval recommended by the algorithm.

[0055] S3045. Obtain the efficiency index corresponding to the marked sampling point in the line graph of each service object, arrange all service objects in descending order of efficiency index, and use it as the second round of receiving sequence.

[0056] S3046. The remaining task orders in the task dispatch sequence are sequentially assigned to the service objects with the same ordinal number in the second round of receiving sequence, and the rest time is selected as the interval between the first and second task order assignments in each service object.

[0057] S3047. When there are still unassigned task orders in the task dispatch sequence, continue to calculate the sum of the expected execution time based on all the task orders already assigned to each service object to obtain the impact time, and the sum of the cost indices to obtain the impact index.

[0058] S3048. After redrawing the line graph, calculate the rest time and efficiency index, establish the third round of receiving sequence, and redistribute the task work order. Use the rest time as the interval between the second and third task work order assignments for each service object.

[0059] Based on the above predictions, the efficiency index of each service object after the optimal rest is obtained, and the objects are reordered according to this index to form a new round of receiving sequence and allocate the remaining tasks.

[0060] This process not only assigns tasks, but also sets up an optimal rotation mechanism between each batch of tasks, achieving synergistic optimization of task scheduling and energy management.

[0061] S3049. Repeat steps S3047 to S3048 until the first step is completed. When the task assignment sequence is established and task work orders are allocated, and there are no unassigned task work orders in the task dispatch sequence, the task assignment is complete.

[0062] When the number of tasks exceeds the number of objects, initiate multiple rounds of allocation. This is the core optimization logic.

[0063] Implement prediction-based, multi-round iterative global task scheduling optimization. Upgrade the static single allocation to dynamic programming that considers the evolution of task queues and individual states, aiming to minimize the long-term overall cost.

[0064] S400: Generate a task list for each service recipient according to the assigned task orders and interval durations. Service recipients execute each task order sequentially according to the order in the task list, recording the execution process information and saving it to the task record. Specifically, this includes:

[0065] S401. Each service object sorts all assigned task orders according to the allocation order, inserts the interval duration between task orders, and generates a task list with time planning.

[0066] S402. The service recipient executes each task work order in the order of all task work orders in the task list within the estimated execution time, and rests during the interval time without executing any task work orders.

[0067] S403. After all task orders in the task list have been completed, record the actual execution time of each task order and the actual rest time between different task orders.

[0068] S404. Analyze the execution time period of each task work order, and package it together with the task requirements and task description as a work order record and store it in the task record.

[0069] This forms a complete PDCA cycle, enabling the entire methodology to learn and evolve continuously, with optimization effects constantly increasing over time and with data accumulation.

[0070] Complete the entire process from optimization decision-making to implementation, and form a closed loop of data feedback. Ensure that the optimization plan is put into practice, and collect the real-world data generated during implementation for continuous calibration and optimization of the model.

[0071] The present invention also provides a cost data optimization system based on a flexible task work order mechanism, including a dynamic perception module, an efficiency analysis module, a cost optimization module, and a data storage module.

[0072] The dynamic perception module is used to receive task orders from clients and collect task records from all service objects within the park.

[0073] It receives task orders submitted by clients in real time and simultaneously collects historical task records of all service objects within the park.

[0074] The records contain details of all work orders completed by each service recipient in the past, such as task content, requirements, and specific execution time periods. This constitutes the raw data pool for subsequent system analysis.

[0075] Ensuring the system has a real-time and comprehensive input data stream includes both new requirements to be processed and accumulated data reflecting the historical capabilities and status of service recipients, provides an accurate and timely data foundation for the entire cost optimization process, and achieves seamless integration of task requests and execution history.

[0076] The efficiency analysis module analyzes all work order records completed by each service object in each cycle based on the task records, calculates the cost index, and fits the efficiency index relationship by combining the interval length of the work order records.

[0077] Conduct in-depth analysis of the historical performance of each service recipient. Specific steps include:

[0078] First, group historical work order records according to a preset cycle.

[0079] Secondly, calculate a comprehensive cost index for each historical work order.

[0080] Finally, the interval between work orders, cumulative working hours, and cumulative workload are analyzed, and these variables are used to fit a unique efficiency index relationship for each service object.

[0081] A nonlinear mathematical model is used to predict how future work efficiency will change with work status.

[0082] By establishing a quantitative personal efficiency model, the system can dynamically predict changes in the efficiency of service recipients when performing subsequent tasks, rather than simply statically assessing their current capabilities.

[0083] This enables the system to understand the impact of fatigue accumulation and rest recovery on work efficiency, providing key scientific basis for forward-looking and intelligent task allocation.

[0084] The cost optimization module calculates the cost index for each task order, sorts them, and performs an initial round of allocation to service recipients. Based on the efficiency index relationship, it predicts the efficiency index changes for each service recipient and iteratively allocates task orders after determining the interval.

[0085] Perform dynamic task allocation decisions. First, calculate and sort the cost index of all new work orders to be allocated, and then perform the first round of allocation based on the baseline efficiency of the service recipients.

[0086] When the number of tasks exceeds the number of service objects, an iterative allocation algorithm is initiated: using the efficiency exponential relationship mentioned above, the predicted efficiency of each service object with assigned tasks is simulated and calculated after a given rest period. Based on this, the service objects are reordered to form a new receiving sequence, and tasks are redistributed. A suggested interval is set between task allocations. This process is repeated until all tasks are allocated.

[0087] The allocation mechanism enables dynamic optimization of global costs. It not only considers the immediate cost of a single task but also anticipates the potential impact of task allocation on the future work efficiency of the service recipients.

[0088] By scientifically setting task intervals, the fatigue accumulation of service recipients was effectively alleviated, thereby improving overall task execution efficiency, reducing long-term operating costs, and achieving optimization from one-time allocation to sustainable scheduling.

[0089] The data storage module generates a task list for each service object, records the task execution process information, and saves it to the task record.

[0090] After the tasks are assigned, a detailed task list containing the task execution order and preset interval duration is generated for each service object.

[0091] The service recipient executes tasks according to this list, and upon completion, the system automatically records the actual execution time of each task and the actual rest time between tasks. This information is then packaged with the original task requirements and descriptions to form a new work order record, which is then updated in the service recipient's task record.

[0092] This forms a closed loop for task execution and a positive cycle for data.

[0093] On the one hand, it ensures that the optimized allocation plan can be delivered and executed accurately.

[0094] On the other hand, the collected real-time execution data is fed back into the system to enrich historical records, calibrate future efficiency models and cost calculations, enabling the entire system to continuously learn and optimize itself based on actual results, and constantly improve the accuracy of prediction and allocation.

[0095] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0096] A multi-dimensional cost quantification model incorporating emotional perception: This technical solution constructs a comprehensive cost index calculation model by combining structured indicators in task requirements with unstructured emotional information in task descriptions. It not only quantifies the objective execution difficulty but also incorporates implicit factors such as communication costs and changes in tolerance space by introducing negative emotional intensity analysis based on an emotional lexicon. This achieves a more comprehensive and accurate assessment of the true complexity of the task, overcoming the limitations of existing technologies that rely solely on explicit indicators.

[0097] Dynamic and predictive personal efficiency modeling: This technical solution constructs a nonlinear personal efficiency index relationship for each service recipient. The model uses rest duration, cumulative working time, and workload as key variables to scientifically characterize the dynamic law of efficiency decline due to fatigue accumulation and nonlinear recovery through rest. It can predict future changes in the service recipient's work efficiency, providing a basis for proactive resource scheduling and overcoming the limitations of existing technologies that treat the executor as a static model.

[0098] Iterative Global Optimization Allocation Based on Simulation Prediction: This technical solution employs a multi-round iterative allocation mechanism. In each round, the future efficiency state of each service object is predicted based on the already allocated tasks, and its optimal rest duration and expected efficiency are recalculated, thereby generating a new receiving sequence. This process achieves a leap from one-time instant matching to dynamic programming that considers long-term impacts, seeking to minimize the overall global cost by simulating the chain reaction of task queues on individual states.

[0099] Adaptive and personalized task pacing management: While assigning tasks, a data-driven, personalized suggested rest period is generated for the intervals between each batch of tasks. This period is optimized using an efficiency recovery function, aiming to ensure the service recipient achieves optimal efficiency per unit of time in subsequent work. This achieves synergy between task scheduling and energy management, ensuring that human resources are maximized at a sustainable pace, overcoming the shortcomings of fixed schedules or ignoring intervals in existing technologies. Attached Figure Description

[0100] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0101] Figure 1 This is a flowchart illustrating the cost data optimization method based on the flexible task work order mechanism of the present invention.

[0102] Figure 2 This is a schematic diagram of the cost data optimization system based on the flexible task work order mechanism of the present invention. Detailed Implementation

[0103] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0104] Example 1: Please refer to Figure 1 This invention provides a cost data optimization method based on a flexible task work order mechanism, including:

[0105] S100 receives task orders from clients and collects task records from all service objects within the park.

[0106] In practice, a task work order refers to a service order generated by the requester through the client according to their needs, which includes task requirements and task description.

[0107] The requester refers to the person who initiates the task order. The task requirements include the set values ​​of various requirements indicators filled in by the requester according to the fixed format of the preset template. The set values ​​refer to the minimum standards that the execution results of the corresponding task order must meet.

[0108] The task description should be supplemented by the requester according to their individual needs.

[0109] The service recipient refers to the person who performs the task order, and the task record includes the service recipient's work order records at different times. The work order record refers to the completed task orders, specifically including the task requirements, task description, and execution time period.

[0110] It enables dynamic sensing and aggregation of task requests and historical data. This not only ensures the timely entry of new task requirements but also builds a database containing the complete work history of each service recipient for subsequent analysis.

[0111] S200. Analyze the task records of each service object and parse all work order records completed within each cycle. Calculate the cost index for each work order record and fit an efficiency index relationship by combining the time intervals between work order records. Specifically, this includes:

[0112] S201, Preset sampling period Obtaining service objects In historical periods Within the task record, extract all work order records. According to the sampling period... After grouping, the cost index for each work order record is calculated separately. Specifically, this includes:

[0113] S2011, Parsing Work Order Records The task requirements and description are analyzed, and the set values ​​of each requirement indicator in the task requirements are examined. And count the total number of all required indicators. Each requirement indicator has a preset upper limit for its value. and lower limit .

[0114] S2012. Keywords in the task description are identified and extracted using an attention mechanism. Each keyword is then mapped using the Sentiment Dictionary SenticNet to obtain the negative sentiment intensity value for each keyword within the range [0,1]. A larger value indicates a higher negative sentiment intensity.

[0115] S2013, the average of the negative sentiment intensity values ​​of all keywords was calculated. Substitute the values ​​into the formula to calculate the work order record. Cost Index Calculate the cost index for each work order record. The formula for calculating the cost index is as follows:

[0116] ;

[0117] ;

[0118] Cost Index It is a dimensionless scalar value used to comprehensively quantify the expected execution complexity and resource consumption level of a task work order.

[0119] Basic execution difficulty (Quantified by task requirements) multiplied by an amplification factor consisting of a negative emotion moderating coefficient (quantified by the intensity of negative emotions in the task description).

[0120] In real-world business scenarios, negative emotions from the requester can significantly increase communication costs and reduce the margin for error, thereby non-linearly raising the overall task execution cost.

[0121] In the formula, For the intensity coefficient of emotional influence, ( This is an empirical constant. It determines the maximum possible amplification of the overall cost index by negative emotions.

[0122] For the nonlinear amplification index of emotion, ( This represents the non-linear amplification effect of negative emotions on difficulty. When... At that time, the effect is linear. When At that time, with the emotional score The closer it gets to 1 (i.e., the more extreme the negative), the more the difficulty it brings will increase dramatically.

[0123] For the first The weighting coefficients of the required indicators must meet the following requirements. The value is determined by domain experts or historical data, representing the relative importance of this indicator in the overall task difficulty assessment.

[0124] For the first The difficulty sensitivity index of the required indicators, ( ), is an adjustable parameter used to characterize the non-linear characteristics of the difficulty increase of the indicator. When At that time, the difficulty and deviation showed a linear relationship.

[0125] when As the deviation term gets closer to 1, the calculated difficulty increases rapidly, indicating that the closer certain indicators (such as project duration and accuracy) are to the limit requirements, the more exponentially the difficulty of achieving them will increase. It is a constant.

[0126] S202. Analyze the execution time period of each work order record within the same group, as well as the interval between work order records. Based on the cost index and interval of each work order record within the group, fit the service target. The efficiency index relationship. Specifically, it includes:

[0127] S2021. All work order records within the same group are arranged chronologically according to their execution time period, and each pair of adjacent work order records is analyzed by sorting. and The interval between them is recorded as the duration of the subsequent work order. The length of rest.

[0128] S2022. The duration of the execution period is used as the execution duration of the corresponding work order record. The cost index is divided by the execution duration to obtain the efficiency index of the corresponding work order record.

[0129] S2023, Calculate work order records by sorting Previously, the sum of the execution times of all work order records within the group was used as the work order record. The duration of the impact is calculated by summing the cost indices of all work order records within the group. The impact index.

[0130] S2024. Analyze the rest duration, efficiency index, impact duration, and impact index of each work order record, and then... Rest duration as the dependent variable Duration of impact and impact index They are used as independent variables respectively.

[0131] S2025. For each work order record, the dependent variable and all independent variables are packaged into a sample, and the total number of all work order records in all groups is counted. According to this The service objects are obtained by fitting the sample. The efficiency index relationship is as follows:

[0132] Preset reference efficiency constant and influence intensity coefficient , will this The independent variables in each sample are used as input values, and the difference between the output value and the dependent variable for each sample is used as the deviation value. The relationship is as follows:

[0133] ;

[0134] In practical implementation, a learnable, complex nonlinear model is used to quantify and predict the dynamic work efficiency of the service recipients. (Logarithmic function) Its widespread application ensures that the influence of all factors conforms to the universal law of diminishing marginal returns. Whether it's the benefits of rest or the losses from accumulated fatigue, the initial changes are significant, but they tend to level off later.

[0135] In the formula, The rest duration elasticity coefficient is denoted by ( ). It is used to adjust the sensitivity of rest duration to efficiency recovery. The higher the value, the more significant the recovery in efficiency can be even with a shorter rest.

[0136] The fatigue time constant is denoted by ( ). ) used to measure the duration of influence After normalization, it represents the standard working hours required for the service recipient to experience significant fatigue.

[0137] This is the workload constant; its value is ( ) used for the influence index The standard is normalized to represent the workload capacity of the service recipient.

[0138] In the specific implementation process, the efficiency index relationship is used to predict the change in the efficiency index of each service object when it takes on the next task. In this way, when allocating tasks, we can not only consider the current cost, but also foresee the impact on the future efficiency (i.e., long-term cost) of the service object after the allocation, so as to achieve truly dynamic and forward-looking cost data optimization.

[0139] The deviations of all samples are summed to obtain the deviation coefficient. The minimum deviation coefficient is obtained by adjusting the baseline efficiency constant and the influence intensity coefficients, and the values ​​of the baseline efficiency constant and the influence intensity coefficients in the formula are fixed.

[0140] S203. Obtain the task records of each service object, calculate the cost index, and fit the respective efficiency index relationship.

[0141] In practice, historical behavioral data is transformed into quantifiable personal capability models. This leap from recording to insight, achieved by establishing a relationship between cost and efficiency indices, creates a dynamic and predictable digital model for each service recipient.

[0142] S300: Calculate and sort the cost index of each task order, and perform the first round of allocation based on the number of all service objects. Predict the efficiency index changes of each service object based on the efficiency index relationship, and iteratively allocate task orders after determining the interval. Specifically, this includes:

[0143] S301. Obtain all task work orders and calculate the cost index for each task work order; arrange all task work orders in descending order of cost index and use them as the task dispatch sequence.

[0144] S302. Extract the baseline efficiency constant from the efficiency index relationship of all service objects, arrange all service objects in descending order of the baseline efficiency constant, and use it as the first round of receiving sequence.

[0145] In the implementation process, task orders are sorted in descending order of cost index (the most difficult tasks are prioritized), and service objects are sorted in descending order of baseline efficiency constant (the most efficient ones are accepted first). The greedy algorithm is used for initialization to quickly match high-difficulty tasks with efficient objects, forming a better initial solution.

[0146] S303. Assign each task order in the task dispatch sequence to the service object with the same ordinal number in the first round of receiving sequence in sequence, and count the number of all task orders in the task dispatch sequence. and the total number of all service recipients .

[0147] S304, when At that time, The result after rounding up As the round number. Iteratively built based on all service objects. The task allocation is completed by receiving the task in a round-robin fashion and then sequentially assigning all task orders in the task dispatch sequence. Specifically, this includes:

[0148] S3041. Obtain the task records of each service object, analyze the cost index and execution time of each work order record, and fit the time relationship between execution time and cost index for each service object.

[0149] S3042. Substitute the cost index of the assigned task work order for each service object into the time relationship to calculate the expected execution time. Use the expected execution time as the impact duration and the cost index as the impact index.

[0150] S3043. Substituting the duration of impact and the impact index into the efficiency index formula, we obtain the relationship between the efficiency index and the rest duration. According to the relation Analysis preset duration The changes in the internal efficiency index are plotted as a line graph.

[0151] S3044, Setting the line chart For each sampling point, calculate the ratio of the efficiency index to the duration, mark the sampling point with the largest ratio, and mark the duration corresponding to the sampling point as the rest duration.

[0152] Substituting the predicted duration of impact and the impact index into the efficiency index formula, we obtain a formula relating the efficiency index and rest duration. .

[0153] In practice, by plotting a line graph and finding the point with the largest ratio, we are essentially solving a local optimum problem for each service object: given a state of fatigue, how long should the rest period be to optimize the recovery of efficiency in the next stage? The duration of this marked sampling point is the data-driven scientific rest interval recommended by the algorithm.

[0154] S3045. Obtain the efficiency index corresponding to the marked sampling point in the line graph of each service object, arrange all service objects in descending order of efficiency index, and use it as the second round of receiving sequence.

[0155] S3046. The remaining task orders in the task dispatch sequence are sequentially assigned to the service objects with the same ordinal number in the second round of receiving sequence, and the rest time is selected as the interval between the first and second task order assignments in each service object.

[0156] S3047. When there are still unassigned task orders in the task dispatch sequence, continue to calculate the sum of the expected execution time based on all the task orders already assigned to each service object to obtain the impact time, and the sum of the cost indices to obtain the impact index.

[0157] S3048. After redrawing the line graph, calculate the rest time and efficiency index, establish the third round of receiving sequence, and redistribute the task work order. Use the rest time as the interval between the second and third task work order assignments for each service object.

[0158] Based on the above predictions, the efficiency index of each service object after the optimal rest is obtained, and the objects are reordered according to this index to form a new round of receiving sequence and allocate the remaining tasks.

[0159] This process not only assigns tasks, but also sets up an optimal rotation mechanism between each batch of tasks, achieving synergistic optimization of task scheduling and energy management.

[0160] S3049. Repeat steps S3047 to S3048 until the first step is completed. When the task assignment sequence is established and task work orders are allocated, and there are no unassigned task work orders in the task dispatch sequence, the task assignment is complete.

[0161] In practice, when the number of tasks exceeds the number of objects, multiple rounds of allocation are initiated. This is the core optimization logic.

[0162] This approach enables prediction-based, multi-round iterative global task scheduling optimization. It upgrades static, single-stage allocation to dynamic programming that considers the evolution of task queues and individual states, aiming to minimize the long-term overall cost (immediate execution cost + future efficiency loss cost).

[0163] S400: Generate a task list for each service recipient according to the assigned task orders and interval durations. Service recipients execute each task order sequentially according to the order in the task list, recording the execution process information and saving it to the task record. Specifically, this includes:

[0164] S401. Each service object sorts all assigned task orders according to the allocation order, inserts the interval duration between task orders, and generates a task list with time planning.

[0165] S402. The service recipient executes each task work order in the order of all task work orders in the task list within the estimated execution time, and rests during the interval time without executing any task work orders.

[0166] S403. After all task orders in the task list have been completed, record the actual execution time of each task order and the actual rest time between different task orders.

[0167] S404. Analyze the execution time period of each task work order, and package it together with the task requirements and task description as a work order record and store it in the task record.

[0168] This forms a complete PDCA (Plan-Do-Check-Act) cycle, enabling the entire process to learn and evolve continuously, with optimization effects constantly increasing over time and with data accumulation.

[0169] Complete the entire process from optimization decision-making to implementation, and form a closed loop of data feedback. Ensure that the optimization plan is put into practice, and collect the real-world data generated during implementation for continuous calibration and optimization of the model.

[0170] Example 2: Please refer to Figure 2 The present invention also provides a cost data optimization system based on a flexible task work order mechanism, including a dynamic perception module, an efficiency analysis module, a cost optimization module and a data storage module.

[0171] The dynamic perception module is used to receive task orders from clients and collect task records from all service objects within the park.

[0172] In the specific implementation process, the system receives task work orders (including task requirements and personalized task descriptions) submitted by clients in real time, and simultaneously collects historical task records of all service objects (i.e. task executors) within the park.

[0173] The records contain details of all work orders completed by each service recipient in the past, such as task content, requirements, and specific execution time periods. This constitutes the raw data pool for subsequent system analysis.

[0174] Ensuring the system has a real-time and comprehensive input data stream includes both new requirements to be processed and accumulated data reflecting the historical capabilities and status of service recipients, provides an accurate and timely data foundation for the entire cost optimization process, and achieves seamless integration of task requests and execution history.

[0175] The efficiency analysis module analyzes all work order records completed by each service object in each cycle based on the task records, calculates the cost index, and fits the efficiency index relationship by combining the interval length of the work order records.

[0176] In the implementation process, a thorough analysis of the historical performance of each service recipient is conducted. Specific steps include:

[0177] First, group historical work order records according to a preset cycle.

[0178] Secondly, a comprehensive cost index is calculated for each historical work order (this index not only quantifies the objective difficulty of the task requirements, but also incorporates the additional communication and management costs brought about by the negative emotions in the task description through sentiment analysis).

[0179] Finally, the interval between work orders (rest), cumulative working hours (impact duration), and cumulative workload (impact index) are analyzed, and these variables are used to fit a unique efficiency index relationship for each service recipient.

[0180] A nonlinear mathematical model is used to predict how future work efficiency will change with work status.

[0181] By establishing a quantitative personal efficiency model, the system can dynamically predict changes in the efficiency of service recipients when performing subsequent tasks, rather than simply statically assessing their current capabilities.

[0182] This enables the system to understand the impact of fatigue accumulation and rest recovery on work efficiency, providing key scientific basis for forward-looking and intelligent task allocation.

[0183] The cost optimization module calculates the cost index for each task order, sorts them, and performs an initial round of allocation to service recipients. Based on the efficiency index relationship, it predicts the efficiency index changes for each service recipient and iteratively allocates task orders after determining the interval.

[0184] In practice, dynamic task allocation decisions are made. First, the cost index of all new work orders to be allocated is calculated and sorted, and then the first round of allocation is carried out based on the baseline efficiency of the service recipients.

[0185] When the number of tasks exceeds the number of service objects, an iterative allocation algorithm is initiated: using the efficiency index relationship mentioned above, the predicted efficiency of each service object with assigned tasks is simulated and calculated after a given rest period. Based on this, the service objects are reordered to form a new receiving sequence, and tasks are redistributed. A suggested interval (rest time) is set between task allocations. This process is repeated until all tasks are allocated.

[0186] The allocation mechanism enables dynamic optimization of global costs. It not only considers the immediate cost of a single task but also anticipates the potential impact of task allocation on the future work efficiency of service recipients (i.e., long-term costs).

[0187] By scientifically setting task intervals, the fatigue accumulation of service recipients was effectively alleviated, thereby improving overall task execution efficiency, reducing long-term operating costs, and achieving optimization from one-time allocation to sustainable scheduling.

[0188] The data storage module generates a task list for each service object, records the task execution process information, and saves it to the task record.

[0189] In the actual implementation process, after the task allocation is completed, a detailed task list containing the task execution order and preset interval duration is generated for each service object.

[0190] The service recipient executes tasks according to this list, and upon completion, the system automatically records the actual execution time of each task and the actual rest time between tasks. This information is then packaged with the original task requirements and descriptions to form a new work order record, which is then updated in the service recipient's task record.

[0191] This forms a closed loop for task execution and a positive cycle for data.

[0192] On the one hand, it ensures that the optimized allocation plan can be delivered and executed accurately.

[0193] On the other hand, the collected real-time execution data (actual duration) is fed back into the system to enrich historical records, calibrate future efficiency models and cost calculations, enabling the entire system to continuously learn and optimize itself based on actual results, and constantly improve the accuracy of prediction and allocation.

[0194] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0195] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cost data optimization method based on a flexible task work order mechanism, characterized by: The method includes: S100: Receives task work orders from clients and collects task records from all service objects within the park; S200. Analyze the task records of each service object and parse all work order records completed in each cycle; calculate the cost index of each work order record respectively, and fit the efficiency index relationship by combining the interval between work order records. S300: Calculate and sort the cost index of each task order, and perform the first round of allocation according to the number of all service objects; predict the efficiency index changes of each service object based on the efficiency index relationship, and iteratively allocate task orders after determining the interval. S400: Generate a task list for each service object according to the assigned task work orders and interval duration. The service object executes each task work order in the order of the task list, records the process information of the task work order execution, and saves it to the task record.

2. The cost data optimization method based on the flexible task work order mechanism according to claim 1, characterized in that: In S100, a task work order refers to a service order generated by the requester through the client according to the request, which specifically includes task requirements and task description; The requester refers to the person who initiates the task order. The task requirements include the set values ​​of various requirements indicators filled in by the requester according to the fixed format of the preset template. The set values ​​refer to the minimum standards that the execution results of the corresponding task order must meet. The task description should be supplemented by the requester according to their individual needs; The service recipient refers to the person who performs the task order. The task record includes the service recipient's work order records at different times. The work order record refers to the completed task work orders, which specifically include the task requirements, task description, and execution time period.

3. The cost data optimization method based on the flexible task work order mechanism according to claim 2, characterized in that: S200 includes: S201, Preset sampling period Obtaining service objects In historical periods Record tasks within the scope and extract all work order records; according to the sampling period. After grouping, the cost index for each work order record is calculated separately; S202. Analyze the execution time period of each work order record within the same group, as well as the interval between work order records; based on the cost index and interval of each work order record within the group, fit the service object... The efficiency index relationship; S203. Obtain the task records of each service object, calculate the cost index, and fit the respective efficiency index relationship.

4. The cost data optimization method based on the flexible task work order mechanism according to claim 3, characterized in that: In S201, the cost index calculation includes: S2011, Parsing Work Order Records The task requirements and description are analyzed, and the set values ​​of each requirement indicator in the task requirements are examined. And count the total number of all required indicators. Each requirement indicator has a preset upper limit for its value. and lower limit ; S2012. Identify and extract keywords from the task description through the attention mechanism, map each keyword through the SenticNet sentiment dictionary, and obtain the negative sentiment intensity value of each keyword in the range [0,1]. S2013, the average of the negative sentiment intensity values ​​of all keywords was calculated. Substitute the values ​​into the formula to calculate the work order record. Cost Index Calculate the cost index for each work order record; the formula for calculating the cost index is as follows: ； ； In the formula, This represents the intensity coefficient of emotional influence. For the non-linear amplification index of emotions, For the first The weighting coefficients of the required indicators are as follows: For the first The difficulty sensitivity index of the required indicators It is a constant.

5. The cost data optimization method based on the flexible task work order mechanism according to claim 3, characterized in that: S202 includes: S2021. All work order records within the same group are arranged chronologically according to their execution time period, and each pair of adjacent work order records is analyzed by sorting. and The interval between them is recorded as the duration of the subsequent work order. Rest duration; S2022. The duration of the execution period is used as the execution duration of the corresponding work order record. The cost index is divided by the execution duration to obtain the efficiency index of the corresponding work order record. S2023, Calculate work order records by sorting Previously, the sum of the execution times of all work order records within the group was used as the work order record. The duration of the impact is calculated by summing the cost indices of all work order records within the group. The impact index; S2024. Analyze the rest duration, efficiency index, impact duration, and impact index of each work order record, and then... Rest duration as the dependent variable Duration of impact and impact index Each is used as an independent variable; S2025. For each work order record, the dependent variable and all independent variables are packaged into a sample, and the total number of all work order records in all groups is counted. According to this The service objects are obtained by fitting the sample. The efficiency index relationship.

6. The cost data optimization method based on the flexible task work order mechanism according to claim 5, characterized in that: The efficiency index relationship fitting includes: Preset reference efficiency constant and influence intensity coefficient , will this The independent variables in each sample are used as input values, and the difference between the output value and the dependent variable for each sample is used as the deviation value; the relationship is as follows: ； In the formula, This is the elasticity coefficient for rest duration. The fatigue time constant, This is the workload constant; The deviations of all samples are summed to obtain the deviation coefficient. The minimum deviation coefficient is obtained by adjusting the baseline efficiency constant and the influence intensity coefficients, and the values ​​of the baseline efficiency constant and the influence intensity coefficients in the formula are fixed.

7. The cost data optimization method based on the flexible task work order mechanism according to claim 5, characterized in that: The S300 includes: S301. Obtain all task work orders and calculate the cost index for each task work order; arrange all task work orders in descending order of cost index and use them as the task dispatch sequence. S302. Extract the baseline efficiency constant from the efficiency index relationship of all service objects, arrange all service objects in descending order of the baseline efficiency constant, and use it as the first round of receiving sequence; S303. Assign each task order in the task dispatch sequence to the service object with the same ordinal number in the first round of receiving sequence in sequence, and count the number of all task orders in the task dispatch sequence. and the total number of all service recipients ; S304, when At that time, The result after rounding up As the round number; iteratively built based on all service objects. The task allocation is completed by receiving the sequence in rounds and then assigning all the task orders in the task dispatch sequence in turn.

8. The cost data optimization method based on the flexible task work order mechanism according to claim 7, characterized in that: S304 includes: S3041. Obtain the task records of each service object, analyze the cost index and execution time of each work order record, and fit the time relationship between execution time and cost index for each service object; S3042. Substitute the cost index of the assigned task work order for each service object into the time relationship to calculate the expected execution time. Use the expected execution time as the impact duration and the cost index as the impact index. S3043. Substituting the duration of impact and the impact index into the efficiency index formula, we obtain the relationship between the efficiency index and the rest duration. According to the relation Analysis preset duration The changes in the internal efficiency index were plotted as a line graph. S3044, Setting the line chart For each sampling point, calculate the ratio of the efficiency index to the duration, mark the sampling point with the largest ratio, and mark the duration corresponding to the sampling point as the rest duration; S3045. Obtain the efficiency index corresponding to the marked sampling points in the line graph of each service object, arrange all service objects in descending order of efficiency index, and use it as the second round of receiving sequence; S3046. The remaining task orders in the task dispatch sequence are sequentially assigned to the service objects with the same ordinal number in the second round of receiving sequence, and the rest time is selected as the interval between the first and second task order assignments in each service object. S3047. When there are still unassigned task orders in the task dispatch sequence, continue to calculate the sum of the expected execution time based on all the task orders already assigned to each service object to obtain the impact time, and the sum of the cost indices to obtain the impact index. S3048. After redrawing the line graph, calculate the rest time and efficiency index, establish the third round of receiving sequence, and redistribute the task work order. Use the rest time as the interval between the second and third task work order distributions for each service object. S3049. Repeat steps S3047 to S3048 until the first step is completed. When the task assignment sequence is established and task work orders are allocated, and there are no unassigned task work orders in the task dispatch sequence, the task assignment is complete.

9. The cost data optimization method based on the flexible task work order mechanism according to claim 7, characterized in that: The S400 includes: S401. Each service object sorts all assigned task orders according to the allocation order, inserts the interval duration between task orders, and generates a task list with time planning. S402. Service recipients shall execute each task work order in the order of all task work orders in the task list within the estimated execution time, and rest during the interval time and not execute any task work orders. S403. After all task orders in the task list have been completed, record the actual execution time of each task order and the actual rest time between different task orders. S404. Analyze the execution time period of each task work order, and package it together with the task requirements and task description as a work order record and store it in the task record.

10. A cost data optimization system based on a flexible task work order mechanism, applied to the cost data optimization method based on a flexible task work order mechanism as described in claim 1, characterized in that: The system includes a dynamic sensing module, an efficiency analysis module, a cost optimization module, and a data storage module; The dynamic perception module is used to receive task orders from clients and collect task records from all service objects within the park; The efficiency analysis module analyzes all work order records completed by each service object in each cycle based on the task records, calculates the cost index, and fits the efficiency index relationship by combining the interval duration of the work order records. The cost optimization module calculates the cost index of each task order, sorts them, and performs the first round of allocation to service objects; it then predicts the efficiency index changes of each service object based on the efficiency index relationship, determines the interval time, and iteratively allocates task orders. The data storage module generates a task list for each service object, records the task execution process information, and saves it to the task record.

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