Automatic filing method and system for personnel files

Abstract

The present invention relates to the field of automated archiving technology, specifically to a method and system for automated archiving of personnel files, comprising the following steps: identifying key contents of personnel files, including work experience and professional level, performing real-time task analysis based on personnel file task data, evaluating the complexity and urgency of archiving tasks, and obtaining task characteristic evaluation results. The present invention, by adopting a multi-objective optimization analysis method, effectively improves the efficiency and accuracy of automated archiving of personnel files, especially in terms of real-time task analysis and priority adjustment, realizes an accurate evaluation of the complexity and urgency of archiving tasks, and based on the evaluation results, performs resource allocation and task scheduling, optimizes archiving strategies, and significantly improves the overall performance of archiving work, which not only speeds up file processing speed, but also improves data security and accessibility, and optimizes subsequent file retrieval and use.

Description

Technical Field

[0001] The present invention relates to the technical field of automated filing, and in particular to an automated filing method and system for personnel files. Background Art

[0002] The field of automated archiving technology focuses on the use of computer systems and software to automatically perform the classification, storage, management and retrieval of files and data. This field integrates technologies such as information technology and database management systems to improve the efficiency and accuracy of data processing, reduce human intervention, and ensure the security and accessibility of data. Automated archiving technology is particularly critical in a variety of industry applications, including healthcare, financial services, education, and human resources management. By achieving efficient and automated processing of files and data, this technology supports organizations to optimize their information management processes, accelerate decision-making, and reduce management costs.

[0003] Among them, the automated filing method of personnel files refers specifically to a series of methods in the field of human resource management that use automation technology to classify, store, update and retrieve employees' personnel files. The purpose of this method is to reduce manual operation errors, improve filing efficiency and file retrieval speed, and thus achieve efficient and accurate management of personnel files. This method supports organizations in decision-making in recruitment, performance evaluation, employee development, and compliance monitoring by achieving fast and accurate file processing, thereby improving organizational operational efficiency and employee satisfaction.

[0004] The traditional automated filing system for personnel files lacks in-depth analysis and optimization of task complexity and urgency, resulting in low efficiency in resource allocation and task scheduling, and inability to flexibly respond to the specific needs of different filing tasks. The lack of effective multi-objective optimization strategies makes it difficult to ensure both filing accuracy and processing speed, leading to information omissions or errors in the filing process, increasing the difficulty of subsequent file retrieval and use, thus affecting the operational efficiency and employee satisfaction of the entire organization. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a method and system for automatically filing personnel files.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for automatically filing personnel files, comprising the following steps:

[0007] S1: Based on the personnel file task data, identify the key content of the personnel file, including work experience and professional level, conduct real-time task analysis, evaluate the complexity and urgency of the file filing task, and obtain the task characteristic evaluation results;

[0008] S2: Based on the evaluation results of the task characteristics, a dynamic model of the archiving process is constructed to capture the optimized task processing path by simulating differentiated process configurations;

[0009] S3: Based on the optimized task processing path and the task characteristic evaluation result, the priority of the archiving task is adjusted, and according to the urgency and complexity of the archiving task, the execution order of the differentiated archiving tasks is automatically adjusted to obtain an optimized task scheduling solution;

[0010] S4: According to the optimized task scheduling scheme, resources are dynamically allocated, resource allocation is adjusted in real time according to currently available resources and archiving task requirements, priorities of differentiated archiving tasks are addressed, and a resource allocation plan is established;

[0011] S5: Based on the resource allocation plan, archiving accuracy and processing time are used as optimization targets through multi-objective optimization to generate and evaluate archiving strategies and obtain multi-objective optimization results;

[0012] S6: Based on the multi-objective optimization results, by establishing a mathematical model of the automated filing process of personnel files, identifying key factors that affect filing accuracy, and dynamically adjusting filing rules to optimize filing accuracy, an optimized filing strategy is obtained.

[0013] The present invention has the following improvements: the task characteristic evaluation result includes the task complexity level, urgency rating, and estimated processing time; the optimized task processing path includes an archiving step sequence and a resource calling sequence; the optimized task scheduling scheme includes a task execution priority list and an archiving allocation scheme; the resource allocation plan includes the resource type, resource quantity, and resource usage time period allocated to differentiated tasks; the multi-objective optimization result includes an optimized combination of archiving strategies, archiving accuracy optimization, and a processing time reduction ratio; the optimized archiving strategy includes an archiving rule adjustment scheme, key parameter adjustment values, and an expected accuracy improvement ratio.

[0014] The present invention is improved in that, according to the personnel file task data, the key contents of the personnel file are identified, including work experience and professional level, real-time task analysis is performed, and the complexity and urgency of the file filing task are evaluated. The specific steps of obtaining the task characteristic evaluation result are as follows:

[0015] S101: According to the personnel file task data, the key contents of the personnel file are screened, including work experience and professional level, and each content is marked, and the content is marked as key indicators and general indicators to obtain key content marking results;

[0016] S102: Based on the key content annotation results, perform complexity assessment on each archive, and assign a corresponding complexity score to each archive by analyzing the composite influence of the number of key indicators and general indicators to obtain the archive complexity assessment result;

[0017] S103: Based on the archive complexity evaluation result, combined with the archive submission time, combined with the time factor and complexity score of the archive task, the urgency of the archive task is calculated to obtain the task characteristic evaluation result.

[0018] The present invention is improved in that, based on the task characteristic evaluation results, a dynamic model of the archiving process is constructed, and the specific steps of capturing and optimizing the task processing path by simulating differentiated process configuration are as follows:

[0019] S201: According to the task characteristic evaluation result, define the status and events in the archiving process, the status includes document receiving, classification, scanning, and verification, and the events include document classification completion and scanning completion, and generate an archiving process status and event definition table;

[0020] S202: According to the archive process state and event definition table, each state is defined as a location of resources and conditions, each event is defined as an activity representing a state transition, and an archive process network model is established by identifying the connection relationship between the location and the activity;

[0021] S203: According to the archived process network model, a simulation run is performed according to the differentiated process configuration, the processing time and resource consumption under each configuration are recorded, and the target configuration is identified by comparing the simulation results of the differentiated configuration to obtain an optimized task processing path.

[0022] The present invention is improved in that, based on the optimized task processing path and the task characteristic evaluation result, the priority of the archiving task is adjusted, and the execution order of the differentiated archiving tasks is automatically adjusted according to the urgency and complexity of the archiving task. The specific steps of obtaining the optimized task scheduling scheme are as follows:

[0023] S301: Based on the optimization task processing path and the task characteristic evaluation result, a sorting algorithm is used to assign a corresponding priority to each task according to the urgency and complexity score of each archiving task, so as to obtain archiving task basic priority information;

[0024] The sorting algorithm is based on the formula:

[0025] ;

[0026] Calculate the priority score of each task and generate the basic priority information of the archived task, where: Represents the urgency score, represents the complexity score, Represents the weighted score of the task type, , , They are the weight coefficients of the urgency score, complexity score, and task type weighted score;

[0027] S302: According to the archiving task basic priority information, identify the tasks that affect the execution order, including the dependencies between the tasks, and optimize the conflicts in the execution order by adjusting the priorities of the tasks to obtain the adjusted archiving task priority information;

[0028] S303: Based on the adjusted archiving task priority information, the execution order of the archiving tasks is readjusted to optimize the dependencies and resource allocation between tasks, and the execution order and estimated processing time of each archiving task are identified to obtain an optimized task scheduling solution.

[0029] The present invention is improved in that, according to the optimization task scheduling scheme, resources are dynamically allocated, resource allocation is adjusted in real time according to the currently available resources and archiving task requirements, and the priorities of differentiated archiving tasks are dealt with. The specific steps of establishing a resource allocation plan are as follows:

[0030] S401: According to the optimized task scheduling scheme, the resource type and quantity required for each task, including equipment and time, are evaluated, and based on the current resource pool status, a corresponding resource requirement list is created for each task to obtain resource requirement matching information;

[0031] S402: According to the resource demand matching information, identify bottlenecks and conflicts in resource allocation, including multiple tasks requiring the same resource at the same time, avoid resource conflicts by adjusting resource allocation strategies and optimizing resource usage plans, and obtain conflict adjustment allocation strategies;

[0032] S403: Based on the conflict adjustment allocation strategy, resources are dynamically allocated, and the resource allocation status is updated in real time in combination with the actual execution time of the archiving task and the resource utilization efficiency, so as to optimize the resource configuration of the target archiving task and establish a resource allocation plan.

[0033] The present invention is improved in that, based on the resource allocation plan, archiving accuracy and processing time are used as optimization targets through multi-objective optimization to generate and evaluate the archiving strategy, and the specific steps of obtaining the multi-objective optimization result are as follows:

[0034] S501: Based on the resource allocation plan, the processing time and required resources of the archiving tasks are listed, and the archiving accuracy and processing time of each task are used as evaluation indicators to create an archiving task evaluation indicator list;

[0035] S502: According to the archiving task evaluation index list, corresponding weights are assigned to the indexes according to the importance of archiving accuracy and processing time, to obtain evaluation indexes after weight adjustment;

[0036] S503: Based on the evaluation index after the weight adjustment, a multi-objective optimization analysis method is used to comprehensively evaluate all archiving tasks, analyze the performance under the differentiated archiving strategies, optimize the archiving strategies, and adjust the execution sequence of archiving tasks and resource allocation to obtain a multi-objective optimization result;

[0037] The multi-objective optimization analysis method is according to the formula:

[0038] ;

[0039] Calculate the performance score of the archiving strategy and obtain the multi-objective optimization result, where: For archiving accuracy, For processing time, For resource efficiency, , , are the weight coefficients for archiving accuracy, processing time, and resource efficiency, respectively.

[0040] The present invention is improved in that, based on the multi-objective optimization results, by establishing a mathematical model of the automated filing process of personnel files, the key factors affecting the filing accuracy are identified, and the filing rules are dynamically adjusted to optimize the filing accuracy. The specific steps of obtaining the optimized filing strategy are as follows:

[0041] S601: Based on the multi-objective optimization results, historical archiving data is collected, including archiving success rate, processing time, and resource utilization, and a mathematical model of the archiving process is established through the data to reflect the process and key parameters of the archiving task, thereby obtaining a basic archiving model;

[0042] S602: According to the basic archiving model, key factors affecting archiving accuracy are identified through the model, including improper resource allocation and processing timeout, the impact of the factors on archiving accuracy is identified, and analysis results of key influencing factors are identified;

[0043] S603: Based on the analysis results of the key influencing factors, the archiving rules are adjusted, including optimizing the resource allocation method, shortening the task processing time, optimizing the archiving process by adjusting the model parameters, optimizing the archiving accuracy, and dynamically adjusting the archiving rules to obtain an optimized archiving strategy.

[0044] An automated personnel file filing system, the system comprising:

[0045] The content recognition module performs text analysis and key content extraction based on personnel file task data, identifies work experience and professional level information, and annotates the extracted information in a critical and general manner to obtain key content annotation records;

[0046] The complexity assessment module assesses the complexity of each archive based on the key content annotation records, analyzes the impact of the number of key indicators and general indicators, assigns a complexity score to each archive, and obtains an archive complexity assessment table;

[0047] The urgency calculation module calculates the urgency of the archive task based on the archive complexity evaluation table and the archive submission time to obtain a task characteristic evaluation result;

[0048] The dynamic model building module captures the processing path and obtains the optimized process configuration scheme by simulating the differentiated process configuration based on the task characteristic evaluation results;

[0049] The priority adjustment module automatically adjusts the task execution order according to the urgency and complexity based on the optimization process configuration scheme and the task characteristic evaluation results, and obtains an adjusted task priority list;

[0050] The resource allocation module performs resource demand analysis and dynamic allocation based on the adjusted task priority list, and adjusts resource allocation in real time based on currently available resources and task requirements, matches the priorities of differentiated tasks, and establishes a resource allocation plan;

[0051] The dynamic adjustment module uses a multi-objective optimization analysis method based on the resource allocation plan, takes archiving accuracy and processing time as optimization goals, generates and evaluates archiving strategies, and identifies factors that affect archiving accuracy by establishing a mathematical model of the archiving process, and adjusts the archiving rules in real time to obtain an optimized archiving strategy.

[0052] Compared with the prior art, the advantages and positive effects of the present invention are:

[0053] In the present invention, this method effectively improves the efficiency and accuracy of automated archiving of personnel files by adopting a multi-objective optimization analysis method, especially in terms of real-time task analysis and priority adjustment, and realizes an accurate assessment of the complexity and urgency of archiving tasks. Based on the assessment results, resource allocation and task scheduling are performed, and the archiving strategy is optimized, which significantly improves the overall performance of the archiving work, speeds up the file processing speed, improves data security and accessibility, and optimizes subsequent file retrieval and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart of a method for automatically filing personnel files proposed by the present invention;

[0055] Figure 2 The present invention provides a detailed flow chart of step S1 in the method for automated filing of personnel files;

[0056] Figure 3 The present invention provides a detailed flow chart of step S2 in the method for automated filing of personnel files;

[0057] Figure 4 The present invention provides a detailed flow chart of step S3 in the method for automated filing of personnel files;

[0058] Figure 5 The present invention provides a detailed flow chart of step S4 in the method for automated filing of personnel files;

[0059] Figure 6 The present invention provides a detailed flow chart of step S5 in the method for automated filing of personnel files;

[0060] Figure 7 The present invention provides a detailed flow chart of step S6 in the method for automated filing of personnel files;

[0061] Figure 8 The present invention proposes a module diagram of an automated filing system for personnel files. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0063] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0064] Example: See Figure 1 The present invention provides a technical solution: a method for automatically filing personnel files, comprising the following steps:

[0065] S1: Based on the personnel file task data, identify the key content of the personnel file, including work experience and professional level, conduct real-time task analysis, evaluate the complexity and urgency of the file filing task, and obtain the task characteristic evaluation results;

[0066] S2: Based on the evaluation results of task characteristics, a dynamic model of the archiving process is constructed to capture the optimized task processing path by simulating differentiated process configurations;

[0067] S3: Based on the optimization task processing path and task characteristic evaluation results, the priority of the archiving task is adjusted. According to the urgency and complexity of the archiving task, the execution order of the differentiated archiving tasks is automatically adjusted to obtain an optimized task scheduling solution.

[0068] S4: Dynamically allocate resources according to the optimized task scheduling plan, adjust resource allocation in real time according to the current available resources and archiving task requirements, respond to the priorities of differentiated archiving tasks, and establish a resource allocation plan;

[0069] S5: Based on the resource allocation plan, through multi-objective optimization, archiving accuracy and processing time are used as optimization goals to generate and evaluate archiving strategies and obtain multi-objective optimization results;

[0070] S6: Based on the multi-objective optimization results, by establishing a mathematical model of the automated filing process of personnel files, the key factors affecting the filing accuracy are identified, and the filing rules are dynamically adjusted to optimize the filing accuracy and obtain the optimized filing strategy.

[0071] The task characteristic evaluation results include the task complexity level, urgency rating, and estimated processing time. The optimized task processing path includes the archiving step sequence and resource call order. The optimized task scheduling plan includes the task execution priority list and the archiving allocation plan. The resource allocation plan includes the resource type, resource quantity and resource usage time period allocated to differentiated tasks. The multi-objective optimization results include the optimal combination of archiving strategies, archiving accuracy optimization, and processing time reduction ratio. The optimized archiving strategy includes the archiving rule adjustment plan, key parameter adjustment values, and expected accuracy improvement ratio.

[0072] See also Figure 2 ,According to the personnel file task data, the key contents of the personnel file, including work experience and professional level, are identified,real-time task analysis is performed, and the complexity and urgency of the file archiving task are evaluated.,The specific steps for obtaining the task characteristic evaluation results are as follows:

[0073] S101: According to the personnel file task data, the key contents of the personnel file are screened, including work experience and professional level, and each content is marked, and the content is marked as key indicators and general indicators. The specific process of obtaining the key content marking results is as follows;

[0074] Sub-step S101 uses the natural language processing method based on the personnel file task data and the NLTK library in the Python programming language for text processing. The text is decomposed into words and phrases by the tokenization function nltk.tokenize, and each word and phrase is tagged with the part of speech tagging function nltk.pos_tag. According to the preset key indicators and general indicator standards, conditional statements are used to filter out words and phrases that meet the key indicators, such as "work experience" and "professional level", and the content is marked as a key indicator. The remaining content that is not marked as a key indicator is automatically classified as a general indicator to generate a key content tagging result;

[0075] S102: Based on the key content annotation results, each archive is evaluated for complexity. By analyzing the composite impact of the number of key indicators and general indicators, a corresponding complexity score is assigned to each archive. The specific process of obtaining the archive complexity evaluation result is as follows;

[0076] In sub-step S102, based on the key content annotation results, a weighted scoring algorithm is used to set the weight of key indicators to 0.7 and the weight of general indicators to 0.3. The sum formula is used to perform weighted calculations on all indicators in each archive, where indicator_value is the number of times the indicator appears or the preset indicator value. The calculated total score is used as the complexity score of the archive. The sorted function in Python is used to sort all archives according to the complexity score to obtain the archive complexity assessment result.

[0077] S103: Based on the archive complexity evaluation result, combined with the archive submission time, combined with the time factor and complexity score of the archive task, the urgency of the archive task is calculated, and the specific process of obtaining the task characteristic evaluation result is as follows;

[0078] Based on the results of the archive complexity assessment, sub-step S103 adopts a time series analysis method and uses the to_datetime function in Python's Pandas library to convert the archive submission time into a date and time format. The number of waiting days for the archive is calculated by subtracting the archive submission date from the current date. Combined with the complexity score of the archive task, the urgency of the archive task is calculated using the formula of complexity score / number of waiting days. The urgency of each archive task is ranked, and an urgency level is assigned to each archive task to obtain the task characteristic assessment results.

[0079] See also Figure 3 ,Based on the evaluation results of task characteristics, a dynamic model of the ,archiving process is constructed. ,The specific steps of capturing the optimized task processing path ,by simulating differentiated process configurations are as follows:

[0080] S201: Based on the task characteristic evaluation results, define the status and events in the archiving process. The status includes document receiving, classification, scanning, and verification. The events include document classification completion and scanning completion. The specific process of generating the archiving process status and event definition table is as follows;

[0081] The S201 sub-step is based on the task characteristic assessment results, adopts data classification and labeling methods, and is implemented in the Python programming language. The pandas library is used to load and preprocess the personnel file data. Then, according to the preset rules, the if-else conditional statement is used. For example, work experience greater than 5 years is marked as a key indicator, professional level evaluation of senior and above is also marked as a key indicator, and the rest of the information is marked as general indicators. Each data in the file is analyzed one by one, the data is appropriately classified and labeled, the information is summarized, and the archiving process status and event definition table are generated.

[0082] S202: According to the archive process state and event definition table, each state is defined as the location of resources and conditions, and each event is defined as an activity representing a state transition. By identifying the connection relationship between the location and the activity, the specific process of establishing the archive process network model is as follows;

[0083] Sub-step S202 uses process modeling technology based on the archive process state and event definition table, and is implemented in Python through the Graphviz software package. Each state in the process, such as document reception, classification, scanning, and verification, is defined as a node, and each event, such as document classification completion and scanning completion, is connected to the corresponding nodes as an edge. A directed graph is created through the graphviz.Digraph() function, and a unique identifier is assigned to each state and event. The relationship between nodes, that is, the connection relationship between positions and activities, is defined, and the relationship is reflected by adding edges in the add_edge method to establish an archive process network model.

[0084] S203: According to the archived process network model, a simulation run is performed according to the differentiated process configuration, and the processing time and resource consumption under each configuration are recorded. By comparing the simulation results of the differentiated configurations, the target configuration is identified, and the specific process of obtaining the optimized task processing path is as follows;

[0085] In sub-step S203, based on the archived process network model, simulation execution technology is used to perform simulation operation of differentiated process configurations in Python using the SimPy library. First, the simulation environment simpy.Environment() is defined, and then the resource usage and task processing order are set according to the process network model. Each activity in the model is converted into a process in the simulation through the env.process() function. Different parameters such as the number of resources and task priority are set, and the simulation env.run() is executed. The processing time and resource consumption of each task in each simulation are recorded, and the results under different configurations are compared. The configuration with the highest simulation efficiency and the lowest resource consumption is selected to obtain the optimized task processing path.

[0086] See also Figure 4 ,Based on the optimization task processing path and task characteristic evaluation results, the priority of archiving tasks is adjusted. According to the urgency and complexity of archiving tasks, the execution order of differentiated archiving tasks is automatically adjusted. The specific steps to obtain the optimized task scheduling solution are as follows:

[0087] S301: Based on the optimization task processing path and task characteristic evaluation results, a sorting algorithm is used to assign a corresponding priority to each task according to the urgency and complexity score of each archiving task. The specific process of obtaining the basic priority information of the archiving task is as follows;

[0088] The S301 sub-step is based on the optimization of the task processing path and the task characteristics evaluation results, and is implemented in the Python programming language using a sorting algorithm. A function assign_priority is defined to traverse each archiving task. The function uses sorted() to sort each task based on its urgency and complexity score. The priority of each task is determined by its position in the sorted list. The higher the position, the higher the priority. This ensures that each task is assigned an appropriate priority based on its urgency and complexity score, and generates the basic priority information for the archiving task.

[0089] Sorting algorithm, according to the formula:

[0090] ;

[0091] Calculate the priority score of each task and generate the basic priority information of the archived task, where: Represents the urgency score, reflecting the urgency of task execution. represents the complexity score, reflecting the amount of work and complexity required to complete the task. Represents the weighted score of the task type, reflecting the importance of different types of tasks in the priority sorting. , , They are the weight coefficients of the urgency score, complexity score and task type weighted score. The weight coefficients are obtained through statistical analysis of the success rate and resource efficiency of past archiving tasks. and Determined by linear regression analysis, It is assessed through expert scoring method.

[0092] The execution process of the improved formula is as follows:

[0093] Through statistical analysis of the success rate and resource efficiency of past archiving tasks, linear regression analysis was used to determine and The expert scoring method is used to score the task type based on its urgency and resource usage. The value is determined to obtain a relatively reasonable coefficient value and define the urgency score of each task. , Complexity Score , and the weighted score of task type The urgency score and complexity score are assigned according to the specific situation of the task. The weighted score of the task type is preset according to the category of the task, such as regular archiving, urgent revision or historical data update. The priority score of each task can be calculated according to the formula to generate the basic priority information of the archiving task.

[0094] S302: According to the basic priority information of the archived tasks, identify the tasks that affect the execution order, including the dependencies between the tasks, and optimize the conflicts in the execution order by adjusting the priorities of the tasks. The specific process of obtaining the adjusted archived task priority information is as follows;

[0095] In sub-step S302, based on the basic priority information of the archived tasks, dependency analysis technology is used to create a task dependency graph using the networkx library in Python, dependency edges between tasks are added through the add_edge method, and the dependency graph is topologically sorted using the networkx.algorithms.dag.topological_sort() function. The priority of the task that causes the conflict in the execution order is identified and adjusted, and the priority of the conflicting tasks is reallocated through program logic to ensure that tasks with higher dependencies are executed first, thereby obtaining the adjusted archived task priority information.

[0096] S303: Based on the adjusted archiving task priority information, the execution order of the archiving tasks is readjusted, the dependency relationship and resource allocation between the tasks are optimized, and the execution order and estimated processing time of each archiving task are identified. The specific process of obtaining the optimized task scheduling solution is as follows;

[0097] In sub-step S303, based on the adjusted archived task priority information, a dynamic programming algorithm is adopted to use Python to realize the readjustment of the task execution order, and the optimize_schedule function is defined to process the task execution order. The optimal execution order is dynamically calculated according to the task priority and dependency relationship within the function, and the resource allocation plan is evaluated at the same time. All task execution sequences are traversed through itertools.permutations, and the estimated processing time is calculated for each sequence. The best sequence, that is, the sequence with the shortest estimated processing time, is selected, and the execution order and estimated processing time are specified for each task to obtain the optimized task scheduling plan.

[0098] See also Figure 5 , according to the optimized task scheduling plan, dynamic resource allocation is carried out, and resource allocation is adjusted immediately according to the current available resources and archiving task requirements to cope with the priorities of differentiated archiving tasks. The specific steps for establishing a resource allocation plan are as follows:

[0099] S401: According to the optimized task scheduling plan, the resource type and quantity required for each task, including equipment and time, are evaluated, and based on the current resource pool status, a corresponding resource requirement list is created for each task. The specific process of obtaining resource requirement matching information is as follows;

[0100] Substep S401 uses a resource evaluation algorithm based on the optimized task scheduling plan and is implemented in Python programming language. First, an evaluate_resources function is defined. The function traverses each task. For each task, according to its specific operation requirements, such as scanning requires a scanner and classification may require more time investment, a dict data structure is used to record the type and quantity of equipment required for each task and the estimated occupancy time. Based on the current state of the resource pool, the state is represented by pandas.DataFrame, which includes the total amount and current available amount of each resource. The function matches available resources for each task according to task requirements and resource pool status to obtain resource demand matching information;

[0101] S402: According to the resource demand matching information, identify bottlenecks and conflicts in resource allocation, including multiple tasks requiring the same resource at the same time, and avoid resource conflicts by adjusting resource allocation strategies and optimizing resource usage plans. The specific process of obtaining conflict adjustment allocation strategies is as follows;

[0102] Sub-step S402 adopts conflict identification and resolution strategies based on resource demand matching information, uses collections.defaultdict in Python to create a resource usage mapping, establishes a task list for each resource type, records all tasks that require resources, and traverses the task list of each resource. It uses for loops and if conditions to identify the situation where the same resource is requested by multiple tasks at the same time, that is, resource conflicts. Then, the adjust_resource_allocation function is defined. The function reallocates conflicting resources, adjusts the resource usage plan of tasks through strategies such as priority sorting and task delay, reduces resource conflicts, and obtains a conflict adjustment allocation strategy.

[0103] S403: Based on the conflict adjustment allocation strategy, resources are dynamically allocated, and the resource allocation status is updated in real time in combination with the actual execution time of the archiving task and the resource utilization efficiency, and the resource configuration of the target archiving task is optimized. The specific process of establishing the resource allocation plan is as follows;

[0104] The S403 sub-step is based on the conflict adjustment allocation strategy, adopts dynamic resource allocation technology, and is implemented through Python. It defines the dynamic_resource_allocation function, which uses a while loop and the current time point current_time to update the resource allocation status in real time according to the adjusted resource allocation plan and the actual execution time of each task. For each task, the available amount of the corresponding resources in the resource pool is dynamically adjusted according to its start time and end time to ensure that high-priority tasks can obtain the required resources first. The resource pool status is updated through the update method, and finally the optimal resource configuration is allocated to each task to establish a resource allocation plan.

[0105] See also Figure 6 ,Based on the resource allocation plan, through multi-objective optimization, archiving accuracy and processing time are used as optimization goals, and archiving strategies are generated and evaluated. The specific steps to obtain the multi-objective optimization results are as follows:

[0106] S501: Based on the resource allocation plan, the processing time and required resources of the archiving tasks are listed, and the archiving accuracy and processing time of each task are used as evaluation indicators. The specific process of creating the archiving task evaluation indicator list is as follows;

[0107] The S501 sub-step is based on the resource allocation plan, adopts data sorting and analysis methods, and is implemented in the Python programming language. A function create_evaluation_list is defined to traverse each archiving task. The pandas.DataFrame structure is used inside the function to store the processing time and required resource information of the archiving task, including the type, quantity and estimated occupancy time of the equipment required for each task. Then, according to the characteristics of the archiving task, such as the number of document pages and the expected accuracy requirements, the archiving accuracy and processing time of each task are listed as evaluation indicators, and a corresponding evaluation indicator list is created for each task to generate an archiving task evaluation indicator list.

[0108] S502: According to the list of evaluation indicators for archiving tasks, corresponding weights are assigned to the indicators according to the importance of archiving accuracy and processing time, and the specific process of obtaining the evaluation indicators after weight adjustment is as follows;

[0109] In sub-step S502, a weight allocation strategy is adopted according to the list of evaluation indicators of archiving tasks, and the numpy library in Python is used for numerical processing. The importance weights of archiving accuracy and processing time are defined, and the weight value of each indicator is stored in numpy.array. For example, the archiving accuracy weight is 0.6, and the processing time weight is 0.4. The numpy.multiply function is used to multiply the evaluation indicator of each task with the corresponding weight to obtain the weighted evaluation value, and the corresponding weight value is assigned to each indicator to obtain the evaluation indicator after weight adjustment.

[0110] S503: Based on the evaluation index after weight adjustment, a multi-objective optimization analysis method is used to comprehensively evaluate all archiving tasks, analyze the performance under differentiated archiving strategies, optimize the archiving strategies, and adjust the execution order of archiving tasks and resource allocation. The specific process of obtaining the multi-objective optimization results is as follows;

[0111] The S503 sub-step adopts a multi-objective optimization analysis method based on the evaluation indicators after weight adjustment, and is implemented in the Python programming language. The optimize_archive_strategy function is defined for comprehensive evaluation. The function uses the minimize function in the scipy.optimize library to find the archiving strategy parameter configuration that maximizes the archiving accuracy and minimizes the processing time based on the evaluation indicators after weight adjustment, such as the resource allocation ratio and task execution sequence. By setting the objective function and constraints of minimize, the archiving strategy with the best performance is found, and then the execution order and resource allocation of the archiving tasks are adjusted according to the optimization results to obtain the multi-objective optimization results.

[0112] Multi-objective optimization analysis method, according to the formula:

[0113] ;

[0114] Calculate the performance score of the archiving strategy and obtain the multi-objective optimization result, where: Archiving accuracy refers to the correctness and quality of the results after the archiving task is completed. It is an indicator to measure the degree of consistency between the archiving results and expectations. Processing time refers to the total time required to complete the archiving task, including the time consumed in all stages such as document processing, data entry, and verification. Resource efficiency reflects the effectiveness of resource use in the archiving process, such as manpower and equipment utilization. High efficiency means achieving the best archiving effect with the least resource consumption. , , They are the weight coefficients of archiving accuracy, processing time and resource efficiency, respectively. The coefficients are determined through historical data analysis and reflect the relative importance of each indicator in the optimization process.

[0115] The execution process of the improved formula is as follows:

[0116] Collect the archiving accuracy, processing time and resource utilization of each task from the historical archiving records, and determine the archiving accuracy through statistical analysis of historical data , Processing time and resource efficiency The actual value of the archive is obtained by using regression analysis based on the performance of historical data. , Processing time and resource efficiency Assign weight coefficient , , ,The performance score of the archiving strategy is calculated through a formula, and the optimal archiving strategy parameter configuration, including resource allocation ratio and task execution sequence, is found. According to the optimization results, the execution order and resource allocation of archiving tasks are adjusted to maximize archiving accuracy, minimize processing time and maximize resource efficiency.

[0117] See also Figure 7 ,Based on the results of multi-objective optimization, a mathematical model of the automated filing process of personnel files is established to identify the key factors affecting the filing accuracy, and the filing rules are dynamically adjusted to optimize the filing accuracy. The specific steps of the optimized filing strategy are as follows:

[0118] S601: Based on the multi-objective optimization results, historical archiving data is collected, including archiving success rate, processing time, and resource utilization. A mathematical model of the archiving process is established through the data to reflect the process and key parameters of the archiving task. The specific process of obtaining the basic archiving model is as follows;

[0119] Based on the multi-objective optimization results, sub-step S601 adopts data collection and analysis methods, which are implemented in Python programming language. The pandas library is used to import historical archive data from the database or CSV file, including key information such as archiving success rate, processing time, and resource utilization. Then, statistical analysis methods are applied, such as the descriptive statistics function provided by pandas.DataFrame.describe(), to conduct a preliminary analysis of the data. The linear regression method numpy.polyfit() is used to establish a mathematical model that describes the relationship between the archiving process flow and key parameters. The model parameters include the relationship coefficients between resource type, quantity, archiving success rate, and processing time, and the basic archiving model is obtained.

[0120] S602: Based on the basic archiving model, key factors affecting archiving accuracy are identified through the model, including improper resource allocation and processing timeout, and the impact of the factors on archiving accuracy is identified. The specific process of identifying the analysis results of key influencing factors is as follows;

[0121] In sub-step S602, based on the basic archiving model, the factor analysis method is used. The FactorAnalysis class of the scikit-learn library in Python is used to load the basic archiving model data and perform the factor analysis process. The FactorAnalysis.fit_transform() method is used to identify the factors that most affect the archiving accuracy, such as improper resource allocation and processing timeout. The influence of each factor is represented by the size of the loading factor. During the identification process, numpy.argsort() is used to sort the loading factors of the influencing factors, so as to determine the key factors that have the greatest impact on the archiving accuracy and identify the analysis results of the key influencing factors.

[0122] S603: Based on the analysis results of key influencing factors, the archiving rules are adjusted, including optimizing the resource allocation method, shortening the task processing time, optimizing the archiving process by adjusting model parameters, optimizing the archiving accuracy, and dynamically adjusting the archiving rules. The specific process of obtaining the optimized archiving strategy is as follows;

[0123] The S603 sub-step is based on the analysis results of key influencing factors and uses parameter optimization technology. It is implemented in Python and defines the optimize_archiving_rules function. The function uses the scipy.optimize.minimize() method to adjust the corresponding parameters in the archiving rules for each key influencing factor based on the analysis results of key influencing factors, such as increasing resource allocation to key tasks and reducing the processing time of non-key tasks. The optimization process is executed by setting the optimization goals to minimize the archiving error rate and maximize resource utilization. The parameters adjusted in the optimization process include the resource allocation ratio, the time threshold for task processing, etc., to obtain the optimized archiving strategy.

[0124] See also Figure 8 , personnel file automatic filing system, the system includes:

[0125] The content recognition module performs text analysis and key content extraction based on personnel file task data, identifies work experience and professional level information, and annotates the extracted information in a critical and general manner to obtain key content annotation records;

[0126] The complexity assessment module assesses the complexity of each archive based on the key content annotation records, analyzes the impact of the number of key indicators and general indicators, assigns a complexity score to each archive, and obtains an archive complexity assessment table;

[0127] The urgency calculation module calculates the urgency of the archive task based on the archive complexity evaluation table and the archive submission time, and obtains the task characteristic evaluation result;

[0128] The dynamic model building module captures the processing path and obtains the optimized process configuration scheme by simulating differentiated process configurations based on the task characteristic evaluation results;

[0129] The priority adjustment module automatically adjusts the task execution order according to urgency and complexity based on the optimization process configuration scheme and task characteristic evaluation results, and obtains the adjusted task priority list;

[0130] The resource allocation module performs resource demand analysis and dynamic allocation based on the adjusted task priority list. It adjusts resource allocation in real time based on the currently available resources and task requirements, matches the priorities of differentiated tasks, and establishes a resource allocation plan.

[0131] The dynamic adjustment module uses a multi-objective optimization analysis method based on the resource allocation plan, takes archiving accuracy and processing time as optimization goals, generates and evaluates archiving strategies, and identifies factors that affect archiving accuracy by establishing a mathematical model of the archiving process. It then adjusts the archiving rules in real time to obtain an optimized archiving strategy.

[0132] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for automatically filing personnel files, characterized in that: The following steps are involved: According to the personnel file task data, identify the key content of the personnel file, including work experience and professional level, conduct real-time task analysis, evaluate the complexity and urgency of the file filing task, and obtain the task characteristic evaluation results; According to the personnel file task data, the key contents of the personnel file are identified, including work experience and professional level, and real-time task analysis is performed to evaluate the complexity and urgency of the filing task. The specific steps to obtain the task characteristic evaluation results are as follows: According to the personnel file task data, the key contents of the personnel file are screened, including work experience and professional level, and each content is marked, and the content is marked as key indicators and general indicators to obtain the key content marking results; Based on the key content annotation results, each archive is evaluated for complexity, and a corresponding complexity score is assigned to each archive by analyzing the composite influence of the number of key indicators and general indicators to obtain the archive complexity evaluation result; Based on the archive complexity evaluation result, combined with the archive submission time, combined with the time factor and complexity score of the archive task, the urgency of the archive task is calculated to obtain the task characteristic evaluation result; Based on the evaluation results of the task characteristics, a dynamic model of the archiving process is constructed to capture the optimized task processing path by simulating differentiated process configurations; Based on the evaluation results of the task characteristics, a dynamic model of the archiving process is constructed. By simulating differentiated process configurations, the specific steps of capturing the optimized task processing path are as follows: According to the task characteristic evaluation results, define the states and events in the archiving process, where the states include document receiving, classification, scanning, and verification, and the events include document classification completion and scanning completion, and generate an archiving process state and event definition table; According to the archive process state and event definition table, each state is defined as a location of resources and conditions, each event is defined as an activity representing a state transition, and an archive process network model is established by identifying the connection relationship between the location and the activity; According to the archived process network model, simulation runs are performed according to differentiated process configurations, processing time and resource consumption under each configuration are recorded, and a target configuration is identified by comparing simulation results of differentiated configurations to obtain an optimized task processing path; Based on the optimized task processing path and task characteristic evaluation results, the priority of the archiving task is adjusted, and according to the urgency and complexity of the archiving task, the execution order of the differentiated archiving tasks is automatically adjusted to obtain an optimized task scheduling solution; Based on the optimization task processing path and task characteristic evaluation results, the priority of the archiving task is adjusted, and the execution order of the differentiated archiving tasks is automatically adjusted according to the urgency and complexity of the archiving task. The specific steps for obtaining the optimized task scheduling solution are as follows: Based on the optimization task processing path and the task characteristic evaluation results, a sorting algorithm is used to assign a corresponding priority to each task according to the urgency and complexity scores of each archiving task, so as to obtain the archiving task basic priority information; The sorting algorithm is based on the formula: ； Calculate the priority score of each task and generate the basic priority information of the archived task, where: Represents the urgency score, represents the complexity score, Represents the weighted score of the task type, , , They are the weight coefficients of the urgency score, complexity score, and task type weighted score; According to the archiving task basic priority information, identify the tasks that affect the execution order, including the dependencies between the tasks, and optimize the conflicts in the execution order by adjusting the priorities of the tasks to obtain the adjusted archiving task priority information; Based on the adjusted archiving task priority information, the execution order of the archiving tasks is readjusted, the dependency relationship and resource allocation between the tasks are optimized, and the execution order and estimated processing time of each archiving task are identified to obtain an optimized task scheduling solution; According to the optimization task scheduling scheme, resources are dynamically allocated, resource allocation is adjusted in real time according to the currently available resources and archiving task requirements, and a resource allocation plan is established to cope with the priorities of differentiated archiving tasks; Based on the resource allocation plan, archiving accuracy and processing time are used as optimization targets through multi-objective optimization to generate and evaluate archiving strategies and obtain multi-objective optimization results; Based on the resource allocation plan, archiving accuracy and processing time are used as optimization targets through multi-objective optimization to generate and evaluate archiving strategies. The specific steps to obtain the multi-objective optimization results are as follows: Based on the resource allocation plan, the processing time and required resources of the archiving tasks are listed, and the archiving accuracy and processing time of each task are used as evaluation indicators to create an archiving task evaluation indicator list; According to the list of evaluation indicators for archiving tasks, corresponding weights are assigned to the indicators according to the importance of archiving accuracy and processing time, so as to obtain evaluation indicators after weight adjustment; Based on the evaluation index after the weight adjustment, a multi-objective optimization analysis method is used to comprehensively evaluate all archiving tasks, analyze the performance under differentiated archiving strategies, optimize the archiving strategies, and adjust the execution order of archiving tasks and resource allocation to obtain multi-objective optimization results; The multi-objective optimization analysis method is according to the formula: ； Calculate the performance score of the archiving strategy and obtain the multi-objective optimization result, where: For archiving accuracy, For processing time, For resource efficiency, , , are the weight coefficients for archiving accuracy, processing time, and resource efficiency, respectively; Based on the multi-objective optimization results, a mathematical model of the automated filing process of personnel files is established to identify key factors affecting filing accuracy, dynamically adjust filing rules, optimize filing accuracy, and obtain an optimized filing strategy.

2. The method for automated filing of personnel files according to claim 1, characterized in that: The task characteristic evaluation results include the task complexity level, urgency rating, and estimated processing time. The optimized task processing path includes the archiving step sequence and resource calling order. The optimized task scheduling plan includes the task execution priority list and the archiving allocation plan. The resource allocation plan includes the resource type, resource quantity and resource usage time period allocated to differentiated tasks. The multi-objective optimization results include the archiving strategy optimization combination, archiving accuracy optimization, and processing time reduction ratio. The optimized archiving strategy includes the archiving rule adjustment plan, key parameter adjustment values, and expected accuracy improvement ratio.

3. The method for automatic filing of personnel files according to claim 1, characterized in that: According to the optimization task scheduling scheme, resources are dynamically allocated, and resource allocation is adjusted in real time according to the current available resources and archiving task requirements to cope with the priorities of differentiated archiving tasks. The specific steps for establishing a resource allocation plan are as follows: According to the optimized task scheduling scheme, the resource type and quantity required for each task, including equipment and time, are evaluated, and based on the current resource pool status, a corresponding resource requirement list is created for each task to obtain resource requirement matching information; According to the resource demand matching information, identify bottlenecks and conflicts in resource allocation, including multiple tasks requiring the same resource at the same time, avoid resource conflicts by adjusting resource allocation strategies and optimizing resource usage plans, and obtain conflict adjustment allocation strategies; Based on the conflict adjustment allocation strategy, resources are dynamically allocated, and the resource allocation status is updated in real time in combination with the actual execution time of the archiving task and the resource utilization efficiency, so as to optimize the resource configuration of the target archiving task and establish a resource allocation plan.

4. The method for automatic filing of personnel files according to claim 1, characterized in that: Based on the multi-objective optimization results, by establishing a mathematical model of the automated filing process of personnel files, identifying the key factors affecting the accuracy of filing, and dynamically adjusting the filing rules to optimize the filing accuracy, the specific steps of obtaining the optimized filing strategy are as follows: Based on the multi-objective optimization results, historical archiving data is collected, including archiving success rate, processing time, and resource utilization. A mathematical model of the archiving process is established through the data to reflect the process and key parameters of the archiving task, and a basic archiving model is obtained; According to the basic archiving model, key factors affecting archiving accuracy are identified through the model, including improper resource allocation and processing timeout, the impact of the factors on archiving accuracy is identified, and the analysis results of key influencing factors are identified; Based on the analysis results of the key influencing factors, the archiving rules are adjusted, including optimizing the resource allocation method and shortening the task processing time. By adjusting the model parameters, the archiving process is optimized, the archiving accuracy is optimized, and the archiving rules are dynamically adjusted to obtain an optimized archiving strategy.

5. The personnel file automatic filing system is characterized by: According to any one of claims 1 to 4, the method for automatic filing of personnel files is implemented, and the system comprises: The content recognition module performs text analysis and key content extraction based on personnel file task data, identifies work experience and professional level information, and performs key and general annotations on the extracted information to obtain key content annotation records; The complexity assessment module assesses the complexity of each archive based on the key content annotation records, analyzes the impact of the number of key indicators and general indicators, assigns a complexity score to each archive, and obtains an archive complexity assessment table; The urgency calculation module calculates the urgency of the archive task based on the archive complexity evaluation table and the archive submission time to obtain a task characteristic evaluation result; The dynamic model building module captures the processing path and obtains the optimized process configuration scheme by simulating the differentiated process configuration based on the task characteristic evaluation results; The priority adjustment module automatically adjusts the task execution order according to the urgency and complexity based on the optimization process configuration scheme and the task characteristic evaluation results, and obtains an adjusted task priority list; The resource allocation module performs resource demand analysis and dynamic allocation based on the adjusted task priority list, and adjusts resource allocation in real time based on currently available resources and task requirements, matches the priorities of differentiated tasks, and establishes a resource allocation plan; The dynamic adjustment module uses a multi-objective optimization analysis method based on the resource allocation plan, takes archiving accuracy and processing time as optimization goals, generates and evaluates archiving strategies, and identifies factors that affect archiving accuracy by establishing a mathematical model of the archiving process, and adjusts the archiving rules in real time to obtain an optimized archiving strategy.

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