Smart Government Service Platform

Abstract

The present invention relates to the field of task management technology, specifically to a smart government service platform, which includes a priority adjustment module, a process adjustment module, an intelligent text mining module, and an intelligent matching optimization module. The present invention realizes the accurate sorting of task priorities by analyzing the historical data and user behavior of the completion of government service tasks, effectively improves the efficiency of task processing, and can adjust the workflow in real time when responding to public health emergencies or natural disasters, ensuring the immediacy and adaptability of government actions, deeply mining government text data and extracting key information, optimizing the decision support system, and enhancing the accuracy of services. By accurately analyzing the degree of matching between text content and work requirements and optimizing information display, the consistency and efficiency of the workflow are significantly improved, processing time and potential errors are reduced, thereby improving the overall response speed and quality of government services.

Description

Technical Field

[0001] The present invention relates to the field of task management technology, and in particular to a smart government service platform. Background Art

[0002] The task management technology field focuses on the development and implementation of tools and systems that help individuals and teams more effectively organize, coordinate, and track tasks and activities. This includes both software and hardware solutions designed to increase productivity, optimize resource allocation, and enhance decision-making. Core functionality typically includes task assignment, progress tracking, resource management, collaboration tools, and data analytics systems, enabling systems ranging from simple personal to-do lists to complex enterprise-level project management.

[0003] The Smart Government Service Platform is an integrated solution designed to optimize the delivery of government services through digitalization, automation, and data-driven decision-making. By integrating multiple functions, including task management, data processing, process automation, and citizen engagement, it supports government agencies in more efficiently managing their internal operations and delivering public services. Its primary uses include streamlining administrative processes, enhancing transparency, and improving the accessibility and responsiveness of government services. Through the Smart Government Platform, we can better respond to public needs, optimize resource allocation, and promptly resolve public issues.

[0004] Traditional government service platforms are slow to respond to emergencies, especially in public health emergencies or natural disasters. Their fixed workflows struggle to adapt quickly to changing circumstances, hindering the timeliness and effectiveness of responses. Traditional platforms also exhibit limitations in analyzing and utilizing large amounts of government text data. They are unable to quickly extract useful information from complex data, resulting in insufficient information support for decision-making. This inadequacy in information extraction and analysis limits the ability to accurately match government needs and optimize resource allocation, impacting service quality and public satisfaction. Summary of the Invention

[0005] The purpose of this invention is to solve the shortcomings of the existing technology and propose a smart government service platform.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: a smart government service platform, which includes:

[0007] The priority adjustment module analyzes the historical data of government service task completion based on the current government service task list and user behavior, calculates the urgency index of official document approval tasks, evaluates the priority of differentiated tasks, and sorts them to obtain an optimized task list;

[0008] The process adjustment module analyzes the data impact of public health events and natural disaster emergencies based on the optimized task list, dynamically adjusts the workflow, and obtains an adaptive process configuration;

[0009] The intelligent text mining module extracts and analyzes key information from contract documents and resumes based on service request text data. It also identifies and ranks keywords and phrases in the text, analyzes the importance and relevance of each information item, calculates information value indicators, and obtains text information analysis results.

[0010] Based on the text information analysis results and adaptive process configuration, the intelligent matching optimization module extracts and classifies the text content required for the workflow, calculates the matching degree between the text content and the government work content, adjusts the display of the text content in the workflow, and generates text matching information.

[0011] The present invention is improved in that the method for calculating the urgency index of the document approval task is:

[0012] Based on the historical data of user behavior and government service task completion, a historical behavior dataset is constructed to extract features associated with document approval tasks, including task submission time, completion time, and user feedback, to obtain a historical feature set.

[0013] Based on the historical feature set, the formula:

[0014] ;

[0015] Calculate the time sensitivity score of a task ;

[0016] in, Score the time sensitivity of the task, and Represents the start and completion time of the task, is the curve steepness adjustment parameter, and are the offset parameter and the square sum base, is the base of natural logarithms;

[0017] Based on the time sensitivity score and the influence of user feedback on urgency, the formula is:

[0018]

[0019] Calculating the urgency index , get the urgency index of the document approval task;

[0020] in, Represents the urgency index of the task, Represents the rating of task urgency in user feedback, and is the weight parameter, is a normalization coefficient used to balance the impact of different task types.

[0021] The present invention is improved in that the steps of obtaining the optimization task list are as follows:

[0022] Based on the urgency index of the document approval task, the formula is:

[0023]

[0024] Calculate the priority of task processing to obtain the task priority ;

[0025] in, is the task priority, is the urgency index, is the weight parameter of urgency, is the reference offset, To adjust the parameters, is the base of natural logarithms;

[0026] According to the task priority By comparing the task value, sort the tasks in descending order to get the optimized task list.

[0027] The present invention is improved in that the method for analyzing the data impact of public health events and natural disaster emergencies is:

[0028] Collect historical data on public health events and natural disasters, including the time, duration, scope, and severity of the events, to generate a historical data set;

[0029] Based on the historical data set, event data associated with the current optimization task list is filtered, and the severity and spatiotemporal correlation of the event are combined to obtain the following formula:

[0030]

[0031] Perform weighted processing on the data to obtain weighted event data:

[0032] in, is the weighted event data, Indicates the severity of the incident. Indicates the duration of the event in days. Indicates the proximity of the event to the current task time, 、 and is the weight coefficient;

[0033] Based on the weighted event data, the formula:

[0034]

[0035] Calculating influence , assess the impact of the event on the current workflow;

[0036] in, For the impact, is the influence sensitivity adjustment parameter, is the model offset parameter, is the base of natural logarithms.

[0037] The present invention is improved in that the steps of obtaining the adaptive process configuration are:

[0038] Based on the impact ,Identify the workflow parameters that need to be adjusted, including task resource parameters and decision thresholds, and obtain the adjustment requirements;

[0039] Based on the adjustment requirements, the formula:

[0040]

[0041] Calculate updated configuration parameters;

[0042] in, is the updated configuration parameter, are the current workflow configuration parameters, is the adjustment sensitivity coefficient, is the adjustment smoothing coefficient, is the influence degree;

[0043] The updated configuration parameters are implemented into the actual workflow, and the workflow is dynamically adjusted to obtain an adaptive workflow configuration.

[0044] The present invention is improved in that the method for marking and sorting keywords and phrases in a text is as follows:

[0045] Based on the service request government text data, the government text data is standardized and preprocessed, including removing stop words, punctuation marks, and irrelevant characters to obtain a cleaned text dataset;

[0046] Based on the cleaned text dataset, the word frequency-inverse document frequency method is applied, using the formula:

[0047]

[0048] Calculate the importance score of each word in the text and identify the keywords in the text;

[0049] in, is a term The importance score of is a term Frequency in the document, is a term that contains The document frequency of is the total number of documents;

[0050] Based on the cleaned text dataset, using the statistical correlation analysis method, the formula:

[0051]

[0052] Calculate the relevance score of phrases and identify key phrases in the text;

[0053] in, is the relevance score, is a candidate phrase, is with Other co-occurring phrases, and are the co-occurrence frequency and the independent frequency, is the smoothing parameter;

[0054] The keywords and key phrases are sorted according to the importance scores and relevance scores to generate a sorted list of keywords and phrases.

[0055] The present invention is improved in that the steps of obtaining the text information analysis results are:

[0056] Based on the importance score and relevance score, the formula:

[0057]

[0058] Calculate a composite score for each keyword and phrase;

[0059] in, For the comprehensive score, is the importance score, is the relevance score, and is the weight coefficient;

[0060] Based on the comprehensive score, the formula:

[0061]

[0062] Calculate the information value of each keyword and phrase, summarize and sort the information value of keywords and phrases, obtain a list of information value indicators, measure the value of text content, and generate text information analysis results;

[0063] in, is the information value, is the keyword relevance score to the document topic, is the position factor of the keyword in the document, is the keyword length, is the adjustment parameter, is the base of natural logarithms.

[0064] The present invention is improved in that the steps of obtaining the text matching information are as follows:

[0065] Based on the text information analysis results and the adaptive process configuration, extract the text content required by the workflow, classify the text content according to the differentiated links of the workflow, and obtain the classified text content;

[0066] Based on the classified text content, the formula:

[0067]

[0068] Calculating the matching score ,identify the matching degree between text content and government work content;

[0069] in, is the matching score, Represents text content The weight of Represents text content Government affairs work The similarity of Indicates the content of government affairs The correlation, Indicates the amount of text content. Indicates the amount of work content, Index for text content, Indexing work content;

[0070] According to the matching score , through the formula:

[0071]

[0072] Calculating display priority ,Adjust the display order of text content in the workflow to obtain text matching information;

[0073] in, The importance of the text content, is the position of the text content in the workflow, and To adjust the parameters, Display priority indicates the order in which text content is displayed in the workflow.

[0074] Compared with the prior art, the advantages and positive effects of the present invention are:

[0075] In the present invention, by analyzing the historical data and user behavior of the completion of government service tasks, the accurate sorting of task priorities is achieved, and the efficiency of task processing is effectively improved. When responding to public health emergencies or natural disasters, the work process can be adjusted in real time, ensuring the immediacy and adaptability of government actions, deeply mining government text data and extracting key information, optimizing the decision support system, and enhancing the accuracy of services. By accurately analyzing the degree of match between text content and work needs and optimizing information display, the consistency and efficiency of the work process are significantly improved, processing time and potential errors are reduced, thereby improving the overall response speed and quality of government services. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 is a platform flow chart of the present invention;

[0077] Figure 2 This is a flow chart of the present invention for calculating the urgency index of a document approval task;

[0078] Figure 3 A flowchart for obtaining an optimization task list for the present invention;

[0079] Figure 4 Flowchart for analyzing data impacts of public health events and natural disaster emergencies for the present invention;

[0080] Figure 5 A flow chart for obtaining an adaptive process configuration for the present invention;

[0081] Figure 6 A flowchart for identifying and sorting keywords and phrases in a text for the present invention;

[0082] Figure 7 A flowchart of obtaining text information analysis results for the present invention;

[0083] Figure 8 This is a flow chart of the present invention for obtaining text matching information. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0085] 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 directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0086] Example

[0087] See also Figure 1 The present invention provides a technical solution: a smart government service platform, which includes:

[0088] The priority adjustment module analyzes the historical data of government service task completion based on the current government service task list and user behavior, calculates the urgency index of official document approval tasks, evaluates the priority of differentiated tasks, and sorts them to obtain an optimized task list;

[0089] The process adjustment module analyzes the impact of public health events and natural disaster emergencies based on the optimized task list, dynamically adjusts the workflow, and obtains an adaptive process configuration;

[0090] The intelligent text mining module extracts and analyzes key information from contract documents and resumes based on service request text data. It also identifies and ranks keywords and phrases in the text, analyzes the importance and relevance of each information item, calculates information value indicators, and obtains text information analysis results.

[0091] Based on the text information analysis results and adaptive process configuration, the intelligent matching optimization module extracts and classifies the text content required for the workflow, calculates the matching degree between the text content and the government work content, adjusts the display of the text content in the workflow, and generates text matching information.

[0092] The optimized task list includes the task urgency ranking and the expected completion time of the task. The adaptive process configuration includes the adjusted workflow steps, real-time response rules and emergency handling strategies. The text information analysis results are specifically key information extraction identifiers, text value scores and correlation analysis data. The text matching information includes the matching degree between text content and work items, optimization parameter sets and work efficiency improvement indicators.

[0093] See also Figure 2 ,The method for calculating the urgency index of the document approval task is;

[0094] Based on the historical data of user behavior and government service task completion, a historical behavior dataset is constructed to extract features associated with document approval tasks, including task submission time, completion time, and user feedback, to obtain a historical feature set.

[0095] Based on the historical feature set, the formula:

[0096]

[0097] Calculate the time sensitivity score of a task ;

[0098] in, Score the time sensitivity of the task, and Represents the start and completion time of the task, is the curve steepness adjustment parameter, and are the offset parameter and the square sum base, respectively, which are used to adjust the flexibility and sensitivity of the time sensitivity score. is the base of natural logarithms;

[0099] Based on the time sensitivity score and the impact of user feedback on urgency, the formula is:

[0100]

[0101] Calculating the urgency index , get the urgency index of the document approval task;

[0102] in, Represents the urgency index of the task, Represents the rating of task urgency in user feedback, and is the weight parameter, is a normalization coefficient used to balance the impact of different task types.

[0103] formula:

[0104]

[0105]

[0106] The meaning and acquisition method of the parameters:

[0107] and : Represents the start time and completion time of the document approval task. The data is directly obtained from the task tracking database of the government service system.

[0108] : A parameter that adjusts the steepness of the curve. Its value is based on statistical analysis of historical data and is determined by optimizing the model to best fit the historical task completion time sensitivity distribution.

[0109] and : is the offset parameter, which is used to adjust the displacement of the curve along the time axis, and are the square and cardinality, both of which are obtained through regression analysis of historical data to enhance the adaptability and discrimination of the model for different task types.

[0110] and : Weight parameter is used to measure the time sensitivity score and user feedback In the urgency index The relative importance of the two factors is determined through expert evaluation or analysis of previous cases.

[0111] : Normalization coefficient, used to balance the influence of various factors and ensure the urgency index Adjustments are made based on the results of model validation within a reasonable numerical range.

[0112] Calculation example:

[0113] Assume that the start time of a document approval task is hours, completion time hours, assuming , ,and .

[0114] Calculating the time sensitivity score :

[0115]

[0116] Assume that we get the task urgency score from user feedback ,as well as , , ;

[0117] Calculating the urgency index :

[0118]

[0119] Urgency Index Determine the urgency of the task.

[0120] See also Figure 3 , the steps to obtain the optimization task list are:

[0121] Based on the urgency index of the document approval task, the formula is:

[0122]

[0123] Calculate the priority of task processing to obtain the task priority ;

[0124] in, is the task priority, is the urgency index, is the weight parameter of urgency, is the reference offset, To adjust the parameters, is the base of natural logarithms;

[0125] According to task priority By comparing the task value, sort the tasks in descending order to get the optimized task list.

[0126] formula:

[0127]

[0128] The meaning and acquisition method of the parameters:

[0129] : Urgency index, obtained from the previous calculation step.

[0130] : Weight parameter, used to adjust the urgency index Sensitivity to the impact of priority. This parameter is usually set by analyzing historical data to find the optimal relationship between urgency and task completion efficiency.

[0131] : Baseline offset, used to adjust the horizontal position of the priority curve to make the priority distribution more reasonable. This offset can be set based on the average urgency index of historical urgent tasks.

[0132] : A tuning parameter used to ensure numerical stability and flexibility in the priority calculation. This parameter is typically adjusted based on stability indicators from model testing.

[0133] : base of natural logarithm, constant Approximately 2.718, which is a standard value in the calculation of exponential functions and is used for smoothing and nonlinear transformation priority values.

[0134] Calculation example:

[0135] Assume the given parameters are as follows: , , , .

[0136] Calculation process:

[0137] According to the formula , substitute the above parameters into the formula:

[0138] Calculate the exponential part:

[0139] .

[0140] calculate Exponential power of : .

[0141] Using approximations, .

[0142] Will Substituting into the formula:

[0143]

[0144] The calculation example shows the use of the urgency index and other parameters to calculate the priority of the task ,in A high value of indicates that the task has a high priority and should be processed first.

[0145] See also Figure 4 , the method for analyzing the data impact of public health events and natural disaster emergencies is:

[0146] Collect historical data on public health events and natural disasters, including the time, duration, scope, and severity of the events, to generate a historical data set;

[0147] Based on the historical data set, filter the event data associated with the current optimization task list, combine the severity and spatiotemporal correlation of the event, and use the formula:

[0148]

[0149] Perform weighted processing on the data to obtain weighted event data:

[0150] in, is the weighted event data, Indicates the severity of the incident. Indicates the duration of the event in days. Indicates the proximity of the event to the current task time, 、 and is the weight coefficient;

[0151] Based on the weighted event data, the formula:

[0152]

[0153] Calculating influence , assess the impact of the event on the current workflow;

[0154] in, For the impact, is the influence sensitivity adjustment parameter, is the model offset parameter, is the base of natural logarithms.

[0155] The meaning and acquisition method of the parameters:

[0156] formula:

[0157]

[0158]

[0159] : Weighted event data, the calculation method involves the severity of public health events and natural disasters , duration and the temporal proximity of the event to the current task weighted.

[0160] 、 and : Weight coefficient, determined based on statistical analysis of historical data or expert experience, to ensure that the weight reflects the actual impact on the workflow.

[0161] : Parameters for adjusting impact sensitivity are used to adjust the sensitivity of impact calculations. They can be determined based on model optimization or regression analysis to best fit historical event data and its impact on the workflow.

[0162] : Model offset parameter, which adjusts the baseline during the calculation process to ensure that the impact calculation does not deviate from the actual value when no event occurs. Set based on the median or mean of the dataset.

[0163] Calculation example:

[0164] Assume a natural disaster event with the following parameters:

[0165] Severity , duration Days, the proximity of the event to the current task time , weight coefficient , , , influence sensitivity parameter , model offset parameter .

[0166] Calculate weighted event data;

[0167]

[0168] Calculate the impact:

[0169]

[0170] calculate :

[0171]

[0172] Calculate the exponent part and denominator:

[0173]

[0174]

[0175] The calculation example shows how to calculate the specific impact on the workflow from the basic characteristics of the event.

[0176] See also Figure 5 , the steps to obtain the adaptive process configuration are:

[0177] Based on influence ,Identify the workflow parameters that need to be adjusted, including task resource parameters and decision thresholds, and obtain the adjustment requirements;

[0178] Based on the adjustment requirements, the formula is:

[0179]

[0180] Calculate updated configuration parameters;

[0181] in, is the updated configuration parameter, are the current workflow configuration parameters, is the adjustment sensitivity coefficient, is to adjust the smoothing coefficient, It is the degree of influence;

[0182] The updated configuration parameters are implemented into the actual workflow, and the workflow is dynamically adjusted to obtain an adaptive process configuration.

[0183] formula:

[0184]

[0185] The meaning and acquisition method of the parameters:

[0186] : Configuration parameters of the current workflow, representing the process configuration values that were set and in use before any adjustments were made. Parameters are obtained from the organization's process management system or process documentation.

[0187] :Adjust the sensitivity coefficient, which is a preset parameter used to adjust the sensitivity according to the impact Adjust the configuration of the workflow. Determines how the impact of configuration changes is linearly affected, and is set through historical data analysis or simulation testing.

[0188] : Adjusts the smoothing coefficient, which is used to refine configuration adjustments and takes into account the quadratic term of the impact to provide smoother configuration adjustments, especially when the impact is high. The coefficient is determined through optimization models or feedback adjustment experiments to ensure that the adjustment does not cause system instability due to sudden changes.

[0189] : Impact, calculated from the previous analysis step, reflects the degree of influence of a specific event on the workflow.

[0190] Calculation example:

[0191] Assumptions: , , , .

[0192] Calculation process:

[0193] Calculate new configuration parameters :

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200] The calculation example shows the influence of the known , sensitivity coefficient and smoothing coefficient , and the current configuration value , calculate the new workflow configuration Ensure dynamic adaptation of workflows to respond to the impact of external events and improve the responsiveness and efficiency of the overall process.

[0201] See also Figure 6 , the method for identifying and sorting keywords and phrases in the text is:

[0202] Based on the service request government text data, the government text data is standardized and preprocessed, including removing stop words, punctuation marks, and irrelevant characters to obtain a cleaned text dataset;

[0203] Based on the cleaned text dataset, the word frequency-inverse document frequency method is applied, through the formula:

[0204]

[0205] Calculate the importance score of each word in the text and identify the keywords in the text;

[0206] in, is a term The importance score of is a term Frequency in the document, is a term that contains The document frequency of is the total number of documents;

[0207] Based on the cleaned text dataset, using the statistical correlation analysis method, through the formula:

[0208]

[0209] Calculate the relevance score of phrases and identify key phrases in the text;

[0210] in, is the relevance score, is a candidate phrase, is with Other co-occurring phrases, and are the co-occurrence frequency and the independent frequency, is the smoothing parameter;

[0211] Sort the keywords and key phrases according to their importance scores and relevance scores to generate a ranked list of keywords and phrases.

[0212] formula:

[0213]

[0214]

[0215] The meaning and acquisition method of the parameters:

[0216] :the term Frequency in the document, i.e., how many times the term appears in a particular document. The parameters are taken directly from the document's term frequency statistics.

[0217] : Contains terms The document frequency of , which indicates the total number of documents containing the term, is obtained by counting the occurrences of each term in the entire document collection.

[0218] : The total number of documents, that is, the total number of documents in the entire document collection. This parameter is known in advance or obtained through simple counting.

[0219] : Candidate phrase, indicating the phrase to be analyzed.

[0220] :and Co-occurring phrases, which represent other phrases that appear in the same document.

[0221] : co-occurrence frequency, indicating candidate phrases and co-occurring phrases The number of occurrences in the same document is obtained by counting the co-occurrence matrix.

[0222] : Independent frequency, indicating co-occurrence phrases The number of times a word appears in all documents is obtained directly from the word frequency statistics of the document.

[0223] : A smoothing parameter used to ensure that the relevance calculation for low-frequency phrases is more stable. This parameter is usually determined empirically or through validation.

[0224] Calculation example:

[0225] Assumptions: Terminology Frequency , containing the term Document frequency , total number of documents , candidate phrases and Co-occurring phrases , candidate phrases and co-occurring phrases The co-occurrence frequency of , co-occurring phrases Independent frequency , smoothing parameter .

[0226] Calculate TF-IDF:

[0227]

[0228] Substitute the parameters:

[0229]

[0230] calculate:

[0231]

[0232]

[0233]

[0234] Calculate the correlation:

[0235]

[0236] Assumptions (i.e. there is only one co-occurring phrase) and substitute the parameters:

[0237]

[0238] Hypothetical candidate phrases Independent frequency :

[0239]

[0240]

[0241]

[0242] The calculation example shows how to calculate the TF-IDF value of a keyword and the relevance of a candidate phrase based on given parameters and formulas, ensuring the accuracy and rationality of the calculation and providing a basis for subsequent keyword and phrase ranking.

[0243] See also Figure 7 , the steps to obtain the text information analysis results are:

[0244] Based on the importance score and relevance score, the formula is:

[0245]

[0246] Calculate a composite score for each keyword and phrase;

[0247] in, For the comprehensive score, is the importance score, is the relevance score, and are weight coefficients, which are used to adjust the influence of TF-IDF score and relevance score respectively;

[0248] Based on the comprehensive score, the formula:

[0249]

[0250] Calculate the information value of each keyword and phrase, summarize and sort the information value of keywords and phrases, obtain a list of information value indicators, measure the value of text content, and generate text information analysis results;

[0251] in, is the information value, is the keyword relevance score to the document topic, is the position factor of the keyword in the document, is the keyword length, is the adjustment parameter, is the base of natural logarithms.

[0252] formula:

[0253]

[0254]

[0255] The meaning and acquisition method of the parameters:

[0256] : Adjust the weight coefficient of the TF-IDF score, determined through model optimization or expert experience, to balance the impact of the TF-IDF score in the comprehensive score;

[0257] : Adjust the weight coefficient of the correlation score, which is determined through model optimization or expert experience to balance the impact of the correlation score in the comprehensive score;

[0258] TF-IDF: The term frequency-inverse document frequency score calculated from the keyword extraction step, which indicates the importance of the keyword in the document;

[0259] Assoc: The relevance score calculated from the key phrase identification step, indicating the relevance of the keyword to other phrases;

[0260] : Comprehensive score, which combines TF-IDF and relevance scores to obtain the total score, used for subsequent information value calculations.

[0261] :Keywords The relevance score to the document topic, obtained through topic modeling or similarity calculation, is used to measure the relevance between keywords and document topics.

[0262] :Keywords The position factor in the document indicates the position of the keyword in the document. Words that are positioned higher usually have higher weights.

[0263] :Keywords The length of the word is used to consider the impact of long and short words on information value.

[0264] : Adjustment parameters are used to adjust the impact of keyword length on information value and ensure that keywords of different lengths are balanced in the calculation.

[0265] Calculation example:

[0266] Assume TF-IDF is 3 and Assoc is 2, , , , , , .

[0267] Calculate the composite score :

[0268]

[0269]

[0270]

[0271]

[0272] Calculating information value indicators :

[0273]

[0274]

[0275]

[0276]

[0277]

[0278]

[0279] The calculation example shows how to calculate the comprehensive score and information value index of keywords based on given parameters and formulas, ensuring the accuracy and rationality of the calculation, thereby providing a basis for subsequent information processing and analysis.

[0280] See also Figure 8 , the steps to obtain text matching information are:

[0281] Based on the text information analysis results and adaptive process configuration, the text content required by the workflow is extracted, and the text content is classified according to the differentiated links of the workflow to obtain the classified text content;

[0282] Based on the classified text content, the formula is:

[0283]

[0284] Calculating the matching score ,identify the matching degree between text content and government work content;

[0285] in, is the matching score, Represents text content The weight of Represents text content Government affairs work The similarity of Indicates the content of government affairs The correlation, Indicates the amount of text content. Indicates the amount of work content, Index for text content, Indexing work content;

[0286] According to the matching score , through the formula:

[0287]

[0288] Calculating display priority ,Adjust the display order of text content in the workflow to obtain text matching information;

[0289] in, The importance of the text content, is the position of the text content in the workflow, and To adjust the parameters, Display priority indicates the order in which text content is displayed in the workflow.

[0290] formula:

[0291]

[0292]

[0293] The meaning and acquisition method of the parameters:

[0294] : Text content The weight is set through historical data or expert experience, indicating the importance of text content in calculating the matching degree.

[0295] : Text content Government affairs work The similarity is usually obtained through similarity calculation method.

[0296] :Content of government affairs work The relevance is obtained through topic modeling or expert scoring, which indicates the importance of work content in the overall government work.

[0297] : Matching score, indicating the degree of match between the text content and the government work content.

[0298] :Keywords The relevance score to the document topic, obtained through topic modeling or similarity calculation.

[0299] :Keywords The position factor in the document is usually obtained by normalizing the position of the keyword in the document.

[0300] :Keywords The length of the keyword is obtained directly by counting the number of characters in the keyword.

[0301] : Adjustment parameters are used to adjust the impact of keyword length on information value and ensure that keywords of different lengths are balanced in the calculation.

[0302] Calculation example:

[0303] Assumption: Text content and Weight and , text content and Government affairs work Similarity and , government affairs work content Correlation and , the importance of text content and , the position of text content in the workflow and , adjust the parameters and .

[0304] Calculating the matching score :

[0305]

[0306]

[0307]

[0308]

[0309]

[0310] Calculating display priority :

[0311]

[0312]

[0313]

[0314]

[0315]

[0316]

[0317] The calculation example shows how to calculate the matching score and display priority based on given parameters and formulas, ensuring the accuracy and rationality of the calculation, thereby providing a basis for the subsequent text content display.

[0318] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. Smart government service platform, characterized by: The platform includes: The priority adjustment module analyzes the historical data of government service task completion based on the current government service task list and user behavior, calculates the urgency index of official document approval tasks, evaluates the priority of differentiated tasks, and sorts them to obtain an optimized task list; The process adjustment module analyzes the data impact of public health events and natural disaster emergencies based on the optimized task list, dynamically adjusts the workflow, and obtains an adaptive process configuration; The intelligent text mining module extracts and analyzes key information from contract documents and resumes based on service request text data. It also identifies and ranks keywords and phrases in the text, analyzes the importance and relevance of each information item, calculates information value indicators, and obtains text information analysis results. The method for marking and sorting keywords and phrases in a text is as follows: Based on the service request government text data, the government text data is standardized and preprocessed, including removing stop words, punctuation marks, and irrelevant characters to obtain a cleaned text dataset; Based on the cleaned text dataset, the word frequency-inverse document frequency method is applied, using the formula: ； Calculate the importance score of each word in the text and identify the keywords in the text; in, is a term The importance score of is a term Frequency in the document, is a term that contains The document frequency of is the total number of documents; Based on the cleaned text dataset, using the statistical correlation analysis method, the formula: ； Calculate the relevance score of phrases and identify key phrases in the text; in, is the relevance score, is a candidate phrase, is with Other co-occurring phrases, and are the co-occurrence frequency and the independent frequency, is the smoothing parameter; Sorting the keywords and key phrases according to the importance scores and relevance scores to generate a ranked list of keywords and phrases; Based on the text information analysis results and adaptive process configuration, the intelligent matching optimization module extracts and classifies the text content required for the workflow, calculates the matching degree between the text content and the government work content, adjusts the display of the text content in the workflow, and generates text matching information.

2. The smart government service platform according to claim 1, characterized in that: The method for calculating the urgency index of the document approval task is: Based on the historical data of user behavior and government service task completion, a historical behavior dataset is constructed to extract features associated with document approval tasks, including task submission time, completion time, and user feedback, to obtain a historical feature set. Based on the historical feature set, the formula: ； Calculate the time sensitivity score of a task ; in, Score the time sensitivity of the task, and Represents the start and completion time of the task, is the curve steepness adjustment parameter, and are the offset parameter and the square sum base, is the base of natural logarithms; Based on the time sensitivity score and the influence of user feedback on urgency, the formula is: ； Calculating the urgency index , get the urgency index of the document approval task; in, Represents the urgency index of the task, Represents the rating of task urgency in user feedback, and is the weight parameter, is a normalization coefficient used to balance the impact of different task types.

3. The smart government service platform according to claim 2, characterized in that: The steps for obtaining the optimization task list are: Based on the urgency index of the document approval task, the formula is: ； Calculate the priority of task processing to obtain the task priority ; in, is the task priority, is the urgency index, is the weight parameter of urgency, is the reference offset, To adjust the parameters, is the base of natural logarithms; According to the task priority By comparing the task value, sort the tasks in descending order to get the optimized task list.

4. The smart government service platform according to claim 1, characterized in that: The method for analyzing the data impact of public health events and natural disaster emergencies is: Collect historical data on public health events and natural disasters, including the time, duration, scope, and severity of the events, to generate a historical data set; Based on the historical data set, event data associated with the current optimization task list is filtered, and the severity and spatiotemporal correlation of the event are combined to obtain the following formula: ； Perform weighted processing on the data to obtain weighted event data: in, is the weighted event data, Indicates the severity of the incident. Indicates the duration of the event in days. Indicates the proximity of the event to the current task time, 、 and is the weight coefficient; Based on the weighted event data, the formula: ； Calculating influence , assess the impact of the event on the current workflow; in, For the impact, is the influence sensitivity adjustment parameter, is the model offset parameter, is the base of natural logarithms.

5. The smart government service platform according to claim 4 is characterized in that: The steps for obtaining the adaptive process configuration are: Based on the impact ,Identify the workflow parameters that need to be adjusted, including task resource parameters and decision thresholds, and obtain the adjustment requirements; Based on the adjustment requirements, the formula: ； Calculate updated configuration parameters; in, is the updated configuration parameter, are the current workflow configuration parameters, is the adjustment sensitivity coefficient, is to adjust the smoothing coefficient, It is the degree of influence; The updated configuration parameters are implemented into the actual workflow, and the workflow is dynamically adjusted to obtain an adaptive workflow configuration.

6. The smart government service platform according to claim 1, characterized in that: The steps for obtaining the text information analysis result are: Based on the importance score and relevance score, the formula: ； Calculate a composite score for each keyword and phrase; in, For the comprehensive score, is the importance score, is the relevance score, and is the weight coefficient; Based on the comprehensive score, the formula: ； Calculate the information value of each keyword and phrase, summarize and sort the information value of keywords and phrases, obtain a list of information value indicators, measure the value of text content, and generate text information analysis results; in, is the information value, is the keyword relevance score to the document topic, is the position factor of the keyword in the document, is the keyword length, is the adjustment parameter, is the base of natural logarithms.

7. The smart government service platform according to claim 1, characterized in that: The steps for obtaining the text matching information are: Based on the text information analysis results and the adaptive process configuration, extract the text content required by the workflow, classify the text content according to the differentiated links of the workflow, and obtain the classified text content; Based on the classified text content, the formula: ； Calculating the matching score ,identify the matching degree between text content and government work content; in, is the matching score, Represents text content The weight of Represents text content Government affairs work The similarity of Indicates the content of government affairs The correlation, Indicates the amount of text content. Indicates the amount of work content, Index for text content, Indexing work content; According to the matching score , through the formula: ； Calculating display priority ,Adjust the display order of text content in the workflow to obtain text matching information; in, The importance of the text content, is the position of the text content in the workflow, and To adjust the parameters, Display priority indicates the order in which text content is displayed in the workflow.

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