Monitoring and early warning method for implementation condition of basic public service standard of key crowd with one old and one small based on index system

Abstract

The invention provides a method for monitoring and early warning implementation conditions of basic public service standards of key people with one old and one small based on an index system. The method belongs to the technical field of public service management, and comprises the following steps: firstly, constructing an index system covering core dimensions such as medical treatment, old-age care, education, young support and social security, determining a weight through an analytic hierarchy process and fuzzy consistency adjustment, formulating a standardized data acquisition template, and acquiring and verifying multi-channel data. And dynamically evaluating the implementation level of the public service standard of each region by using a weighted scoring model and an improved TOPSIS algorithm in combination with a time sequence. According to the system, real-time automatic early warning of service glide risks is realized through trend modeling and anomaly detection, and targeted rectification suggestions are provided by adopting hierarchical notification and a causal analysis mechanism. All early warning and rectification results are returned to a system database, self-learning and continuous optimization of an index system and an evaluation model are supported, and scientific decision and closed-loop management of public service governance are realized.

Description

Technical Field

[0001] This invention belongs to the field of public service management technology, and more specifically, it relates to a method for monitoring and early warning of the implementation of basic public service standards for key groups such as the elderly and children based on an indicator system. Background Technology

[0002] With the increasing aging of society and changes in population structure, ensuring public services for key groups such as the elderly and children has become a crucial issue in social governance. To improve service quality and management efficiency, various regions have gradually established service standard systems covering multiple dimensions, including health, education, and elderly care. However, in practice, problems such as difficulty in implementing standards, significant deviations in execution, and hidden risks of changes are common.

[0003] On the one hand, traditional supervision and evaluation methods mainly rely on manual inspections and periodic spot checks, resulting in delayed information transmission and difficulty in timely and comprehensive understanding of service implementation dynamics. This makes it difficult to expose hidden problems early and respond quickly. On the other hand, existing evaluation methods are mostly static assessments, lacking real-time data monitoring and anomaly early warning mechanisms during the standard implementation process, which cannot effectively guide the precise allocation of resources and efficient intervention. Furthermore, with the increase in data volume and business dimensions, how to achieve multi-source data collection, continuous optimization of evaluation models, and promote intelligent and closed-loop management of the standard implementation process have become urgent technical challenges. Therefore, there is an urgent need for a technical solution that can dynamically monitor the standard implementation status, intelligently identify abnormal risks, automatically output rectification suggestions, and support continuous optimization to improve the scientific nature and responsiveness of public service governance for key populations. Summary of the Invention

[0004] This invention aims to address the technical problems existing in the implementation of public service standards for key population groups, such as difficulties in standard implementation, untimely monitoring of implementation effects, lack of intelligent assessment and early warning, and insufficient closed-loop data collection and model optimization. It aims to achieve dynamic monitoring of standard implementation, intelligent early warning of abnormal risks, and automatic recommendation and feedback of rectification measures, thereby improving the scientific, refined, and continuous improvement capabilities of public service governance.

[0005] To achieve the above objectives, the present invention employs the following technical solution: the method comprises: A basic public service standard indicator system was established, and the weights of various indicators were initially assigned using the analytic hierarchy process (AHP), and the weight allocation was corrected using the fuzzy consistency adjustment method. Develop standardized data collection templates, organize data collection from multiple channels, and screen for obvious data anomalies through verification. The collected data is classified and organized according to the previously determined indicator system, and the implementation of standards in each region and object is initially quantitatively evaluated through a weighted scoring model. An improved TOPSIS multi-objective decision-making algorithm is introduced to incorporate changes in the time dimension into the evaluation system, so as to achieve a comprehensive comparison and dynamic display of the implementation level of public service standards. Dynamic monitoring utilizes trend modeling methods based on historical data to automatically identify and detect anomalies in the service indicator trends of monitored objects, and captures potential service decline risk areas in real time, laying the foundation for subsequent early warning. When monitoring signals indicate deviations or continuous declines in standard implementation, multi-level early warnings are triggered. A risk grading and tiered notification mechanism is adopted, and causal analysis methods are combined to identify key factors that lead to the problem, providing management decision-makers with specific and feasible rectification suggestions and intervention priorities in a timely manner. The results of the early warning processing are uploaded to the system database for self-learning and continuous optimization of the indicator system, data collection process, evaluation and early warning mechanism.

[0006] One proposed approach involves establishing a basic public service standard indicator system, which includes: comprehensively collecting and sorting out relevant policy documents; combining expert interviews and field research to select a preliminary indicator library covering core dimensions such as medical services, elderly care services, educational resources, childcare, and social security; and using the Analytic Hierarchy Process (AHP) and fuzzy consistency adjustment method to determine the basic public service standard indicator system and weight distribution for key groups such as the elderly and children.

[0007] In one approach, the development of standardized data collection templates includes: developing unified and standardized data collection templates based on the specific content and data requirements of each indicator; conducting multi-channel data collection; utilizing statistical annual reports, special data reports, real-time business reporting systems, business surveys, and resident follow-ups; and using an intelligent verification system to perform logical verification, interval constraints, historical comparisons, and cross-channel matching on the collected data to ensure data quality and accuracy.

[0008] In one approach, the classification and organization includes: after data collection and cleaning, classifying and standardizing the data according to the indicator system, using a weighted scoring model to quantitatively evaluate the implementation of service standards, and introducing time series analysis based on the improved TOPSIS multi-objective decision-making algorithm to dynamically reflect the progress or regression trends of each region and object in the implementation of public service standards, thereby achieving comprehensive comparison and visualization.

[0009] In one approach, the dynamic monitoring includes: after completing the quantitative assessment of the standard implementation level, conducting stratified and regional dynamic monitoring of key service targets based on weighted scoring and TOPSIS assessment results; using trend modeling methods to model and predict the time series of various service indicators; and automatically identifying potential service decline risks through an anomaly detection system, generating monitoring reports and risk warnings in real time.

[0010] In one approach, triggering multi-level early warnings includes: when the dynamic monitoring system detects abnormal signals of deviation or decline in standard implementation, automatically activating a multi-level early warning process, classifying the abnormal event by risk, pushing early warning information through a graded notification mechanism, integrating causal analysis tools to identify key variables leading to service decline, and combining risk levels and analysis results to output specific and actionable rectification suggestions and intervention priorities, thereby achieving time-limited rectification and closed-loop management.

[0011] In one approach, the early warning processing results are uploaded to the system database, including: after an early warning is triggered and rectification actions are taken, all processing results and key data are structured and fed back to the database. The system runs a self-learning and model iteration process regularly based on the fed-back data. By analyzing the processing effectiveness, the system automatically optimizes indicator weights, filters indicators, improves the data collection process, and revises the early warning model and algorithm parameters. This achieves closed-loop self-optimization of the evaluation and early warning mechanism, thereby enhancing the scientific nature and sustainability of public service governance.

[0012] Beneficial effects of this invention: This invention, by introducing dynamic monitoring, intelligent early warning, and continuous optimization mechanisms, achieves full-process, closed-loop management of the implementation of public service standards for key populations. Compared to traditional methods, this invention can collect and analyze multi-source data in real time, promptly identify anomalies and weaknesses in the service execution process, and automatically push rectification suggestions, effectively improving the speed of problem response and the accuracy of governance. Simultaneously, through a model self-learning and feedback mechanism, it continuously optimizes evaluation standards and intervention measures, enhancing the adaptability and personalization of public services, promoting the overall improvement of service quality, and possessing high promotional value and application prospects. Attached Figure Description

[0013] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0014] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0015] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.

[0016] like Figure 1 As shown, a monitoring and early warning method for the implementation of basic public service standards for key populations, namely the elderly and children, based on an indicator system, is described below. The specific steps are as follows: Step 1: First, based on a review of policy documents and expert interviews, establish a basic public service standard indicator system for key populations, including the elderly and children, covering core areas such as healthcare, elderly care, education, childcare, and social security. In this process, the Analytic Hierarchy Process (AHP) is used to initially assign weights to various indicators, and a fuzzy consistency adjustment method is used to refine the weight allocation. This enhances the flexibility and adaptability of the indicator system, ensuring it scientifically and comprehensively reflects the implementation goals of the service standards.

[0017] In the construction phase of the indicator system, the first step is to comprehensively collect and sort out relevant policy documents, and analyze in detail the service standards, plans, and indicator requirements of the national, provincial, municipal, and local governments in the field of "elderly and children." Simultaneously, expert interviews and field research are conducted to ensure that the selected indicators not only comply with legal requirements but also meet actual needs. Based on the sorting and research results, a preliminary indicator library covering core dimensions such as medical services, elderly care services, educational resources, childcare, and social security is selected. To scientifically determine the importance and weight of each indicator, the Analytic Hierarchy Process (AHP) is introduced. First, the indicators are structurally layered, with the target layer, criterion layer, and indicator layer arranged in an orderly manner. Then, multiple experts are invited to conduct pairwise comparisons of the relative importance of indicators at each level, constructing a judgment matrix. ,in Representing the The first indicator is relative to the first The importance assessment values ​​for each indicator are then used. The matrix is ​​decomposed using maximum eigenvalue decomposition to calculate the eigenvectors. The preliminary weight distribution of the indicators was obtained by normalization. ,Right now .

[0018] Since expert subjective judgments are often ambiguous and inconsistent, a fuzzy consistency adjustment method is further introduced to correct the weights. Specifically, a fuzzy judgment matrix is ​​defined, and a membership function is used... To reflect the reliability of the weights of each indicator, a fuzzy consistency test formula is then used. The consistency level of the evaluation judgment matrix is ​​then assessed. If the consistency requirements are not met, the original judgments must be adjusted or new expert opinions introduced, and the process is iteratively revised until the preset consistency threshold is reached. Ultimately, a basic public service standard indicator system for key populations of the elderly and children is obtained, which is both supported by expert knowledge and can be dynamically optimized and adjusted, laying a solid foundation for subsequent rigorous quantitative assessment and monitoring and early warning.

[0019] Step 2: After determining the indicator system, develop standardized data collection templates and organize data collection through multiple channels, including government statistics, reports from service agencies, business surveys, and resident feedback, to ensure the breadth and representativeness of the data. During the data collection process, use an intelligent verification system to screen for obvious data anomalies and duplications, improving the accuracy and usability of the data foundation.

[0020] After the indicator system is determined, the first step is to develop a unified and standardized data collection template based on the specific content and data requirements of each indicator. This template clarifies the data type, format, collection frequency, and data source for each indicator, ensuring that all data collection activities are highly standardized and comparable. Subsequently, multi-channel data collection is organized, making full use of statistical annual reports and special data reports provided by government departments, combined with the real-time business reporting system of service institutions, and further supplemented by regular business surveys and resident follow-ups with representative households or individuals, to achieve comprehensive coverage and multi-dimensional reflection of the service status of key populations. To ensure the quality and accuracy of the data, all collected data will be simultaneously imported into an intelligent verification system. This system, through setting a series of rules, including logical verification of indicator data, interval constraints, historical comparison, and cross-channel matching, can automatically screen out obvious outliers, missing values, and duplicate data, and issue alerts or marks for suspected errors, facilitating manual review and subsequent correction. Through this process, not only is the breadth and representativeness of data sources guaranteed, but the accuracy and usability of the data foundation are also significantly improved, providing solid data support for the scientific evaluation and dynamic monitoring of the subsequent implementation of the standards.

[0021] Step 3: The collected data is categorized and organized according to the previously determined indicator system, and a weighted scoring model is used to conduct a preliminary quantitative assessment of the standard implementation status of each region and object. To ensure the fairness and interpretability of the assessment, an improved TOPSIS multi-objective decision-making algorithm is introduced into the scoring process, incorporating time-dimensional changes into the evaluation system to achieve a comprehensive comparison and dynamic display of the implementation level of public service standards.

[0022] After data collection and cleaning, all data was categorized and organized according to the previously established indicator system to ensure that data for each service area could be accurately mapped to the corresponding indicators. The data was also standardized to eliminate dimensional differences between different regions and data sources. Subsequently, a weighted scoring model was used to conduct a preliminary quantitative assessment of the implementation of service standards in each region and for each target group. Specifically, this involved normalizing the values ​​of each indicator... Compared with the previously determined weights Multiply and sum the results to obtain the overall score for each evaluated object. ,Right now This reflects the overall level of achievement of public service standards by the target at the current point in time.

[0023] To further enhance the fairness and interpretability of the evaluation system and effectively identify performance across multiple dimensions, an improved TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) multi-objective decision-making algorithm was introduced in the scoring process. The improvement lies in considering not only the performance of each object at each point in time but also incorporating historical data, adding a time dimension. By constructing a time-series indicator matrix, normalized data from each time period are integrated into the TOPSIS decision space. In practice, the first step is to determine the positive ideal solution. and negative ideal solution Then, the distance from each evaluation object to the positive and negative ideal solutions is calculated separately. and The formula is Finally, the overall score pass The calculation shows that the closer the value is to 1, the better the performance of a region or object across all periods and all indicators. By introducing time series analysis and improving the TOPSIS algorithm, it is possible to dynamically reflect the progress or regression trends of various regions and objects in the implementation of public service standards, realize a comprehensive comparison and visualization of the standard implementation level, and lay a quantitative foundation for subsequent monitoring and early warning analysis.

[0024] Step 4: Subsequently, based on the quantitative assessment results, dynamic monitoring is implemented in a tiered and regional manner. Utilizing trend modeling methods based on historical data, the system automatically identifies and detects anomalies in the service indicator trends of the monitored objects. The system can capture potential service decline risk areas in real time, laying the foundation for subsequent early warnings.

[0025] After completing the quantitative assessment of the standard implementation levels for various regions and targets, the system enters a phase of dynamic monitoring based on hierarchical and regional classifications. According to weighted scoring and TOPSIS assessment results, the system categorizes key service targets into different levels (e.g., high, medium, low) and different geographical regions, enabling differentiated and targeted continuous monitoring. To comprehensively capture the dynamic changes in service standard implementation, a trend modeling method based on historical data is employed to model and analyze the time series of various service indicators. Specifically, for each monitored target and its related indicators, a time series is first constructed. Models such as Autoregressive Moving Average (ARMA) or Autoregressive Integrated Moving Average (ARIMA) are applied for fitting, such as... ,in For a certain period of time, the standardized index value is... These represent the autoregressive and moving average coefficients, respectively. This is the noise term.

[0026] After the model is fitted, a future trend line is generated by predicting the values ​​for one or more future periods. At the same time, the system processes the residual sequence. Continuous monitoring should be conducted, utilizing statistical anomaly detection methods such as z-score ( ,in For the mean, Using standard deviation (or control chart techniques), the system automatically marks data points that deviate significantly from historical patterns. When the indicator value of a certain region or object is significantly lower than the historical forecast range (e.g., below the lower bound of the confidence interval) within a continuous period, or when abnormal fluctuations occur, the system will automatically identify it as a potential service decline risk and generate monitoring reports and risk warnings in real time.

[0027] This hierarchical and regional trend modeling and anomaly detection system can accurately track the dynamic changes in service quality and standard implementation, providing decision-makers with real-time risk clues and a scientific basis for subsequent early warning mechanisms and resource intervention.

[0028] Step 5: When monitoring signals indicate a deviation from or continuous decline in standard implementation, the system automatically triggers multi-level early warnings. The early warning module employs an innovative risk grading and tiered notification mechanism, combined with causal analysis methods, to correlate key factors that may lead to problems, and promptly provides management decision-makers with specific and feasible rectification suggestions and intervention priorities.

[0029] When the dynamic monitoring system detects abnormal signals such as deviation from standard implementation or continuous decline, the early warning mechanism automatically activates a multi-level early warning process. First, the system classifies the abnormal event into risk levels based on the magnitude of the decline, its duration, and the weight of key indicators, such as general early warning, major early warning, and emergency early warning. Each level corresponds to different response time limits and intervention requirements to ensure that resources are prioritized for the areas with the most urgent needs. Early warning information is pushed to relevant business departments, supervisors, and regional managers through a tiered notification mechanism, and a visual early warning report can be generated simultaneously on the management platform, improving response efficiency and transparency.

[0030] To enhance the scientific rigor and relevance of early warning systems, the system integrates causal analysis tools to rapidly uncover the underlying causes of anomalies. Specifically, the system collects multidimensional data related to abnormal indicators and uses methods such as regression analysis, decision trees, and Granger causality tests to identify key variables and impact paths highly correlated with service decline. For instance, if the health service standards for the elderly decline continuously, the system automatically compares historical changes in variables such as medical service resource input, personnel changes, and resident satisfaction to determine the most likely root cause of the problem. The analysis results serve as a crucial basis for management recommendations, assisting decision-makers in accurately identifying areas for improvement.

[0031] In terms of providing rectification suggestions, the system combines the actual risk level and causal analysis results to provide managers with a tiered, specific, and actionable action list. For example, for general warnings, it suggests routine inspections and staff training; for major or urgent warnings, it proposes more targeted measures such as increasing funding, adjusting service processes, and conducting focused home visits. Simultaneously, the system automatically prioritizes interventions, guiding managers to address the issues with the greatest impact on overall service, thus achieving time-bound rectification, dynamic tracking, and closed-loop management. Through the organic integration of warning, analysis, suggestion, and response mechanisms, the system provides intelligent and scientific decision support for continuous improvement of standard implementation and risk control.

[0032] Step 6: Finally, all early warning processing results and rectification effectiveness are fed back into the system database for system self-learning and continuous optimization of the indicator system, data collection process, and evaluation and early warning mechanism. The entire process constructs a closed-loop, self-improving method for monitoring and early warning of the implementation of public service standards for key populations such as the elderly and children, providing solid data and intelligent support for public service governance.

[0033] After an early warning is triggered and corrective actions are implemented, all relevant results, including the early warning response process, specific intervention measures, implementation progress, and evaluation of corrective effectiveness, are systematically recorded by the system and fed back into the database. To ensure the comprehensiveness and accuracy of the data, the system automatically collects changes in key indicators before and after corrective actions, operation logs during the corrective process, and feedback from managers and frontline staff. This data, along with existing monitoring and evaluation data, is stored together, becoming important material for subsequent analysis and model optimization.

[0034] Based on the feedback data, the system regularly runs a self-learning and model iteration process. First, by analyzing the handling effectiveness and actual improvement of different early warning events, the sensitivity and directionality of the current indicator system are assessed. If certain indicators are found to be consistently ineffective or have excessively high / low weights in early warning and rectification, the system will automatically adjust indicator weights or perform indicator filtering using machine learning methods (such as feature importance analysis or Bayesian optimization) to enhance the scientific rigor of monitoring and early warning. Second, the system will also optimize the data collection process based on new data. For example, by analyzing data gaps, it may identify information lags or blind spots in certain business processes, promptly proposing improvement suggestions to promote the improvement and automation of the data collection interface.

[0035] In terms of assessment and early warning mechanisms, the system automatically revises algorithm parameters such as anomaly detection, risk classification, and causal analysis by retrospectively analyzing the historical accuracy and timeliness of the early warning model, thereby improving future identification efficiency and the relevance of rectification suggestions. Furthermore, the system supports administrators in archiving personalized feedback and adjustment suggestions during the early warning response process, incorporating them into a self-optimization cycle to improve business rules and response procedures. Ultimately, this closed-loop system achieves end-to-end management from data collection, quantitative assessment, dynamic monitoring, and intelligent early warning to rectification feedback and self-improvement, providing continuous intelligent support and a data foundation for public service governance for key populations such as the elderly and children, and promoting the high-quality and sustainable development of standard implementation.

[0036] Example: With increasing societal pressures from an aging population and declining birth rates, ensuring basic public services for key groups such as the elderly and children has become a crucial issue in urban governance. A municipal government decided to develop and deploy a monitoring and early warning system based on an indicator system to achieve quantitative monitoring, dynamic evaluation, and intelligent early warning across five core dimensions: medical services, elderly care services, educational resources, childcare, and social security. This will enable continuous optimization of resource allocation and service supply.

[0037] 1. Policy review and preliminary indicator selection By collecting data from the "Standards for Services for the Elderly," the "Regulations on the Protection of Children's Welfare," and related policies, and combining expert interviews and field research, the following preliminary indicator database has been compiled.

[0038] 2. Metrics System Architecture (Partial Example) 3. Weight Determination Process The Analytic Hierarchy Process (AHP) was used to organize more than ten experts to conduct pairwise comparisons of the above indicators. Combined with the fuzzy consistency adjustment method, the final weights are as follows: III. Data Acquisition and Preprocessing 1. Data Acquisition Template Design The standardized data collection template covers data coding, units, collection methods, and time points. Data sources include the Municipal Health Commission, Civil Affairs Bureau, Education Bureau, and Social Security Bureau.

[0039] 2. Sample table of collected data The data is automatically validated and outliers are screened (e.g., the number of elderly care beds in Area A increased abnormally in 2022, which was corrected after manual verification).

[0040] IV. Quantitative Assessment of Standard Implementation and TOPSIS Decision-Making 1. Data normalization and weighted scoring To normalize all regional data to the [0,1] interval, the following formula is used: Normalized value = (actual value - minimum value) / (maximum value - minimum value) After normalization, each indicator is multiplied by its weight and summed to obtain the comprehensive score for each region.

[0041] 2. Improve the TOPSIS algorithm Taking into account the changes between years, data from 2021 to 2023 were included to calculate the distance from the ideal solution and the negative ideal solution. Combined with time weights, the service level of the region was dynamically evaluated.

[0042] Example table of TOPSIS analysis: The system provides a dynamic and visual representation of the annual trends and changes in service levels across different districts.

[0043] The historical score trend is modeled using an autoregressive moving average (ARMA) model to identify downward inflection points or abrupt drops.

[0044] For example, in Zone D, the number of family doctors per thousand elderly people was below the warning line of 2.5 for two consecutive quarters, which the system automatically identified as a high-risk signal.

[0045] Yellow alert: A single indicator shows a slight decline; attention is advised. Orange alert: Multiple indicators are at a critical level, requiring rectification. Red alert: Key indicators deviate significantly, mandatory intervention required. The system automatically sends notifications to relevant personnel via SMS / email / App. Introduce causal analysis models (such as multiple regression and decision trees) to correlate the reasons for the decline in services within the region, such as human resource allocation, budget adjustments, and the impact of the pandemic.

[0046] Output of causal analysis in region D: High turnover rate of family doctors (due to excessive pressure from key performance assessments) Funding disbursements for some elderly care projects have been delayed. Suggestions for rectification: 1. Prioritize the allocation of subsidies for community family doctors. 2. Simplify performance evaluation indicators 3. Supervise the monthly disbursement of funds to ensure the expansion of bed capacity. Once approved, the rectification suggestions will be included in the supervision list and will be completed within one month. The system will automatically track the progress of the rectification.

[0047] All rectification records, processing results, and regional adjustment data are uploaded to the system database in a structured format.

[0048] The system periodically analyzes the common characteristics of successful rectifications and automatically optimizes the indicator weights. Delete long-term irrelevant or ineffective indicators Upgrade verification rules for data acquisition processes prone to anomalies. The parameters of the early warning model are adaptively fine-tuned based on historical data. After six months of self-learning and optimization, it was found that the contribution of "community care point coverage rate" to the elderly care service score was underestimated. The weight was adjusted from 8% to 12%, making the model prediction more in line with actual governance needs.

[0049] This embodiment illustrates that by relying on a scientific indicator system, standardized data collection, advanced multi-objective decision-making models, and intelligent early warning mechanisms, it is possible to achieve dynamic and closed-loop management of public services for key populations such as the elderly and children throughout the entire process, which is of great practical significance for promoting the refined governance of urban public services.

[0050] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0051] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring and early warning of the implementation of basic public service standards for key populations, namely the elderly and children, based on an indicator system, characterized in that: The method includes: A basic public service standard indicator system was established, and the weights of various indicators were initially assigned using the analytic hierarchy process (AHP), and the weight allocation was corrected using the fuzzy consistency adjustment method. Develop standardized data collection templates, organize data collection from multiple channels, and screen for obvious data anomalies through verification. The collected data is classified and organized according to the previously determined indicator system, and a weighted scoring model is used to conduct a preliminary quantitative assessment of the standard implementation status of each region and object. An improved TOPSIS multi-objective decision-making algorithm is introduced to incorporate time dimension changes into the evaluation system, so as to achieve a comprehensive comparison and dynamic display of the implementation level of public service standards. Dynamic monitoring utilizes trend modeling methods based on historical data to automatically identify and detect anomalies in the service indicator trends of monitored objects, and captures potential service decline risk areas in real time, laying the foundation for subsequent early warning. When monitoring signals indicate deviations or continuous declines in standard implementation, multi-level early warnings are triggered. A risk classification and tiered notification mechanism is adopted, and causal analysis methods are combined to identify key factors that lead to the problem, providing management decision-makers with specific and feasible rectification suggestions and intervention priorities in a timely manner. The results of the early warning processing are uploaded to the system database for self-learning and continuous optimization of the indicator system, data collection process, evaluation and early warning mechanism.

2. The method for monitoring and early warning of the implementation of basic public service standards for key populations "elderly and children" based on an indicator system as described in claim 1, characterized in that: The establishment of a basic public service standard indicator system includes: comprehensively collecting and sorting out relevant policy documents, combining expert interviews and field research, selecting a preliminary indicator library covering the core dimensions of medical services, elderly care services, education resources, childcare security, and social security, and determining the basic public service standard indicator system and weight distribution for key groups of "the elderly and children" through the analytic hierarchy process (AHP) and fuzzy consistency adjustment method.

3. The method for monitoring and early warning of the implementation of basic public service standards for key populations "elderly and children" based on an indicator system as described in claim 1, characterized in that: The aforementioned development of standardized data collection templates includes: developing unified and standardized data collection templates based on the specific content and data requirements of each indicator; conducting multi-channel data collection; utilizing statistical annual reports, special data reports, real-time business reporting systems, business surveys, and resident follow-ups; and using intelligent verification systems to perform logical verification, interval constraints, historical comparisons, and cross-channel matching on the collected data to ensure data quality and accuracy.

4. A method for monitoring and early warning of the implementation of basic public service standards for key populations "elderly and children" based on an indicator system, as described in claim 1, is characterized in that: The classification and organization process includes: after data collection and cleaning, classifying and standardizing the data according to the indicator system, using a weighted scoring model to quantitatively evaluate the implementation of service standards, and introducing time series analysis based on the improved TOPSIS multi-objective decision-making algorithm to dynamically reflect the progress or regression trends of each region and object in the implementation of public service standards, so as to achieve comprehensive comparison and visualization.

5. A method for monitoring and early warning of the implementation of basic public service standards for key populations "elderly and children" based on an indicator system, as described in claim 1, characterized in that: The dynamic monitoring includes: after completing the quantitative assessment of the standard implementation level, conducting stratified and regional dynamic monitoring of key service targets based on weighted scoring and TOPSIS assessment results; using trend modeling methods to model and predict the time series of various service indicators; and automatically identifying potential service decline risks through an anomaly detection system, generating monitoring reports and risk warnings in real time.

6. A method for monitoring and early warning of the implementation of basic public service standards for key populations "elderly and children" based on an indicator system, as described in claim 1, is characterized in that: The aforementioned multi-level early warning triggering includes: when the dynamic monitoring system detects abnormal signals of deviation or decline in standard implementation, automatically activating the multi-level early warning process, classifying the abnormal event by risk, pushing early warning information through a graded notification mechanism, integrating causal analysis tools to identify key variables that lead to service decline, and combining risk level and analysis results to output specific and actionable rectification suggestions and intervention priorities, thereby achieving time-limited rectification and closed-loop management.

7. A method for monitoring and early warning of the implementation of basic public service standards for key populations "elderly and children" based on an indicator system, as described in claim 1, characterized in that: The aforementioned early warning processing results are uploaded to the system database, including: after the early warning is triggered and rectification actions are taken, all processing results and key data are structured and fed back to the database. The system regularly runs a self-learning and model iteration process based on the fed-back data. By analyzing the processing effectiveness, it automatically optimizes indicator weights, filters indicators, improves the data collection process, and revises the early warning model and algorithm parameters to achieve closed-loop self-optimization of the evaluation and early warning mechanism, thereby improving the scientific nature and sustainability of public service governance.

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