Intelligent detection method for implementation standard reaching rate of basic public service standard of key people with one old and one small

Abstract

The invention discloses an intelligent detection method for the implementation standard reaching rate of basic public service standards of key people with one old and one small, and belongs to the field of smart cities. By integrating automatic acquisition and management of multi-source heterogeneous data and combining a service standard knowledge base and an intelligent evaluation model, automatic monitoring, dynamic standard-reaching rate analysis and abnormal early warning of basic public service key indexes of key people such as pension, children and the like are realized. The system supports index weight adaptive adjustment and multi-dimensional visual display, and can discover service short boards in real time and assist decision optimization. According to the invention, the efficiency and accuracy of monitoring are improved, manual statistical errors are reduced, and scientific and accurate'complementation of shortages' and high-quality service support are provided for public service management departments.

Description

Technical Field

[0001] This invention belongs to the field of smart cities, and more specifically relates to an intelligent detection method for the compliance rate of basic public service standards for key groups such as the elderly and children. Background Technology

[0002] Society's demand for basic public services for key groups such as the elderly and children is growing. Governments at all levels and relevant departments attach great importance to the service and support work for these key groups, and have successively introduced a series of basic public service standards and assessment requirements to promote the standardization and equalization of the service system.

[0003] However, due to the involvement of multiple departments, the complexity of service items, and the heterogeneity and frequent updates of data sources, traditional population service evaluation and compliance monitoring rely heavily on manual statistics, decentralized reporting, and manual review. This results in problems such as incomplete data collection, inconsistent statistical standards, omissions, or false reporting, making it difficult to achieve dynamic, accurate, and scientific assessment of service compliance rates. Furthermore, existing analytical methods primarily present static results, lacking real-time monitoring and trend analysis of abnormal fluctuations in service compliance rates, making it difficult to promptly identify weaknesses in service delivery and hindering continuous improvement in service quality. Therefore, there is an urgent need to leverage modern information technologies such as big data and artificial intelligence to establish an integrated intelligent monitoring method covering "basic data collection, standardized management, intelligent analysis and early warning, and automated reporting," enabling comprehensive, dynamic, and intelligent monitoring of the compliance rate of basic public service standards for key populations such as the elderly and children. Summary of the Invention

[0004] This invention aims to address the technical challenges in existing monitoring of the compliance rate of basic public services for key populations such as the elderly and children. These challenges include incomplete data collection, inconsistent statistical standards, low efficiency of manual statistics, difficulty in timely detection and tracing of anomalies, and a lack of intelligence and dynamism in compliance rate assessment. The invention proposes an integrated detection method based on multi-source data fusion, intelligent anomaly detection, personalized indicator weight adaptive adjustment, and automated report analysis. This method enables scientific, accurate, dynamic, and traceable intelligent assessment and early warning of the compliance rate of basic public services for key populations such as the elderly and children.

[0005] To achieve the above objectives, the present invention employs the following technical solution: the method comprises: Basic data collection and storage: Collect basic information on key groups such as the elderly and children in the jurisdiction, including population statistics, health status, service needs, and historical service records; Construction of a service standard knowledge base: Establish / maintain a standard knowledge base for basic public services for the elderly and children, including service items, compliance requirements, assessment details, service frequency and quality parameters; Service data is collected in multiple dimensions and anomaly detection is performed. A multi-dimensional fusion anomaly detection algorithm is adopted, which is based on a combination of multimodal data adaptive hybrid clustering and isolated forest algorithm to discover missing items, false data entry and data anomalies. The achievement rate assessment index system is constructed by adopting the analytic hierarchy process and adaptive weight optimization algorithm, and introducing an AHP weight self-learning adjustment mechanism based on personalized needs feedback of the population to establish a multi-dimensional evaluation index system covering service coverage, usage rate, satisfaction and sustainability. Compliance identification and early warning: Based on the time series trend identification and anomaly tracing AI model, the collected service data is dynamically tracked to identify the compliance rate, short-term anomalies and long-term downward trends, and the causes are traced. The system automatically generates and analyzes compliance results, combining the determined compliance rate, gap analysis, and improvement suggestions to generate compliance analysis reports by level, region, and service item.

[0006] In one solution, the basic data collection and storage includes: connecting with the community management system, the medical and health management information platform, the student management database of schools and childcare institutions in the jurisdiction, and the social security and civil affairs service system; using a unified data interface standard; performing permission verification, data desensitization, data format standardization and data cleaning; and mapping multi-source heterogeneous data into the basic data center in a unified manner, supporting timed synchronization and incremental update mechanisms.

[0007] In one solution, the construction of the service standard knowledge base includes: connecting with national and local policies, normative documents and industry standards, performing semantic analysis through a policy parsing engine, and extracting service items, service scope, service targets, implementing entities, frequency requirements, service quality parameters and assessment details; It adopts an ontology modeling approach for structured expression and supports version management, multi-regional standard customization, and manual verification and expert review mechanisms to ensure the authority and practical value of the knowledge base.

[0008] In one solution, the multidimensional collection and anomaly detection of service data includes: associating basic information with various third-party service data with a unique identifier as the primary key to construct a multidimensional comprehensive database covering population attributes, health records, service history, service frequency, and geographical distribution; An algorithm combining adaptive hybrid clustering and isolated forest based on multimodal data is adopted. First, Gaussian mixture model is used to adaptively cluster the feature dataset to screen out abnormal samples. Then, the isolated forest algorithm is introduced to further detect and judge the initially screened abnormal and marginal samples. This enables the identification of multiple types of data anomalies, such as missing service data, false data entry, short-term high-frequency services, and service logic conflicts. All suspected anomalies are archived, marked, and associated with the original data for manual verification and subsequent analysis.

[0009] In one approach, the construction of the compliance rate assessment indicator system includes: using the analytic hierarchy process to decompose the overall compliance rate into core evaluation dimensions such as service coverage, usage rate, satisfaction, and service continuity; calculating the initial weights of each indicator using an indicator comparison judgment matrix and eigenvector method; and verifying the compliance rate by combining the consistency ratio.

[0010] In one scheme, the compliance identification and early warning includes: conducting time series monitoring and analysis of the comprehensive compliance score of each service unit, using time series modeling methods such as autoregressive moving average for trend analysis and future change prediction, and combining sliding window dynamic threshold detection to identify short-term anomalies; For anomalies and downward trends, input multidimensional indicator scores, service data, and anomaly detection tags. Use decision trees to automatically identify key related indicators and their importance, enabling intelligent tracing and precise location of anomalies in the compliance rate. The results are pushed through a visual dashboard and regular reports to provide early warnings.

[0011] In one solution, the automated generation and reporting analysis of the compliance results includes: automatically compiling compliance rates and trend analysis for each unit, region, and service type, and tracing the source of anomalies; summarizing the results by administrative division, service institution, and service type in multiple dimensions; conducting gap analysis and summarizing the main reasons affecting the compliance rate; and proposing improvement suggestions based on the source tracing results of indicator anomalies.

[0012] In one approach, the construction of the target achievement rate assessment indicator system includes: introducing an AHP weight self-learning adjustment mechanism based on personalized needs feedback from the population; collecting service feedback from key populations in real time; obtaining the evaluation of the importance of each indicator from the actual needs side through subjective scoring; dynamically optimizing the comprehensive weight of each indicator; and achieving multi-dimensional indicator synergy and adaptive weight adjustment.

[0013] Beneficial effects of this invention: By integrating technologies such as automatic collection and governance of multi-source heterogeneous data, intelligent management of standard knowledge bases, multi-dimensional analysis of compliance rates, and intelligent early warning, the monitoring efficiency and accuracy of compliance rates for basic public services for key populations such as the elderly and children have been significantly improved. This method enables real-time dynamic collection and automated analysis of service data, timely detection of anomalies and weaknesses in service provision, and effectively reduces data omissions, delays, and inconsistencies caused by manual statistics and decentralized reporting.

[0014] By introducing adaptive indicator weight adjustments and trend-based intelligent analysis, the evaluation model can be flexibly optimized for different regions and service scenarios, improving the scientific rigor and relevance of analysis and decision-making. Furthermore, the automatically generated analysis reports provide management departments with intuitive decision-making support, enabling continuous optimization and precise improvement of public service provision, ultimately promoting the equalization and high-quality development of public services for key groups such as the elderly and children. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0016] 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.

[0017] 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.

[0018] like Figure 1 As shown, a smart detection method for the compliance rate of basic public service standards for key groups such as the elderly and children includes: Step 1: Basic Data Collection and Storage Collect basic information on key groups (the elderly and children) within the jurisdiction, including population statistics, health status, service needs, and historical service records.

[0019] Data sources include: community management systems, medical institutions, educational institutions, social security, etc.

[0020] In the implementation of this invention, the basic data collection and storage stage, as the first step in intelligent detection, must ensure the comprehensiveness, accuracy, and security of the data. This step first involves data integration with various business management systems within the jurisdiction, including community management systems, medical and health management information platforms, student management databases of schools and childcare institutions within the jurisdiction, as well as social security and civil affairs services.

[0021] Adopting a unified data interface standard, the system connects to basic data from various sources, covering key populations such as the elderly and children, including their ID card information, household registration and address, health status, chronic disease and key disease records, types and frequency of public services received, education and growth records, and social security participation information. During data collection, access rights are verified and data is anonymized for each data source to prevent the leakage of sensitive information. Simultaneously, a built-in data format standardization and data cleaning module automatically handles missing values, outliers, and duplicate records, ensuring the standardization and uniformity of the entered data. Finally, the processed and verified multi-source heterogeneous data is uniformly mapped into the basic data center, providing high-quality, structured data support for subsequent service standard integration and the implementation of intelligent detection algorithms. Furthermore, to ensure timely data updates and dynamic tracking, a timed synchronization and incremental update mechanism is supported to ensure that the information of key populations in the jurisdiction remains up-to-date.

[0022] Step 2: Building a Service Standards Knowledge Base In accordance with relevant national and local policies, establish / maintain a standard knowledge base for basic public services for the elderly and children, including service items, compliance requirements, assessment details, service frequency, and quality parameters.

[0023] In the process of building the service standards knowledge base, the first step is to connect with relevant policies, normative documents, and industry standards issued by national and local governments at all levels regarding basic public services for key groups such as the elderly and children. This involves comprehensively collecting and organizing service requirements and compliance standards for the elderly and children in various fields such as health, education, welfare, medical care, and social interaction. A policy parsing engine is then used to perform semantic analysis on the original policy texts, automatically extracting and summarizing core elements such as service items, service scope, service recipients, implementing entities, frequency requirements, service quality parameters, and specific assessment rules.

[0024] Subsequently, the knowledge base employs ontology modeling to structurally represent service standards at different levels and categories, forming a scalable, multi-level knowledge network that ensures the integrity and organization of the standard content. To adapt to policy updates and local differences, it supports version management and multi-regional standard customization, enabling the simultaneous maintenance of localized standard clauses for different jurisdictions. The knowledge base also integrates manual verification and expert review mechanisms to check the legality, accuracy, and applicability of automatically collected and modeled results, ensuring the authoritativeness and practical value of the knowledge base content. Finally, after multiple rounds of processing and optimization, the service standard knowledge base achieves a digital translation from policy to business rules, providing a machine-readable and automatically reasoning standard foundation for subsequent service compliance determination and intelligent detection algorithms, thus improving overall adaptability and intelligence.

[0025] Step 3: Multi-dimensional collection and anomaly detection of service data By combining collected population data and third-party service data, a multi-dimensional fusion anomaly detection algorithm is adopted. Based on a combination of multimodal data adaptive hybrid clustering and isolated forest algorithm, possible omissions, false data entry and data anomalies are automatically detected.

[0026] In the multi-dimensional data collection and anomaly detection phase, the basic information of key populations such as the elderly and children collected in the early stages is first fused with various third-party service data (medical, educational, social services, etc.) in multiple dimensions. All data are linked using a unique identifier (ID number or unified code) as the primary key to construct a comprehensive database covering multiple dimensions such as population attributes, health records, service history, service frequency, and geographical distribution. For each piece of service data, a feature vector is extracted. ,in Let represent the i-th feature dimension (service type, service time, service frequency, health status score, etc.), and d be the total number of features. In this way, all personnel samples are uniformly mapped into a d-dimensional feature space.

[0027] Building upon this foundation, an innovative algorithm combining adaptive hybrid clustering and isolated forest based on multimodal data was adopted. First, a Gaussian Mixture Model (GMM) was used to adaptively cluster the feature dataset. This method assumes that all sample points are generated by a mixture of several Gaussian distributions ("clusters"). The probability density function of the model is: in, This represents the input feature vector. Represents the set of all model parameters. This represents the number of components in a Gaussian distribution. For the first The mixing coefficients of the Gaussian components (satisfying) ,and ), For the first The mean vector of Gaussian components, Let its covariance matrix be... Indicates For the mean, The covariance is a multivariate Gaussian distribution. Based on the GMM clustering results, each data point is assigned a probability belonging to each Gaussian component. When the probability value of a data point is very low in all clusters, it is initially judged as an outlier.

[0028] Next, the Isolation Forest algorithm is introduced to further detect the anomalous or marginal samples initially screened out. Isolation Forest focuses on identifying outliers that are significantly different from the majority of samples and easily "isolated." Its basic idea is to repeatedly and randomly split the data dimensions to construct a random tree. When a sample... Average path length in a tree When the score is significantly shorter than most samples, it indicates that the sample is more likely to be isolated and has a higher probability of being an anomaly. The formula for calculating the anomaly score is: in, Indicates the sample to be tested. It is the total number of samples in the dataset. It is a sample Average path length, It is the normalization factor, approximately , Harmonic number. Abnormal score. The score ranges from 0 to 1, with higher scores indicating a greater likelihood of an outlier.

[0029] Through the aforementioned two-stage fusion algorithm, the global distribution recognition capability of clustering models and the local sensitivity of isolated forests can be fully integrated in a multimodal, high-dimensional feature space. This enables the sensitive identification of missing service data, false data entry, and other data anomalies (such as short-term high-frequency services and service logic conflicts). All detected suspected anomalies are archived, marked, and linked to the original data for subsequent manual verification, service compliance assessment, and problem tracing.

[0030] Step 4: Construction of the compliance rate assessment indicator system By employing the Analytic Hierarchy Process (AHP) and an adaptive weight optimization algorithm, and introducing an AHP weight self-learning adjustment mechanism based on personalized needs feedback from the population, a multi-dimensional evaluation index system covering service coverage, usage rate, satisfaction, and sustainability is established, laying the foundation for subsequent judgments.

[0031] During the construction phase of the compliance rate assessment indicator system, a comprehensive indicator system covering core evaluation dimensions such as service coverage, usage rate, satisfaction, and service continuity was designed based on the service standard knowledge base and multi-dimensional service data.

[0032] First, the Analytic Hierarchy Process (AHP) is used to decompose the overall goal (the service compliance rate for the elderly and children) into sub-goals and specific indicators at each level. For example, the top level is the overall compliance rate, and the lower levels include service coverage (the proportion of the target population who actually receive services), usage rate (the proportion of actual service usages to the total number of available services), satisfaction (quantified through questionnaires, ratings, etc.), and sustainability (service uninterrupted rate or the proportion of people receiving continuous services). AHP constructs an indicator comparison and judgment matrix. (in This represents the importance of the i-th indicator relative to the j-th indicator (typically taking values ​​from 1 to 9). The initial weights of each indicator are calculated using the eigenvector method. Weight Vector satisfy ,in To determine the largest eigenvalue of a matrix, the consistency ratio (CR) is used to test the matrix's reasonableness.

[0033] To better adapt to the personalized needs of different regions and populations, a creative adaptive weight optimization algorithm is introduced, namely, an "AHP weight self-learning adjustment mechanism incorporating personalized needs feedback from the population." This mechanism collects service feedback from key populations (the elderly, children, and their families) in real time, obtaining subjective ratings or rankings of the importance of indicators based on actual needs through questionnaires, online interactions, and satisfaction ratings. Let the subjective weight assigned by the k-th user to the i-th indicator be... It can use feedback from all users within a certain period to calculate the average feedback value of each indicator. (in To effectively reflect the number of users, and then combine it with the initial weight of AHP. Forming a comprehensive weight: .in It is an adaptive adjustment coefficient ( The weights can be dynamically adjusted based on the sample size and confidence level. As more real feedback accumulates, the weight self-learning adjustment mechanism continuously optimizes the matching effect of the actual importance of each indicator.

[0034] Ultimately, the compliance rate is determined based on a comprehensive indicator score: for the j-th service unit (a region, an institution, or a population group), its compliance rate can be expressed as... in, It is the standardized score of the service unit on the i-th metric. The final weights for each indicator are determined. The entire system ensures the synergy of multi-dimensional indicators and adaptive learning of weights, taking into account policy objectives, objective data, and subjective experience, thus laying a solid foundation for scientifically and dynamically determining the service compliance rate for the elderly and children.

[0035] Step 5: Compliance Identification and Early Warning Based on the indicator system in step 4, the collected service data is dynamically tracked using a creative algorithm (creative algorithm 3: AI model based on time series trend identification and anomaly tracing) to identify short-term anomalies and long-term trend declines, and to trace the causes.

[0036] In the intelligent compliance identification and early warning stage, based on the dynamic weighted multidimensional indicator system constructed in step 4, the collected service data is continuously monitored and analyzed.

[0037] First, the overall compliance score for each service unit (a region, institution, or key population group) will be calculated. Organized in time series format, i.e. , where t represents a point in time (month, quarter, year). Let be the pass rate of the j-th unit at time t.

[0038] The study employs an AI model for time series trend identification and outlier tracing. For trend identification, it first utilizes time series modeling methods such as Autoregressive Moving Average (ARMA / ARIMA) to analyze... Perform stationarity and trend analysis. Taking the ARIMA model as an example, the model form is as follows: in, express The difference (after eliminating the non-stationary trend). It is the autoregressive coefficient. The moving average coefficient, The noise level is white noise. The model identifies long-term trends by predicting the change in the pass rate over a future period. For sudden anomalies, a sliding window dynamic threshold detection is introduced: let the average pass rate over K historical periods be... Standard deviation is If at the current time t, there is It is then judged as a short-term anomaly. The threshold coefficient is 2 or 3.

[0039] For anomalies and declining trends, an anomaly tracing AI model is further utilized. This model takes multi-dimensional indicator scores, service data, and anomaly detection tags before and after the anomaly as input features, and uses a decision tree to automatically discover the causal relationship between the anomalies in each indicator and the overall decline in compliance. The model can output the most likely associated indicator for each anomaly (a sudden drop in satisfaction for a certain type of service, a sudden change in coverage, etc.) and calculate the feature importance score based on the Gini importance of the tree model, using the formula: By combining the above steps, the following can be achieved: 1) Dynamically track service compliance rates and capture long-term downward trends; 2) Intelligently identify short-term abnormal fluctuations and issue timely alerts; 3) Automatically locate and trace the key indicators and data dimensions of abnormal occurrences, providing a basis for management departments to intervene precisely and optimize measures. All results are pushed to relevant decision-makers in real time through a visual dashboard and regular reports, forming an intelligent, closed-loop early warning and response mechanism.

[0040] Step 6: Automated generation and report analysis of compliance results It automatically combines assessed compliance rates, gap analysis, and improvement suggestions to generate compliance status analysis reports categorized by level, region, and service item. It also supports intelligent text summarization algorithms to automatically extract key findings and risk alerts.

[0041] In step 6, "Automatic Generation and Report Analysis of Compliance Results," the entire process of automated report generation and intelligent analysis was achieved. First, based on the compliance rate data for each unit, region, and service item determined in the preceding steps, as well as the results of dynamic trend analysis and anomaly tracing, the compliance status was automatically compiled by level, region, and service type. All analytical content was categorized and summarized according to multiple dimensions such as administrative division, service agency, and service type, ensuring that the report could meet the management and decision-making needs of different levels.

[0042] Next, a gap analysis is conducted. By comparing the current compliance rate with the target standard in each region, service shortcomings and weaknesses are automatically identified, clearly indicating which regions and service items have significant room for improvement, the specific gap size, and the main risks that have recently emerged. Simultaneously, based on the results of anomaly tracing, the main reasons affecting the compliance rate are automatically summarized, such as uneven service coverage, low usage rates, sharp drops in satisfaction, and poor sustainability. Targeted improvement suggestions are then proposed, such as strengthening publicity, optimizing processes, increasing resource allocation, and enhancing training.

[0043] To enhance report readability and decision-making efficiency, a built-in intelligent text summarization algorithm automatically transforms a large amount of structured and unstructured analytical conclusions into concise and accurate natural language summaries. The algorithm can extract the core performance, major risks, key findings, and improvement recommendations for each region or unit, and automatically generate risk alerts for significant anomalies or trend changes. Finally, the output analysis report can be distributed on demand to various decision-making, management, and implementation agencies, supports export in multiple formats (PDF, Word, web pages, etc.), and can be linked with large visualization dashboards to achieve closed-loop management of compliance monitoring and improvement efforts.

[0044] 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.

[0045] 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 intelligently detecting the compliance rate of basic public service standards for key populations, namely the elderly and children, characterized by: The method includes: Basic data collection and storage: Collect basic information on key groups such as the elderly and children in the jurisdiction, including population statistics, health status, service needs, and historical service records; Construction of a service standard knowledge base: Establish / maintain a standard knowledge base for basic public services for the elderly and children, including service items, compliance requirements, assessment details, service frequency, and quality parameters; Service data is collected in multiple dimensions and anomaly detection is performed. A multi-dimensional fusion anomaly detection algorithm is adopted, which is based on a combination of multimodal data adaptive hybrid clustering and isolated forest algorithm to discover missing items, false data entry and data anomalies. The achievement rate assessment index system is constructed by adopting the analytic hierarchy process and adaptive weight optimization algorithm, and introducing an AHP weight self-learning adjustment mechanism based on personalized needs feedback of the population to establish a multi-dimensional evaluation index system covering service coverage, usage rate, satisfaction and sustainability. Compliance identification and early warning: Based on the time series trend identification and anomaly tracing AI model, the collected service data is dynamically tracked to identify the compliance rate, short-term anomalies and long-term downward trends, and the causes are traced. The system automatically generates and analyzes compliance results, combining the determined compliance rate, gap analysis, and improvement suggestions to generate compliance analysis reports by level, region, and service item.

2. The intelligent detection method for the compliance rate of basic public service standards for key populations "elderly and children" as described in claim 1, characterized in that: The aforementioned basic data collection and storage includes: data connection with community management systems, medical and health management information platforms, student management databases of schools and childcare institutions in the jurisdiction, social security and civil affairs service systems, using unified data interface standards, performing permission verification, data anonymization processing, data format standardization and data cleaning, and uniformly mapping multi-source heterogeneous data into the basic data center, supporting timed synchronization and incremental update mechanisms.

3. The intelligent detection method for the compliance rate of basic public service standards for key populations "elderly and children" as described in claim 1, characterized in that: The construction of the service standard knowledge base includes: connecting with national and local policies, normative documents and industry standards, performing semantic analysis through a policy parsing engine, and extracting service items, service scope, service targets, implementing entities, frequency requirements, service quality parameters and assessment details; It adopts an ontology modeling approach for structured expression and supports version management, multi-regional standard customization, and manual verification and expert review mechanisms to ensure the authority and practical value of the knowledge base.

4. The intelligent detection method for the compliance rate of basic public service standards for key populations "elderly and children" as described in claim 1, characterized in that: The aforementioned multidimensional collection and anomaly detection of service data includes: associating basic information with various third-party service data using a unique identifier as the primary key to construct a multidimensional comprehensive database covering population attributes, health records, service history, service frequency, and geographical distribution; An algorithm combining adaptive hybrid clustering and isolated forest based on multimodal data is adopted. First, Gaussian mixture model is used to adaptively cluster the feature dataset to screen out abnormal samples. Then, the isolated forest algorithm is introduced to further detect and judge the initially screened abnormal and marginal samples. This enables the identification of multiple types of data anomalies, such as missing service data, false data entry, short-term high-frequency services, and service logic conflicts. All suspected anomalies are archived, marked, and associated with the original data for manual verification and subsequent analysis.

5. The intelligent detection method for the compliance rate of basic public service standards for key populations "elderly and children" as described in claim 1, characterized in that: The construction of the compliance rate assessment index system includes: using the analytic hierarchy process to decompose the overall compliance rate into core evaluation dimensions such as service coverage, usage rate, satisfaction, and service continuity; calculating the initial weight of each index through the index comparison judgment matrix and eigenvector method; and verifying the compliance rate by combining the consistency ratio.

6. The intelligent detection method for the compliance rate of basic public service standards for key populations "elderly and children" as described in claim 1, characterized in that: The aforementioned compliance identification and early warning includes: conducting time series monitoring and analysis of the comprehensive compliance score of each service unit, using the autoregressive moving average time series modeling method for trend analysis and future change prediction, and combining sliding window dynamic threshold detection to identify short-term anomalies; For anomalies and downward trends, input multidimensional indicator scores, service data, and anomaly detection tags. Use decision trees to automatically identify key related indicators and their importance, enabling intelligent tracing and precise location of anomalies in the compliance rate. The results are pushed through a visual dashboard and regular reports to provide early warnings.

7. The intelligent detection method for the compliance rate of basic public service standards for key populations "elderly and children" as described in claim 1, characterized in that: The automated generation and report analysis of the compliance results include: automatically compiling compliance rates and trend analysis for each unit, region, and service type; tracing the source of anomalies; summarizing the results by administrative division, service institution, and service type in multiple dimensions; conducting gap analysis and summarizing the main reasons affecting the compliance rate; and proposing improvement suggestions based on the source tracing results of indicator anomalies.

8. The intelligent detection method for the compliance rate of basic public service standards for key populations "elderly and children" as described in claim 5, characterized in that: The aforementioned achievement rate assessment indicator system includes: introducing an AHP weight self-learning adjustment mechanism based on personalized needs feedback from the population; collecting service feedback from key populations in real time; obtaining the evaluation of the importance of each indicator from the actual needs side through subjective scoring; dynamically optimizing the comprehensive weight of each indicator; and realizing multi-dimensional indicator synergy and adaptive weight adjustment.

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