Multi-dimensional fusion medical resource intelligent monitoring and early warning method

Abstract

The invention discloses a multi-dimensional fusion medical resource intelligent monitoring and early warning method. According to the method and the device, the integrity and the accuracy of the medical resource data are improved through a multi-dimensional data fusion and dynamic calibration mechanism. Multi-source data of a medical institution system, a regional health platform, wearable equipment and the like are integrated, comprehensive data coverage and timely updating are ensured, and resource monitoring blind areas caused by information islands are reduced. Through the processing flows of noise filtering, missing value filling, abnormal value identification and the like, the influence of data errors on resource evaluation is reduced, quantitative analysis of key indexes such as medical resource supply and patient demands is closer to the actual situation, and the timeliness of medical resource risk early warning and the effectiveness of decision support are enhanced. The hierarchical early warning model constructed based on the multi-modal feature matrix can identify potential risks from multiple dimensions of resource exhaustion, service saturation, regional propagation and the like, and ensures reasonable distribution and efficient utilization of medical resources in different scenes.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of medical resource monitoring, and specifically relates to a multi-dimensional fusion medical resource intelligent monitoring and early warning method. BACKGROUND

[0002] The medical resource monitoring and early warning system is an intelligent management system constructed based on big data, artificial intelligence and Internet of Things technology, aiming to monitor and predictively analyze the distribution, use and supply-demand changes of medical resources in real time, so as to improve the allocation efficiency and emergency response capability of medical resources. The system integrates multi-dimensional data such as hospital beds, drug inventory, medical equipment, number of medical staff and geographical location, combines historical trends and influencing factors of sudden events (such as infectious disease outbreaks and natural disasters), and constructs a dynamic model to intelligently warn possible resource shortages or surpluses. At the same time, the system can realize information sharing and linkage scheduling with government health management departments, medical institutions and emergency centers, and provide scientific basis for policy making, resource allocation and emergency decision making. The medical resource monitoring and early warning system plays an important role in optimizing medical resource allocation, alleviating medical supply and demand contradictions and improving public health event response capability, and is a key component of the smart medical system.

[0003] However, the prior art is prone to form information islands in medical resource monitoring, resulting in monitoring blind spots, and lacks dynamic calibration mechanism for data processing, resulting in large data errors and affecting evaluation accuracy. At the same time, the risk early warning model has single dimension, which makes the resource scheduling efficiency low and the response time long. SUMMARY

[0004] The purpose of the present application is to solve the above-mentioned problems, and to provide a multi-dimensional fusion medical resource intelligent monitoring and early warning method.

[0005] The technical scheme adopted by the present application is as follows: a multi-dimensional fusion medical resource intelligent monitoring and early warning method, the method comprising the following steps:

[0006] S1: continuously access the HIS system of medical institutions, electronic medical records, regional health platforms, disease control data and wearable device health data, and uniformly gather the data scattered in different systems to a central database through a standardized interface to provide complete data sources for subsequent data processing.

[0007] S2: noise filtering, missing value filling and outlier identification are performed on the gathered data, the calibrated data is synchronized to a multi-dimensional feature fusion module through the establishment of a data quality scoring mechanism, and the quality evaluation results are fed back to the data gathering layer to optimize the data collection strategy.

[0008] S3: Integrate medical resource supply dimensions (such as beds, equipment, medical staff), patient demand dimensions (such as the number of visits, the proportion of severe cases), and spatial geographic dimensions (such as regional population density) to construct a multi-modal feature matrix. The fusion results directly support the construction of dynamic resource elasticity portraits.

[0009] S4: Based on the fused features, generate dynamic elasticity portraits of medical resources through time series pattern mining and demand prediction algorithms. Real-time capture of resource load change trends. The portrait results are synchronized to the intelligent early warning model and resource scheduling module, realizing the linkage of monitoring and early warning and resource allocation.

[0010] S5: Combine dynamic resource portraits with historical early warning cases to establish a three-level early warning model that includes resource depletion risk, service capacity saturation risk, and regional transmission risk. The early warning signals output by the model are automatically pushed to the early warning classification and decision support module.

[0011] S6: Generate differentiated response strategies based on the early warning level: mild early warning triggers internal resource adjustment recommendations, moderate early warning activates regional coordination mechanisms, and severe early warning triggers emergency resource reserve calling strategies. Decision schemes are synchronized to the resource dynamic scheduling module.

[0012] S7: Based on the decision support scheme, real-time adjust the distribution state of medical resources through supply and demand matching algorithms. The scheduling results are fed back to the real-time monitoring module, and the scheduling effect data is returned to the data quality calibration module to optimize data processing accuracy.

[0013] S8: Track the whole process of early warning response and resource scheduling, collect key indicators such as response timeliness and resource utilization rate, and form an effect evaluation report to provide a basis for dynamic resource elasticity portrait update and data quality calibration.

[0014] S9: Integrate monitoring feedback data, regularly update dynamic resource elasticity portraits and early warning models, and deposit typical cases and optimization strategies into a knowledge base to benefit data source optimization in the data aggregation layer and dimension expansion in the feature fusion layer.

[0015] In a preferred embodiment, in step S1, the real-time convergence of multi-source medical data is achieved through a distributed data gateway, which uses the HL7 FHIR R4 standard interface protocol to connect to the HIS system electronic medical record area health platform CDC database and wearable device manufacturer API of the medical institutions, the data access frequency is set to 5 minutes / once incremental synchronization, and the full data is automatically backed up at 2 a.m. every day. The central database uses a distributed storage architecture with 3 copies of storage nodes deployed across regions covering at least 1 node in each of the east, central and west regions, the data compression ratio is controlled between 1:3 and 1:5, the metadata management is realized through Apache Atlas to ensure data traceability and real-time data synchronization with the data quality dynamic calibration module.

[0016] In a preferred embodiment, in step S2, the data quality dynamic calibration module first performs noise filtering when started, uses a moving average filtering method, uses a distance weighted filling strategy for missing value filling, and uses statistical tests combined with an abnormality recognition model for double verification of abnormal values. The data quality scoring mechanism includes integrity, consistency, timeliness, accuracy and other main dimensions, and the scoring results are used to generate quality reports periodically and synchronized to the data convergence layer for optimizing the sampling frequency and interface priority of the data source. The calibrated data is stored in Parquet format to ensure that the column storage compression rate meets the expected standard.

[0017] In a preferred embodiment, in step S3, the multi-dimensional feature collaborative fusion stage first normalizes the data features using the min-max normalization method to map all feature values to the 0-1 interval, selects the core features by the recursive feature elimination algorithm combined with the variance inflation factor, and sets the variance inflation factor threshold to 5. The final multi-modal feature matrix contains 128 dimensions, including 32 dimensions of medical resource supply, 48 dimensions of patient demand, 24 dimensions of spatial geography, and 24 dimensions of time series features. The feature fusion uses a method combining attention mechanism and tensor decomposition, the attention weight is normalized by the Softmax function, the fused feature matrix is updated every 15 minutes and directly pushed to the dynamic resource elasticity portrait construction module, and the feature importance score result is fed back to the data quality calibration module to optimize the sensitivity of abnormal value identification.

[0018] In a preferred embodiment, in step S4, the dynamic resource elasticity portrait construction adopts a multivariate time series fusion prediction model to realize real-time capture of resource load trend and elasticity feature description through a three-layer architecture. First, based on a sliding time window, a time series slice is performed on the fusion feature matrix (containing 12 types of core indicators such as bed occupancy rate, medical staff working hours, and regional patient volume), and an improved bidirectional LSTM network is used to extract long and short term dependence features, wherein the number of hidden layer neurons is dynamically adjusted through Bayesian optimization. Then, an attention mechanism is introduced to dynamically weight the multi-dimensional features, and the weight calculation combines Shapley value and feature importance score, focusing on key early warning indicators such as critical illness conversion rate and emergency resource response time (weight proportion dynamically allocated range 15%-35%). Finally, an exponential smoothing-ARIMA combined model is used to perform short-term prediction on the time series features, generate a probability distribution curve of resource load, and combine historical fluctuation coefficients (1.5 times threshold of standard deviation in the past 30 days) to construct an elasticity threshold matrix. The matrix is compared with the prediction result in real time to form a three-dimensional elasticity portrait containing "overload risk probability", "resource redundancy", and "demand fluctuation sensitivity", which is automatically updated every 15 minutes and synchronized to the early warning model and dispatch module, realizing the leap from "static threshold judgment" to "dynamic elasticity adaptation".

[0019] The resource elasticity threshold dynamic correction formula is:

[0020]

[0021] In the formula:

[0022] REI t represents the resource elasticity index at time t (value range 0-2, ≥1.5 triggers an alarm);

[0023] D t represents the current actual demand for resources (such as the number of patients and occupied beds);

[0024] C t represents the maximum capacity of resources at time t (static configuration value, such as total bed number and medical staff number); ΔD t+4 represents the demand prediction increment in the next 4 hours (output by the ARIMA model);

[0025] σ t-30 represents the demand fluctuation standard deviation in the past 30 days (reflecting historical volatility);

[0026] μ t-30 represents the demand mean in the past 30 days (reflecting the baseline level);

[0027] α, β, γ represent dynamic weight coefficients (sum is 1, real-time optimization through gradient descent, initial values are 0.5, 0.3, and 0.2, respectively).

[0028] The formula realizes quantitative description of resource elasticity by fusing real-time supply-demand ratio, short-term prediction change rate and historical fluctuation characteristics, and the weight coefficient is dynamically adjusted according to the warning level (for example, the β weight is increased to 0.4 in severe warning, to enhance the sensitivity to short-term surge risk).

[0029] In a preferred embodiment, in the step S5, the hierarchical warning model is constructed based on the dynamic resource elasticity portrait and the historical warning case library, which contains 120,000 warning records in the past 5 years. A three-level warning classifier is trained through a classification model, in which there are 28 characteristic dimensions for resource exhaustion risk, 35 for service capacity saturation risk, and 22 for regional transmission risk. The hierarchical warning model is constructed based on the dynamic resource elasticity portrait and the historical warning case library, and a three-level warning classifier is trained through a classification model, covering resource exhaustion risk, service capacity saturation risk, regional transmission risk and other scenarios. The model training adopts a cross-validation method to improve performance by reasonably setting the learning rate and iteration number, and the warning threshold is determined by a statistical optimization method. The warning signal output by the model includes risk level, risk probability, key influencing factors and other elements, which are pushed to the warning grading and decision support module in real time through a message queue cluster.

[0030] In a preferred embodiment, in the step S6, after receiving the warning signal, the warning grading and decision support module first performs secondary verification of the risk level, adjusts the warning level in combination with the regional population density and the baseline value of medical resources, triggers the internal resource adjustment suggestion generation algorithm based on the optimized allocation method to calculate the optimal allocation scheme between departments for the mild warning level 1, automatically starts the regional coordination mechanism to call the standby resource pool of medical institutions within 30 kilometers for the moderate warning level 2, and triggers the emergency resource reserve calling strategy to call the allocation process of the national emergency medical material reserve warehouse for the severe warning level 3, with a response time requirement ≤ 2 hours. After the decision scheme is generated, the electronic seal system automatically stamps the department seal and synchronizes to the resource dynamic scheduling module, and the scheme execution progress data is accessed to the real-time monitoring module to realize full-process tracking.

[0031] In a preferred embodiment, in the step S7, the scheduling decision variables of the resource dynamic scheduling optimization module include medical staff scheduling adjustment with a minimum time unit of 30 minutes, bed type conversion between general beds and ICU beds, and medical equipment cross-regional allocation with a transportation time threshold of 4 hours. The scheduling results are updated every 30 minutes and pushed to the department terminals through the hospital intranet system, and the evaluation data are returned to the data quality calibration module every hour for weight distribution optimization in the feature fusion stage.

[0032] In a preferred embodiment, in step S8, the real-time monitoring and effect feedback module collects key indicators through the Internet of Things sensor and hospital information system interface, the monitoring indicator system includes early warning response timeliness, resource utilization rate, patient satisfaction, scheduling execution accuracy and other primary indicators, and the data collection is uploaded to the cloud database after pre-processing by the edge computing node. The effect evaluation report is automatically generated daily, the performance of each dimension is comprehensively evaluated by using the weighted scoring method, and the report results are stored in multiple formats for manual review and system interface calling, providing trend data support for dynamic resource elasticity portrait update and data quality calibration.

[0033] In a preferred embodiment, in step S9, the closed-loop iteration and knowledge sedimentation module sets a bi-weekly iteration cycle to perform full-process optimization every 14 days, the model update adopts an incremental training method to adjust the model parameters based on the number of new data samples in the last 14 days being greater than or equal to 5,000, the historical case database adopts ontology to construct a knowledge graph including 6 major categories, 32 subcategories, 128 types of response strategies, and 96 quantitative indicators of effect evaluation of three core entities. The knowledge base storage adopts a graph database cluster deployment of three core nodes to support query language, and the reverse-feeding mechanism realizes priority adjustment of the data source of the data aggregation layer through an API interface, updates the access strategy and dimension expansion of the feature fusion layer every quarter, and adds 5-8 feature dimensions every year, to ensure that the system continuously adapts to new demands of medical resource monitoring.

[0034] To sum up, due to the adoption of the above technical solutions, the present application has the following beneficial effects:

[0035] 1. In the present application, the multi-dimensional data fusion and dynamic calibration mechanism improves the completeness and accuracy of medical resource data. By integrating medical institution systems, regional health platforms, wearable devices and other multi-source data, and combining real-time synchronization and regular backup strategies, the data coverage is comprehensive and timely, reducing the resource monitoring blind spots caused by information silos. At the same time, through noise filtering, missing value filling and outlier identification processes, the influence of data errors on resource evaluation is reduced, making the quantitative analysis of key indicators such as medical resource supply and patient demand more realistic.

[0036] 2. In the present application, the timeliness of medical resource risk early warning and the effectiveness of decision support are improved. The hierarchical early warning model based on multi-modal feature matrix can identify potential risks from multiple dimensions such as resource depletion, service saturation and regional transmission, and improve the accuracy of early warning signals by combining a dynamic threshold adjustment mechanism. In addition, through hierarchical response strategies such as internal adjustment, regional coordination and emergency reserve calling, the resource scheduling efficiency is optimized, the response time from risk identification to measure implementation is shortened, and the rational allocation and efficient use of medical resources in different scenarios are ensured. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The flow principle diagram of the present application. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0039] Embodiment:

[0040] Referring to Figure 1 A multi-dimensional fusion medical resource intelligent monitoring and early warning method, the method comprising the following steps:

[0041] S1: Continuously access the HIS system of medical institutions, electronic medical records, regional health platforms, disease control data and wearable device health data, and uniformly gather the data scattered in different systems to the central database through standardized interfaces to provide complete data sources for subsequent data processing.

[0042] S2: Noise filtering, missing value filling and outlier identification are performed on the gathered data, the calibrated data is synchronized to the multi-dimensional feature fusion module through the establishment of a data quality scoring mechanism, and the quality evaluation results are fed back to the data gathering layer to optimize the data acquisition strategy.

[0043] S3: Integrate medical resource supply dimensions (such as beds, equipment, medical staff), patient demand dimensions (such as the number of visits, the proportion of severe cases) and spatial geographic dimensions (such as regional population density) and other features, construct a multi-modal feature matrix, and the fusion results directly support the construction of dynamic resource elasticity portrait.

[0044] S4: Based on the fused features, generate a dynamic elasticity portrait of medical resources through time series pattern mining and demand prediction algorithm, real-time capture resource load change trend, portrait results are synchronized to intelligent early warning model and resource scheduling module, realize monitoring and early warning and resource allocation linkage.

[0045] S5: Combine the dynamic resource portrait with historical early warning cases, establish a three-level early warning model including resource exhaustion risk, service capacity saturation risk and regional transmission risk, and the early warning signals output by the model are automatically pushed to the early warning grading and decision support module.

[0046] S6: According to the early warning level, generate differentiated response strategies: mild early warning triggers internal resource adjustment suggestions, moderate early warning starts regional coordination mechanism, severe early warning triggers emergency resource reserve calling strategy, decision scheme is synchronized to resource dynamic scheduling module.

[0047] S7: Based on the decision support scheme, the supply and demand matching algorithm is used to adjust the distribution state of medical resources in real time, and the scheduling result is fed back to the real-time monitoring module. At the same time, the scheduling effect data is returned to the data quality calibration module to optimize the data processing accuracy.

[0048] S8: Track the whole process of early warning response and resource scheduling, collect key indicators such as response timeliness and resource utilization, and form an effect evaluation report to provide a basis for dynamic resource elasticity portrait update and data quality calibration.

[0049] S9: Integrate monitoring feedback data, regularly update dynamic resource elasticity portrait and early warning model, and deposit typical cases and optimization strategies into knowledge base to benefit data source optimization and dimension expansion of feature fusion layer of data aggregation layer.

[0050] In step S1, real-time multi-source medical data aggregation is achieved through a distributed data gateway. HL7 FHIR R4 standard interface protocol is used to connect HIS system electronic medical record regional health platform CDC database and wearable device manufacturer API. The data access frequency is set to 5 minutes / once incremental synchronization, and the full data is automatically backed up at 2 am every day. The central database uses a distributed storage architecture with 3 copies of storage nodes deployed across regions, covering at least one node in each of the east, central and west regions. The data compression ratio is controlled between 1:3 and 1:5. Metadata management is realized through Apache Atlas to track data lineage and ensure that the data aggregation process is traceable and synchronized with the data quality dynamic calibration module in real time.

[0051] In step S2, the data quality dynamic calibration module first performs noise filtering when started. Moving average filtering method is used, missing value filling uses distance weighted filling strategy, and abnormal value identification uses statistical test combined with abnormal identification model double check. The data quality scoring mechanism includes integrity, consistency, timeliness, accuracy and other main dimensions. The scoring results generate quality reports regularly and synchronize to the data aggregation layer to optimize the sampling frequency and interface priority of the data source. The calibrated data is stored in Parquet format to ensure that the column storage compression rate meets the expected standard.

[0052] In step S3, the multi-dimensional feature collaborative fusion stage first normalizes the data. The min-max normalization method is used to map all feature values to the interval of 0 to 1. Feature selection is performed by the recursive feature elimination algorithm combined with the variance inflation factor. The variance inflation factor threshold is set to 5. The finally constructed multi-modal feature matrix contains 128 dimensions, including 32 dimensions of medical resource supply, 48 dimensions of patient demand, 24 dimensions of spatial geography, and 24 dimensions of time series features. Feature fusion adopts a method combining attention mechanism and tensor decomposition. The attention weight is normalized by the Softmax function. The fused feature matrix is updated every 15 minutes and directly pushed to the dynamic resource elasticity portrait construction module. At the same time, the feature importance score result is fed back to the data quality calibration module to optimize the sensitivity of abnormal value identification.

[0053] In step S4, the dynamic resource elasticity portrait construction adopts a multivariate time series fusion prediction model to realize real-time capture of resource load trend and elasticity feature description through a three-layer architecture. First, based on a sliding time window, the fusion feature matrix (containing 12 types of core indicators such as bed utilization rate, medical staff working hours, and regional visit volume) is time series sliced. The improved bidirectional LSTM network is used to extract long and short term dependence features, and the number of hidden layer neurons is dynamically adjusted through Bayesian optimization. Then, an attention mechanism is introduced to dynamically weight the multi-dimensional features. The weight calculation combines Shapley value and feature importance score, focusing on key warning indicators such as critical illness conversion rate and emergency resource response time (weight allocation range dynamically allocated 15% to 35%). Finally, an exponential smoothing-ARIMA combined model is used to predict the short-term trend of the time series features, generate a probability distribution curve of resource load, and construct an elasticity threshold matrix combined with historical fluctuation coefficients (1.5 times the threshold of the standard deviation in the past 30 days). The matrix is compared with the prediction result in real time to form a three-dimensional elasticity portrait containing "overload risk probability", "resource redundancy", and "demand fluctuation sensitivity". The portrait is automatically updated every 15 minutes and synchronized to the warning model and dispatch module, realizing the transition from "static threshold judgment" to "dynamic elasticity adaptation".

[0054] The resource elasticity threshold dynamic correction formula is:

[0055]

[0056] In the formula:

[0057] REI t represents the resource elasticity index at time t (value range 0-2, ≥1.5 triggers an alarm);

[0058] D t represents the current actual resource demand (such as real-time number of visits, number of occupied beds);

[0059] Ct Resource maximum capacity at time t (static configuration value, such as total bed number, medical staff number);

[0060] ΔD t+4 Demand prediction increment for the next 4 hours (output by ARIMA model);

[0061] σ t-30 Standard deviation of demand fluctuation in the past 30 days (reflecting historical volatility);

[0062] μ t-30 Mean demand in the past 30 days (reflecting baseline level);

[0063] α, β, γ represent dynamic weight coefficients (sum to 1, optimized in real time by gradient descent, initial values are 0.5, 0.3, and 0.2 respectively).

[0064] This formula realizes the quantitative characterization of resource elasticity by integrating real-time supply-demand ratio, short-term prediction rate of change, and historical fluctuation characteristics, and the weight coefficients are dynamically adjusted according to the warning level (such as increasing β weight to 0.4 in severe warning to enhance the sensitivity to short-term surge risk).

[0065] In step S5, the hierarchical warning model is constructed based on dynamic resource elasticity portrait and historical warning case library, containing 120,000 warning records in the past 5 years, and a three-level warning classifier is trained through a classification model, with 28 feature dimensions for resource depletion risk, 35 for service capacity saturation risk, and 22 for regional transmission risk. The hierarchical warning model is constructed based on dynamic resource elasticity portrait and historical warning case library, and a three-level warning classifier is trained through a classification model, covering resource depletion risk, service capacity saturation risk, and regional transmission risk scenarios. The model training uses cross-validation method to improve performance by reasonably setting learning rate and iteration number, and the warning threshold is determined by statistical optimization method. The warning signal output by the model includes risk level, risk probability, and key influencing factors, which are pushed to the warning grading and decision support module in real time through message queue cluster.

[0066] In step S6, after receiving the early warning signal, the early warning grading and decision support module first performs secondary verification of the risk level, adjusts the early warning level in combination with the regional population density and the baseline value of medical resources, triggers the internal resource adjustment suggestion generation algorithm based on the optimized allocation method to calculate the optimal allocation scheme between departments, automatically starts the regional coordination mechanism to call the standby resource pool of medical institutions within 30 kilometers, triggers the emergency resource reserve calling strategy to call the allocation process of the national emergency medical material reserve warehouse, and responds to the time requirement of ≤2 hours. After the decision scheme is generated, the department seal is automatically affixed through the electronic seal system and is synchronized to the resource dynamic scheduling module, and the scheme execution progress data is accessed to the real-time monitoring module to realize full-process tracking.

[0067] In step S7, the scheduling decision variables of the resource dynamic scheduling optimization module include medical staff scheduling adjustment with a minimum time unit of 30 minutes, bed type conversion between general beds and ICU beds, and medical equipment cross-regional allocation and transportation time threshold of 4 hours. The scheduling results are updated every 30 minutes and pushed to the department terminals through the hospital intranet system, and the evaluation data are returned to the data quality calibration module every hour for weight distribution in the optimization feature fusion stage.

[0068] In step S8, the real-time monitoring and effect feedback module collects key indicators through Internet of Things sensors and hospital information system interfaces, and the monitoring indicator system includes early warning response time, resource utilization rate, patient satisfaction, and scheduling execution accuracy as primary indicators. After edge computing node preprocessing, the data is uploaded to the cloud database. The effect evaluation report is automatically generated every day, and the weighted scoring method is used to comprehensively evaluate the performance of each dimension. The report results are stored in multiple formats and used for manual review and system interface calling, providing trend data support for dynamic resource flexible portrait updating and data quality calibration.

[0069] In step S9, the closed-loop iteration and knowledge sedimentation module sets a bi-weekly iteration cycle to perform full-process optimization every 14 days. The model update adopts incremental training based on the number of new data samples ≥5000 in the last 14 days to adjust the model parameters. The historical case library adopts ontology to construct a knowledge graph, including 6 major categories, 32 small categories, 128 types of coping strategies, 96 quantitative indicators of standard process effect evaluation, and three core entities. The knowledge base storage adopts graph database cluster deployment of three core nodes to support query language. The counterproductive mechanism adjusts the data source priority of the data aggregation layer through the API interface to update the access strategy and dimension expansion of the feature fusion layer every quarter, and adds 5-8 feature dimensions every year to ensure that the system continuously adapts to new demands of medical resource monitoring.

[0070] From the above, it can be seen that:

[0071] In the present application, the completeness and accuracy of medical resource data are improved through multi-dimensional data fusion and dynamic calibration mechanism. By integrating multi-source data such as medical institution system, regional health platform, wearable devices, and combining real-time synchronization and regular backup strategy, the data coverage is comprehensive and timely updated, reducing the resource monitoring blind area caused by information silos. At the same time, through noise filtering, missing value filling and outlier identification processing, the influence of data error on resource evaluation is reduced, making the quantitative analysis of key indicators such as medical resource supply and patient demand more close to the actual situation.

[0072] In the present application, the timeliness of medical resource risk early warning and the effectiveness of decision support are strengthened. The hierarchical early warning model based on multi-modal feature matrix can identify potential risks from multiple dimensions such as resource depletion, service saturation, and regional transmission, and improve the accuracy of early warning signals through dynamic threshold adjustment mechanism. In addition, through hierarchical response strategies such as internal adjustment, regional coordination, and emergency reserve calling, the resource scheduling efficiency is optimized, the response time from risk identification to measure execution is shortened, and the reasonable allocation and efficient use of medical resources in different scenarios are ensured.

[0073] It should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0074] The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-dimensional integrated medical resource intelligent monitoring and early warning method, characterized by: The method comprises the following steps: S1: Continuously access the HIS system of medical institutions, electronic medical records, regional health platforms, disease control data, and health data from wearable devices. Through standardized interfaces, data scattered across different systems are unified into a central database, providing a complete data source for subsequent data processing. S2: Noise filtering, missing value filling, and outlier identification are performed on the aggregated data. By establishing a data quality scoring mechanism, the calibrated data is synchronized to the multi-dimensional feature fusion module, and the quality assessment results are fed back to the data aggregation layer to optimize the data collection strategy. S3: Integrate features such as medical resource supply, patient demand, and spatial geography to construct a multimodal feature matrix. The fusion results directly support the construction of a dynamic resource elasticity profile. S4: Based on the fusion features, a dynamic elasticity profile of medical resources is generated through time series pattern mining and demand forecasting algorithms, capturing the changing trend of resource load in real time. The profile results are synchronized to the intelligent early warning model and resource scheduling module; S5: Combine dynamic resource profiling with historical warning cases to establish a three-level warning model that includes resource depletion risk, service capacity saturation risk, and regional transmission risk. The warning signals output by the model are automatically pushed to the warning classification and decision support module; S6: Generate differentiated response strategies based on warning levels: mild warnings trigger internal resource adjustment suggestions, moderate warnings activate regional coordination mechanisms, and severe warnings trigger emergency resource reserve deployment strategies. Decision plans are synchronized to the resource dynamic scheduling module. S7: Based on the decision support solution, the distribution of medical resources is adjusted in real time through the supply and demand matching algorithm. The scheduling results are fed back to the real-time monitoring module. At the same time, the scheduling effect data is sent back to the data quality calibration module to optimize data processing accuracy. S8: Track the entire early warning response and resource scheduling process, collect key indicators such as response time and resource utilization, and generate effect evaluation reports to provide a basis for dynamic resource elasticity profile updates and data quality calibration. S9: Integrate monitoring feedback data, regularly update dynamic resource elasticity portraits and early warning models, and accumulate typical cases and optimization strategies into a knowledge base to feed back data source optimization at the data aggregation layer and dimension expansion at the feature fusion layer.

2. The multi-dimensional integrated medical resource intelligent monitoring and early warning method according to claim 1, characterized in that: In step S1, real-time aggregation of multi-source medical data is achieved through a distributed data gateway, which uses the HL7 FHIR R4 standard interface protocol to connect to the medical institution HIS system, the electronic medical record, the regional health platform, the CDC database, and the wearable device manufacturer API. The data access frequency is set to 5 minutes / time incremental synchronization, and the full data is automatically backed up at 2 am every day; The central database adopts a distributed storage architecture with three copies of storage nodes deployed across regions, covering at least one node each in the east, central and west regions. The data compression ratio is controlled between 1:3 and 1:

5.

3. The multi-dimensional integrated medical resource intelligent monitoring and early warning method according to claim 1, characterized in that: In step S2, when the data quality dynamic calibration module is started, noise filtering is first performed using a moving average filtering method, missing values ​​are filled using a distance-weighted filling strategy, and outlier identification is double-checked through statistical tests combined with anomaly recognition models; the data quality scoring mechanism includes major dimensions such as integrity, consistency, timeliness, and accuracy. The scoring results are regularly generated into quality reports synchronized to the data aggregation layer for optimizing the sampling frequency and interface priority of the data source. The calibrated data is stored in Parquet format to ensure that the column storage compression rate meets the expected standard.

4. The multi-dimensional integrated medical resource intelligent monitoring and early warning method according to claim 1, characterized in that: In the step S3, in the multi-dimensional feature collaborative fusion stage, the data is firstly normalized using the min-max normalization method to map all eigenvalues ​​to the range of 0 to 1. Feature selection is performed by combining the recursive feature elimination algorithm with the variance inflation factor to screen the core features. The variance inflation factor threshold is set to 5. The multimodal feature matrix finally constructed contains 128 dimensions, including 32 dimensions of medical resource supply, 48 dimensions of patient demand, 24 dimensions of spatial geography, and 24 dimensions of time series features.

5. The multi-dimensional integrated medical resource intelligent monitoring and early warning method according to claim 1, characterized in that: In step S4, the dynamic resource elasticity profile is constructed using a multivariate time series fusion prediction model, which uses a three-layer architecture to capture resource load trends in real time and characterize elasticity features. First, the fusion feature matrix is ​​time-series sliced ​​based on a sliding time window, and an improved bidirectional LSTM network is used to extract long-term and short-term dependency features, where the number of hidden layer neurons is dynamically adjusted through Bayesian optimization. Then, an attention mechanism was introduced to dynamically weight multi-dimensional features. The weight calculation combined Shapley values ​​with feature importance scores, focusing on key early warning indicators such as critical illness conversion rate and emergency resource response time. Finally, an exponential smoothing-ARIMA combined model was used to perform short-term forecasts of time series features, generating a probability distribution curve for resource load and constructing an elasticity threshold matrix based on historical fluctuation coefficients. The formula for dynamic correction of resource elasticity threshold is: Where: REI t represents the resource elasticity index at time t; D t Indicates the actual demand for current resources; C t represents the maximum capacity of the resource at time t; ΔD t+4 It represents the demand forecast increment for the next 4 hours; σ t-30 Indicates the standard deviation of demand fluctuations over the past 30 days; μ t-30 Indicates the average demand value for the past 30 days; α, β, and γ represent dynamic weights.

6. The multi-dimensional integrated medical resource intelligent monitoring and early warning method according to claim 1, characterized in that: In step S5, a hierarchical warning model is constructed based on a dynamic resource elasticity portrait and a historical warning case library, which contains 120,000 warning records in the past five years. A three-level warning classifier is trained through a classification model, in which resource depletion risk corresponds to 28 feature dimensions, service capacity saturation risk corresponds to 35, and regional transmission risk corresponds to 22.

7. The multi-dimensional integrated medical resource intelligent monitoring and early warning method according to claim 1, characterized in that: In step S6, after receiving the warning signal, the warning classification and decision support module first performs a secondary verification of the risk level and adjusts the warning level in combination with the regional population density and the baseline value of medical resources. A mild warning level 1 triggers an internal resource adjustment suggestion generation algorithm to calculate the optimal resource allocation plan between departments based on the optimization allocation method. A moderate warning level 2 automatically starts the regional coordination mechanism to call the backup resource pool of medical institutions within 30 kilometers through the smart contract. A severe warning level 3 triggers the emergency resource reserve calling strategy to call the allocation process of the national emergency medical material reserve warehouse with a response time requirement of ≤2 hours.

8. The multi-dimensional integrated medical resource intelligent monitoring and early warning method according to claim 1, characterized in that: In step S7, the scheduling decision variables of the resource dynamic scheduling optimization module include three dimensions: the minimum time unit of 30 minutes for adjusting medical staff shifts, the bed type conversion between ordinary beds and ICU beds, and the transportation time threshold of 4 hours for cross-regional allocation of medical equipment; the scheduling results are updated every 30 minutes and pushed to the terminals of each department through the hospital intranet system, and the evaluation data are transmitted back to the data quality calibration module every hour to optimize the weight distribution in the feature fusion stage.

9. The multi-dimensional integrated medical resource intelligent monitoring and early warning method according to claim 1, characterized in that: In step S8, the real-time monitoring and effect feedback module collects key indicators through the Internet of Things sensors and the hospital information system interface. The monitoring indicator system includes first-level indicators such as early warning response time, resource utilization, patient satisfaction, and scheduling execution accuracy. Data collection is pre-processed by edge computing nodes and then uploaded to the cloud database.

10. The multi-dimensional integrated medical resource intelligent monitoring and early warning method according to claim 1, characterized in that: In step S9, the closed-loop iteration and knowledge precipitation module sets a two-week iteration cycle to perform full-process optimization every 14 days. The model update adopts an incremental training method to adjust the model parameters based on the new data sample size of ≥5,000 in the past 14 days. The historical case library uses ontology to construct a knowledge graph including 6 major categories, 32 subcategories of warning scenarios, 128 standard process effect evaluations, 96 quantitative indicators and three core entities.

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