Postpartum health data management system based on cloud

Abstract

The invention relates to the technical field of data management, in particular to a cloud-based postpartum health data management system, which comprises a data flow optimization module, a security backup management module, an index adjustment module and a query efficiency optimization module. By analyzing the real-time flow and the access mode, the system can provide higher data access speed, the key point is that the system can adjust and adapt to the quickly changed data access requirement in real time, the optimization not only improves the data access efficiency, but also optimizes the security backup of the data, and the security backup of the data is improved by selecting the most suitable backup center and backup frequency. The multi-dimensional self-adaptive index is introduced, the time of large-scale health data retrieval is shortened, the method is particularly important for health information urgently needing to be processed, the optimal data query mode is evaluated and selected, the operation cost is further reduced, the economic efficiency of a management system is enhanced, and meanwhile the data management response speed is increased through asynchronous processing.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a cloud-based postpartum health data management system. Background Art

[0002] Data management technology involves data collection, storage, maintenance, processing and analysis. This field includes database management systems (DBMS), data warehouses and data lake technologies, as well as related data integration and data quality improvement technologies. Data management enables enterprises to ensure the integrity, security and accessibility of data, and supports real-time data analysis and decision making. With the development of cloud computing, cloud-based data management has also received increasing attention because it provides scalability, flexibility and cost-effectiveness, allowing users to access and collaborate on data globally.

[0003] Among them, the cloud-based postpartum health data management system refers to a system that uses a cloud computing platform to manage and analyze the health data of postpartum women. The system collects health indicators, physiological data, etc. of postpartum women, and uses cloud technology for data storage, processing and analysis, thereby providing real-time monitoring and health management services. Its uses include monitoring postpartum recovery progress, providing health advice, preventing and managing postpartum-related health problems, thereby improving the health level and quality of life of postpartum women.

[0004] The existing system lacks sufficient flexibility to adjust data storage strategies in real time, which is particularly evident during peak data access periods. If it cannot adapt to changes in access patterns in a timely manner, it will lead to data congestion and access delays. In terms of data security, backups in traditional systems are mostly performed periodically rather than based on real-time risk assessments, resulting in damage to the data recovery time window and data integrity when sudden problems occur in the data center. Data indexes are static and do not support adjustment, making them inefficient when processing large amounts of dynamically changing health data. In terms of data query costs, the lack of cost-benefit analysis will lead to improper use of resources and increase the cost of the management system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a cloud-based postpartum health data management system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a cloud-based postpartum health data management system, the system comprising:

[0007] The data flow optimization module is based on the cloud platform, analyzes the health monitoring data flow and access mode, calculates the optimal data storage location, and adjusts the data liquidity management process according to the real-time data flow to obtain the liquidity adjustment configuration;

[0008] The security backup management module adjusts the configuration based on the liquidity, evaluates the operational stability and data security risks of the cloud platform, sets the data backup frequency, and selects the optimal backup center to perform the data backup operation, and obtains a backup recovery plan;

[0009] The index adjustment module adjusts the consulting and support data index structure of the cloud platform based on the backup and recovery plan, establishes a multi-dimensional adaptive index, and optimizes the data retrieval speed and efficiency to obtain an index performance optimization result;

[0010] The query efficiency optimization module evaluates the data query cost based on the index performance optimization result, selects the query execution process with the optimal cost, and updates the query result to the user interface through asynchronous processing to obtain data query response information.

[0011] The present invention is improved in that the step of calculating the optimal data storage location is specifically as follows:

[0012] Based on the cloud platform, extract health monitoring data traffic, count and record the access frequency of data points, and generate hourly access frequency data;

[0013] Based on the hourly access frequency data, the total traffic of each data point in the different time periods is calculated, and the access frequency and traffic data are combined to obtain the traffic frequency analysis result;

[0014] Based on the flow frequency analysis results, the formula is adopted:

[0015]

[0016] Calculate the cost efficiency of each data center CX i , determine the optimal data storage location, where N c Represents the total number of statistical time periods, Cf ij represents the access frequency of the i-th data center in the j-th time period, Cv ij Represents the traffic of the i-th data center in the j-th time period.

[0017] The present invention is improved in that the steps of obtaining the liquidity adjustment configuration are specifically as follows:

[0018] Collecting data traffic of multiple cloud platforms, including data transmission rate and total data volume of each node, and generating real-time data traffic records;

[0019] Based on the real-time data flow records, traffic peaks and valleys are identified, periodic changes in data flow are determined through time series prediction, and data liquidity analysis results are generated;

[0020] Based on the data liquidity analysis results, the formula is adopted:

[0021]

[0022] Adjust the data transmission and storage processes to generate a liquidity adjustment configuration, where RS(t) represents the resource optimization configuration at time t, β R is the weight parameter of bandwidth utilization, P Rmax is the maximum bandwidth capacity, LR i is the traffic load of the ith node, f Ri (t) is the traffic frequency of the ith node at time t, n r is the total number of monitoring nodes in the network.

[0023] The present invention is improved in that the steps of evaluating the operation stability and data security risk are specifically as follows:

[0024] Based on the liquidity adjustment configuration, extract key information from the cloud platform logs, including system failure events, unauthorized access information, and data leakage events, to build a preliminary security event set;

[0025] Based on the preliminary set of security events, the random forest algorithm is used to identify abnormal behaviors and potential security threats. The formula is used in combination with current operation data and historical security events:

[0026]

[0027] Calculate the risk index DR i , and obtain safety and stability analysis information, where γ d is the correction factor, wd k is the weight of the kth risk factor, xd ik is the performance of the ith event on the kth risk factor, K d is the number of risk factors;

[0028] Utilizing the security and stability analysis information, the operational stability and security risks of the data center are evaluated according to industry standards and risk thresholds, the frequency and severity of events at different risk levels are analyzed, and operational stability and data security risk assessment results are obtained.

[0029] The present invention is improved in that the steps of obtaining the backup recovery plan are specifically as follows:

[0030] Based on the operational stability and data security risk assessment information, the data is graded according to its criticality using the formula:

[0031]

[0032] Determine the data backup frequency, where fV(d) represents the backup frequency of data d, v d is the value indicator of the data, s dis the sensitivity index of the data, c q and d q is the influence coefficient of the adjustment factor;

[0033] Based on the data backup frequency, combined with the cloud platform's data processing capabilities, physical security, geographic location, and network connection speed, regular and irregular backup tasks are set to form a backup operation log;

[0034] Based on the backup operation log, a backup recovery plan is formulated, the plan including a recovery time objective and a data recovery point objective.

[0035] The present invention is improved in that the steps of establishing the multidimensional adaptive index are specifically as follows:

[0036] Based on the backup and recovery plan, analyze the data index structure of the current cloud platform, identify performance bottlenecks caused by data volume growth, collect key performance indicators, and generate index structure evaluation records;

[0037] Based on the index structure evaluation records, a multi-dimensional adaptive index matching the differential data access pattern is established, using the formula:

[0038]

[0039] Get the optimal index dimension configuration, where FB(d B ,t) represents the data index dimension after dynamic adjustment according to time at time t, d B represents the original data dimension, t represents the time factor, r B Adjust the parameters for time.

[0040] The present invention is improved in that the steps for optimizing the data retrieval speed and efficiency are specifically as follows:

[0041] Extract query response time and access frequency data from the cloud platform's data access logs, identify high-frequency access patterns and performance bottlenecks in data retrieval, and generate performance analysis logs;

[0042] Based on the performance analysis log, the formula is adopted:

[0043]

[0044] Perform query optimization and match query load to generate data retrieval optimization results OS (c u ,p u ), where c u represents the query complexity, p u Indicates the query parameter, k u is the sensitivity coefficient, m u It is the query parameter balance point.

[0045] The present invention is improved in that the step of obtaining the data query response information is specifically as follows:

[0046] Based on the index performance optimization results, combined with the query response time and query processing cost data, the formula is adopted:

[0047]

[0048] The query process for calculating the optimal cost, where CZ represents the query cost, t Z represents the query time, r Z represents resource consumption, q Z represents the query load, α Z is the time weight coefficient, β Z is the resource consumption weight coefficient;

[0049] Based on the optimal cost query process, the query results are updated to the user interface through an asynchronous processing mechanism to optimize waiting time and user experience, and the efficiency of asynchronous operations and the result update frequency are monitored to obtain data query response information.

[0050] Compared with the prior art, the advantages and positive effects of the present invention are:

[0051] In the present invention, by analyzing real-time traffic and access patterns, the system can provide faster data access speed. The key is to be able to adjust in real time to adapt to rapidly changing data access needs. This optimization not only improves data access efficiency, but also optimizes data security backup. By selecting the most suitable backup center and backup frequency and introducing multi-dimensional adaptive indexing, the time for large-scale health data retrieval is reduced, which is especially important for health information that needs to be processed urgently. Evaluating and selecting the optimal data query method further reduces operating costs and enhances the economic efficiency of the management system. At the same time, it also improves data management response speed through asynchronous processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a system flow chart of the present invention;

[0053] Figure 2 A flow chart for calculating the optimal data storage location in the present invention;

[0054] Figure 3 A flow chart for obtaining the liquidity adjustment configuration in the present invention;

[0055] Figure 4 This is a flow chart for evaluating the operational stability and data security risks in the present invention;

[0056] Figure 5 A flowchart for obtaining a backup recovery plan in the present invention;

[0057] Figure 6 A flowchart of establishing a multi-dimensional adaptive index in the present invention;

[0058] Figure 7 It is a flow chart for optimizing data retrieval speed and efficiency in the present invention;

[0059] Figure 8 This is a flow chart for obtaining data query response information in the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0062] Example

[0063] See also Figure 1 The present invention provides a technical solution: a cloud-based postpartum health data management system comprising:

[0064] The data flow optimization module is based on the cloud platform, analyzes the health monitoring data flow and access mode, calculates the optimal data storage location, and adjusts the data liquidity management process according to the real-time data flow to obtain the liquidity adjustment configuration;

[0065] The security backup management module adjusts the configuration based on liquidity, evaluates the operational stability and data security risks of the cloud platform, sets the data backup frequency, and selects the optimal backup center to perform data backup operations and obtain a backup recovery plan;

[0066] The index adjustment module adjusts the consulting and support data index structure of the cloud platform based on the backup and recovery plan, establishes a multi-dimensional adaptive index, and optimizes the data retrieval speed and efficiency to obtain index performance optimization results;

[0067] The query efficiency optimization module evaluates the data query cost based on the index performance optimization results, selects the query execution process with the optimal cost, and updates the query results to the user interface through asynchronous processing to obtain data query response information.

[0068] Liquidity adjustment configuration includes flow optimization parameters, priority standards, and scheduling schedules. The backup recovery plan specifically includes backup node selection, recovery time objectives, and data integrity standards. Index performance optimization results include index structure segmentation, response time optimization results, and load distribution configuration. Data query response information specifically refers to query priority settings, response speed indicators, and resource utilization efficiency.

[0069] See also Figure 2 , the calculation steps of the optimal data storage location are as follows:

[0070] Based on the cloud platform, extract health monitoring data traffic, count and record the access frequency of data points, and generate hourly access frequency data;

[0071] Collect health monitoring data from the cloud platform, especially the access frequency of statistical data points every hour. The system automatically extracts access records from the server's log files. The records contain timestamps and data request details. Each record will be parsed to determine the frequency of access, thereby generating an access frequency data point for each hour. This data will be stored in a temporary database for use in subsequent steps, ensuring real-time updating and accuracy of the data, because each data point is generated based on actual server access records.

[0072] Based on the hourly access frequency data, calculate the total traffic of each data point in the different time periods, merge the access frequency and traffic data, and obtain the traffic frequency analysis results;

[0073] Based on the collected hourly access frequency data, further analyze the total traffic of each data point in different time periods, including combining the access frequency with the amount of data transmitted in each access, calculating the total traffic, and summarizing the hourly data to obtain the traffic situation for the whole day. In addition, it is necessary to evaluate the peak and valley values ​​of the traffic to identify data access patterns. The analysis will help understand the usage of the data and optimize the data storage and processing strategies, thereby providing support for further decision-making.

[0074] Based on the flow frequency analysis results, the formula is used:

[0075]

[0076] Calculate the cost efficiency of each data center CX i , determine the optimal data storage location, where N c Represents the total number of statistical time periods, Cfij represents the access frequency of the i-th data center in the j-th time period, Cv ij Represents the traffic of the i-th data center in the j-th time period;

[0077] A data center has access frequency and traffic records in three time periods:

[0078] 10 accesses / hour, 100MB / access;

[0079] 20 accesses / hour, 150MB / access;

[0080] 30 accesses / hour, 200MB / access;

[0081] Calculated according to the formula:

[0082]

[0083] The result shows that the average traffic cost efficiency of this data center is 3333MB access / hour, which means that a larger amount of data can be processed at a lower cost per hour. Therefore, if the processing of other data centers is similar, it can be determined as the optimal data storage location.

[0084] See also Figure 3 , the specific steps to obtain the liquidity adjustment configuration are:

[0085] Collect data traffic from multiple cloud platforms, including the data transmission rate and total data volume of each node, and generate real-time data traffic records;

[0086] Incoming and outgoing data packets are captured in real time at each network node. The size and timestamp of each data packet are recorded for detailed traffic analysis, ensuring the accuracy and real-time nature of the monitoring data. All collected data is transmitted to the monitoring system in real time. The monitoring system uses the data to generate real-time traffic reports. The reports show the data traffic status of each node, as well as bottlenecks and traffic peaks, so that network strategies can be adjusted in a timely manner to optimize data flows.

[0087] Based on real-time data flow records, traffic peaks and valleys are identified, and periodic changes in data flow are determined through time series prediction to generate data liquidity analysis results;

[0088] The collected data is used to generate data mobility patterns, including identifying traffic patterns, such as daily peak and trough times of traffic. This information is critical for predicting future traffic trends. By analyzing the patterns, periodic changes and abnormal traffic in data flows can be identified, so that network resources can be adjusted according to actual data mobility needs, such as by increasing bandwidth or reconfiguring routing to cope with expected high-traffic events. This not only improves the operational efficiency of the data center, but also provides a basis for further liquidity management.

[0089] Based on the results of data liquidity analysis, the formula is adopted:

[0090]

[0091] Adjust the data transmission and storage processes to generate a liquidity adjustment configuration, where RS(t) represents the resource optimization configuration at time t, β R is the weight parameter of bandwidth utilization, P Rmax is the maximum bandwidth capacity, LR i is the traffic load of the ith node, f Ri (t) is the traffic frequency of the ith node at time t, n r is the total number of monitoring nodes in the network;

[0092] In a network environment, the following data was collected:

[0093] P Rmax =1000Mbps;

[0094] Assume β R =0.8 and α R =0.05;

[0095] For the traffic load LR of three nodes i =[100,200,300]Mbps

[0096] Flow frequency f Ri (t) = [0.5, 0.3, 0.2], substitute into the formula to calculate:

[0097] RS(t)=0.8·1000-0.05·(100·0.5+200·0.3+300·0.2)

[0098] RS(t)=800-0.05·(50+60+60)

[0099] RS(t)=800-8.5=791.5

[0100] The result shows that under the current parameter settings, the optimized bandwidth available to the network is 791.5Mbps, indicating that by reasonably setting weight parameters and managing traffic load, network resources can be effectively utilized and resource waste can be avoided during high traffic periods.

[0101] See also Figure 4 , the specific steps for evaluating operational stability and data security risks are as follows:

[0102] Based on the liquidity adjustment configuration, key information is extracted from the cloud platform logs, including system failure events, unauthorized access information, and data leakage events, to build a preliminary security event set;

[0103] Extract key information including system failure events, unauthorized access attempts, and data leakage events from cloud platform logs. By organizing the information, log entries containing detailed data such as timestamps, event types, and severity are converted into an analyzable format and stored in a preliminary security event data set, providing the necessary basic data for subsequent in-depth analysis.

[0104] Based on the preliminary set of security events, the random forest algorithm is used to identify abnormal behaviors and potential security threats. Combining current operation data and historical security events, the formula is used:

[0105]

[0106] Calculate the risk index DR i , and obtain safety and stability analysis information, where γ d is a correction factor that takes into account the influence of environmental variables and operating mode, wd k is the weight of the kth risk factor, xd ik is the performance of the ith event on the kth risk factor, K d is the number of risk factors;

[0107] Three safety events were detected, each with three risk factors:

[0108] Unauthorized access attempt (xd i1 ), system failure (xd i2 ), data leakage (xd i3 );

[0109] Set the correction factor γ d =1.2 to enhance the model's response sensitivity;

[0110] The weights are set to wd1=0.3, wd2=0.4, wd3=0.3 respectively;

[0111] If the risk factor of the first event is expressed as xd 11=2,xd 12 =5,xd 13 =3, then the risk index of the event is calculated as follows:

[0112] DR i =1.2·(0.3·2+0.4·5+0.3·3)

[0113] DR i =1.2·(0.6+2.0+0.9)=1.2·3.5=4.2

[0114] The result shows that the risk index of the first event is 4.2, which represents a relatively high security risk, suggesting that the security team should give priority to such high-risk events.

[0115] Using security and stability analysis information, we evaluate the operational stability and security risks of the data center based on industry standards and risk thresholds, analyze the frequency and severity of events at different risk levels, and obtain operational stability and data security risk assessment results;

[0116] According to the security and stability analysis report, a comprehensive risk assessment of the data center is conducted. If the risk threshold is 4.0, which is determined based on past data analysis and industry standards, the calculated risk index is used for comparison. i If the threshold is exceeded, it indicates that there is a high-risk event in the data center and immediate attention is required. The system will automatically mark the event that exceeds the threshold as a high-priority processing object to ensure that the cloud data center can respond quickly and take necessary security measures, reduce the probability of potential security incidents in the future, and ensure the long-term stable operation and data security of the data center.

[0117] See also Figure 5 , the specific steps for obtaining the backup recovery plan are:

[0118] Based on the operational stability and data security risk assessment information, the data is classified into criticality levels using the formula:

[0119]

[0120] Determine the data backup frequency, where fV(d) represents the backup frequency of data d, v d is the value indicator of the data, s d is the sensitivity index of the data, c q and d q is the influence coefficient of the adjustment factor;

[0121] The following data was collected:

[0122] The value v of data item d d =0.8;

[0123] Sensitivity index d =0.6;

[0124] Adjustment coefficient c q and d q , the calculation process of the backup frequency is as follows:

[0125]

[0126] It shows that data item d has a very high backup frequency, close to daily backup, to ensure data security and instant recovery capabilities. The backup frequency close to 1 means that this is high-value and high-sensitivity data, which needs frequent backup to maximize protection and recovery efficiency.

[0127] Based on the frequency of data backup, combined with the cloud platform's data processing capabilities, physical security, geographic location, and network connection speed, set regular and irregular backup tasks to form a backup operation log;

[0128] Choose the most suitable backup center for data backup. The decision depends on the backup center's data processing capabilities, physical security, geographical location and network connection speed with the main data center. Combined with the established backup frequency strategy, arrange regular and temporary backup tasks. During the backup process, monitor its status in real time to ensure the smooth operation. The backup operation log will record detailed operation information, including data size, backup time, backup speed and any abnormalities. The information will be used to evaluate the performance and reliability of the backup center, thereby providing data support for future backup tasks, ensuring the efficiency of backup activities and the security of data, while optimizing resource allocation and reducing costs.

[0129] Develop a backup recovery plan based on the backup operation log. The plan includes the recovery time objective and data recovery point objective to ensure that critical data can be quickly and effectively restored in the event of data loss or system failure.

[0130] The backup recovery plan is refined based on the backup operation log, describing the recovery process in the event of data loss or system failure, including the recovery time objective (RTO) and data recovery point objective (RPO). The plan will detail the recovery strategy and steps for each data type, as well as the required resources and expected recovery time. The recovery plan also includes regular recovery drills to ensure the effectiveness of the recovery process and the team's responsiveness. The recovery strategy evaluation is based on the previous backup operation log to determine the most effective recovery time point and data integrity, ensuring that critical operations can be quickly restored in any emergency, with the goal of minimizing data loss.

[0131] See also Figure 6 , the specific steps for establishing a multidimensional adaptive index are:

[0132] Based on the backup and recovery plan, analyze the data index structure of the current cloud platform, identify the performance bottleneck caused by the growth of data volume, collect key performance indicators, and generate index structure evaluation records;

[0133] Analyze the data index structure of the current cloud platform, identify performance bottlenecks, and collect key performance indicators. The analysis process is based on actual data access logs and system monitoring reports, from which key indicators such as data query frequency, response time, and system load are extracted. Through preliminary analysis of the data, a comprehensive report on the efficiency of the current index structure is generated. The report points out the correlation and bottleneck areas between data growth and query efficiency, providing a basis for further index structure optimization.

[0134] Based on the index structure evaluation records, a multi-dimensional adaptive index matching the differential data access pattern is established, using the formula:

[0135]

[0136] Get the optimal index dimension configuration, where FB(d B ,t) represents the data index dimension after dynamic adjustment according to time at time t, d B represents the original data dimension, t represents the time factor, which is used to adjust the adaptability of the index over time, and r B It is the time adjustment parameter, which determines the influence of the time variable on the index dimension;

[0137] The original dimension d of the collected data B is 5, the current time t is 10, and the adjustment coefficient r B is 8, calculated according to the formula:

[0138]

[0139] The result shows that under the given parameter settings, the optimized index dimension is about 2.886, which means that the system should reduce the dimension to this value to optimize performance and adapt to current data access needs.

[0140] See also Figure 7 , the specific steps for optimizing data retrieval speed and efficiency are:

[0141] Extract query response time and access frequency data from the cloud platform's data access logs, identify high-frequency access patterns and performance bottlenecks in data retrieval, and generate performance analysis logs;

[0142] Query response time and access frequency data are extracted from the cloud platform's data access logs, and high-frequency access patterns and performance bottlenecks in data retrieval are analyzed and determined, including detailed descriptions of key performance indicators and bottleneck areas. This will provide basic data and reference basis for subsequent data retrieval optimization and set clear goals and directions for improving system configuration.

[0143] Based on the performance analysis log, the formula is used:

[0144]

[0145] Perform query optimization and match query load to generate data retrieval optimization results OS (c u ,p u ), where c u Indicates query complexity, covering the amount of data involved in the query and the difficulty of processing. u Indicates the query parameters, including query conditions and ranges, k u is the sensitivity coefficient, m u It is the balance point of query parameters;

[0146] The data collected are:

[0147] Query complexity c u is 50, query parameter p u is 30, the equilibrium point m u is 20, the adjustment coefficient k u is 5, and the calculation is:

[0148]

[0149] The results show that under the current parameter settings, the optimized query efficiency index is approximately 26.35, reflecting the effect and performance improvement of the query optimization algorithm under the given query load and system configuration.

[0150] See also Figure 8 , the specific steps for obtaining data query response information are:

[0151] Based on the index performance optimization results, combined with the query response time and query processing cost data, the formula is used:

[0152]

[0153] The query process for calculating the optimal cost, where CZ represents the query cost, t Z represents the query time, r Z represents resource consumption, q Z represents the query load, α Z is the time weight coefficient, which indicates the influence of time factor in total cost calculation, βZ is the resource consumption weight coefficient, which indicates the influence of resource usage in cost calculation;

[0154] The following data were monitored:

[0155] Query time t Z is 200 milliseconds, and the resource consumption is Z is 50, the query quantity is q Z is 100, and the time weight α Z is 0.5, resource weight β Z is 0.3. Then the cost is calculated as follows:

[0156]

[0157] The result shows that the average cost of each query is 1.15 considering the current weights of time and resources, and the average cost can include computing resources, storage resources, and network resources.

[0158] Based on the query process with the best cost, the query results are updated to the user interface through the asynchronous processing mechanism to optimize the waiting time and user experience, and the efficiency of asynchronous operations and the frequency of result updates are monitored to obtain data query response information;

[0159] Based on the optimized index performance results, a comprehensive evaluation of data query costs is conducted, which takes into account processing time and resource consumption. By analyzing the resource requirements and time costs of various query types, the cost drivers are refined and identified, and key performance indicators are further integrated to optimize query costs, thereby guiding the selection of the most economical query execution process, aiming to reduce unnecessary resource consumption and improve query efficiency, thereby ensuring a balance between cost and performance.

[0160] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A cloud-based postpartum health data management system, characterized in that: The system comprises: The data flow optimization module is based on the cloud platform, analyzes the health monitoring data flow and access mode, calculates the optimal data storage location, and adjusts the data liquidity management process according to the real-time data flow to obtain the liquidity adjustment configuration; The security backup management module adjusts the configuration based on the liquidity, evaluates the operational stability and data security risks of the cloud platform, sets the data backup frequency, and selects the optimal backup center to perform the data backup operation, and obtains a backup recovery plan; The index adjustment module adjusts the consulting and support data index structure of the cloud platform based on the backup and recovery plan, establishes a multi-dimensional adaptive index, and optimizes the data retrieval speed and efficiency to obtain an index performance optimization result; The query efficiency optimization module evaluates the data query cost based on the index performance optimization result, selects the query execution process with the optimal cost, and updates the query result to the user interface through asynchronous processing to obtain data query response information.

2. The cloud-based postpartum health data management system according to claim 1, characterized in that: The steps for calculating the optimal data storage location are specifically as follows: Based on the cloud platform, extract health monitoring data traffic, count and record the access frequency of data points, and generate hourly access frequency data; Based on the hourly access frequency data, the total traffic of each data point in the different time periods is calculated, and the access frequency and traffic data are combined to obtain the traffic frequency analysis result; Based on the flow frequency analysis results, the formula is adopted: Calculate the cost efficiency of each data center CX i , determine the optimal data storage location, where N c Represents the total number of statistical time periods, Cf ij represents the access frequency of the i-th data center in the j-th time period, Cv ij Represents the traffic of the i-th data center in the j-th time period.

3. The cloud-based postpartum health data management system according to claim 1, characterized in that: The steps for obtaining the liquidity adjustment configuration are specifically as follows: Collecting data traffic of multiple cloud platforms, including data transmission rate and total data volume of each node, and generating real-time data traffic records; Based on the real-time data flow records, traffic peaks and valleys are identified, periodic changes in data flow are determined through time series prediction, and data liquidity analysis results are generated; Based on the data liquidity analysis results, the formula is adopted: Adjust the data transmission and storage processes to generate a liquidity adjustment configuration, where RS(t) represents the resource optimization configuration at time t, β R is the bandwidth utilization weight parameter, is the maximum bandwidth capacity, LR i is the traffic load of the ith node, f Ri (t) is the traffic frequency of the ith node at time t, n r is the total number of monitoring nodes in the network.

4. The cloud-based postpartum health data management system according to claim 1, characterized in that: The steps for evaluating the operational stability and data security risks are specifically as follows: Based on the liquidity adjustment configuration, extract key information from the cloud platform logs, including system failure events, unauthorized access information, and data leakage events, to build a preliminary security event set; Based on the preliminary set of security events, the random forest algorithm is used to identify abnormal behaviors and potential security threats. The formula is used in combination with current operation data and historical security events: Calculate the risk index DR i , and obtain safety and stability analysis information, where γ d is the correction factor, wd k is the weight of the kth risk factor, xd ik is the performance of the ith event on the kth risk factor, K d is the number of risk factors; Utilizing the security and stability analysis information, the operational stability and security risks of the data center are evaluated according to industry standards and risk thresholds, the frequency and severity of events at different risk levels are analyzed, and operational stability and data security risk assessment results are obtained.

5. The cloud-based postpartum health data management system according to claim 1, characterized in that: The steps for obtaining the backup recovery plan are specifically as follows: Based on the operational stability and data security risk assessment information, the data is classified by criticality using the formula: Determine the data backup frequency, where fV(d) represents the backup frequency of data d, v d is the value indicator of the data, s d is the sensitivity index of the data, c q and d q is the influence coefficient of the adjustment factor; Based on the data backup frequency, combined with the cloud platform's data processing capabilities, physical security, geographic location, and network connection speed, regular and irregular backup tasks are set to form a backup operation log; Based on the backup operation log, a backup recovery plan is formulated, the plan including a recovery time objective and a data recovery point objective.

6. The cloud-based postpartum health data management system according to claim 1, characterized in that: The steps of establishing the multidimensional adaptive index are specifically as follows: Based on the backup and recovery plan, analyze the data index structure of the current cloud platform, identify performance bottlenecks caused by data volume growth, collect key performance indicators, and generate index structure evaluation records; Based on the index structure evaluation records, a multi-dimensional adaptive index matching the differential data access pattern is established, using the formula: Get the optimal index dimension configuration, where FB(d B ,t) represents the data index dimension after dynamic adjustment according to time at time t, d B represents the original data dimension, t represents the time factor, r B Adjust the parameters for time.

7. The cloud-based postpartum health data management system according to claim 1, characterized in that: The steps for optimizing the data retrieval speed and efficiency are specifically as follows: Extract query response time and access frequency data from the cloud platform's data access logs, identify high-frequency access patterns and performance bottlenecks in data retrieval, and generate performance analysis logs; Based on the performance analysis log, the formula is adopted: Perform query optimization and match query load to generate data retrieval optimization results OS (c u ,p u ), where c u represents the query complexity, p u Indicates the query parameter, k u is the sensitivity coefficient, m u It is the query parameter balance point.

8. The cloud-based postpartum health data management system according to claim 1, characterized in that: The steps for obtaining the data query response information are specifically as follows: Based on the index performance optimization results, combined with the query response time and query processing cost data, the formula is adopted: The query process for calculating the optimal cost, where CZ represents the query cost, t Z represents the query time, r Z represents resource consumption, q Z represents the query load, α Z is the time weight coefficient, β Z is the resource consumption weight coefficient; Based on the optimal cost query process, the query results are updated to the user interface through an asynchronous processing mechanism to optimize waiting time and user experience, and the efficiency of asynchronous operations and the result update frequency are monitored to obtain data query response information.

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