A data processing method for an elderly care service platform and the elderly care service platform itself.

Abstract

This invention relates to the field of data processing technology, specifically disclosing a data processing method for an elderly care service platform and the platform itself. After initializing server configuration parameters, the method can stably receive and instantly provide feedback on the integrity of elderly care service data, ensuring the accuracy and reliability of data input. Furthermore, it intelligently monitors and processes server operating parameters, initially adjusting the configuration to adapt to real-time load, effectively improving system response speed and stability. By continuously monitoring data application performance parameters and making secondary adjustments, the platform can dynamically optimize resource allocation, maximizing server performance utilization. This not only significantly improves the processing efficiency of elderly care service data but also ensures the accuracy of data processing, providing the elderly with a more personalized and efficient elderly care service experience. Simultaneously, the intelligent resource allocation adjustment enables the platform to maintain efficient and stable operation when facing service demands of different scales.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a data processing method for an elderly care service platform and the elderly care service platform itself. Background Technology

[0002] With the increasing aging of the population, the issue of elderly care has become a major social problem that urgently needs to be addressed globally. Traditional elderly care models, such as family care and institutional care, are no longer able to meet the growing diverse and personalized needs of the elderly. Therefore, building a smart elderly care service platform using modern information technology has become an effective way to solve the current elderly care problem.

[0003] For example, invention patent CN115496643A discloses an elderly care service platform system and data processing method. The system includes a data platform server, a regulatory agency terminal, at least one operation center terminal, at least one elderly care service institution terminal, and at least one elderly care service provider terminal. The data platform server is used to receive elderly care service supervision data uploaded by the regulatory agency terminal, the operation center terminal, the elderly care service institution terminal, and the elderly care service provider terminal, respectively. The regulatory agency terminal is used to operate the elderly care service supervision data uploaded by the operation center terminal to the data platform server. The operation center terminal is used to determine the target terminal under the operation center terminal and the first target operation method according to the business structure of the elderly care service platform system, and operate the elderly care service supervision data uploaded by the target terminal to the data platform server according to the first target operation method.

[0004] For example, the invention patent with publication number CN115994842A discloses a system for collecting and processing data for an elderly care service platform. This system includes a data collection module and a data collection module. The data collection module includes an employee management module, a service work order management module, a service order management module, a data log tracking module, an anomaly data processing module, and an offline data processing module. The system comprises these modules. The relevant data collection modules achieve non-intrusive data collection of business code through the data log tracking module, and send the data to the anomaly data processing module and the offline data processing module for asynchronous data processing.

[0005] However, in the process of implementing the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: when the elderly care service data is abnormal during transmission or processing, the existing technology often lacks an effective response mechanism, which may lead to data loss, error accumulation or platform crash, thereby affecting the accuracy and timeliness of elderly care services. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a data processing method and an elderly care service platform, which can effectively solve the problems mentioned in the background section.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides a data processing method for an elderly care service platform, comprising: Step 1, initializing the configuration parameters of the server to which the elderly care service platform belongs; the initialized elderly care service platform receives elderly care service data and provides integrity feedback on the elderly care service data; Step 2, acquiring and processing the operating parameters of the server to which the elderly care service platform belongs, thereby adjusting the configuration parameters of the server to which the elderly care service platform belongs in one step, and monitoring the data application performance parameters of the elderly care service platform; Step 3, processing the data application performance parameters of the elderly care service platform, thereby adjusting the configuration parameters of the server to which the elderly care service platform belongs in a second step; Step 4, processing the elderly care service data to obtain the processing result of the elderly care service data, and providing result feedback.

[0008] As a further method, the initialization of the configuration parameters of the server to which the elderly care service platform belongs specifically involves the following initialization process: the configuration parameters of the server to which the elderly care service platform belongs include the data types to be received for each priority and the compression threshold for each data type to be received for each priority; obtain each data type to be received by the elderly care service platform, and initialize the corresponding priority for each data type in sequence to obtain the data types to be received for each priority; obtain the reference load set of the server to which the elderly care service platform belongs, and match the compression threshold for each data type to be received for each priority from the elderly care database; initialize the data types to be received for each priority and the compression threshold for each data type to be received for each priority, thereby completing the initialization of the configuration parameters of the server to which the elderly care service platform belongs.

[0009] As a further method, the configuration parameters of the server to which the elderly care service platform belongs are adjusted. Specifically, the adjustment process is as follows: by processing the operating parameters of the server to which the elderly care service platform belongs, the abnormal operating factors of the server to which the elderly care service platform belongs in the first period are obtained. Based on the abnormal operating factors of the server to which the elderly care service platform belongs in the first period, the compression expansion value of the received data type corresponding to each priority and the compression requirement of the server to which the elderly care service platform belongs are matched from the elderly care database. Based on the compression threshold of the received data type corresponding to each priority and the corresponding compression expansion value, the compression threshold of the received data type corresponding to each priority in the configuration parameters of the server to which the elderly care service platform belongs is adjusted.

[0010] As a further method, the configuration parameters of the server to which the elderly care service platform belongs are adjusted a second time. Specifically, the second adjustment process is as follows: The data application efficiency parameters of the elderly care service platform are comprehensively processed to obtain the data application completeness index for each target user at the end of the first cycle; the data application completeness index for each target user at the end of the first cycle is compared with a data application completeness threshold; if the data application completeness index for each target user at the end of the first cycle is greater than or equal to the data application completeness threshold, then no further adjustment is made to the configuration parameters of the server to which the elderly care service platform belongs; if any target user's data application completeness index at the end of the first cycle is less than the data application completeness threshold, then data with an index less than the data application completeness threshold is counted. For each target user corresponding to the application integrity index, the data application integrity factor of each data type belonging to each target user at the end of the first cycle is obtained, and the average data application integrity factor of the data type corresponding to each priority is calculated through data analysis. Based on the average data application integrity factor of the data type corresponding to each priority, the compression reduction value of the data type corresponding to each priority is matched from the elderly care database. Based on the compression threshold of the data type corresponding to each priority and the corresponding compression reduction value, the compression threshold of the data type corresponding to each priority in the configuration parameters of the elderly care service platform's server is adjusted a second time. After the adjustment is completed, the elderly care service platform continues to receive elderly care service data in the next adjacent first cycle.

[0011] A second aspect of the present invention provides an elderly care service platform that applies the data processing method described above, comprising: a server and an elderly care service platform; the elderly care service platform is used to visualize elderly care service data; the server is used to process the elderly care service data.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0013] (1) This invention provides a data processing method and an elderly care service platform, which realizes efficient processing of elderly care service data and optimized configuration of server resources. After initializing the server configuration parameters, the platform can stably receive and provide timely feedback on the integrity of elderly care service data, ensuring the accuracy and reliability of data input. Furthermore, the platform intelligently monitors and processes server operating parameters, adjusts the configuration for the first time to adapt to real-time load, effectively improving the system response speed and stability. By continuously monitoring data application performance parameters and making secondary adjustments, the platform can dynamically optimize resource configuration and maximize the use of server performance. This not only significantly improves the processing efficiency of elderly care service data, but also ensures the accuracy and timeliness of data processing, providing the elderly with a more personalized and efficient elderly care service experience. At the same time, the intelligent resource configuration adjustment enables the platform to maintain efficient and stable operation when facing service demands of different scales, providing solid technical support for the comprehensive upgrading of elderly care services.

[0014] (2) By deeply analyzing the reception of complete indexes, this invention has precisely optimized the configuration parameters of the server to which the elderly care service platform belongs. This measure has significantly improved the performance of the elderly care service platform and ensured that the data integrity reception requirements can be fully met, thereby achieving the dual advantages of efficient service and accurate data.

[0015] (3) This invention conducts in-depth analysis and processing of abnormal factors during operation, and makes secondary adjustments to the configuration parameters of the server to which the elderly care service platform belongs. This aims to ensure that the service can strictly meet the integrity requirements during use, effectively avoiding information loss during data compression caused by improper configuration. Even if the data is over-compressed inadvertently during the data compression process, causing some data to appear missing, this invention can recover it through intelligent data analysis. Thus, while ensuring data integrity, it also greatly improves the efficiency and security of data processing, providing solid technical support and guarantee for the elderly care service platform. Attached Figure Description

[0016] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] Reference Figure 1 As shown, the first aspect of the present invention provides a data processing method for an elderly care service platform, including: Step 1, initializing the configuration parameters of the server to which the elderly care service platform belongs, the elderly care service platform receiving elderly care service data after initialization, and providing integrity feedback on the elderly care service data.

[0020] The aforementioned elderly care service platform is used to visualize elderly care service data; the aforementioned server is used to process elderly care service data; the following description of the elderly care service platform's processing of elderly care service data refers to the processing of elderly care service data by the server to which the elderly care service platform belongs.

[0021] Specifically, the process for providing integrity feedback on elderly care service data is as follows: The reception status parameters of the elderly care service data within the first period are acquired and analyzed to obtain the reception integrity index for the data within the first period. If the reception integrity index is greater than or equal to the reception integrity threshold, a successful reception integrity feedback is provided for the elderly care service data within the first period. If the reception integrity index is less than the reception integrity threshold, the configuration parameters of the server to which the elderly care service platform belongs are adjusted based on the abnormal operation factors of the server within the first period. After adjustment, the elderly care service data for the first period is re-uploaded from the backup data, and a successful reception integrity feedback is provided. The aforementioned reception integrity threshold represents the minimum value within the reasonable range of the reception integrity index, extracted from the elderly care database. The aforementioned successful reception integrity feedback specifically refers to the elderly care service platform automatically generating a data reception success instruction and saving it in the data log to confirm the integrity of the data reception. The aforementioned backup data refers to a copy of the same data as the original data that is pre-stored during the data reception process of the elderly care service platform to prevent data loss, damage, or incomplete reception.

[0022] In this embodiment, the duration of data reception by the elderly care service platform is divided into several adjacent first periods. Each first period represents a small fixed duration of data reception by the elderly care service platform, so as to conduct orderly and detailed management of the server to which the platform belongs. Through this division, the data reception status of the server in different time periods can be monitored and evaluated more clearly, providing strong support for the operation and maintenance and optimization of the server.

[0023] Furthermore, the specific analysis process for the reception integrity index of the elderly care service data in the first period is as follows: The effective load value of the received data packets, the fluctuation value of the sequence number of the received data packets, and the throughput-received data packet effective load value factor of the elderly care service platform's server at each moment in the first period are obtained and analyzed. This yields the reception integrity factor of the elderly care service data at each moment in the first period. Specifically, this involves weighting and amplifying the deviation between the effective load value of the received data packets and the corresponding defined effective load value, the ratio of the fluctuation value of the sequence number of the received data packets to the defined fluctuation value of the sequence number of the received data packets, and the deviation between the throughput-received data packet effective load value factor and the reference throughput-received data packet effective load value factor. The reception integrity factor of the elderly care service data at each moment in the first period represents the quantitative data on the influence of the effective load value of the received data packets, the fluctuation value of the sequence number of the received data packets, and the throughput-received data packet effective load value factor on the reception integrity of the elderly care service data at each moment in the first period. This data is used to comprehensively quantify the reception integrity of the elderly care service data at each moment in the first period. The aforementioned received data packets have... The effective load value refers to the amount of data in the effective load of all data packets received by the server of the elderly care service platform at each moment in the first period. It can be obtained by parsing the received data packets using network packet capture tools (such as network packet analysis) to extract the effective load portion and calculate its overall data volume. The received data packet sequence number fluctuation value refers to the degree of difference between the sequence number of the data packets received by the server of the elderly care service platform at each moment in the first period and the expected sequence number. During data transmission, data packets are usually arranged according to a certain sequence number to ensure the order and integrity of the data. The similarity algorithm (such as cosine similarity) is used to calculate the similarity between the actual received data packet sequence number at each moment and the expected sequence number of the elderly care data service platform at that moment. Then, the similarity is subtracted from 1 to obtain the received data packet sequence number fluctuation value. The throughput-received data packet effective load value factor is a comprehensive indicator that combines throughput and received data packet effective load value. It reflects the degree of matching between the actual amount of data received by the server and the data processing capacity. It is obtained by dividing the throughput by the total amount of received data packets. The throughput refers to the amount of data successfully transmitted by the elderly care service platform per unit time, which can be monitored by network performance monitoring tools (such as network bandwidth testing tools).

[0024] The data reception completeness index for elderly care services in the first period is obtained by integrating the data reception completeness factor at each moment in the first period, representing the overall reception completeness of the elderly care service data in the first period.

[0025] The completeness factor of the received elderly care service data at each time point in the first period is specifically expressed as follows:

[0026]

[0027] In the formula, TOL_CDC is the data reception completeness index for elderly care services in the first period, and CDC is the data reception completeness index. p For elderly care service data, the complete factor of reception at time p within the first cycle, where p∈[p1, p2], where p is any time point within the first cycle, p1 is the start time point of the first cycle, and p2 is the end time point of the first cycle, RPV p ΔRPV is the effective payload value of the data packets received by the server of the elderly care service platform at time p in the first period. p The FSN is the effective payload value of the received data packets at time p within the first period, stored in the elderly care database and belonging to the server of the elderly care service platform. p ΔFSN is the fluctuation value of the sequence number of the data packets received by the server of the elderly care service platform at time p in the first period. p TR_DV is the value of the sequence number fluctuation of the received data packets at time p within the first period, stored in the elderly care database and belonging to the server of the elderly care service platform. p ΔTR_DV is the throughput factor of the server belonging to the elderly care service platform at time p in the first period, which is the effective payload value of the received data packets. p ED1 is the reference throughput-received data packet payload value factor of the server belonging to the elderly care service platform stored in the elderly care database at time p in the first period. ED2 is the influence value corresponding to the preset received data packet payload value in the elderly care database, ED3 is the influence value corresponding to the preset received data packet sequence number fluctuation value in the elderly care database, and ED3 is the influence value corresponding to the preset throughput-received data packet payload value factor in the elderly care database.

[0028] The above definition of the received data packet payload value represents the minimum allowable value of the received data packet payload value; the above definition of the received data packet sequence number fluctuation value represents the maximum allowable value of the received data packet sequence number fluctuation value; the above reference throughput-received data packet payload value factor represents the reference value of the throughput-received data packet payload value factor.

[0029] The impact value corresponding to the received data packet payload value mentioned above represents the degree of influence of the unit value of the received data packet payload value on the reception integrity factor; the impact value corresponding to the received data packet sequence number fluctuation value mentioned above represents the degree of influence of the unit value of the received data packet sequence number fluctuation value on the reception integrity factor; the impact value corresponding to the throughput-received data packet payload value factor mentioned above represents the degree of influence of the unit value of the throughput-received data packet payload value factor on the reception integrity factor; the elderly care database stores the correspondence between the received data packet payload value, the received data packet sequence number fluctuation value, and the throughput-received data packet payload value factor and their corresponding impact values. For example, by inputting the received data packet payload value, the received data packet sequence number fluctuation value, and the throughput-received data packet payload value factor into the elderly care database, the elderly care database can match the impact value corresponding to the received data packet payload value, the impact value corresponding to the received data packet sequence number fluctuation value, and the impact value corresponding to the throughput-received data packet payload value factor, all of which have a value range between 0 and 1.

[0030] It's important to explain that when a server has sufficient memory, it can efficiently process received data packets, keeping the effective payload value of the received data packets within the expected range, while ensuring stable sequence numbers with minimal fluctuations. However, if the server runs out of memory, the processing speed will decrease, leading to a reduction in the actual effective payload value received, and even potential data packet loss, resulting in a significant deviation from the corresponding reference value. This processing delay and data loss will further cause fluctuations in the data packet sequence numbers, increasing the fluctuation value. Simultaneously, insufficient memory will limit the server's processing capacity, leading to a decrease in throughput and a reduced match between throughput and the effective payload value of received data packets. The throughput-effective payload value factor will also deviate from the corresponding reference value. These parameter changes are interrelated and collectively affect the reception integrity factor, i.e., the integrity and accuracy of data reception. An anomaly in any parameter can lead to a decrease in the reception integrity factor, thereby affecting the overall data service quality of the elderly care service platform.

[0031] The elderly care database is a database used to store parameters involved in the data processing of the elderly care service platform.

[0032] Furthermore, the initialization process for the configuration parameters of the server to which the elderly care service platform belongs is as follows: the configuration parameters of the server to which the elderly care service platform belongs include the data types to be received corresponding to each priority and the compression threshold of the data types to be received corresponding to each priority. The data types to be received by the elderly care service platform are obtained, and the priorities corresponding to each data type are initialized sequentially to obtain the data types to be received corresponding to each priority. The aforementioned data types to be received by the elderly care service platform refer to the data types that have been defined as acceptable at the initial design stage of the platform. The sequential initialization of the priorities corresponding to each data type refers to the data administrator setting the correspondence rules between each data type and its priority, and then initializing the priorities of each data type.

[0033] The reference load set of the server to which the elderly care service platform belongs is obtained, and the compression threshold of the received data type corresponding to each priority is matched from the elderly care database. The specific matching process is as follows: using a similarity algorithm (Jaccard similarity coefficient), the reference load set of the server to which the elderly care service platform belongs is compared with each reference load set in the elderly care database, and the reference load set with the highest similarity is found. The compression threshold of each priority received data type corresponding to the reference load set with the highest similarity in the elderly care database is extracted. These thresholds are the compression thresholds of each priority received data type matched in this embodiment. The reference load set of the server to which the elderly care service platform belongs refers to the set of performance parameters set when the server is manufactured. The performance parameters include the maximum number of concurrent connections, etc., which can be extracted from the server's specifications.

[0034] Initialize the data types to be received for each priority and the compression threshold for each data type to be received for each priority, thereby completing the initialization of the configuration parameters of the server to which the elderly care service platform belongs. That is, each priority contains multiple data types, which are regarded as a whole and have corresponding compression thresholds. The compression threshold refers to the maximum amount of compressed data that is allowed to be achieved during the compression process of data of each priority.

[0035] It should be explained that in each first cycle, the elderly care service platform compresses data according to the stored compression threshold. When entering the next adjacent first cycle, the data compression process continues according to the compression threshold. Each first cycle corresponds to a different compression threshold. In an example embodiment, in a certain first cycle, the compression threshold for the received data type corresponding to priority 3 is 900 MB, the compression threshold for the received data type corresponding to priority 2 is 700 MB, and the compression threshold for the received data type corresponding to priority 1 is 300 MB. According to the set compression threshold, the data of the elderly care service platform is compressed, and the total compressed data volume is 1900 MB. When compressing data of each priority, the compression process starts from the data with the longest storage time.

[0036] It should be explained that priority 1 is high priority, and the types of data to be received corresponding to priority 1 include, but are not limited to, emergency alarm information (such as fall detection, emergency calls, etc.) and real-time health monitoring data (such as heart rate, blood pressure, etc.). Priority 2 is medium priority, and the types of data to be received corresponding to priority 2 include, but are not limited to, daily activity records (such as walking, meals, etc.) and regular health check reports. Priority 3 is low priority, and the types of data to be received corresponding to priority 3 include, but are not limited to, entertainment content (such as music, videos, etc.) and non-real-time health advice.

[0037] Step 2: Obtain and process the operating parameters of the server to which the elderly care service platform belongs, thereby adjusting the configuration parameters of the server to which the elderly care service platform belongs, and monitoring the data application performance parameters of the elderly care service platform.

[0038] In one specific embodiment, the present invention performs a precise optimization adjustment on the configuration parameters of the server to which the elderly care service platform belongs by deeply analyzing the reception of complete indexes. This measure significantly improves the performance of the elderly care service platform and ensures that the reception requirements for data integrity are fully met, thereby achieving the dual advantages of efficient service and accurate data.

[0039] Specifically, the adjustment of the configuration parameters of the server belonging to the elderly care service platform is carried out in one step. The adjustment process is as follows: by processing the operating parameters of the server belonging to the elderly care service platform, the abnormal operating factors of the server belonging to the elderly care service platform in the first period are obtained. Based on the abnormal operating factors of the server belonging to the elderly care service platform in the first period, the compression expansion value of the receiving data type corresponding to each priority and the compression requirement of the server belonging to the elderly care service platform are matched from the elderly care database. The specific matching process is as follows: the elderly care database stores the compression expansion value and compression requirement of the receiving data type corresponding to each priority for each abnormal operating factor interval. The abnormal operating factor interval of the server belonging to the elderly care service platform in the first period is queried from the elderly care database. The compression expansion value and compression requirement of the receiving data type corresponding to each priority for each abnormal operating factor interval stored in the elderly care database are the compression expansion value and compression requirement of the receiving data type corresponding to each priority matched in this embodiment. The above-mentioned compression expansion value refers to the value of increasing the original compression threshold.

[0040] Based on the compression threshold and corresponding compression extension value of the received data type corresponding to each priority, the compression threshold of the received data type corresponding to each priority in the configuration parameters of the server to which the elderly care service platform belongs is adjusted once. Specifically, the compression threshold of the received data type corresponding to each priority is added to the corresponding compression extension value, and the result of the addition is re-marked as the compression threshold of the received data type corresponding to each priority, which is one adjustment. In an example embodiment, in a certain first cycle, the compression threshold of the received data type corresponding to priority 3 is 900 MB, the compression threshold of the received data type corresponding to priority 2 is 700 MB, and the compression threshold of the received data type corresponding to priority 1 is 300 MB. The matched compression extension value of the received data type corresponding to priority 3 is 180 MB, and the compression extension value of the received data type corresponding to priority 2 is 140 MB. If the compression extension value of the data type corresponding to priority 1 is 60 MB, then after one adjustment, the compression threshold of the data type corresponding to priority 3 is 1080 MB, the compression threshold of the data type corresponding to priority 2 is 840 MB, and the compression threshold of the data type corresponding to priority 1 is 360 MB. Therefore, within this first cycle, the data type corresponding to priority 3 can still compress 180 MB of data, the data type corresponding to priority 2 can still compress 140 MB of data, and the data type corresponding to priority 1 can still compress 60 MB of data. When the current compression requirement of the server to which the elderly care service platform belongs is determined to be 300 MB, compression is performed in order from low to high priority. Specifically, the data type corresponding to priority 3 is compressed by 180 MB first, and then the data type corresponding to priority 2 is compressed by 120 MB to meet the compression requirement.

[0041] Furthermore, the specific processing procedure for the abnormal operation factors of the server belonging to the elderly care service platform during the first period is as follows: The operating parameters of the server belonging to the elderly care service platform include the average memory utilization rate, the maximum memory paging file utilization rate, and the peak CPU load during the first period. The average memory utilization rate refers to the average percentage of memory used by the server belonging to the elderly care service platform during the first period, which can be extracted from the task manager of the server belonging to the elderly care service platform. The maximum memory paging file utilization rate refers to the maximum percentage of memory paging file used by the server belonging to the elderly care service platform during the first period, which can be extracted from the task manager of the server belonging to the elderly care service platform. The peak CPU load refers to the highest number of processes recorded as being in a runnable and uninterruptible state by the server belonging to the elderly care service platform during the first period, which can be extracted from the task manager of the server belonging to the elderly care service platform.

[0042] Obtain the defined memory utilization rate of the server belonging to the elderly care service platform, and perform difference processing with the average memory utilization rate of the server belonging to the elderly care service platform in the first period. The processing result is marked as the reserve memory utilization rate of the server belonging to the elderly care service platform in the first period. The defined memory utilization rate represents the maximum allowable value of the average memory utilization rate, which is extracted from the elderly care database. The reserve memory utilization rate represents the difference between the defined memory utilization rate and the average memory utilization rate in the first period.

[0043] The reception integrity index of the elderly care service data in the first period is processed by multiplication inverse. Simultaneously, corresponding influence factors are assigned to the reserved memory utilization rate, maximum memory paging file utilization rate, and peak CPU load of the elderly care service platform server in the first period. Based on the influence factors corresponding to the reserved memory utilization rate, maximum memory paging file utilization rate, and peak CPU load, these parameters are amplified to derive the operational anomaly factor of the elderly care service platform server in the first period. This operational anomaly factor represents the quantitative data on the impact of the reception integrity index, reserved memory utilization rate, maximum memory paging file utilization rate, and peak CPU load on the operational anomalies of the elderly care service platform server in the first period, and is used to comprehensively quantify the degree of operational anomalies of the elderly care service platform server in the first period.

[0044] The specific expression for the operational anomaly factor of the server to which the elderly care service platform belongs during the first period is as follows:

[0045]

[0046] In the formula, TAF is the operational anomaly factor of the server belonging to the elderly care service platform in the first period, TOL_CDC is the reception integrity index of elderly care service data in the first period, B is the influence factor corresponding to the preset reception integrity index in the elderly care database, RMU is the reserve memory utilization rate of the server belonging to the elderly care service platform in the first period, ΔRMU is the preset reference reserve memory utilization rate in the elderly care database, MU is the maximum memory paging file utilization rate of the server belonging to the elderly care service platform in the first period, JMU is the preset defined maximum memory paging file utilization rate in the elderly care database, ZF is the peak CPU load of the server belonging to the elderly care service platform in the first period, JZF is the preset defined peak CPU load in the elderly care database, ho1 is the influence factor corresponding to the preset reserve memory utilization rate in the elderly care database, ho2 is the influence factor corresponding to the preset maximum memory paging file utilization rate in the elderly care database, and ho3 is the influence factor corresponding to the preset peak CPU load in the elderly care database.

[0047] The above reference reserve memory utilization rate represents a reference value for reserve memory utilization; the above defined maximum memory paging file utilization rate represents the maximum allowed value for maximum memory paging file utilization; the above defined peak CPU load represents the maximum allowed value for peak CPU load.

[0048] The aforementioned impact factors corresponding to the Complete Received Index are used to de-unitize the Complete Received Index, representing the numerical value of the unit value of the Complete Received Index on the operational anomaly factor; the aforementioned impact factors corresponding to the Reserve Memory Utilization Rate represent the numerical value of the unit value of the Reserve Memory Utilization Rate on the operational anomaly factor; the aforementioned impact factors corresponding to the Maximum Memory Paging File Utilization Rate represent the numerical value of the unit value of the Maximum Memory Paging File Utilization Rate on the operational anomaly factor; the aforementioned impact factors corresponding to the CPU Peak Load represent the numerical value of the unit value of the CPU Peak Load on the operational anomaly factor; the pension database stores the correspondence between the Complete Received Index, Reserve Memory Utilization Rate, Maximum Memory Paging File Utilization Rate, and CPU Peak Load and their corresponding impact factors. For example, by inputting the Complete Received Index, Reserve Memory Utilization Rate, Maximum Memory Paging File Utilization Rate, and CPU Peak Load into the pension database, the database can match the impact factors corresponding to the Complete Received Index, Reserve Memory Utilization Rate, Maximum Memory Paging File Utilization Rate, and CPU Peak Load, all with values ​​ranging from 0 to 1.

[0049] It's important to explain that the Data Integrity Index (DII) is a crucial indicator for measuring the integrity of data received by an elderly care service platform. A high IDI directly reflects the stability and reliability of the server during data reception and processing. Generally, a high IDI means a lower probability of data loss or corruption during transmission and processing, indicating relatively good server performance. Conversely, a low IDI usually suggests problems with the server during data reception and processing, potentially caused by insufficient memory resources, which exacerbates server anomalies. Conversely, a low IDI in the first cycle of the elderly care service platform's server indicates relatively abundant unused memory resources. These extra memory resources can provide strong support for data reception and processing, ensuring data integrity. Data transmission and processing will not be affected by insufficient memory, thus helping to improve the data reception integrity index of elderly care services. Conversely, when the maximum utilization rate of the server's memory paging file is high, it usually indicates that the server's physical memory resources are close to or have reached saturation, and it has to rely on virtual memory (i.e., the memory paging file) to meet additional memory requirements. In this case, frequent read and write operations of the memory paging file will lead to a decrease in system performance because the access speed of virtual memory is much lower than that of physical memory. This performance degradation may affect the efficiency of data reception and processing, thus negatively impacting the data reception integrity index. At the same time, a high memory paging file utilization rate may also increase the server load, affecting parameters such as peak CPU load, exacerbating the server's operating pressure, and thus affecting the normal operation of the elderly care service platform.

[0050] Step 3: Process the data application performance parameters of the elderly care service platform, thereby making secondary adjustments to the configuration parameters of the server to which the elderly care service platform belongs.

[0051] In one specific embodiment, the present invention performs secondary adjustments to the configuration parameters of the server to which the elderly care service platform belongs by deeply analyzing and processing abnormal factors during operation. This aims to ensure that the service can strictly meet the integrity requirements during use, effectively avoiding information loss during data compression caused by improper configuration. Even if the data is inadvertently over-compressed during the data compression process, causing some data to appear missing, the present invention can recover it through intelligent data analysis. Thus, while ensuring data integrity, it also greatly improves the efficiency and security of data processing, providing solid technical support and guarantee for the elderly care service platform.

[0052] Specifically, the secondary adjustment of the configuration parameters of the server to which the elderly care service platform belongs involves the following process: comprehensively processing the data application efficiency parameters of the elderly care service platform to obtain the data application completeness index of each target user at the end of the first cycle; comparing the data application completeness index of each target user at the end of the first cycle with the data application completeness threshold; if the data application completeness index of each target user at the end of the first cycle is greater than or equal to the data application completeness threshold, then no secondary adjustment is made to the configuration parameters of the server to which the elderly care service platform belongs; the aforementioned data application completeness threshold represents the minimum value of the reasonable range of the data application completeness index, extracted from the elderly care data volume.

[0053] If any target user's data application completeness index is less than the data application completeness threshold at the end of the first period, then the data application completeness factors for each data type of the target user at the end of the first period are calculated. The average data application completeness factor for each priority data type is then determined through data analysis. Specifically, the analysis process is as follows: First, target users whose data application completeness index is less than the data application completeness threshold at the end of the first period are identified. For these users, the data application completeness factors for each data type of the target user at the end of the first period are obtained. Then, based on the priority of the data type, the data application completeness factors for the same priority are aggregated. Finally, the average data application completeness factor for each priority is calculated through mean analysis. The average data application completeness factor for the received data type; in an example embodiment, assuming that only target user 1, target user 2, and target user 3 have a data application completeness index less than the data application completeness threshold at the end of the first period, where the data application completeness factor of received data type 1 belonging to target user 1 at the end of the first period is D_1, the data application completeness factor of received data type 2 belonging to target user 1 at the end of the first period is D_2, the data application completeness factor of received data type 1 belonging to target user 2 at the end of the first period is D_3, and the data application completeness factor of received data type 1 belonging to target user 3 at the end of the first period is D_4, and data type 1 and data type 2 both belong to priority 2, then the average data application completeness factor of the received data type corresponding to priority 2 is... The average data for other priority data types can be obtained similarly using complete factorial data analysis.

[0054] Based on the average data application complete factor of the receiving data type corresponding to each priority, the compression reduction value of the receiving data type corresponding to each priority is matched from the pension database. The specific matching process is as follows: The pension database stores the compression reduction value corresponding to each average data application complete factor interval of the receiving data type corresponding to each priority. The average data application complete factor interval of the receiving data type corresponding to each priority stored in the pension database is then queried. The compression reduction value corresponding to the average data application complete factor interval of the receiving data type corresponding to each priority stored in the pension database is the compression reduction value of the receiving data type corresponding to each priority. The compression reduction value refers to the value of reducing the original compression threshold.

[0055] Based on the compression threshold and corresponding reduction value of the received data type corresponding to each priority, the compression threshold of the received data type corresponding to each priority in the configuration parameters of the server to which the elderly care service platform belongs is adjusted a second time. After the adjustment is completed, the elderly care service platform continues to receive elderly care service data in the next adjacent first cycle. Specifically, the compression threshold of the received data type corresponding to each priority is subtracted from the corresponding reduction value, and the result of the subtraction is re-marked as the compression threshold of the received data type corresponding to each priority, which is the second adjustment. In an example embodiment, the compression threshold of the received data type corresponding to priority 3 is 1080 MB, the compression threshold of the received data type corresponding to priority 2 is 840 MB, and the compression threshold of the received data type corresponding to priority 1 is 360 MB. The reduction value of the received data type corresponding to priority 3 is matched. The compression reduction value for the data type corresponding to priority 2 is 42 MB, and the compression reduction value for the data type corresponding to priority 1 is 36 MB. After the second adjustment, the compression threshold for the data type corresponding to priority 3 is 972 MB, the compression threshold for the data type corresponding to priority 2 is 798 MB, and the compression threshold for the data type corresponding to priority 1 is 324 MB. At the same time, the data corresponding to the compression reduction value is restored, that is, 108 MB of data is restored from the compressed data corresponding to priority 3, 42 MB of data is restored from the compressed data corresponding to priority 2, and 36 MB of data is restored from the compressed data corresponding to priority 1. The data is restored from the compressed data with the closest time. When performing the data recovery operation, the principle of closest time will be followed, and the data whose compression time is closest to the current time will be restored first to ensure the timeliness and integrity of the data.

[0056] Furthermore, the data of each target user at the end of the first period is analyzed using a completeness index, and the specific analysis process is as follows:

[0057] Based on the operational anomaly factors of the server belonging to the elderly care service platform in the first period, the data application integrity index loss value is matched from the elderly care database. The specific matching process is as follows: The elderly care database stores the data application integrity index loss value corresponding to each operational anomaly factor interval. The database stores the operational anomaly factor intervals belonging to the operational anomaly factors of the server belonging to the elderly care service platform in the first period. The data application integrity index loss value corresponding to the operational anomaly factor intervals stored in the elderly care database is the data application integrity index loss value matched in this embodiment. The data application integrity index loss value represents the part that needs to be subtracted from the data application integrity index due to server operational anomalies. This value reflects the negative impact of operational anomalies on data application integrity. That is, in the actual data application integrity index, I need to deduct this part of loss or damage caused by the anomaly.

[0058] The data application performance parameters of the elderly care service platform include the field fill rate of each data type belonging to each target user at the end of the first cycle, the data start time of each data type belonging to each target user, and the data growth rate of each data type belonging to each target user at the end of the first cycle. The field fill rate refers to the proportion of data fields actually filled by each data type belonging to each target user at the end of the first cycle, which can be extracted from the data logs of the elderly care service platform. The data start time refers to the earliest time point when the data of each data type belonging to each target user was recorded in the elderly care service platform, which can be extracted from the data logs of the elderly care service platform. The data growth rate refers to the proportion of data volume increase of each data type belonging to each target user at the end of the first cycle compared to the start time of the first cycle, which can be extracted from the data logs of the elderly care service platform.

[0059] The difference between the data start time of each target user's data type and the reference data start time of each target user's data type stored in the elderly care database is processed, and the processing result is marked as the data start time deviation duration of each target user's data type. The aforementioned reference data start time point represents the reference value of the data start time point; the aforementioned data start time deviation duration represents the interval duration between the data start time point and the reference data start time point.

[0060] The data application completeness index of elderly care service data at the end of the first period is obtained by integrating the data reception completeness index, field fill rate of each data type of each target user at the end of the first period, data start time deviation duration of each data type of each target user at the end of the first period, and data growth rate of each data type of each target user at the end of the first period. The influence of missing values ​​of the data application completeness index is eliminated, and the data application completeness index of each target user at the end of the first period is finally obtained. The data application completeness index of each target user at the end of the first period represents the quantitative data on the influence of the reception completeness index, field fill rate, data start time deviation duration, data growth rate, and missing values ​​of the data application completeness index on the data application completeness of each target user at the end of the first period. It is used to comprehensively quantify the data application completeness of each target user at the end of the first period.

[0061] The data of each target user at the end of the first period is applied using a complete index, the specific expression of which is:

[0062]

[0063] In the formula, SFR i Apply a completeness index to the data of the i-th target user at the end of the first period, where i is the target user's ID (i = {1, 2, 3, ..., g}), g is the total number of target users, TOL_CDC is the completeness index of elderly care service data in the first period, and fg is the influence weight corresponding to the preset completeness index in the elderly care database. iu Apply the integrity factor a to the data of the u-th received data type belonging to the i-th target user at the end of the first period. u Apply the influence weights corresponding to the complete factor to the u-th pre-defined data type in the elderly care database, TF iu DW represents the field fill rate of the u-th received data type belonging to the i-th target user at the end of the first period. iu DR is the data start time deviation duration for the u-th received data type belonging to the i-th target user. iu ΔDR is the data growth rate of the u-th received data type belonging to the i-th target user at the end of the first period. iuΔSFR is the reference data growth rate of the u-th receiving data type belonging to the i-th target user in the pension database at the end of the first period. ΔSFR is the data application integrity index loss value. u is the number of each receiving data type, u={1,2,3,...,d}, d is the total number of receiving data types. po1 is the influence weight corresponding to the pre-set field fill rate in the pension database. po2 is the influence weight corresponding to the pre-set data start time deviation duration in the pension database. po3 is the influence weight corresponding to the pre-set data growth rate in the pension database.

[0064] The above reference data growth rate represents a reference value for the data growth rate.

[0065] The aforementioned influence weights corresponding to the Completeness of Received Index are used to de-unitize the Completeness of Received Index, representing the numerical value of the unit value of the Completeness of Received Index on the application of the Completeness of Data Index; the aforementioned influence weights corresponding to the Completeness of Data Application Factor are used to de-unitize the Completeness of Data Application Factor, representing the numerical value of the unit value of the Completeness of Data Application Factor on the application of the Completeness of Data Index; the aforementioned influence weights corresponding to the Field Fill Rate are used to de-unitize the Field Fill Rate, representing the numerical value of the unit value of the Field Fill Rate on the application of the Completeness of Data Factor; the aforementioned influence weights corresponding to the Data Start Time Deviation Duration are used to de-unitize the Data Start Time Deviation Duration, representing the numerical value of the unit value of the Data Start Time Deviation Duration on the application of the Completeness of Data Factor. The numerical value of the degree; the influence weight corresponding to the above data growth rate, which represents the degree of influence of the unit value of the data growth rate on the data application completeness factor. The pension database stores the correspondence between the reception completeness index, the data application completeness factor, the field filling rate, the data start time deviation duration, and the data growth rate and their corresponding influence weights. For example, if the reception completeness index, the data application completeness factor, the field filling rate, the data start time deviation duration, and the data growth rate are input into the pension database, the pension database can match the influence weights corresponding to the reception completeness index, the data application completeness factor, the field filling rate, the data start time deviation duration, and the data growth rate, all of which have values ​​between 0 and 1.

[0066] It's important to explain that the Data Application Completeness Index is a core indicator for measuring the effectiveness of data application on an elderly care service platform. It is subtly influenced by a variety of factors. Specifically, the data reception completeness index within the first cycle lays a solid foundation for data application and serves as the starting point for calculating the Data Application Completeness Index. Data application completeness factors for each data type received by each target user are preset in the elderly care database based on the characteristics and usage scenarios of the data types. These factors directly reflect the relative importance and contribution of different data types in the application and influence the Data Application Completeness Index through weighted calculations. The field fill rate, as a direct reflection of data quality and completeness, has a direct positive impact on the Data Application Completeness Index: a higher fill rate generally results in a higher Data Application Completeness Index. Deviations in data start time can reduce the effectiveness of data application due to untimely data, and this timeliness deficiency will be reflected in the Data Application Completeness Index. The longer the deviation in data start time, the more it indicates excessive data compression or poor data collection integrity, further leading to a decrease in field fill rate. Comparing the data growth rate with the preset reference data growth rate in the elderly care database reveals whether the dynamic changes in data application meet expectations. If the actual growth rate is higher or lower than the reference value, it may indicate problems in data collection, generation, or compression storage. This difference directly affects the data application completeness index. Finally, data loss due to server malfunctions or other reasons will further exacerbate the decrease in field fill rate, the increase in the deviation in data start time, and the abnormality in data growth rate, thus significantly reducing the overall data application efficiency. In summary, the data application completeness index is the result of the intertwining and combined effects of these factors. Their specific relationships and mutual influences jointly determine the final value of the data application completeness index, thus comprehensively reflecting the efficiency and status of data application on the elderly care service platform.

[0067] Step 4: Process the elderly care service data, obtain the processing results, and provide feedback on the results.

[0068] Furthermore, the feedback process is as follows: Historical service reports and elderly care service data for each target user within the first cycle are obtained. Based on this data and the corresponding historical service reports, a service report for each target user is generated. The historical service reports are extracted from the data logs of the elderly care service platform. These historical service reports are written documents that provide an in-depth analysis and summary of each target user's situation based on existing data from the platform. These reports comprehensively analyze and summarize relevant data to fully demonstrate service usage, behavioral characteristics, and changes in needs. The elderly care service data for each target user within the first cycle can be extracted from the elderly care service data received by the platform. The generation of service reports for each target user specifically involves the elderly care service platform first obtaining the elderly care service data for each target user within the first cycle. This data includes, but is not limited to, service records and health records. In addition to monitoring health status and providing satisfaction feedback, the elderly care service platform also obtains historical service reports from each target user. These reports, generated based on historical data, reflect the past service status of the target users. The platform compares the elderly care service data for the first period with the data in the historical service reports to identify differences and changes. Using data analysis tools (such as Super Business Intelligence Analytics), the platform analyzes these changes to understand the development trend of user service status, potential needs, and possible problems. Based on the results of data comparison and analysis, the platform automatically updates the data content in the historical service reports. This includes adding new service records, updating user health status information, and adjusting satisfaction ratings. After the update is completed, the platform generates a new service report containing the user's latest service status, health status, needs, and potential problems. The platform then marks this report as a service report.

[0069] The system compares the historical service reports of each target user with their corresponding service reports to obtain the changed service data for each target user. This data is then combined with the service reports of each target user and labeled as the processing result of the elderly care service data for each target user. Feedback is then provided to each target user regarding the processing result of their elderly care service data. It should be explained that the changed service data for each target user refers to the data differences or changes identified by comparing the historical service reports with the service reports. This can be obtained through difference analysis algorithms. For example, categorical difference analysis can be used to compare the historical service reports and the category labels in the service reports to identify differences. For instance, cross-tabulations (contingency tables) can be used to show the correlation between two categorical variables, thereby identifying changes in service type or health status. The feedback of the processing result of the elderly care service data for each target user refers to sending the processing result of the elderly care service data for each target user in the form of a document to the corresponding service administrator of each target user. At the same time, the elderly care service platform can also visualize the processing result of the elderly care service data for each target user.

[0070] It should be explained that after receiving the data for the next adjacent first period, the service reports of each target user will continue to be marked as the historical service reports of each target user.

[0071] In one specific embodiment, the present invention provides a data processing method for an elderly care service platform, which realizes efficient processing of elderly care service data and optimized configuration of server resources. After initializing server configuration parameters, the platform can stably receive and provide real-time feedback on the integrity of elderly care service data, ensuring the accuracy and reliability of data input. Furthermore, the platform intelligently monitors and processes server operating parameters, initially adjusting the configuration to adapt to real-time load, effectively improving system response speed and stability. By continuously monitoring data application performance parameters and making secondary adjustments, the platform can dynamically optimize resource configuration and maximize the utilization of server performance. This not only significantly improves the processing efficiency of elderly care service data but also ensures the accuracy and timeliness of data processing, providing the elderly with a more personalized and efficient elderly care service experience. At the same time, intelligent resource configuration adjustment enables the platform to maintain efficient and stable operation when facing service demands of different scales, providing solid technical support for the comprehensive upgrading of elderly care services.

[0072] A second aspect of the present invention provides an elderly care service platform that applies the data processing method described above, comprising: a server and an elderly care service platform; the elderly care service platform is used to visualize elderly care service data; the server is used to process the elderly care service data.

[0073] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A data processing method for an elderly care service platform, characterized in that, include: Step 1: Initialize the configuration parameters of the server to which the elderly care service platform belongs. After initialization, the elderly care service platform receives elderly care service data and provides integrity feedback on the elderly care service data. Step 2: Obtain and process the operating parameters of the server to which the elderly care service platform belongs, thereby adjusting the configuration parameters of the server to which the elderly care service platform belongs, and monitoring the data application performance parameters of the elderly care service platform; Step 3: Process the data application performance parameters of the elderly care service platform, thereby making secondary adjustments to the configuration parameters of the server to which the elderly care service platform belongs; Step 4: Process the elderly care service data, obtain the processing results, and provide feedback on the results; The secondary adjustment of the configuration parameters of the server to which the elderly care service platform belongs is specifically as follows: By comprehensively processing the data application efficiency parameters of the elderly care service platform, a data application completeness index for each target user at the end of the first cycle is obtained. Compare the data application completeness index of each target user at the end of the first cycle with the data application completeness threshold; If the data application completeness index of each target user at the end of the first cycle is greater than or equal to the data application completeness threshold, then the configuration parameters of the server to which the elderly care service platform belongs will not be adjusted again. If a target user's data application completeness index is less than the data application completeness threshold at the end of the first period, then the target users corresponding to the data application completeness index that is less than the data application completeness threshold are counted, the data application completeness factor of each type of received data belonging to each target user at the end of the first period is obtained, and the average data application completeness factor of each priority type of received data is analyzed. Based on the average data of the data types received for each priority, apply the integrity factor to match the compression reduction value of the data types received for each priority from the pension database. Based on the compression threshold of the received data type corresponding to each priority and the corresponding compression reduction value, the compression threshold of the received data type corresponding to each priority in the configuration parameters of the server to which the elderly care service platform belongs is adjusted a second time. After the adjustment is completed, the elderly care service platform continues to receive elderly care service data in the next adjacent first cycle. The data of each target user at the end of the first period is analyzed using a complete index, and the specific analysis process is as follows: Based on the abnormal operation factors of the server to which the elderly care service platform belongs in the first cycle, the data application integrity index loss value is matched from the elderly care database. The data application performance parameters of the elderly care service platform include the field fill rate of each data type of each target user at the end of the first cycle, the data start time of each data type of each target user, and the data growth rate of each data type of each target user at the end of the first cycle. The difference between the data start time of each target user and the reference data start time of each target user and the data type stored in the elderly care database is processed, and the processing result is marked as the data start time deviation duration of each target user and the data type. The data application completeness index of elderly care service data at the end of the first period is obtained by integrating the data reception completeness index, field fill rate of each data type of each target user at the end of the first period, data start time deviation duration of each data type of each target user at the end of the first period, and data growth rate of each data type of each target user at the end of the first period. The influence of missing values ​​of the data application completeness index is eliminated, and the data application completeness index of each target user at the end of the first period is finally obtained. The data application completeness index of each target user at the end of the first period represents the quantitative data on the influence of the reception completeness index, field fill rate, data start time deviation duration, data growth rate, and missing values ​​of the data application completeness index on the data application completeness of each target user at the end of the first period. It is used to comprehensively quantify the data application completeness of each target user at the end of the first period.

2. The data processing method for an elderly care service platform according to claim 1, characterized in that: The initialization process for the configuration parameters of the server to which the elderly care service platform belongs is as follows: The configuration parameters of the server to which the elderly care service platform belongs include the data type of the received data corresponding to each priority and the compression threshold of the data type of the received data corresponding to each priority. Obtain the data types of the elderly care service platform and initialize the corresponding priorities of each data type in sequence to obtain the data types of each priority. Obtain the reference load set of the server to which the elderly care service platform belongs, and match the compression threshold of the received data type corresponding to each priority from the elderly care database; Initialize the data types received for each priority level and the compression threshold for each priority level's data types received, thereby completing the initialization of the configuration parameters for the server to which the elderly care service platform belongs.

3. The data processing method for an elderly care service platform according to claim 1, characterized in that: The process for providing completeness feedback on elderly care service data is as follows: Acquire and analyze the reception status parameters of elderly care service data in the first period to obtain the reception integrity index of elderly care service data in the first period; If the reception integrity index of elderly care service data in the first period is greater than or equal to the reception integrity threshold, then the reception integrity of elderly care service data in the first period is successfully reported. If the reception integrity index of elderly care service data in the first cycle is less than the reception integrity threshold, the configuration parameters of the server to which the elderly care service platform belongs will be adjusted once according to the abnormal operation factor of the server in the first cycle. After the adjustment is completed, the elderly care service data in the first cycle will be re-uploaded from the backup data, and a successful reception integrity feedback will be sent.

4. The data processing method for an elderly care service platform according to claim 3, characterized in that: The specific analysis process for the completeness index of elderly care service data reception in the first period is as follows: The system acquires and analyzes the effective load value of received data packets, the fluctuation value of the sequence number of received data packets, and the throughput-received data packet effective load value factor of the server belonging to the elderly care service platform at each moment within the first period. Specifically, it performs weighted aggregation and data amplification processing on the deviation of the effective load value of received data packets from the corresponding defined effective load value, the ratio of the fluctuation value of the sequence number of received data packets to the defined fluctuation value of the sequence number of received data packets, and the deviation of the throughput-received data packet effective load value factor from the reference throughput-received data packet effective load value factor, to obtain the reception integrity factor of elderly care service data at each moment within the first period. The reception integrity factor of elderly care service data at each moment within the first period represents the quantitative data on the influence of the effective load value of received data packets, the fluctuation value of the sequence number of received data packets, and the throughput-received data packet effective load value factor on the reception integrity of elderly care service data at each moment within the first period, and is used to comprehensively quantify the reception integrity of elderly care service data at each moment within the first period. The data reception completeness index for elderly care services in the first period is obtained by integrating the data reception completeness factors at each time point in the first period.

5. The data processing method for an elderly care service platform according to claim 1, characterized in that: The configuration parameters of the server to which the elderly care service platform belongs are adjusted once. The specific adjustment process is as follows: By processing the operating parameters of the server to which the elderly care service platform belongs, the abnormal operating factors of the server to which the elderly care service platform belongs in the first period are obtained. Based on the abnormal operating factors of the server to which the elderly care service platform belongs in the first period, the compression amount extension value of the received data type corresponding to each priority and the compression requirement of the server to which the elderly care service platform belongs are matched from the elderly care database. Based on the compression threshold of the received data type corresponding to each priority and the corresponding compression extension value, the compression threshold of the received data type corresponding to each priority in the configuration parameters of the server to which the elderly care service platform belongs is adjusted once.

6. The data processing method for an elderly care service platform according to claim 5, characterized in that: The specific handling process for the abnormal operation factors of the server to which the elderly care service platform belongs during the first cycle is as follows: The operating parameters of the server to which the elderly care service platform belongs include the average memory utilization rate of the server to which the elderly care service platform belongs in the first period, the maximum memory paging file utilization rate of the server to which the elderly care service platform belongs in the first period, and the peak CPU load of the server to which the elderly care service platform belongs in the first period. Obtain the defined memory utilization rate of the server to which the elderly care service platform belongs, and perform difference processing with the average memory utilization rate of the server to which the elderly care service platform belongs in the first period. The processing result is marked as the reserve memory utilization rate of the server to which the elderly care service platform belongs in the first period. The reception integrity index of the elderly care service data in the first period is processed by multiplication inverse. Simultaneously, corresponding influence factors are assigned to the reserved memory utilization rate, maximum memory paging file utilization rate, and peak CPU load of the elderly care service platform server in the first period. Based on the influence factors corresponding to the reserved memory utilization rate, maximum memory paging file utilization rate, and peak CPU load, these parameters are amplified to derive the operational anomaly factor of the elderly care service platform server in the first period. This operational anomaly factor represents the quantitative data on the impact of the reception integrity index, reserved memory utilization rate, maximum memory paging file utilization rate, and peak CPU load on the operational anomalies of the elderly care service platform server in the first period, and is used to comprehensively quantify the degree of operational anomalies of the elderly care service platform server in the first period.

7. The data processing method for an elderly care service platform according to claim 1, characterized in that: The specific feedback process is as follows: Obtain historical service reports and elderly care service data for each target user in the first cycle. Based on the elderly care service data and corresponding historical service reports for each target user in the first cycle, derive the service report for each target user. The historical service reports of each target user are compared with the corresponding service reports to obtain the changed service data of each target user. The data is then combined with the service reports of each target user and marked as the processing result of the elderly care service data of each target user. Feedback is then given to the processing result of the elderly care service data of each target user.

8. A senior care service platform applying the data processing method as described in any one of claims 1-7, characterized in that: include: Servers and elderly care service platforms; The elderly care service platform is used to visualize elderly care service data; The server is used to process elderly care service data.

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