A medical and nursing service system cross-region deployment method and system

Abstract

This invention discloses a method and system for cross-regional deployment of an integrated medical and elderly care service system. It acquires functional module information and regional medical environment configuration information; performs differentiated granular decomposition through business boundary identification and reusability assessment to form a standardized service component set; identifies differences between components and the regional environment; classifies the impact of historical deployment failures; and generates a component adaptation strategy based on the inheritance and overriding mechanism of the upper-level regional configuration template. It extracts regionally sensitive parameters and performs incremental difference encoding and version chain encapsulation to form a traceable and rollbackable regional configuration package; parses component dependencies and performs deep availability verification through cascading health checks; orchestrates a phased cloud deployment queue according to resource priority; detects the target environment's readiness status; divides the system into batches based on business similarity; and triggers deployment instructions batch by batch to complete cross-regional integrated deployment, achieving large-scale promotion under controllable risks.

Description

Technical Field

[0001] This invention relates to the field of smart healthcare technology, and in particular to a method and system for cross-regional deployment of an integrated medical and elderly care service system. Background Technology

[0002] As a crucial public welfare project to address population aging, the integrated medical and elderly care service system needs to be promoted and covered across multiple administrative levels, including provinces, cities, and counties. However, different regions exhibit variations in infrastructure conditions such as network bandwidth, computing resources, and storage capacity. Furthermore, policy constraints, including medical insurance policy interfaces, data security regulations, and reporting requirements from the National Health Commission, also differ from place to place. These regional differences pose challenges to the large-scale deployment of the service system.

[0003] Existing cross-regional integrated deployment solutions have the following shortcomings: First, the modular division of service units lacks consideration for the possibility of reuse between regions, and general functions and regionally customized functions are mixed together, resulting in a large amount of repetitive development in each region, long deployment cycles, and high maintenance costs; Second, the adaptation process of the operating environment relies on manual experience judgment, lacks systematic analysis of historical deployment problems, and the configuration adjustment is not targeted enough, with similar problems recurring in different regions; Third, the handling of dependencies when services are started is relatively crude, failing to fully verify the deep availability of upstream services, resulting in situations where the service appears to start successfully but actually fails to work properly, and the risk control measures during the gradual promotion process are also relatively weak. Summary of the Invention

[0004] This invention discloses a method and system for cross-regional deployment of integrated medical and elderly care services. It aims to solve the problems of module reuse difficulties and inefficient environmental adaptation in the multi-regional promotion of service systems. The method classifies and differentiates functional modules to form a standardized service component set, assesses the impact of environmental differences by associating historical deployment records, and generates adaptation strategies through configuration inheritance. It incrementally differentiates regional parameters by encoding and encapsulating version chains to generate rollbackable regional configuration packages. Then, it orchestrates deployment sequences based on cascading dependency deep verification and divides them into batches according to business similarity, ultimately realizing the integrated deployment of integrated medical and elderly care services in differentiated regional environments.

[0005] The first aspect of this invention proposes a method for cross-regional deployment of an integrated medical and elderly care service system, comprising the following steps: Obtain functional module information and regional medical environment configuration information of the integrated medical and elderly care service system, and perform microservice component decomposition to form a standardized service component set based on the functional module information; The standardized service component set and the regional medical environment configuration information are used to locate medical environment difference items, and the difference impact of the medical environment difference items is evaluated to generate a difference impact level. Based on the difference impact level, the configuration item differentiation mapping is implemented to generate a component adaptation strategy. The component adaptation strategy execution parameter template is extracted to form a configuration parameter template library. Based on the configuration parameter template library, a set of region-sensitive parameters is identified. Based on the set of region-sensitive parameters, a parameter version snapshot is encapsulated to generate a region configuration package. The region configuration package is subjected to dependency parsing to extract the medical service call chain. The medical service call chain is then subjected to dependency availability pre-verification and orchestration to obtain the deployment sequence identifier. Based on the deployment sequence identifier, the chain is arranged according to resource priority to form a cloud-based phased deployment queue. The target environment readiness detection is performed on the cloud-based phased deployment queue to obtain the environment readiness status. Based on the environment readiness status, the deployment scope is defined in batches to generate batch deployment identifiers. Based on the batch deployment identifiers, the standardized service component set is triggered to generate cloud-based batch deployment instructions, thereby completing the cross-regional integrated deployment of the cloud platform for the integrated medical and elderly care service system.

[0006] A second aspect of this invention proposes a cross-regional deployment system for integrated medical and elderly care services, comprising: The component splitting module is used to obtain functional module information and regional medical environment configuration information of the integrated medical and elderly care service system, and to perform microservice component splitting to form a standardized service component set based on the functional module information. The difference adaptation module is used to locate medical environment difference items using the standardized service component set and the regional medical environment configuration information, evaluate the difference impact of the medical environment difference items to generate a difference impact level, and implement a configuration item difference mapping to generate a component adaptation strategy based on the difference impact level. The configuration encapsulation module is used to extract the component adaptation strategy execution parameter template to form a configuration parameter template library, identify the regional sensitive parameter set based on the configuration parameter template library, and encapsulate the parameter version snapshot based on the regional sensitive parameter set to generate a regional configuration package. The dependency orchestration module is used to perform dependency parsing on the regional configuration package to extract the medical service call chain, perform dependency availability pre-verification orchestration on the medical service call chain to obtain the deployment sequence identifier, and arrange the deployment sequence identifier according to resource priority to form a cloud-based phased deployment queue. The batch deployment module is used to perform target environment readiness detection on the cloud-based phased deployment queue to obtain the environment readiness status, define the batch deployment scope based on the environment readiness status to generate batch deployment identifiers, and trigger the standardized service component set to generate cloud-based batch deployment instructions based on the batch deployment identifiers, thereby completing the cross-regional integrated deployment of the cloud platform for the integrated medical and elderly care service system.

[0007] The beneficial effects of this invention are reflected in the following points: First, it performs a graded assessment based on the cross-regional reuse potential of business domains. High-reusability business domains use coarse-grained encapsulation, allowing the same component to be directly deployed to multiple regions. Low-reusability business domains use fine-grained splitting, so that regional customization only requires replacing specific components without affecting the overall architecture. Medical environment differences are associated with historical deployment failure records, and failure association weights are calculated. The weight values ​​quantify the historical probability of deployment failure caused by various differences. High-weight difference items are preferentially retrieved from the parent region and inherited from configuration templates that have passed medical production verification, avoiding the trial-and-error process of configuring from scratch. Second, after comparing regional sensitive parameters with the baseline version, only the changed parameters are coded with differences. The encoding records the old and new values ​​to support bidirectional restoration. Parent-child version associations are established between parameter packages to form a traceable version chain. Each version embeds a rollback script pointing to the parent version. When a problem occurs in a new version, it can be rolled back step by step along the version chain to a stable state. Regional tag injection makes the configuration package carry a clear ownership identifier. The cloud distribution system routes the configuration package to the corresponding region based on tag matching, eliminating the incorrect application of the configuration package. Finally, dependency availability detection not only calls the component's own health interface, but also recursively checks the health status of its cascading dependencies. The combined results of both determine deep availability, identifying components that appear healthy but are actually unable to work due to abnormal underlying dependencies. Ready regions are sorted according to their business similarity to benchmark regions. Regions with high similarity are prioritized for the first batch of deployments, as their deployment experience has greater reference value for subsequent batches. Status checkpoints are set between batches, and subsequent batches can only proceed after the previous batch has been confirmed as successful. Deployment anomalies can be detected and handled within a small scope. Attached Figure Description

[0008] The accompanying drawings illustrate specific examples of the technical solutions described in this invention and, together with the detailed embodiments, form part of the specification, serving to explain the technical solutions, principles, and effects of this invention.

[0009] Figure 1 This is a flowchart illustrating a cross-regional deployment method for an integrated medical and elderly care service system according to the present invention.

[0010] Figure 2 This is a structural block diagram of a cross-regional deployment system for an integrated medical and elderly care service system according to the present invention. Detailed Implementation

[0011] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0012] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0013] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0014] The technical solutions of the embodiments of this application will be described below.

[0015] like Figure 1 As shown, this embodiment of the invention provides a method for cross-regional deployment of an integrated medical and elderly care service system, including the following steps S110-S150: Step S110: Obtain the functional module information and regional medical environment configuration information of the integrated medical and elderly care service system, and perform microservice component decomposition to form a standardized service component set based on the functional module information.

[0016] Specifically, this involves acquiring information on the functional modules and regional medical environment configurations of the integrated medical and elderly care service system. This system encompasses multiple business areas, including health management, medical treatment, rehabilitation nursing, and daily living assistance. Each business area corresponds to different functional modules, and the overall architecture of the integrated medical and elderly care service system determines the organization and boundary division of these modules. The names, responsibilities, interface definitions, and dependencies of each functional module are extracted from the system's architecture design documents and business requirement specifications. This information is then structured to form functional module information. The functional module information is organized in the form of a module list, with each module entry including a module identifier, its business area, a description of its core functions, and a list of external service interfaces. For example, the health record management module is responsible for storing and querying the basic health data of service recipients, while the medication reminder module is responsible for generating medication plans based on prescription information and pushing reminder notifications. The responsibilities of each module are clearly defined. Simultaneously, the infrastructure status of each deployment target area is collected, including network bandwidth, computing resource quotas, storage capacity, and medical security compliance requirements. These regional characteristic parameters are then compiled to form regional medical environment configuration information. Regional healthcare environment configuration information is indexed and organized by region identifier. Network conditions in remote areas may be limited, while computing resources in developed areas are relatively abundant. There are also differences in medical insurance policy interfaces and data security regulations in different regions. The security compliance requirements in regional healthcare environment configuration information cover data residency policies, privacy protection standards, and medical industry regulatory regulations.

[0017] In some embodiments, the step of splitting microservice components based on the functional module information to form a standardized service component set includes: identifying business boundaries of the functional module information to obtain business domain division results; performing cross-regional reusability assessment based on the business domain division results to generate a reusability grading identifier; adapting the splitting granularity of the business domain division results according to the reusability grading identifier to form a component splitting scheme; and standardizing and encapsulating the functional module information according to the component splitting scheme to form a standardized service component set.

[0018] Business boundary identification is performed on functional module information to obtain business domain division results. The core functional descriptions and external service interface lists of each module in the functional module information are analyzed to identify the semantic relationship between different modules. The division of business boundaries follows the domain-driven design principle, grouping functional modules with similar business semantics and shared data models into the same business domain, and classifying functional modules with independent business semantics and isolated data models into different business domains. Modules such as health record management, physical examination report parsing, and health risk assessment in the functional module information all revolve around the health status of service recipients in terms of business semantics, and can be classified into the health management business domain; modules such as appointment registration, online consultation, and electronic prescriptions revolve around medical service processes, and can be classified into the medical service business domain. The interface call relationships between modules in the functional module information are analyzed; modules with frequent calls and large data exchange volumes tend to be classified into the same business domain to reduce cross-domain communication overhead. The identified business domains and their contained functional modules are organized into a business domain division result. The business domain division result is presented in the form of a domain list. Each business domain entry contains a domain identifier, a domain name, and a list of contained module identifiers. Different business domains interact with each other through explicitly defined interfaces.

[0019] Based on the business domain segmentation results, cross-regional reusability assessments are conducted to generate reusability grading labels. The functional characteristics of each business domain in the business domain segmentation results are analyzed to assess its reusability potential across different deployment regions. For each business domain in the business domain segmentation results, a reusability metric assessment is performed from three dimensions: functional universality, data model consistency, and external dependency region relevance. The functional universality score G is obtained by statistically analyzing the ratio of the number of region-independent interfaces within the business domain to the total number of interfaces; a higher ratio results in a higher G value. The data model consistency score D is obtained by comparing the differences in data structure of the business domain in each target region; D takes the highest value when the data structure is completely consistent, and decreases accordingly when there are differences in fields or formats. The external dependency region relevance score E is obtained by identifying the type of dependency of the business domain on external systems; E is higher when depending on general standard interfaces, and lower when depending on region-specific policy interfaces. The weighted summation of the assessment results from the three dimensions yields a comprehensive reusability score, calculated using the formula R = W1 × G + W2 × D + W3 × E, where W1, W2, and W3 are the corresponding weights. Based on the overall reuse score, each business domain in the business domain division results is assigned a reuse level identifier. The reuse level identifier is divided into three levels: high reuse, medium reuse, and low reuse. Business domains with high reuse level do not require code modification when deployed in each region, while business domains with low reuse level require adaptation development for the policy interface of the target region. The reuse level identifier corresponds one-to-one with each business domain in the business domain division results.

[0020] Based on the reusability classification, the business domain division results are split into component decomposition schemes with adapted granularity. For business domains with a high reusability classification, a coarser decomposition granularity is adopted, encapsulating the entire business domain into one or a few microservice components. Modules within these coarse-grained components communicate via local calls, avoiding unnecessary network overhead. For example, the three functional modules in the health management business domain division—health record management, physical examination report parsing, and health risk assessment—can be encapsulated into a single health management microservice component. For business domains with a medium reusability classification, a moderate decomposition granularity is adopted, separating the common functional modules and region-specific functional modules within the domain. The common parts form reusable components, while the region-specific parts form components requiring adaptation. These two types of components interface with each other through standard interfaces. For business domains with a low reuse level, a finer-grained decomposition is adopted. Each functional module within the domain is independently split into a single microservice component. This fine-grained decomposition ensures that only specific components need to be replaced in each region without affecting the operation of other components. For example, the medical insurance settlement business domain's modules for medical insurance policy query, cost calculation, and settlement reconciliation are encapsulated as independent components for easy replacement by each region as needed. All business domains in the decomposition are traversed, and the corresponding decomposition granularity strategy is determined based on their reuse level. This strategy is then applied to each domain to generate specific component boundary definitions. The decomposition results for all business domains are summarized and organized to form a complete component decomposition scheme. In this scheme, the boundary division of each component matches its reuse level; high-reuse components use coarse-grained boundaries, while low-reuse components use fine-grained boundaries.

[0021] Following the component decomposition scheme, functional module information is standardized and encapsulated to form a standardized service component set. Based on the component boundaries defined in the scheme, the functional module code, configuration files, and resource dependency lists for each component are extracted from the functional module information. The extracted components undergo interface standardization, uniformly adopting RESTful or gRPC protocols for interface calls and JSON or Protocol Buffers formats for data exchange structures. A standardized deployment description file is generated for each component, containing resource requirement declarations, environment variable configuration templates, health check endpoints, and log output specifications. For example, the health record component's deployment description file declares a minimum resource requirement of 2 CPU cores and 4GB of memory. The environment variable configuration template reserves two parameterized configuration items: a database connection string and a cache service address. Deployment in different regions can be adapted to the local environment through environment variable injection. Components marked as high reusability in the component decomposition scheme have parameterized deployment description files, adapting to different regional operating environments through environment variable injection. Components marked as low reusability have reserved regional adaptation extension points in their deployment description files, allowing for the injection of customized implementation logic in each region. The code packages, configuration templates, and deployment description files of each component are uniformly packaged into a container image. Version identifiers and digital signatures are embedded during the packaging process. Version identifiers are used for component version management and upgrade / rollback, while digital signatures are used to verify image integrity and prevent tampering. All packaged components are incorporated into a unified component repository, forming a standardized service component set. Each component in the standardized service component set follows consistent packaging specifications and interface contracts. All modules in the functional module information, after boundary delineation and standardized packaging according to the component splitting scheme, have been included in the standardized service component set.

[0022] Step S120: Locate medical environment differences using standardized service component sets and regional medical environment configuration information; assess the impact of differences on medical environment differences to generate impact levels; and implement configuration item differentiation mapping to generate component adaptation strategies based on impact levels.

[0023] Specifically, standardized service component sets and regional healthcare environment configuration information are used to identify discrepancies in the healthcare environment. The deployment description files of each component in the standardized service component set declare the environmental requirements for component operation. Resource requirement declarations and environment dependency declarations required for component operation are extracted from the standardized service component set. Resource requirement declarations include minimum CPU core count, memory capacity, and storage space; environment dependency declarations include operating system version, middleware type, and network protocol support. The environment dependency declarations of each component in the standardized service component set are compared item by item with the network bandwidth, computing resource quotas, storage capacity, and security compliance requirements recorded in the regional healthcare environment configuration information. The regional healthcare environment configuration information provides the infrastructure status and policy constraints of each deployment target region. The comparison process between the standardized service component set and the regional healthcare environment configuration information identifies configuration items with discrepancies. For example, the online consultation component in the standardized service component set requires support for the WebSocket long-connection protocol. If the regional healthcare environment configuration information shows that the network devices in a certain region do not support this protocol, a protocol type difference occurs. Similarly, the storage capacity required by the health record storage component exceeds the quota limit for a certain region in the regional healthcare environment configuration information, resulting in a resource capacity difference. All discrepancies identified during the comparison process are summarized, with each discrepancy recorded including the component to which it belongs, the discrepancy configuration name, the component's required value, and the actual value for the region. The summarized results are organized into a list of healthcare environment discrepancies, grouped by region. All healthcare environment discrepancies within the same region are listed together, and the quantity and type distribution of these discrepancies reflect the degree of difference between each region and the standard deployment requirements.

[0024] In some embodiments, the step of assessing the impact of the differences in the medical environment to generate a difference impact level includes: extracting difference type labels from the differences in the medical environment; obtaining failure association weights by associating historical deployment failure records with the difference type labels; classifying the failure association weights by threshold levels to generate an impact level result; and determining the difference impact level based on the impact level result.

[0025] Extract difference type tags from medical environment difference items. Based on the difference configuration names and differences of each medical environment difference item in the list, the medical environment difference items are categorized into a predefined type system. The type system includes main categories such as resource capacity, protocol compatibility, version matching, medical security policy, and network connectivity, with each category further divided into subtypes. For example, insufficient CPU cores are categorized under the computing resource subtype of the resource capacity category; inconsistent database versions are categorized under the middleware version subtype of the version matching category; and differences in SSL certificate configuration are categorized under the encryption configuration subtype of the medical security policy category. Assign a corresponding difference type tag to each medical environment difference item in the list. The difference type tag uses a hierarchical encoding format of "main type - subtype". The WebSocket subtype of the protocol compatibility category is encoded as "PROTO-WS", and the database version subtype of the version matching category is encoded as "VER-DB". This hierarchical encoding design allows for both coarse-grained statistical analysis of medical environment difference items by main type and fine-grained attribution and investigation by subtype. Medical environment differences under the same difference type label have similar impact characteristics and processing methods. The assignment of difference type labels allows the impact assessment of medical environment differences to draw on historical experience data of similar differences, and newly identified medical environment differences do not need to be assessed for their potential risks from scratch.

[0026] Failure association weights are obtained by associating historical deployment failure records with difference type tags. The historical deployment record database is queried to retrieve past failure cases and their cause analyses during cross-regional integration deployments. The historical deployment records cover deployment attempts and failure reason archives for each region. Historical failure cases are categorized and statistically analyzed according to the configuration difference type tags that caused the failures, obtaining the historical failure count and failure rate corresponding to each difference type tag. Network connectivity difference type tags resulted in a high number of failures in historical deployments. In a certain province, deployment failures due to network firewall configuration issues accounted for a large proportion of the total deployment failures in remote mountainous areas. Resource capacity difference type tags resulted in relatively few complete failures, and in some cases only affected service response performance rather than causing the service to fail to start. The historical failure count Fi and total occurrence count Ti are retrieved according to the difference type tag, and the failure association weight W = Fi / Ti × (1 + ln(1 + Fi)), where ln is the natural logarithm. The logarithmic term in the formula gives additional weight to types with a high number of failures, reflecting the risk accumulation effect of high-frequency failure types. The failure association weights are normalized and mapped to the range of 0 to 1. The closer the value is to 1, the more likely the difference type label has historically been to cause deployment failure. The difference type labels of each medical environment difference item are associated and stored with their corresponding failure association weights, forming a mapping table from medical environment difference items to failure association weights. The failure association weights quantify the potential risk level of various medical environment difference items.

[0027] The failure association weights are categorized by threshold to generate impact grading results. Thresholds are set for the failure association weights, dividing them into different impact intervals. The upper threshold is set at 0.7, and the lower threshold at 0.3, with the threshold settings referencing the statistical distribution characteristics of historical deployment data. Differences with failure association weights higher than the upper threshold are classified into the high-impact interval; these differences have a high probability of causing deployment failure or service startup anomalies in historical deployments. Differences with failure association weights lower than the lower threshold are classified into the low-impact interval; these differences usually do not cause deployment failure, and even if they do, the impact is mostly a slight performance degradation. Differences with failure association weights between the upper and lower thresholds are classified into the medium-impact interval, requiring targeted handling based on specific circumstances and component characteristics. The threshold settings can be adjusted according to business tolerance. For core medical service components with high deployment success rate requirements, a stricter threshold classification can be used, placing more differences into the high-impact interval. The failure association weights of each difference are iterated, and the impact level of the difference is determined based on the interval its value falls into. Protocol compatibility differences with a failure association weight of 0.82 were classified into the high-impact range, while resource capacity differences with a failure association weight of 0.15 were classified into the low-impact range. The impact level of each difference was recorded to form an impact grading result. This result uses the difference identifier as an index to record the impact level corresponding to each difference, and covers all differences in the medical environment difference list.

[0028] The impact level of the difference is determined based on the impact grading results. Each difference item in the impact grading results has been labeled with a specific impact level. These impact levels are then converted into standardized level identifiers. A level corresponds to a high impact level, B level to a medium impact level, and C level to a low impact level. The impact grading results show that the same component may have multiple difference items in the same region. For example, in a certain county-level region, the health record component exhibits both database version and storage capacity differences. The impact grading results indicate that these differences may belong to different impact levels: database version difference is grade A, while storage capacity difference is grade C. The impact levels of multiple difference items for the same component in the same region are summarized, and the highest level is used as the overall impact level for that component in that region. The overall impact level for the aforementioned health record component is determined to be grade A. The determined impact levels are then written back to the list of medical environment difference items, with each difference item and each component-region combination labeled with its corresponding impact level.

[0029] In some embodiments, the step of generating a component adaptation strategy by implementing configuration item differentiation mapping based on the difference impact level includes: filtering high-impact configuration items from the difference impact level; querying the parent configuration template of the high-impact configuration items to obtain an inheritable configuration set; generating an inheritance coverage mapping table by performing sub-region coverage configuration based on the inheritable configuration set; and generating a component adaptation strategy based on the inheritance coverage mapping table.

[0030] High-impact configuration items are selected from the difference impact levels. Based on the list of medical environment difference items with marked difference impact levels, difference items with a level A are selected. Difference items with a level A impact level correspond to critical constraints for the normal operation of components. If these difference items with a level A impact level are not adapted, the components will fail to deploy successfully in the target area or fail immediately after startup, directly affecting the availability of medical and elderly care services. The selected level A difference items are compiled into a high-impact configuration item list. Each entry in the high-impact configuration item list includes the component to which the difference belongs, the difference configuration name, the component's required value, and the actual value for the region. This information provides a complete context for developing adaptation solutions. The high-impact configuration item list is grouped by region, and specific adaptation solutions are developed for each region. Centralized processing of high-impact configuration items in the same region can improve the efficiency of the adaptation work. The high-impact configuration item list of a certain county-level region includes two items: database connection protocol incompatibility and missing medical insurance interface address. These two high-impact configuration items must be adapted before deployment to ensure that the components start normally and provide services externally.

[0031] For example, the step of querying the parent configuration template of the high-impact configuration item to obtain an inheritable configuration set includes: extracting the configuration item hierarchy path from the high-impact configuration item; locating the parent region based on the configuration item hierarchy path to obtain the parent region identifier; retrieving the parent region identifier from existing configuration templates to obtain the parent configuration record; and performing validity verification based on the parent configuration record to form an inheritable configuration set.

[0032] Extract the configuration item hierarchy path from high-impact configuration items. Based on the regional affiliation information of each high-impact configuration item in the list, the regional affiliation information uses a regional hierarchy code to represent the region's position in the overall architecture. The regional hierarchy code follows the format "Provincial Code - Municipal Code - County Code". The regional hierarchy code for a provincial region only contains the provincial code, while the regional hierarchy code for a county region contains the complete three-level code, such as "GD-SZ-NS" representing Nanshan District, Shenzhen City, Guangdong Province. Read the regional hierarchy code of the region to which each high-impact configuration item belongs from the high-impact configuration items, and construct the complete configuration item hierarchy path by combining it with the component identifier and configuration item name. The format of the configuration item hierarchy path is "Regional Hierarchical Code / Component Identifier / Configuration Item Name", and this configuration item hierarchy path uniquely identifies the position of the configuration item in the hierarchical architecture. The database connection configuration for a county-level region within a high-impact configuration item has a hierarchical path that is a combination of the county's third-level region code, component identifier, and configuration item name. The complete hierarchical path is in the form of "GD-SZ-NS / health-archive / db-connection". The hierarchical paths of each high-impact configuration item in the high-impact configuration item list are extracted to form a path list. This path list maintains a one-to-one correspondence with the high-impact configuration item list, allowing the original configuration item record to be located by using the hierarchical path.

[0033] The parent region identifier is obtained by locating the parent region based on the configuration item hierarchical path. The regional hierarchical code portion of the configuration item hierarchical path is parsed to identify the current region's level in the hierarchical architecture. The level determination is based on the number of code segments contained in the regional hierarchical code. For a county-level region's configuration item hierarchical path, its parent region is a city-level region. The first two code segments are extracted from the regional hierarchical code as the parent region identifier; for example, the parent region identifier for the "GD-SZ-NS" configuration item hierarchical path is "GD-SZ," which represents Shenzhen. For a city-level region's configuration item hierarchical path, its parent region is a province-level region. The first code segment is extracted from the regional hierarchical code as the parent region identifier; for example, the parent region identifier for the "GD-SZ" configuration item hierarchical path is "GD," which represents Guangdong Province. Provincial-level region configuration item hierarchical paths are at the highest level and can be set to use a nationally unified configuration template as their parent reference. The national template's parent region identifier is "CN," and it includes standardized default configurations for each component. Based on the regional hierarchical encoding extraction rules for configuration item hierarchical paths, the parent region identifier corresponding to each configuration item is determined. The extraction rules are implemented through string truncation operations. The parent region identifier adopts the same format as the regional hierarchical encoding. Each configuration item hierarchical path is associated with its corresponding parent region identifier, and each high-impact configuration item is associated with the parent region identifier it should refer to.

[0034] The system retrieves existing configuration templates for the parent region identifier to obtain parent configuration records. It queries the configuration repository for existing configuration templates corresponding to the parent region identifier. The configuration repository is indexed by region identifier and component identifier, employing a two-level index structure to support rapid retrieval of configuration records for specific components in specific regions. Search criteria include the parent region identifier, component identifier, and configuration item name. The search results return the configuration value, configuration time, and configuration status of the configuration item in the region corresponding to the parent region identifier. If multiple versions of configuration records exist for the region corresponding to the parent region identifier, the latest version with a configuration status marked as production-ready is returned first. Test environment configurations, batch deployment configurations, or obsolete historical configuration versions are not included in the returned results. The provincial region corresponding to the parent region identifier has deployed and operated a medical and elderly care service system for more than two years. Its key configuration items, such as database connection configuration, cache service configuration, and message queue configuration, all have searchable parent configuration records, which have accumulated a wealth of production operation experience. The configuration values, version numbers, verification status, and last update times of each retrieved configuration item are organized into a parent configuration record set, and each entry in the parent configuration record set corresponds to a configuration item in the list of high-impact configuration items.

[0035] An inheritable configuration set is formed by validating the parent configuration records. Each parent configuration record in the set undergoes validity checks, including checks on configuration version timeliness, configuration dependency compatibility, and configuration security compliance. Only parent configuration records that pass all three checks are included in the inheritable set. Configuration version timeliness checks whether the configuration time of the parent record is within its validity period. Parent configuration records that haven't been updated for a long time may be outdated and need to be re-evaluated. Components with rapidly iterating technology stacks have shorter validity periods. Configuration dependency compatibility checks whether the runtime environment on which the parent configuration record depends is compatible with the target region. If the parent region uses MySQL 8.0, but the target region only supports MySQL 5.7, the parent configuration record is not applicable. Connection parameters and driver versions may also differ. Configuration security compliance checks whether the parent configuration record meets the security compliance requirements of the target region. Configurations involving data storage locations must comply with local data residency policies. Some regions have mandatory requirements for local storage of medical data. All parent configuration records that pass all validations will be included in the inheritable configuration set. Parent configuration records that fail validation will be marked with the reason why they cannot be inherited and excluded. The target region needs to develop its own configuration scheme for the non-inheritable configuration items. Each configuration item in the inheritable configuration set has been confirmed to be safe for application in the target region, and the configuration values ​​in the inheritable configuration set have been verified in the production environment of the parent region to have high reliability and stability.

[0036] An inheritance overriding mapping table is generated based on the inheritable configuration set to cover sub-regional configurations. Each configuration item in the inheritable configuration set carries complete configuration definition information, including the configuration item name and configuration value. The configuration values ​​in the inheritable configuration set serve as the starting point for the basic configuration of the target region. The environmental differences between the target region and its parent region are analyzed to identify which configuration items in the inheritable configuration set can be directly inherited and which require overriding modifications for the target region. Common configurations such as service port numbers, log levels, and thread pool sizes configured in the parent region of the inheritable configuration set can be directly inherited and used, as these configurations do not involve regional differences. The medical insurance interface address configuration in the inheritable configuration set needs to be replaced with the local interface address of the target region. The calling addresses and authentication methods of the provincial medical insurance platform interface and the county-level medical insurance platform interface differ; direct inheritance will result in interface call failures. For configuration items that need to be overridden, the overriding value is determined based on the actual environment and integration requirements of the target region. An inheritance overriding mapping relationship is established for the configuration items, recording the configuration item name, the original value inherited from the parent, the overriding value for the target region, and a description of the overriding reason. The inheritance and overriding mapping relationships of all configuration items are organized into an inheritance and overriding mapping table. The inheritance and overriding mapping table clearly shows the inheritance and difference relationships between the target area configuration and the parent area configuration. The inheritance and overriding mapping table is organized by component, and the configuration mapping relationship of each component is presented independently.

[0037] Component adaptation strategies are generated based on the inheritance overriding mapping table. The inheritance overriding mapping table fully records the configuration relationships of each component, including inheritance from the parent region and local overriding. Converting these mapping relationships into an executable adaptation scheme is the final step in configuring differentiated mapping. The set of final configuration values ​​that each component needs to apply in the target region is extracted from the inheritance overriding mapping table. For directly inherited configuration items, the final configuration value equals the original value inherited from the parent region in the inheritance overriding mapping table; for configuration items that need to be overridden, the final configuration value equals the overriding value recorded in the inheritance overriding mapping table for the target region. When deploying a health record component in a county-level region, the inheritance overriding mapping table shows that log level configuration, connection pool size configuration, and timeout configuration are all directly inherited from the city-level region, while medical insurance interface address configuration and data storage path configuration need to be overridden to values ​​specific to the county. The set of final configuration values ​​for each component is organized into a configuration adaptation scheme, which records each configuration item and its target value in key-value pairs. The configuration adaptation scheme for each component is associated with the overriding reason explanation recorded in the inheritance overriding mapping table to form a complete component adaptation strategy. Component adaptation strategies are organized on a component-by-component basis, including the configuration adaptation scheme for that component in each target region. The component adaptation strategy may differ in different county-level regions. The network configuration in coastal counties tends to favor high bandwidth and low latency, while the network configuration in inland counties focuses on network outage tolerance. Component adaptation strategies support querying the summary of adaptation schemes for all components in a specific region by region, and also support querying the distribution of configuration differences of that component in different regions by component.

[0038] Step S130: Extract the component adaptation strategy execution parameter template to form a configuration parameter template library, identify the region sensitive parameter set based on the configuration parameter template library, and encapsulate the parameter version snapshot based on the region sensitive parameter set to generate a region configuration package.

[0039] Specifically, the execution parameter templates of the component adaptation strategy are extracted to form a configuration parameter template library. The configuration adaptation scheme for each component in the component adaptation strategy includes the configuration items and their values ​​that the component needs to apply in each target region. These configuration items cover multiple categories such as database connection, caching service, message queue, interface address, and security authentication. The configuration adaptation schemes for each component in each region within the component adaptation strategy are traversed, and the configuration parameter definitions are extracted. The configuration parameter definitions include parameter names and parameter types. The configuration parameters of the same component in different regions within the component adaptation strategy are compared and analyzed to identify configuration items with the same parameter definition but different parameter values ​​depending on the region. For example, the medical insurance interface address parameter has the same parameter definition in each region but different specific address values, and the database connection string parameter points to different database instances in each region. In the component adaptation strategy, the configuration adaptation scheme for the health record component in various cities of Guangdong Province includes three types of parameters: medical insurance interface address, data storage path, and log level. The medical insurance interface address has different values ​​in each city, while the log level has the same value. When extracting the medical insurance interface address parameter as a template, the placeholder "${region.medical_insurance_url}" is used to indicate that its value needs to be filled according to the region. The extracted configuration parameter definitions are deduplicated and merged; only one template is retained for identical parameter definitions. Regional differences in parameter values ​​are expressed through placeholders or variable references. According to the component classification in the component adaptation strategy, the parameter templates of each component are grouped and organized into a configuration parameter template library. The parameter templates of the online consultation component are classified under the medical service component category in the configuration parameter template library, and the parameter templates of the health record component are classified under the health management component category. The classification organization method of the configuration parameter template library is consistent with the component classification in the component adaptation strategy.

[0040] Identify a set of region-sensitive parameters based on the configuration parameter template library. Analyze the value characteristics of each parameter template in the library to identify which parameters are strongly correlated with the deployment region. Some parameters in the library maintain consistent values ​​across all regions; common parameters such as log format configuration and timeout configuration are region-independent, and these parameters use the same values ​​in each region. Other parameters must be customized based on the target region; parameters such as service endpoint addresses, region-specific keys, and localized resource paths are region-sensitive. Medical insurance interface addresses and health commission data reporting interface addresses differ across regions, and these parameters must be set with different values ​​in different regions. Label each parameter template in the library with region sensitivity, based on criteria including whether the parameter value differs due to region in the component adaptation strategy, whether the parameter name contains region-related semantics, and whether the parameter value references region environment variables. Extract the region-sensitive parameters from the library to form a set of region-sensitive parameters. The regional sensitive parameter set is grouped by component. The sensitive parameter list for each component records the parameter name and parameter type. When deploying across regions, special attention needs to be paid to the correctness of the values ​​of the parameters in the regional sensitive parameter set.

[0041] In some embodiments, the step of encapsulating parameter version snapshots to generate a region configuration package based on the region-sensitive parameter set includes: performing baseline version comparison on the region-sensitive parameter set to extract a subset of difference parameters; performing difference compression encoding on the subset of difference parameters to generate a compressed difference package; constructing a rollback version chain on the compressed difference package to generate a versioned parameter package; and injecting region tags into the versioned parameter package to form a region configuration package.

[0042] Baseline version comparison is performed on the regional sensitive parameter set to extract a subset of differing parameters. The currently deployed configuration version for the target region is obtained as the baseline version. The baseline version records the values ​​of each sensitive parameter in the regional sensitive parameter set at the time of the last successful deployment. The latest target values ​​of each parameter in the regional sensitive parameter set are compared item by item with the corresponding parameter values ​​in the baseline version to identify parameters whose values ​​have changed. When the medical insurance interface address in the regional sensitive parameter set is migrated from the old provincial platform interface to the new national unified platform interface, the old and new values ​​of this parameter differ, thus it is considered a changed parameter. If the log storage path configuration in the regional sensitive parameter set remains unchanged, this parameter is not included in the scope of change. Parameters initially configured in the regional sensitive parameter set that do not exist in the baseline version are marked as newly added parameters; parameters that exist in the baseline version but have been removed from the regional sensitive parameter set are marked as deleted parameters; obsolete old version data reporting interface parameters may be deleted. The changed parameters, added parameters, and deleted parameters are summarized to form a subset of differential parameters. Each parameter in the subset inherits parameter type information from the regional sensitive parameter set, which is used to determine the data format during subsequent encoding. The subset of differential parameters only contains parameters that have changed compared to the baseline version, and the amount of data is usually much smaller than the complete parameter set.

[0043] Based on the subset of difference parameters, a compressed difference package is generated through differential compression encoding. Each parameter change record in the subset is encoded using the structure "Operation Type + Parameter Path + Parameter Value". Operation types include add, modify, and delete, identified by "A", "M", and "D" respectively. For modify parameters in the subset, both the old and new values ​​are recorded in the encoding to support bidirectional difference restoration, allowing for rollback from the new version to the old version when needed. The encoding format for database port change records in the subset is "M|db_port|3306|3307", where M represents the modification operation, and a vertical bar separates the parameter path and the old and new values. The encoded subset of difference parameters is then compressed using the LZ4 or ZSTD algorithm, suitable for text data, to achieve a high compression ratio. If the subset contains a large number of parameter change records with similar structures, such as batch-updated interface address parameters, the compression algorithm can effectively eliminate redundancy and obtain a smaller output volume, reducing the size by approximately 80% compared to the complete parameter set. During compression, sensitive content in parameter values ​​is encrypted using AES. A region-specific encryption key is used to ensure configuration security; compressed difference packets from different regions cannot be decrypted by each other. The region-specific key is used to decrypt configuration content. The compressed and encrypted data is then encapsulated into compressed difference packets. These packets are small in size and offer guaranteed security, making them suitable for transmission between regions with limited network bandwidth.

[0044] A versioned parameter package is generated by constructing a rollback version chain for the compressed difference package. A unique version identifier is assigned to each compressed difference package, using an incremental sequence number combined with a timestamp to ensure global uniqueness; for example, "V20250315-001" represents the first version generated on March 15, 2025. An association is established between the compressed difference package and its baseline version, recording the parent version identifier of the current version, forming an inheritance chain between compressed difference package versions. The version chain supports tracing back from any version to the initial version and rolling back from the current version to any historical version. When a service anomaly occurs in a region after applying a new configuration, the version chain can be used to locate the previous stable version. A rollback script is embedded in the compressed difference package. The rollback script defines the steps to restore the current version configuration to the parent version configuration, recording the list of parameters to be restored and the target values ​​to be restored. The version identifier, version chain association information, and rollback script are integrated with the compressed difference package to form a versioned parameter package. Versioned parameter packages have complete version management capabilities, supporting operations such as version query, version comparison, and version rollback. The version chain structure of versioned parameter packages ensures that the configuration change process is traceable and reversible.

[0045] A regional configuration package is formed by injecting regional tags into the versioned parameter package. The regional metadata information of the target deployment region is obtained, including attributes such as the regional identifier and regional level. This regional metadata is injected as tag information into the metadata area of ​​the versioned parameter package. Tag injection uses key-value pairs, such as "region_id=GD-SZ-NS" and "region_level=county". The injected regional tags give the versioned parameter package a clear regional affiliation identifier, allowing the configuration distribution system to route the versioned parameter package to the correct target region based on the regional tags, preventing the versioned parameter package from being incorrectly applied to other regions. The versioned parameter package with injected tags is then subjected to an integrity signature. The integrity signature covers the package body content and tag metadata, and is used to verify whether the configuration package has been tampered with. The configuration can only be applied after successful verification. The signed data package is named the regional configuration package, and the filename of the regional configuration package includes the regional identifier and version identifier, such as "config-GD-SZ-NS-V20250315-001.pkg". A regional configuration package is the basic unit for configuration distribution during cross-regional integrated deployment. Each region pulls a regional configuration package that matches its own regional label from the configuration center and applies the parameter configurations contained therein.

[0046] Step S140: Perform dependency parsing on the regional configuration package to extract the medical service call chain, perform dependency availability pre-verification and orchestration on the medical service call chain to obtain the deployment sequence identifier, and arrange the deployment sequence identifier according to resource priority to form a cloud-based phased deployment queue.

[0047] Specifically, dependency parsing is performed on the regional configuration package to extract the medical service call chain. The component list and deployment description files of each component in the regional configuration package declare other components or basic services that the component depends on for operation. For example, the health record query component depends on the data access interface provided by the health record storage component, and the online consultation component depends on the identity verification service provided by the user authentication component. These dependencies determine the startup order constraints of the components. The dependency declaration fields of each component in the regional configuration package are parsed to identify the call relationships and data flow between components. The dependencies of all components in the regional configuration package are summarized to construct a directed dependency graph between components. Nodes in the directed dependency graph represent components, and directed edges represent dependencies, with the direction of the edges pointing from the dependent to the dependent. Connectivity analysis is performed on the directed dependency graph to identify mutually independent component subgraphs and component groups with complex dependencies. Mutually independent subgraphs can be deployed in parallel, while complex dependency groups need to be deployed in strict order. The dependency paths in the directed dependency graph are extracted to form a medical service call chain, which records the complete dependency path from the leaf component to the root component in the form of a linked list. Database access components appear as a common dependency of multiple business components in multiple medical service call chains. These components need to be deployed first to meet the startup conditions of downstream components.

[0048] In some embodiments, the step of performing dependency availability pre-verification and orchestration on the medical service call chain to obtain a deployment sequence identifier includes: extracting an upstream dependency component list from the medical service call chain; performing service availability detection on the upstream dependency component list to obtain an available state set; determining dependency satisfaction based on the available state set to generate a dependency ready identifier; and performing topological sorting on the medical service call chain according to the dependency ready identifier to form a deployment sequence identifier.

[0049] The upstream dependency component list is extracted from the medical service call chain. Each component node in the medical service call chain records a list of upstream components or services necessary for its operation. The upstream dependencies of the online consultation component include user authentication components, message push components, and medical record storage components. The online consultation component can only start normally and provide services after all these upstream dependencies are ready. The component nodes on each dependency path in the medical service call chain are traversed. Each node in the medical service call chain contains a direct upstream dependency declaration, which records the upstream component's identifier, interface address, communication protocol, and timeout configuration. The dependency declarations of each component node in the medical service call chain are summarized to form the upstream dependency component list. Each entry in the upstream dependency component list contains information such as the upstream component identifier, component type, service address, and health check endpoint. The component type distinguishes whether the dependency is an infrastructure service or a business application component. Different types of components use different availability detection methods. When the same upstream component is depended on by multiple downstream components, only one record is retained to avoid duplicate detection. The upstream dependency component list is organized by component type, with infrastructure dependencies and business application dependencies listed separately to facilitate differentiated detection strategies.

[0050] For example, the step of performing service availability detection on the upstream dependent component list to obtain an available state set includes: extracting a cascading dependency list from the upstream dependent component list; making layer-by-layer health interface calls to the cascading dependency list to obtain cascading response status; performing deep availability determination based on the cascading response status to generate a single component available state; and aggregating the single component available states to form an available state set.

[0051] Extract the cascading dependency list from the upstream dependency component list. The upstream components in the upstream dependency component list may not run independently; their normal operation depends on higher-level components or services. For example, the user authentication component relies on a database service to store user credentials and permission information, and also relies on a caching service to accelerate token verification and session management, forming a multi-layered cascading dependency structure. Traverse each upstream component in the upstream dependency component list, recursively parsing the dependency declarations of each component to obtain the complete cascading dependency path. Organize the upstream components and their cascading dependencies into a tree structure. The root node of the tree is the directly dependent component in the upstream dependency component list, and the child nodes are the cascading dependencies at each level. The depth of the tree reflects the complexity of the dependency chain. After flattening the tree structure into the cascading dependency list, arrange them by dependency depth. The greater the depth, the further away from the dependency level of the original component to be deployed. The deepest component is usually the most basic infrastructure service. The cascading dependency list records the dependency depth of each component and the identifier of the parent dependent component. The user authentication component is at the first level, and its dependent database service and caching service are at the second level. The hierarchical information of the cascading dependency list is used for subsequent layer-by-layer detection to ensure that verification starts from the lowest level of dependency, avoiding the situation where the seemingly healthy component is actually not working due to ignoring the abnormality of the cascading dependency.

[0052] The cascading dependency list is processed layer by layer using health check endpoints to obtain cascading response statuses. The deepest level components in the cascading dependency list are typically infrastructure services such as databases, caches, and message queues. The availability of these services is fundamental to the normal operation of the entire dependency chain. Performing health checks layer by layer upwards from the deepest level of the cascading dependency list helps identify underlying issues early and avoids ineffective upper-level checks. Following the hierarchical order in the cascading dependency list, each component in the list uses a timeout control mechanism when calling its health check endpoint. The timeout period is dynamically configured based on the component type and network environment. Calls exceeding the response time limit are considered failures, preventing slow responses from a single component from blocking the overall detection progress. For example, a health check endpoint call to the database service in the cascading dependency list returning a status code of 200 and a response time of 15 milliseconds indicates normal service. A health check endpoint call to the cache service returning a timeout error after exceeding the 500 millisecond response time limit is recorded as unavailable. The health check endpoints return the component's running status code and status details. A status code of 200 indicates the component is running healthily, while a status code of 503 indicates the component service is unavailable. The health check response results of each component in the cascading dependency list are organized into cascading response states by hierarchy and upstream component identifier. The cascading response states are indexed by hierarchy, and within the same hierarchy, they are arranged by upstream component identifier for easy batch processing. The cascading response states fully record the health status of each component at each level in the dependency chain, and abnormal states of deeper components will propagate upwards along the dependency chain.

[0053] Based on the cascading response status, a deep availability assessment is performed to generate the availability status of a single component. The health check results of each component in the cascading response status, combined with the cascading dependencies between components, are used for a comprehensive availability assessment. Deep availability assessment checks not only the health status of the component itself but also the health status of its cascading dependencies in the cascading response status. Only when both are normal can the component be considered truly available. For example, if the user authentication component's own health check returns a 200 status code, seemingly healthy, but the cascading response status shows that its dependent cache service returns a 503 status code, then the user authentication component is considered unavailable in the deep availability assessment. This is because the cache service anomaly in the cascading response status will prevent the authentication component from providing token verification functionality, and even if the authentication component process itself is running, it cannot complete business processing. The deep availability status of each component is calculated recursively upwards from the deepest level of the cascading response status. The availability status of a downstream component depends on the availability status of the upstream components in the cascading response status; if any upstream component is unavailable, the downstream component is also considered unavailable. Each first-level dependency component in the cascading dependency list eventually obtains a single component availability state. The single component availability state is an enumeration value, including three states: available, unavailable, and degraded available. Degraded available means that the core function of the component is normal but some auxiliary functions are limited. For example, when some nodes of the cache service fail but the main node is normal, it can be determined as degraded available, and downstream components can run in a degraded manner.

[0054] The availability status of individual components is aggregated to form an availability status set. The availability status of each upstream component in the upstream dependency component list, after deep availability assessment, needs to be aggregated to form a complete dependency readiness view. The aggregated result provides comprehensive dependency status information for deployment decisions. During the aggregation process, detailed availability status information for each component is retained, including status values ​​and specific reasons for anomalies. When the availability status of an upstream component is unavailable, the aggregation record also records the specific reason for this unavailability, such as "caching service connection timeout" or "database service insufficient disk space." The aggregation statistics calculate the distribution of the number of components with an available status, the number of components with an unavailable status, and the number of components with a degraded available status. The distribution reflects the overall health of the target region's dependency environment; all available indicates ideal conditions and immediate deployment, while unavailable components require prior problem repair. The aggregated results are organized into an availability status set, with the upstream component identifier as the key and the individual component availability status and its details as the value. The availability status set provides a panoramic view of the dependency readiness status in the target region. The available state set supports filtering by state value and by component type, allowing you to quickly locate problematic dependency components.

[0055] Dependency satisfaction is determined based on the availability state set to generate a dependency ready flag. The availability status of each upstream component in the availability state set determines whether downstream components can start successfully. Dependency satisfaction is determined for each component to be deployed in the medical service call chain, taking into account the distinction between necessary and optional dependencies. The determination logic checks whether all necessary upstream dependencies declared by the component are available in the availability state set. If all necessary dependencies are available, the dependency is considered satisfied; if any necessary dependency is unavailable, the dependency is considered unsatisfied. The health record query component depends on the health record storage component and the user authentication component. If the availability state set shows both are available, the health record query component's dependency is satisfied and it can enter the deployment ready state. Dependencies in the availability state set that have been downgraded are judged based on whether their restricted functions affect the core functions of downstream components. Restricted non-critical functions are considered satisfied, while restricted core functions are considered unsatisfied. If some nodes of the cache service in the availability state set fail but the master node is normal, it can be considered downgraded to available, and downstream components can run in a downgraded manner. The determination result of each component to be deployed forms a dependency readiness flag, which is in Boolean form. A true value indicates that the component's dependencies have been met and it can be deployed, while a false value indicates that the component has unmet dependencies and needs to wait. Components with a false dependency readiness flag need to be re-determined after their upstream dependencies have become ready.

[0056] The healthcare service call chain is topologically sorted based on dependency readiness flags to form deployment sequence flags. Components with true dependency readiness flags constitute the current set of deployable components. All upstream dependencies of these components meet the deployment requirements, allowing for safe deployment without startup failure due to missing dependencies. After filtering components with true dependency readiness flags, a topological sorting algorithm is applied to the healthcare service call chain, outputting nodes sequentially starting with nodes with an in-degree of zero. An in-degree of zero indicates that the component does not depend on other components to be deployed in the healthcare service call chain, or that its dependencies have been deployed. These components can be deployed immediately. After each node is output, it is removed from the healthcare service call chain, and the in-degree values ​​of its downstream components in the chain are updated. Newly generated nodes with an in-degree of zero are added to the output queue for further processing. The database access component, as a common dependency of multiple business components, has an in-degree of zero and is output first. Subsequently, the health record storage component and medication record component, which depend on it, are output sequentially, followed by the upper-level business components that depend on these components. The output order of topology sorting determines the deployment sequence identifier of each component. Components that are sorted earlier receive smaller deployment sequence identifier numbers, indicating higher deployment priority. Deployment sequence identifiers are integers starting from 1 and incrementing. Components with a false dependency readiness flag are not assigned a deployment sequence identifier until their dependencies are satisfied. The sorted deployment sequence identifiers ensure that the component deployment order conforms to dependency constraints.

[0057] Based on deployment sequence identifiers and resource priority, a cloud-based phased deployment queue is formed. After the components are initially arranged according to their deployment sequence identifiers, the cloud resource scheduling strategy and deployment window limitations of the target region need to be further considered. Simply deploying components one by one according to their deployment sequence identifiers may lead to low resource utilization. Components with similar deployment sequence identifiers and complementary resource requirements can be grouped into the same deployment phase. Parallel deployment of compute-intensive and storage-intensive components can make full use of different types of resources and avoid situations where one type of resource is idle while another is strained. Components with significantly different deployment sequence identifiers or conflicting resource requirements are assigned to different deployment phases. Deploying two components that both require a large amount of memory simultaneously may lead to memory overflow and should be staggered. Components within each deployment phase are then reordered according to resource priority. Core business components receive cloud resource allocation before auxiliary function components, and service components directly used by users are deployed before backend management components. The phased component deployment plan is organized into a cloud-based phased deployment queue, which records the list of components to be deployed and the resource quota for each phase. The phase division of the cloud-based phased deployment queue takes into account both the dependency order constraints determined by the deployment sequence identifier and the resource utilization efficiency. Checkpoints are set between each phase of the cloud-based phased deployment queue to verify the successful deployment of the previous phase before starting the subsequent phase.

[0058] Step S150: Perform target environment readiness detection on the cloud phased deployment queue to obtain the environment readiness status, define the batch deployment scope based on the environment readiness status and generate batch deployment identifiers, and trigger the standardized service component set to generate cloud batch deployment instructions based on the batch deployment identifiers to complete the cross-regional integrated deployment of the cloud platform of the medical and elderly care service system.

[0059] Specifically, a target environment readiness check is performed on the cloud-based phased deployment queue to obtain the environment readiness status. The list of components to be deployed in each stage of the cloud-based phased deployment queue and the resource quotas for each stage determine the deployment conditions required for the target region. The readiness check includes four aspects: computing resource availability, storage space sufficiency, network connectivity, and security policy compliance. The computing resource availability check verifies whether the CPU and memory quotas of the target region meet the total resource requirements of the components in the cloud-based phased deployment queue. If a region has 16GB of available memory and the total memory requirement of the components to be deployed in the cloud-based phased deployment queue is 12GB, then the resources are considered sufficient, while reserving a certain amount of resource margin to cope with runtime load fluctuations. The storage space sufficiency check verifies whether the disk quotas of the target region meet the data storage and log storage requirements of the components in the cloud-based phased deployment queue. It also needs to consider the log growth rate and data retention period to ensure that long-term operation will not exhaust the storage space. Network connectivity testing verifies whether the communication link between the target area and the cloud management platform is normal. The issuance of batch deployment instructions and the reporting of deployment status in the cloud depend on this link. It also checks whether the internal network of the target area meets the bandwidth and latency requirements for inter-component communication. Security policy compliance testing verifies whether the security configuration of the target area allows the ports of the components to be deployed to be open and access the network. Some areas may have strict outbound traffic restrictions, requiring whitelist configuration in advance. The results of each test item are summarized to form the environment readiness status. The environment readiness status records the pass status and specific test values ​​of each test item on a target area basis. Areas that pass all tests are marked as ready. Areas in the environment readiness status that fail any tests are marked as not ready, and the reasons for failure are recorded. The reasons for failure are used to guide maintenance personnel in troubleshooting, such as "Insufficient memory quota, needs to be expanded to 16GB" or "Port 8080 is blocked by the firewall, needs to add a permission rule."

[0060] In some embodiments, the step of defining the batch deployment scope and generating batch deployment identifiers based on the environment readiness status includes: filtering ready area nodes from the environment readiness status; performing business similarity assessment on the ready area nodes to obtain similarity ranking results; performing batch priority allocation based on the similarity ranking results to generate a batch node list; and determining the batch deployment identifiers based on the batch node list.

[0061] The process involves selecting ready region nodes from the environment readiness list. Region nodes marked as "ready" in the environment readiness log indicate that their computing resource availability, storage space sufficiency, network connectivity, and security policy compliance have all passed tests, fulfilling the basic conditions for deployment. They can be safely deployed without failure due to environmental issues. The process iterates through the region detection records in the environment readiness list, compiling these ready region nodes into a list of ready region nodes. Each entry in this list contains information such as region identifier, region level, and resource reserves. This information provides a basis for subsequent batch priority allocation decisions. Resource reserves information records the remaining computing and storage resources of the ready region node after meeting deployment requirements. For example, a city-level ready region node has 8GB of memory reserves, sufficient to accommodate additional monitoring component deployments. Ready region nodes with larger resource reserves have better fault tolerance during deployment; even if actual component resource consumption exceeds estimates, system crashes are unlikely. The list of ready regional nodes is grouped and statistically analyzed by region level to understand the distribution of ready regional nodes at the provincial, municipal, and county levels. The hierarchical distribution influences the formulation of the phased deployment strategy. Typically, pilot projects are first conducted at the county level, and then expanded upwards level by level. The list of ready regional nodes excludes regions that are not ready due to insufficient resources or network anomalies, ensuring that the scope of subsequent phased deployments is determined only in regions with the necessary deployment conditions.

[0062] A business similarity assessment is performed on the ready regional nodes to obtain similarity ranking results. Each region in the ready regional node list is compared with a benchmark region. The benchmark region is typically a successful model region with an operational medical and elderly care service system, whose business scale, user characteristics, and operational experience are representative, and whose deployment configuration and operational parameters have been fully validated and can serve as a reference template for other regions. For each ready regional node in the ready regional node list, similarity is assessed from four dimensions: business scale similarity, user structure similarity, service type distribution similarity, and operational model similarity. Business scale similarity compares the daily active users and average daily service order volume of the two regions; the closer the values ​​are, the higher the similarity. User structure similarity compares the age distribution and health status distribution of users in the two regions. A county-level ready regional node and the benchmark region both have approximately 60% elderly users and a similar proportion of users with chronic diseases, indicating a high degree of user structure similarity. Service type distribution similarity compares the composition of medical and elderly care service categories offered in the two regions; a high degree of similarity is indicated by similar proportions of health management, rehabilitation nursing, and daily living care. The weighted sum of similarity across the four dimensions yields a comprehensive similarity score, calculated using the formula S = Σ(i=1 to 4)wi × si, where wi is the weight of each dimension and si is the similarity value for each dimension. The weights can be adjusted based on business priorities; by default, each dimension has equal weight (wi = 0.25). The comprehensive similarity scores are then sorted from highest to lowest to form a similarity ranking. Regions ranked higher in the similarity ranking are most similar to the baseline region. The ranking determines the priority allocation for each batch. The final similarity ranking is achieved once all ready region nodes have been evaluated.

[0063] A batch priority list is generated based on the similarity ranking results. The ranking and overall similarity score of each region in the similarity ranking results determine its batch priority. A higher ranking indicates a more similar region to the benchmark region, and the greater the reference value for batch verification in that region. Based on the ranking results, the top 10% of regions are assigned the highest batch priority. These regions are most similar to the benchmark region in characteristics, and the experience and problems discovered during batch verification in these regions are most universally applicable, effectively guiding the deployment of subsequent batches. The middle 50% of regions are assigned a medium batch priority, serving as the targets for the second and third batches, and will be deployed sequentially after the first batch of deployments has passed verification and experience has been summarized. Regions with lower rankings are assigned a lower batch priority. These regions differ significantly from the benchmark region, with marked differences in business characteristics or user structure, and may require targeted adaptation and adjustments. They are deployed in later batches to learn from the lessons of previous batches. The batch priority coding format for each region is "batch number - sequence number within the batch", such as "01-003" representing the third region deployed in the first batch. This coding format facilitates sorting and batch grouping. The batch priority code is associated with the region identifier to form a batch node list. The batch node list is organized with the batch number as the main index, and regions within the same batch are arranged according to their sequence number within the batch. The batch node list completely records the batch deployment order and batch affiliation information of each ready region.

[0064] The batch deployment identifier is determined based on the batch node list. The batch priority code for each region in the batch node list includes the batch number and sequence number within the batch. Regions with the same batch number are grouped into the same batch, forming a batch-organized batch scope structure. This batch structure facilitates batch operations and batch-level status management. The batch division information in the batch node list is parsed, and each batch sets batch trigger conditions and progress rules. The next batch is automatically triggered after the first batch is deployed and all component health checks pass. If any batch fails to deploy or a component malfunctions, subsequent batches are paused and troubleshooting is initiated. After the problem is fixed and verified, batch progress can be manually resumed. This mechanism ensures that problems are detected and handled promptly without escalating. The overall batch deployment plan generates batch deployment identifiers, which are stored in a structured format and contain complete batch management information, including the total number of batches, the list of regions included in each batch, batch trigger conditions, and batch progress status. The first batch deployment in the batch node list includes three regional nodes in Nanshan District, Futian District, and Luohu District of Shenzhen. The batch trigger condition in the batch deployment identifier is set to "all components in the previous batch have passed health checks and have been running stably for 24 hours." The initial progress status of the first batch is "Pending Deployment." The list of regions included in each batch in the batch deployment identifier directly references the regional identifiers in the batch node list to avoid data redundancy and maintain consistency for easy maintenance and updates. After the first batch of deployment regions has completed deployment and been running stably for 24 hours, the progress status of the first batch in the batch deployment identifier is updated to "Completed." The system automatically updates the progress status of the next batch to "Pending Deployment" and triggers the deployment process based on the batch trigger conditions.

[0065] Based on the batch deployment identifier, the standardized service component set is triggered to generate cloud batch deployment instructions. After the list of regions included in each batch of the first batch deployment region in the batch deployment identifier is determined, the container images and region configuration packages corresponding to these regions are extracted from the standardized service component set. Before deployment, the digital signature of the container image and the integrity signature of the region configuration package in the standardized service component set are verified. After successful verification, the encrypted configuration content in the region configuration package is decrypted using the region-specific key. The decrypted configuration parameters are used for environment variable injection when the component starts. The phase order of the cloud phased deployment queue determines the generation order of the cloud batch deployment instructions. The cloud batch deployment instructions include component image addresses, configuration parameters, resource quotas, and startup commands. Based on the batch information in the batch deployment identifier, the cloud batch deployment instructions are sent to the edge deployment agent of the first batch deployment region. After receiving the cloud batch deployment instructions, the edge agent performs local deployment operations, including pulling container images from the standardized service component set, loading region configuration packages, allocating runtime resources, and starting component instances. After each component starts successfully and passes the health check, the deployment of this phase is considered successful, and then the next phase of cloud batch deployment instructions is triggered to continue. After the first batch of deployment areas has been successfully deployed and running stably for 24 hours, the next batch of cloud deployment instructions will be triggered according to the batch trigger conditions of the batch deployment identifier, and the deployment will be expanded batch by batch until all ready areas are completed, thus completing the cross-regional integrated deployment of the cloud platform for the integrated medical and elderly care service system.

[0066] To implement the cross-regional deployment method of the integrated medical and elderly care service system corresponding to the above method embodiments, in order to achieve the corresponding functions and technical effects. See also Figure 2 , Figure 2 This diagram illustrates a structural block diagram of a cross-regional deployment system 200 for an integrated medical and elderly care service system provided in an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The cross-regional deployment system 200 for an integrated medical and elderly care service system provided in this embodiment includes: The component splitting module 201 is used to obtain the functional module information and regional medical environment configuration information of the integrated medical and elderly care service system, and to perform microservice component splitting to form a standardized service component set based on the functional module information. The difference adaptation module 202 is used to locate medical environment difference items using the standardized service component set and the regional medical environment configuration information, evaluate the difference impact of the medical environment difference items to generate a difference impact level, and implement a configuration item difference mapping to generate a component adaptation strategy based on the difference impact level. The configuration encapsulation module 203 is used to extract the component adaptation strategy execution parameter template to form a configuration parameter template library, identify a set of region-sensitive parameters based on the configuration parameter template library, and encapsulate a parameter version snapshot based on the set of region-sensitive parameters to generate a region configuration package. Dependency orchestration module 204 is used to perform dependency parsing on the regional configuration package to extract the medical service call chain, perform dependency availability pre-verification orchestration on the medical service call chain to obtain the deployment sequence identifier, and arrange the deployment sequence identifier according to resource priority to form a cloud-based phased deployment queue. The batch deployment module 205 is used to perform target environment readiness detection on the cloud-based phased deployment queue to obtain the environment readiness status, define the batch deployment scope based on the environment readiness status to generate a batch deployment identifier, and trigger the standardized service component set to generate cloud-based batch deployment instructions based on the batch deployment identifier, thereby completing the cross-regional integrated deployment of the cloud platform for the integrated medical and elderly care service system.

[0067] The aforementioned cross-regional deployment system 200 for integrated medical and elderly care services can implement a cross-regional deployment method for integrated medical and elderly care services as described in the above method embodiments. The options in the above method embodiments are also applicable to this embodiment and will not be detailed here. The remaining content of this application's embodiments can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0068] The purpose of the above embodiments is to reproduce and derive the technical solution of the present invention by way of example, and to fully describe the technical solution, purpose and effect of the present invention. The purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosure of the present invention, and not to limit the scope of protection of the present invention.

[0069] The above embodiments are not an exhaustive list based on the present invention, and there may be many other embodiments not listed. Any substitutions and improvements made without departing from the concept of the present invention are within the protection scope of the present invention.

Claims

1. A medical and nursing service system cross-regional deployment method, characterized in that, include: Obtain functional module information and regional medical environment configuration information of the integrated medical and elderly care service system, and perform microservice component decomposition to form a standardized service component set based on the functional module information; The standardized service component set and the regional medical environment configuration information are used to locate medical environment difference items, and the difference impact of the medical environment difference items is evaluated to generate a difference impact level. Based on the difference impact level, the configuration item differentiation mapping is implemented to generate a component adaptation strategy. The component adaptation strategy execution parameter template is extracted to form a configuration parameter template library. Based on the configuration parameter template library, a set of region-sensitive parameters is identified. Based on the set of region-sensitive parameters, a parameter version snapshot is encapsulated to generate a region configuration package. The region configuration package is subjected to dependency parsing to extract the medical service call chain. The medical service call chain is then subjected to dependency availability pre-verification and orchestration to obtain the deployment sequence identifier. Based on the deployment sequence identifier, the chain is arranged according to resource priority to form a cloud-based phased deployment queue. The target environment readiness detection is performed on the cloud-based phased deployment queue to obtain the environment readiness status. Based on the environment readiness status, the deployment scope is defined in batches to generate batch deployment identifiers. Based on the batch deployment identifiers, the standardized service component set is triggered to generate cloud-based batch deployment instructions, thereby completing the cross-regional integrated deployment of the cloud platform for the integrated medical and elderly care service system.

2. The method of claim 1, wherein, The step of splitting microservice components into a standardized service component set based on the functional module information includes: The business domain division results are obtained by identifying the business boundaries of the aforementioned functional module information; Based on the business domain division results, a cross-regional reuse assessment is performed to generate a reuse level identifier; Based on the reusability classification identifier, the business domain division result is split at a granularity that is adapted to form a component splitting scheme. The functional module information is standardized and encapsulated according to the component decomposition scheme to form a standardized service component set.

3. The method of claim 1, wherein, The process of assessing the impact of differences in the medical environment to generate a difference impact level includes: Extract the difference type labels from the medical environment differences; Based on the aforementioned difference type labels, historical deployment failure records are associated to obtain failure association weights; The failure association weights are divided into threshold-based classifications to generate impact classification results; The degree of difference in influence is determined based on the results of the influence grading.

4. The method according to claim 1, characterized in that, The step of implementing a configuration item differentiation mapping to generate a component adaptation strategy based on the difference impact level includes: Select high-impact configuration items from the aforementioned difference impact levels; For the high-impact configuration items, query the parent configuration template to obtain the inheritable configuration set; Based on the inheritable configuration set, sub-region coverage configuration is performed to generate an inheritance coverage mapping table; Component adaptation strategies are generated based on the inheritance overlay mapping table.

5. The method according to claim 1, characterized in that, The step of encapsulating parameter version snapshots to generate a region configuration package based on the region-sensitive parameter set includes: Baseline version comparison is performed on the set of sensitive parameters in the region to extract a subset of the differences in parameters; Based on the subset of difference parameters, perform difference compression encoding to generate a compressed difference packet; The compressed difference package is rolled back to build a version chain to generate a versioned parameter package; The versioned parameter package is injected with region tags to form a region configuration package.

6. The method according to claim 1, characterized in that, The step of performing dependency availability pre-verification orchestration on the medical service call chain to obtain the deployment sequence identifier includes: Extract the list of upstream dependent components from the medical service call chain; Perform service availability detection on the list of upstream dependent components to obtain an available status set; Based on the available state set, a dependency satisfaction determination is performed to generate a dependency ready identifier; The medical service call chain is topologically sorted based on the dependency readiness identifier to form a deployment sequence identifier.

7. The method according to claim 1, characterized in that, The step of defining the batch deployment scope and generating batch deployment identifiers based on the environmental readiness status includes: Filter ready region nodes from the environmental ready state; The ready area nodes are evaluated for business similarity to obtain similarity ranking results; Based on the similarity ranking results, batch priority allocation is performed to generate a batch node list; The batch deployment identifier is determined based on the batch node list.

8. The method according to claim 4, characterized in that, The step of querying the parent configuration template to obtain the inheritable configuration set for the high-impact configuration item includes: Extract the configuration item hierarchy path from the high-impact configuration items; Based on the configuration item hierarchy path, locate the upper-level region and obtain the upper-level region identifier; The existing configuration template is retrieved to obtain the upper-level configuration record for the upper-level region identifier; The validity of the parent configuration record is validated to form an inheritable configuration set.

9. The method according to claim 6, characterized in that, The step of performing service availability detection on the list of upstream dependent components to obtain an available status set includes: Extract the cascade dependency list from the upstream dependent component list; The cascading dependency list is processed by calling the health interface layer by layer to obtain the cascading response status; Based on the cascaded response status, a deep availability determination is performed to generate the availability status of a single component; The available states of the individual components are aggregated to form an available state set.

10. A cross-regional deployment system for integrated medical and elderly care services, characterized in that, include: The component splitting module is used to obtain functional module information and regional medical environment configuration information of the integrated medical and elderly care service system, and to perform microservice component splitting to form a standardized service component set based on the functional module information. The difference adaptation module is used to locate medical environment difference items using the standardized service component set and the regional medical environment configuration information, evaluate the difference impact of the medical environment difference items to generate a difference impact level, and implement a configuration item difference mapping to generate a component adaptation strategy based on the difference impact level. The configuration encapsulation module is used to extract the component adaptation strategy execution parameter template to form a configuration parameter template library, identify the regional sensitive parameter set based on the configuration parameter template library, and encapsulate the parameter version snapshot based on the regional sensitive parameter set to generate a regional configuration package. The dependency orchestration module is used to perform dependency parsing on the regional configuration package to extract the medical service call chain, perform dependency availability pre-verification orchestration on the medical service call chain to obtain the deployment sequence identifier, and arrange the deployment sequence identifier according to resource priority to form a cloud-based phased deployment queue. The batch deployment module is used to perform target environment readiness detection on the cloud-based phased deployment queue to obtain the environment readiness status, define the batch deployment scope based on the environment readiness status to generate batch deployment identifiers, and trigger the standardized service component set to generate cloud-based batch deployment instructions based on the batch deployment identifiers, thereby completing the cross-regional integrated deployment of the cloud platform for the integrated medical and elderly care service system.

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