Internal operation comprehensive digital management method and system based on whole-process of disease control

Abstract

The application discloses a kind of based on whole process's inside operation comprehensive digital management method and system of disease control, method includes: constructing disease control business whole scene data acquisition system, formulating unified data standard specification, clear data coding rule and cross-scene mapping logic, build integrated digital business model, simultaneously define cross-module process node, permission rule and data flow mechanism. Match calibration is completed using business process and model node two-way mapping coding method, realize process digital configuration, and form traceable process track and data footprint by time sequence stamp embedding. Business characteristics are mined relying on business feature vector extraction and clustering fusion algorithm, complete business classification and extract the importance features of various businesses. Construct sensitive level dynamic evaluation algorithm, establish dynamic grading standard, implement classification control and hierarchical permission configuration to business, realize the dynamic adaptation of business control and data security. Efficiency of business operation and data security level are improved.

Description

Technical Field

[0001] This invention belongs to the field of digital management technology, and more specifically, relates to a comprehensive digital management method and system for the internal operation of disease control based on the entire process. Background Technology

[0002] Currently, disease control and prevention (CDC) internal operations management faces multiple practical challenges. Digital infrastructure development suffers from fragmentation, insufficient standardization, and lagging security controls, severely impacting the efficient and standardized operation of CDC services. In existing CDC operations management, data collection lacks a unified system; different business modules collect data independently without unified data standards, specifications, and coding rules. This leads to chaotic cross-scenario data mapping, resulting in the ineffective integration of medical institution diagnostic data, laboratory testing data, material management data, and cross-institutional collaborative data. This creates multiple data silos, resulting in low data utilization and difficulty in supporting end-to-end business analysis and decision-making.

[0003] Meanwhile, the digitalization level of disease control business processes is low, with a lack of precise correspondence between business process links and digital model nodes, leading to frequent adaptation discrepancies. The processes also lack flexibility in configuration, making it difficult to quickly adapt to the dynamic adjustment needs of disease control operations. Furthermore, the lack of effective trajectory recording and data traceability mechanisms during process execution makes operations untraceable. In the event of anomalies, it is difficult to quickly locate the problematic link and identify the responsible party, impacting operational efficiency and regulatory compliance.

[0004] Furthermore, the diverse range of disease control services makes it difficult for existing management methods to scientifically classify and precisely control various business operations. This hinders the accurate identification of core and non-core business functions, leading to inefficient resource allocation. Regarding data security control, the lack of a dynamic sensitivity level assessment mechanism and the prevalence of fixed grading methods prevent real-time adjustments to control strategies based on changes in business importance and data leakage risks. This can result in both the risk of core sensitive data leakage and the potential for over-control that disrupts normal business operations.

[0005] To address the prominent issues in the digital management of internal operations of disease control and prevention (CDC), improve the standardization, efficiency, and security of CDC operations management, ensure the orderly conduct of the entire CDC business process, and meet the actual needs of public health prevention and control and internal management, there is an urgent need for a comprehensive digital management method for internal operations of CDC based on the entire process. This method should comprehensively address core pain points such as data integration, process adaptation, business classification, and security control, and provide reliable technical support for the internal operations management of CDC. Summary of the Invention

[0006] This invention aims to address the current problems in the digital management of disease control operations, such as inconsistent data standards, process adaptation deviations, insufficient business traceability, and rigid security controls. It constructs an integrated management model for the entire process, improves operational efficiency and data security, and provides stable and reliable technical support for the standardized operation of disease control.

[0007] To address the aforementioned deficiencies or improvement needs of existing technologies, as a first aspect of this invention, the present invention provides a comprehensive digital management method for the internal operations of disease control based on the entire process, comprising: S1. Construct a full-scenario data collection system for disease control business, formulate unified data standards and specifications, clarify data coding rules and cross-scenario mapping logic, build an integrated digital business model, and simultaneously define cross-module process nodes, permission rules and data flow mechanisms. S2. A bidirectional mapping coding method between business process and model node is adopted to uniquely code and match business process links with digital model nodes. The adaptation deviation is calibrated through dynamic adaptation and adjustment methods, and the digital configuration of the process is completed based on the calibration results. At the same time, a full-process node time stamp embedding method is adopted to automatically generate traceable process node trajectory records and data traces. S3. Based on the business attributes and data characteristics of disease control, a business feature vector extraction-clustering fusion algorithm is adopted to mine the core business characteristics of disease control and complete the business classification. At the same time, the business importance features corresponding to each business category are extracted. S4. Construct a dynamic data sensitivity level assessment algorithm, combine business importance weights and data leakage risk coefficients, establish dynamically updated data classification standards, implement classified management and control of core disease control business based on classification results and classification standards, set hierarchical access and operation permissions for disease control business data with different sensitivity levels, and complete the dynamic adaptation of business management and data security.

[0008] Furthermore, the disease control business full-scenario data collection system in S1 includes, but is not limited to: It directly connects to medical institutions' diagnosis and treatment systems to automatically capture infectious disease reporting and case-related data; it reads sample reception, testing items, test results, and testing time data through the laboratory information management system; it synchronizes information on material warehousing, outbound, inventory balance, and allocation flow in real time from the disease control material management system; it collects operational data on the entire process of business initiation, approval, and completion from various internal departments; and it aggregates collaborative business interaction data from health, community, and third-party testing institutions through standardized interfaces. All collected data is imported into the digital business model after field alignment and format verification according to unified standards.

[0009] Furthermore, the construction process of the integrated digital business model in S1 is as follows: The core business processes of disease control were reviewed, and the specific business needs, data collection scope, data format requirements, and connection logic between each process were clarified. The correspondence between each process and the corresponding collected and processed data was determined, and the specific requirements for data entry, review, and transfer were clarified. By combining unified data standards and specifications, each business process is associated with and bound to the corresponding data collection scope, data verification rules, and data processing flow. The input and output standards and data transmission formats of each business node are clarified, the triggering conditions, flow paths, and exception handling mechanisms between each business node are defined, and the operating permissions, approval processes, and responsible entities of each business node are also clarified.

[0010] Furthermore, the specific process of the bidirectional mapping encoding method for business processes and model nodes in S2 is as follows: Let the set of business process steps be . Any one of the business process steps is , This is the unique sequence number of this business process step within the overall disease control business process; the set of digital model nodes is... Any of the digital model nodes is , This is the unique serial number of the digital model node within the integrated digital business model; According to the unified data coding rules, respectively and Assign a composite unique code that includes business attributes, data type, and process sequence. and Construct a mapping matching feature function: in To map and match feature values ​​between business process steps and digital model nodes. Encode the length of the overlapping fields in both. Set a valid threshold for mapping the total field length for both encodings. Construct a mapping and determination relationship: This indicates that the mapping match is valid. This indicates an adaptation bias, based on The business process links and digital model nodes corresponding to the positioning deviations are re-corresponded to the coding fields according to the coding rules, and the mapping matching feature values ​​are recalculated until all mapping matches are valid, thus completing the bidirectional mapping coding matching.

[0011] Furthermore, the specific process of calibrating the adaptation deviation through the dynamic adaptation adjustment method in S2 is as follows: Construct a mapping topology structure between business process steps and digital model nodes, and define the set of mapping paths as follows. A single mapping path is ,in A unique number for the mapping path. Define the set of encoding matching anomalies as follows: The abnormal element is ,in A unique identifier for the abnormal element. The formula for topology path correction is as follows: in, For the calibrated mapping path, A set of codes for business process steps. For the set of node codes of the digital model, A unique set of characteristic fields for coding each stage of a business process. Encode a unique set of feature fields for each node in the digital model; based on the calibrated mapping path Update the correspondence between business process steps and digital model nodes, and establish closed-loop verification rules, the expression of which is as follows: in, This is the set of encoding mismatches after this verification. This is the set of encoding mismatches after the last verification. This is the set of valid codes corresponding to the calibrated mapping path; path correction and loop closure verification are performed iteratively until the anomaly set is reached. The empty set is used to complete the adaptation deviation calibration, so that the business process links and digital model nodes form a stable topological correspondence.

[0012] Furthermore, the full-process node time stamp embedding method in S2 is specifically as follows: Based on the bidirectional mapping encoding relationship between business processes and model nodes, a hash chain generation system for time stamps is constructed, defining the encoding set of business process stages as follows: The set of digital model node codes is The set of business operation execution times is Let the mapping pair between a single business process step and a model node be . ,in , Define the initial hash value as Then the formula for generating the time stamp is: In the formula For the first The time-series hash stamp generated by this operation For string concatenation operations, For the first The standardized timestamps for interactions between business operations and model nodes are generated using a secure hash algorithm. By performing a hash operation between the hash value of the previous moment and the current encoding and time information, the chained association and unique generation of timestamps are achieved, ensuring that there is no risk of collision between timestamps of any two operations.

[0013] Furthermore, the process of mining core disease control business characteristics and completing business classification in S3 is carried out based on an improved density peak clustering algorithm: First, the Euclidean distance between any two business feature vectors is calculated to quantify the differences in features between businesses. Next, the local density of each business feature point is calculated to reflect the degree of clustering of similar businesses around the business point. Then, the minimum distance from each business point to a point with higher density is calculated to identify cluster centers. After that, business points with high local density and high minimum distance are selected as cluster centers, and the remaining business points are assigned to the category to which the corresponding cluster center belongs, completing the business clustering. Finally, the silhouette coefficient is introduced to quantify and evaluate the classification effect, and the clustering results are iteratively optimized based on the evaluation results to ensure that the classification fits the actual business needs of disease control.

[0014] Furthermore, the process of constructing the data grading standard in S4 is as follows: First, based on the importance characteristics obtained after clustering and classifying various disease control services, the importance weight of the corresponding services is calculated. This weight is used to reflect the coreness of the services in the overall operation system. Then, combined with the probability and scope of data leakage in the flow, storage and interaction links, the data leakage risk coefficient is determined. The service importance weight and the data leakage risk coefficient are weighted and integrated to obtain the data sensitivity evaluation value. Sensitivity levels are divided according to the distribution range of data sensitivity evaluation values ​​to form an initial classification standard. Then, by real-time access to newly added business data and security event records, the business importance weight and data leakage risk coefficient are dynamically updated, and the sensitivity level division range is adjusted synchronously to achieve dynamic updates of the data classification standard. Based on the business classification results and dynamically updated data grading standards, the core business of disease control is subject to classified and graded management. Access permissions, operating scope and approval processes are configured for data of different sensitivity levels to ensure that business management strategies and data security requirements are dynamically adapted.

[0015] As a second aspect of the present invention, a comprehensive digital management system for internal operations of disease control based on the entire process is also provided, comprising: The data collection system building unit is used to construct a full-scenario data collection system for disease control business, formulate unified data standards and specifications, clarify data coding rules and cross-scenario mapping logic, build an integrated digital business model, and simultaneously define cross-module process nodes, permission rules and data flow mechanisms. The process digitization configuration unit is used to uniquely code and match business process links with digital model nodes using a bidirectional mapping coding method between business processes and model nodes. It calibrates the adaptation deviation through a dynamic adaptation and adjustment method and completes the process digitization configuration based on the calibration results. At the same time, it uses a full-process node time stamp embedding method to automatically generate traceable process node trajectory records and data traces. The business classification unit is used to mine the core business characteristics of disease control and complete business classification based on the business attributes and data characteristics of disease control, using a business feature vector extraction-clustering fusion algorithm, while extracting the business importance features corresponding to each business category. The access control unit is used to build a dynamic data sensitivity level assessment algorithm. It combines business importance weights and data leakage risk coefficients to establish dynamically updated data classification standards. Based on the classification results and classification standards, it implements classified control of core disease control business, sets hierarchical access and operation permissions for disease control business data with different sensitivity levels, and completes the dynamic adaptation of business control and data security.

[0016] As a third aspect of the invention, a computer-readable storage medium is also provided, on which a computer program is stored, which is executed by a processor, according to any one of the claims, a comprehensive digital management method for the internal operation of disease control based on the entire process.

[0017] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: 1. This invention provides a comprehensive digital management method for the internal operations of disease control based on the entire process. It constructs a full-scenario data collection system for disease control operations, establishes unified data standards and coding rules, sets cross-scenario data mapping logic, and builds an integrated digital business model. This model clarifies cross-module process nodes, permission rules, and data flow mechanisms, enabling unified collection and standardized processing of multi-source heterogeneous data. This approach connects multiple data sources, including medical institutions, laboratories, material management, and cross-institutional collaboration, eliminating data silos and ensuring the orderly aggregation and standardized interaction of various business data within a unified framework. This provides a stable and reliable data foundation for subsequent process digitization, business analysis, and security control, while also making the business connections between different links within disease control clearer and providing a unified basis for data interaction.

[0018] 2. The comprehensive digital management method for internal disease control operations based on the entire process of this invention achieves unique code matching by employing a two-way mapping coding method between business processes and model nodes. This is combined with a dynamic adaptation and adjustment method to calibrate adaptation deviations, enabling digital configuration of the process. Simultaneously, a full-process node time stamp embedding method generates traceable process trajectories and data trails. The two-way mapping coding accurately establishes the correspondence between actual business operations and the digital model, while dynamic calibration effectively reduces process adaptation errors and improves system stability. The time stamp embedding technology completely records the execution sequence of business operations, forming an immutable operational trajectory. This ensures the traceability and verifiability of business processes and provides authentic and complete data support for process optimization, responsibility definition, and anomaly tracing, thereby enhancing the standardization and controllability of internal disease control operations.

[0019] 3. This invention provides a comprehensive digital management method for the internal operations of disease control based on the entire process. It mines and classifies business features through business feature vector extraction and clustering fusion algorithms, simultaneously extracts business importance features, and then constructs a dynamic data sensitivity level assessment algorithm. Combining business importance weights and data leakage risk coefficients, a dynamic grading standard is established to achieve classified control and tiered permission configuration. The feature-based clustering algorithm can objectively classify disease control business categories, accurately reflecting the core nature of different businesses and providing a basis for differentiated management. The dynamic sensitivity level assessment can adjust the grading results in real time according to changes in business attributes and risks. Combined with tiered access and operation permission settings, it adapts to business operation needs while ensuring core data security, achieving a dynamic balance between business control efficiency and data security protection. Attached Figure Description

[0020] Figure 1 This is a flowchart of a comprehensive digital management method for internal operations of disease control based on the entire process, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the system units in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0022] Example 1 Please refer to Figure 1 This embodiment 1 provides a comprehensive digital management method for the internal operations of disease control based on the entire process, including: S1. Construct a full-scenario data collection system for disease control business, formulate unified data standards and specifications, clarify data coding rules and cross-scenario mapping logic, build an integrated digital business model, and simultaneously define cross-module process nodes, permission rules and data flow mechanisms. S2. A bidirectional mapping coding method between business process and model node is adopted to uniquely code and match business process links with digital model nodes. The adaptation deviation is calibrated through dynamic adaptation and adjustment methods, and the digital configuration of the process is completed based on the calibration results. At the same time, a full-process node time stamp embedding method is adopted to automatically generate traceable process node trajectory records and data traces. S3. Based on the business attributes and data characteristics of disease control, a business feature vector extraction-clustering fusion algorithm is adopted to mine the core business characteristics of disease control and complete the business classification. At the same time, the business importance features corresponding to each business category are extracted. S4. Construct a dynamic data sensitivity level assessment algorithm, combine business importance weights and data leakage risk coefficients, establish dynamically updated data classification standards, implement classified management and control of core disease control business based on classification results and classification standards, set hierarchical access and operation permissions for disease control business data with different sensitivity levels, and complete the dynamic adaptation of business management and data security.

[0023] This embodiment 1 further elaborates on the above steps.

[0024] (1) Data acquisition system construction Currently, various operational data in the disease control field are scattered across different systems and institutions, with diverse data sources and inconsistent standards. This easily leads to problems such as data inconsistencies, inefficient processes, and difficulties in collaborative management. Therefore, it is necessary to establish a comprehensive and standardized data collection and operational system. Under this premise, the first step is to build a data collection system covering all disease control operational scenarios. By uniformly formulating data standards and specifications, clarifying data coding methods and the data correspondence logic between different scenarios, a foundation is laid for the integration and interoperability of multi-source data.

[0025] This data collection system interfaces with multiple key business systems, enabling the automatic aggregation of multi-dimensional data. It can retrieve infectious disease reporting and case-related information from medical institution treatment systems, obtain sample and testing process data from laboratory information management systems, synchronize material flow and inventory information from material management systems, and collect operational data from internal departments throughout the entire process from initiation and approval to completion. Furthermore, it integrates collaborative data from health departments, communities, and third-party testing institutions through standardized interfaces. All collected data undergoes field alignment and format validation according to unified rules to ensure data consistency before being integrated into a unified business management model.

[0026] Building upon this foundation, an integrated digital business model is further constructed. First, a comprehensive review of all core business processes in disease control is conducted, clearly defining the business needs, data collection boundaries, format requirements, and inter-process connections for each process. The correspondence between business processes and raw / processed data is established, and specific requirements for data entry, review, and transfer are standardized. By integrating unified data standards, business processes are tightly linked to the collection scope, verification rules, and processing procedures. The input / output standards and data transmission formats for each business node are clearly defined, and the triggering conditions, operational paths, and anomaly handling methods for data transfer between nodes are established. Simultaneously, the operational permissions, approval processes, and responsible parties for each node are clearly defined, ensuring the orderly operation of the entire disease control business under a unified model.

[0027] For example, when a suspected cluster of viral outbreaks occurs within the jurisdiction, the data collection system directly obtains case information, medical records, and infectious disease report cards from the receiving medical institutions. It also extracts sample collection time, testing items, and final positive results from the laboratory system. Simultaneously, it tracks the quantity and distribution of disinfectants and emergency supplies, records the entire process of epidemic reporting, epidemiological investigation assignment, and review and reporting within the disease control system, and connects with data collected from the community regarding key location management and close contact tracing. All data is integrated into the business model after standardized field organization and format verification. The system automatically pushes data to the corresponding handling positions according to the established process, restricting access to sensitive case information to designated personnel. In case of missing data or delays in the process, an automatic reminder and verification mechanism is activated to ensure smooth coordination and clear responsibilities at each stage of epidemic response.

[0028] (2) Digital configuration of processes In addition, there are often adaptation discrepancies between current disease control business processes and digital models. The business links and model nodes are not accurately matched, and the process execution lacks traceable trajectory records, resulting in unstable digital operation of business and difficulty in defining operational responsibilities. Therefore, it is necessary to achieve efficient adaptation between business processes and digital models through coding matching, deviation calibration and time sequence traceability technologies.

[0029] First, a bidirectional mapping encoding method between business process and model nodes is adopted. For example, let the set of business process steps be... Any one of the business process steps is , This is the unique sequence number of this business process step within the overall disease control business process; the set of digital model nodes is... Any of the digital model nodes is , This is the unique serial number of the digital model node within the integrated digital business model; According to the unified data coding rules, respectively and Assign a composite unique code that includes business attributes, data type, and process sequence. and Construct a mapping matching feature function: in To map and match feature values ​​between business process steps and digital model nodes. Encode the length of the overlapping fields in both. Set a valid threshold for mapping the total field length for both encodings. Construct a mapping and determination relationship: This indicates that the mapping match is valid. This indicates an adaptation bias, based on The business process links and digital model nodes corresponding to the positioning deviations are re-corresponded to the coding fields according to the coding rules, and the mapping matching feature values ​​are recalculated until all mapping matches are valid, thus completing the bidirectional mapping coding matching.

[0030] After matching is complete, any potential adaptation deviations are calibrated using a dynamic adaptation adjustment method. This requires first constructing a mapping topology between business process steps and digital model nodes, defining the mapping path set as follows: A single mapping path is ,in A unique number for the mapping path. Define the set of encoding matching anomalies as follows: The abnormal element is ,in A unique identifier for the abnormal element. The formula for topology path correction is as follows: in, For the calibrated mapping path, A set of codes for business process steps (including core feature fields such as business operations and data requirements). A set of encodings for nodes in a digital model (including core feature fields such as functional modules and data interfaces). A unique set of characteristic fields for coding each stage of a business process. Encode a unique set of feature fields for each node in the digital model; based on the calibrated mapping path Update the correspondence between business process steps and digital model nodes, and establish closed-loop verification rules, the expression of which is as follows: in, This is the set of encoding mismatches after this verification. This is the set of encoding mismatches after the last verification. This is the set of valid codes corresponding to the calibrated mapping path; path correction and loop closure verification are performed iteratively until the anomaly set is reached. The empty set is used to complete the adaptation deviation calibration, so that the business process links and digital model nodes form a stable topological correspondence, and the digital configuration of the process is completed based on this.

[0031] Simultaneously, a full-process node time stamp embedding method is adopted to achieve traceable management of business processes. Specifically, based on the bidirectional mapping encoding relationship between business processes and model nodes, a hash chain generation system for time stamps is constructed, and the encoding set of business process stages is defined as follows: The set of digital model node codes is The set of business operation execution times is Let the mapping pair between a single business process step and a model node be . ,in , Define the initial hash value as Then the formula for generating the time stamp is: In the formula For the first The time-series hash stamp generated by this operation For string concatenation operations, For the first The standardized timestamps for interactions between business operations and model nodes are generated using a secure hash algorithm. By performing a hash operation between the hash value of the previous moment and the current encoding and time information, the chained association and unique generation of timestamps are achieved, ensuring that there is no risk of collision between timestamps of any two operations.

[0032] Simultaneously, a continuity verification model for time stamps is established, defining the verification quantity based on the difference between adjacent time stamps as follows: ,pass The numerical uniqueness is used to determine the continuity of the time sequence link. If the preset numerical characteristics of the hash chain are met, the time stamp generation and record are determined to be continuous and valid; otherwise, the time chain reconstruction mechanism is triggered; the hash chain is regenerated based on the latest business code and time information. Construct a time-series trajectory tracing query model, assuming the business process step to be queried is coded as follows: The digital model node is coded as The query time is Then the source tracing calculation expression is: By employing a dual hash calculation involving reverse derivation and forward verification, the system enables rapid location of the temporal trajectory of any business operation node and model interaction node. Combined with the immutable nature of the hash chain, it ensures the authenticity and integrity of process node trajectory records and data traces.

[0033] (3) Business Classification The current disease control services are diverse, with significant differences in operational models and data characteristics among different services. Without a scientific classification method, it is difficult to identify core and non-core services, thus failing to provide a basis for subsequent differentiated management. Therefore, it is necessary to combine the inherent attributes and data characteristics of disease control services, and use feature extraction and clustering fusion algorithms to uncover the characteristics of core services and complete scientific classification, while simultaneously extracting the importance characteristics of each type of service to support subsequent management.

[0034] First, the attributes and data characteristics of various disease control services are sorted out, and key dimensions such as the number of business links, data interaction frequency, approval level, scope of personnel involved, and event response cycle are extracted. These dimensions are transformed into quantifiable business feature vectors. At the same time, the extracted basic features are weighted and fused to highlight features related to the core attributes of disease control services and reduce the interference of weakly correlated features on the classification results, forming an optimized feature vector, which lays the foundation for subsequent clustering and classification.

[0035] After constructing feature vectors and performing weighted feature fusion for disease control services, an improved density peak clustering algorithm is used to perform service classification calculations: First, the Euclidean distance between any two service feature vectors is calculated to quantify the feature differences between different services. The calculation formula is as follows: In the formula For the first The and the first The distance between each business feature vector and The corresponding feature vectors at the th Feature values ​​in each dimension The total dimension of the feature vectors; Based on the Euclidean distance results, the local density of each service feature point is further calculated to reflect the degree of clustering of similar services around that service point. The calculation formula is as follows: In the formula For the first Local density of each business point To determine the cutoff distance, this value is adaptively determined through statistical analysis of disease control business characteristics, avoiding classification bias caused by manual setting. Then, the minimum distance from each business point to a point with higher density is calculated. This metric is used to identify cluster centers, and the calculation formula is as follows: For the service points with the highest density, their minimum distance is directly set to the maximum value of the distances between all service points to ensure complete and reliable cluster center identification; Business points with both high local density and high minimum distance are selected as cluster centers. The number of cluster centers is the final number of categories for disease control business. After determining the cluster centers, each remaining business point is assigned to the category of the nearest and densest cluster center, thus completing the clustering of all disease control business. Finally, the silhouette coefficient is introduced to quantitatively evaluate the classification performance. The calculation formula is as follows: In the formula For the first The average distance between each business point and other business points within the same category This represents the average distance between the business point and the nearest outlier business point; the closer the contour coefficient is to 1, the more closely the business classification effect matches the actual operation scenario.

[0036] Based on the evaluation results, the clustering process is iteratively optimized, and relevant parameters are adjusted until the classification effect meets the actual business needs. At the same time, after the classification is completed, the business importance characteristics corresponding to each type of business are extracted to clarify the core degree of each type of business in the overall operation of disease control.

[0037] (4) Access Control In addition to the issues mentioned above, disease control operational data also contains a large amount of sensitive information. Furthermore, the importance and data leakage risks vary across different business operations. Existing fixed data classification methods cannot adapt to dynamic changes in business operations, easily leading to insufficient protection of core data or excessive control that impacts operational efficiency. Therefore, it is necessary to construct a dynamic data sensitivity level assessment algorithm, combining business importance with data leakage risk to establish dynamic classification standards, achieving a precise alignment between business control and data security.

[0038] First, the business importance characteristics are normalized to obtain standardized business importance weights, calculated using the following formula: ,in For the first Importance weight of business categories For the first Key characteristic values ​​of similar business functions This represents the total number of business categories under control. This weight directly reflects the core importance of each business category within the overall disease control operation system, with core businesses having a higher weight and non-core businesses having a relatively lower weight, providing a basis for subsequent sensitivity level assessments.

[0039] Next, a comprehensive analysis of the security risks of various business data throughout their entire lifecycle is conducted. The analysis focuses on the probability of data leakage in different scenarios, including storage, transmission, internal access, and cross-organizational interaction. Simultaneously, the impact of a data breach on public health security, personal privacy, and the operational order of disease control is assessed. Combining these two factors, a data leakage risk coefficient is calculated using the following formula: ,in For the first The data leakage risk coefficient corresponding to this type of business. For the probability of data leakage, To determine the extent of the impact of the leak, and The weighting coefficient is set according to the risk control and security management requirements and meets the following conditions. .

[0040] Then, the business importance weight is combined with the data leakage risk coefficient to calculate the comprehensive evaluation value of the data sensitivity level. The calculation formula is as follows: ,in For the first Data sensitivity evaluation value for similar businesses and A balance coefficient set for safety management and control and satisfying Based on the numerical range of data sensitivity evaluation values, multiple sensitivity levels are defined to form a dynamic data grading standard. The calculation formula is as follows: in For the first Data sensitivity level for similar business operations and The threshold for classifying security levels is dynamically adjusted based on the security policy.

[0041] Based on the business classification results and sensitivity level results, differentiated access and operation permissions are configured for data of different levels to achieve dynamic adaptation between business control and data security. The permission adaptation determination formula is as follows: ,in For the first The permission configuration results for similar business functions. For the first Classification attributes of business types This is the permission matching function. Through the permission matching mechanism, business control policies and data security requirements are dynamically adapted, ensuring the security of core sensitive data without affecting normal business operations.

[0042] Example 2 Please refer to Figure 2 This embodiment 2 provides a comprehensive digital management system for the internal operations of disease control based on the entire process, including: The data collection system building unit is used to construct a full-scenario data collection system for disease control business, formulate unified data standards and specifications, clarify data coding rules and cross-scenario mapping logic, build an integrated digital business model, and simultaneously define cross-module process nodes, permission rules and data flow mechanisms. The process digitization configuration unit is used to uniquely code and match business process links with digital model nodes using a bidirectional mapping coding method between business processes and model nodes. It calibrates the adaptation deviation through a dynamic adaptation and adjustment method and completes the process digitization configuration based on the calibration results. At the same time, it uses a full-process node time stamp embedding method to automatically generate traceable process node trajectory records and data traces. The business classification unit is used to mine the core business characteristics of disease control and complete business classification based on the business attributes and data characteristics of disease control, using a business feature vector extraction-clustering fusion algorithm, while extracting the business importance features corresponding to each business category. The access control unit is used to build a dynamic data sensitivity level assessment algorithm. It combines business importance weights and data leakage risk coefficients to establish dynamically updated data classification standards. Based on the classification results and classification standards, it implements classified control of core disease control business, sets hierarchical access and operation permissions for disease control business data with different sensitivity levels, and completes the dynamic adaptation of business control and data security.

[0043] Example 3 This embodiment 3 also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can realize any step of a comprehensive digital management method for the internal operation of disease control based on the entire process.

[0044] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0045] For a description of the computer-readable storage medium provided in this application, please refer to the above method embodiments; further details will not be repeated here.

[0046] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A comprehensive digital management method for the internal operations of disease control based on the entire process, characterized in that, include: S1. Construct a full-scenario data collection system for disease control business, formulate unified data standards and specifications, clarify data coding rules and cross-scenario mapping logic, build an integrated digital business model, and simultaneously define cross-module process nodes, permission rules and data flow mechanisms. S2. A bidirectional mapping coding method between business process and model node is adopted to uniquely code and match business process links with digital model nodes. The adaptation deviation is calibrated through dynamic adaptation and adjustment methods, and the digital configuration of the process is completed based on the calibration results. At the same time, a full-process node time stamp embedding method is adopted to automatically generate traceable process node trajectory records and data traces. S3. Based on the business attributes and data characteristics of disease control, a business feature vector extraction-clustering fusion algorithm is adopted to mine the core business characteristics of disease control and complete the business classification. At the same time, the business importance features corresponding to each business category are extracted. S4. Construct a dynamic data sensitivity level assessment algorithm, combine business importance weights and data leakage risk coefficients, establish dynamically updated data classification standards, implement classified management and control of core disease control business based on classification results and classification standards, set hierarchical access and operation permissions for disease control business data with different sensitivity levels, and complete the dynamic adaptation of business management and data security.

2. The comprehensive digital management method for internal operations of disease control based on the entire process, as described in claim 1, is characterized in that... The disease control business full-scenario data collection system in S1 includes, but is not limited to: It directly connects to medical institutions' diagnosis and treatment systems to automatically capture infectious disease reporting and case-related data; it reads sample reception, testing items, test results, and testing time data through the laboratory information management system; it synchronizes information on material warehousing, outbound, inventory balance, and allocation flow in real time from the disease control material management system; it collects operational data on the entire process of business initiation, approval, and completion from various internal departments; and it aggregates collaborative business interaction data from health, community, and third-party testing institutions through standardized interfaces. All collected data is imported into the digital business model after field alignment and format verification according to unified standards.

3. The comprehensive digital management method for internal operations of disease control based on the entire process, as described in claim 1, is characterized in that... The construction process of the integrated digital business model in S1 is as follows: The core business processes of disease control were reviewed, and the specific business needs, data collection scope, data format requirements, and connection logic between each process were clarified. The correspondence between each process and the corresponding collected and processed data was determined, and the specific requirements for data entry, review, and transfer were clarified. By combining unified data standards and specifications, each business process is associated with and bound to the corresponding data collection scope, data verification rules, and data processing flow. The input and output standards and data transmission formats of each business node are clarified, the triggering conditions, flow paths, and exception handling mechanisms between each business node are defined, and the operating permissions, approval processes, and responsible entities of each business node are also clarified.

4. The comprehensive digital management method for internal operations of disease control based on the entire process, as described in claim 1, is characterized in that... The specific process of the bidirectional mapping encoding method for business processes and model nodes in S2 is as follows: Let the set of business process steps be . Any one of the business process steps is , This is the unique sequence number of this business process step within the overall disease control business process; the set of digital model nodes is... Any of the digital model nodes is , This is the unique serial number of the digital model node within the integrated digital business model; According to the unified data coding rules, respectively and Assign a composite unique code that includes business attributes, data type, and process sequence. and Construct a mapping matching feature function: in To map and match feature values ​​between business process steps and digital model nodes. Encode the length of the overlapping fields in both. Set a valid threshold for mapping the total field length for both encodings. Construct a mapping and determination relationship: This indicates that the mapping match is valid. This indicates an adaptation bias, based on The business process links and digital model nodes corresponding to the positioning deviations are re-corresponded to the coding fields according to the coding rules, and the mapping matching feature values ​​are recalculated until all mapping matches are valid, thus completing the bidirectional mapping coding matching.

5. The comprehensive digital management method for internal operations of disease control based on the entire process, as described in claim 1, is characterized in that... The specific process of calibrating the adaptation deviation through the dynamic adaptation adjustment method in S2 is as follows: Construct a mapping topology structure between business process steps and digital model nodes, and define the set of mapping paths as follows. A single mapping path is ,in A unique number for the mapping path. ; Define the set of encoding matching anomalies as The abnormal element is ,in A unique identifier for the abnormal element. The formula for topology path correction is as follows: in, For the calibrated mapping path, A set of codes for business process steps. For the set of node codes of the digital model, A unique set of characteristic fields for coding each stage of a business process. Encode a unique set of feature fields for each node in the digital model; based on the calibrated mapping path Update the correspondence between business process steps and digital model nodes, and establish closed-loop verification rules, the expression of which is as follows: in, This is the set of encoding mismatches after this verification. This is the set of encoding mismatches after the last verification. This is the set of valid codes corresponding to the calibrated mapping path; path correction and loop closure verification are performed iteratively until the anomaly set is reached. The empty set is used to complete the adaptation deviation calibration, so that the business process links and digital model nodes form a stable topological correspondence.

6. The comprehensive digital management method for internal operations of disease control based on the entire process, as described in claim 1, is characterized in that... The specific method for embedding the full-process node time stamp in S2 is as follows: Based on the bidirectional mapping encoding relationship between business processes and model nodes, a hash chain generation system for time stamps is constructed, defining the encoding set of business process stages as follows: The set of digital model node codes is The set of business operation execution times is Let the mapping pair between a single business process step and a model node be . ,in , Define the initial hash value as Then the formula for generating the time stamp is: In the formula For the first The time-series hash stamp generated by this operation For string concatenation operations, For the first The standardized timestamps for interactions between business operations and model nodes are generated using a secure hash algorithm. By performing a hash operation between the hash value of the previous moment and the current encoding and time information, the chained association and unique generation of timestamps are achieved, ensuring that there is no risk of collision between timestamps of any two operations.

7. The comprehensive digital management method for internal operations of disease control based on the entire process, as described in claim 1, is characterized in that... The process of mining core business characteristics of disease control and classifying business in S3 is carried out based on an improved density peak clustering algorithm: First, the Euclidean distance between any two business feature vectors is calculated to quantify the differences in features between businesses. Next, the local density of each business feature point is calculated to reflect the degree of clustering of similar businesses around the business point. Then, the minimum distance from each business point to a point with higher density is calculated to identify cluster centers. After that, business points with high local density and high minimum distance are selected as cluster centers, and the remaining business points are assigned to the category to which the corresponding cluster center belongs, completing the business clustering. Finally, the silhouette coefficient is introduced to quantify and evaluate the classification effect, and the clustering results are iteratively optimized based on the evaluation results to ensure that the classification fits the actual business needs of disease control.

8. The comprehensive digital management method for internal operations of disease control based on the entire process, as described in claim 1, is characterized in that... The process of constructing the data classification standard in S4 is as follows: First, based on the importance characteristics obtained after clustering and classifying various disease control services, the importance weight of the corresponding service is calculated. This weight is used to reflect the coreness of the service in the overall operation system. Then, by combining the probability and scope of data leakage in the flow, storage and interaction stages, the data leakage risk coefficient is determined, and the business importance weight and the data leakage risk coefficient are weighted and integrated to obtain the data sensitivity evaluation value; Sensitivity levels are divided according to the distribution range of data sensitivity evaluation values ​​to form an initial classification standard. Then, by real-time access to newly added business data and security event records, the business importance weight and data leakage risk coefficient are dynamically updated, and the sensitivity level division range is adjusted synchronously to achieve dynamic updates of the data classification standard. Based on the business classification results and dynamically updated data grading standards, the core business of disease control is subject to classified and graded management. Access permissions, operating scope and approval processes are configured for data of different sensitivity levels to ensure that business management strategies and data security requirements are dynamically adapted.

9. A comprehensive digital management system for internal operations of disease control based on the entire process, characterized in that, include: The data collection system building unit is used to construct a full-scenario data collection system for disease control business, formulate unified data standards and specifications, clarify data coding rules and cross-scenario mapping logic, build an integrated digital business model, and simultaneously define cross-module process nodes, permission rules and data flow mechanisms. The process digitization configuration unit is used to uniquely code and match business process links with digital model nodes using a bidirectional mapping coding method between business processes and model nodes. It calibrates the adaptation deviation through a dynamic adaptation and adjustment method and completes the process digitization configuration based on the calibration results. At the same time, it uses a full-process node time stamp embedding method to automatically generate traceable process node trajectory records and data traces. The business classification unit is used to mine the core business characteristics of disease control and complete business classification based on the business attributes and data characteristics of disease control, using a business feature vector extraction-clustering fusion algorithm, while extracting the business importance features corresponding to each business category. The access control unit is used to build a dynamic data sensitivity level assessment algorithm. It combines business importance weights and data leakage risk coefficients to establish dynamically updated data classification standards. Based on the classification results and classification standards, it implements classified control of core disease control business, sets hierarchical access and operation permissions for disease control business data with different sensitivity levels, and completes the dynamic adaptation of business control and data security.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor as described in any one of claims 1-8: a comprehensive digital management method for the internal operations of disease control based on the entire process.

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