Multi-region collaborative planning and scheduling method for infectious disease emergency resources

Abstract

This invention discloses a multi-regional collaborative planning and scheduling method for infectious disease emergency resources, relating to the field of public health emergency management technology. The method includes the following steps: constructing collaborative responsibility units for infectious disease emergency response based on the entire cross-border transmission chain of infectious diseases; generating unique link anchor codes for each responsibility unit and executing dynamic link update rules. This invention utilizes the construction of collaborative responsibility units based on the entire cross-border transmission chain of infectious diseases and the generation of link anchor codes; establishing a mechanism for the transformation of resource ownership and collaborative rights, as well as a distributed ledger and rights symbiotic compensation mechanism; driving scheduling with anchor codes; and iteratively optimizing scheduling data parameters. This addresses the problems of overall lack of management of port infectious disease emergency resources, information barriers, scheduling lags, configuration imbalances, and the disconnect between theory and practice. It achieves efficient collaborative scheduling of resources, eliminates information barriers and collaborative gaps, improves response efficiency, reduces transmission risks, fills research gaps, and forms a theoretical-practice closed loop.

Description

Technical Field

[0001] This invention relates to the field of public health emergency management technology, specifically a multi-regional collaborative planning and scheduling method for infectious disease emergency resources. Background Technology

[0002] The risk of cross-border transmission of emerging infectious diseases continues to rise globally. As key nodes in the prevention and control of imported infectious diseases, ports of entry rely heavily on multi-regional collaborative planning and allocation of emergency resources to determine the effectiveness of epidemic response. The World Health Organization has repeatedly warned of the potential for large-scale epidemics caused by unknown pathogens. Experiences from imported outbreaks such as SARS and MERS demonstrate that sudden outbreaks of infectious diseases at ports of entry place stringent demands on the coordinated allocation of cross-regional emergency resources. Currently, the core issue in the management of emergency resources for imported emerging infectious diseases at ports of entry is a lack of overall cohesion.

[0003] In port epidemic prevention and control practices, fragmented governance has hindered the formation of a unified multi-regional collaborative planning and dispatch system for emergency resources. Information barriers exist across regions and departments, preventing the interconnection of data on emergency resource reserves, needs, and allocation. This directly leads to a lack of top-level coordination in resource planning, delayed dispatch response, and imbalanced allocation among regions. Furthermore, existing research and practice have not integrated the concept of holistic governance into emergency resource dispatch. There is a gap in specialized research on multi-regional collaborative planning and dispatch of infectious disease emergency resources in port scenarios, resulting in a disconnect between theory and practice and failing to provide effective support for cross-regional resource linkage.

[0004] This core issue directly hinders the formation of cross-regional collaborative efforts in the dispatch of emergency resources at ports of entry. When faced with sudden imported outbreaks of new infectious diseases, it is difficult to quickly match resources and coordinate support, significantly reducing the efficiency of emergency response to epidemics at ports, exacerbating the risk of cross-border transmission and spread of the epidemic, and seriously threatening national public health security and the stable operation of the economy and society. Therefore, there is an urgent need to construct a multi-regional collaborative planning and dispatch model for infectious disease emergency resources adapted to port scenarios. In view of this, this paper proposes a multi-regional collaborative planning and dispatch method for infectious disease emergency resources to overcome the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-regional collaborative planning and scheduling method for infectious disease emergency resources, so as to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, this invention provides a multi-regional collaborative planning and scheduling method for infectious disease emergency resources, comprising the following steps: Based on the entire chain of cross-border transmission of infectious diseases, an emergency collaborative responsibility unit for infectious diseases is constructed, and a unique link anchor code is generated for the responsibility unit and the link dynamic update rule is executed. Maintain the independent ownership of emergency resources for each entity, establish rules for the transformation of resource ownership and collaborative rights, adopt a distributed rights ledger model and implement rules for rights symbiosis compensation; Driven by link anchor point coding, emergency resource reserves, demand response, and cross-regional collaborative planning and scheduling are completed; After resource scheduling is completed, the parameters are iteratively optimized based on the full-process scheduling data, forming a theoretical-practice closed loop.

[0007] Furthermore, the link anchor point code is generated by quantifying the number of propagation link nodes, node risk weights, node functional characteristic values, bit operation rules, basic identifiers of responsible units, and the risk level of the propagation link.

[0008] Furthermore, collaborative rights and interests are quantified and converted into collaborative rights and interests points by means of resource reserve compliance rate, information reporting timeliness score, and cross-regional allocation response speed score, combined with corresponding weight coefficients.

[0009] Furthermore, the trigger threshold for the rights-sharing compensation rule is determined by the mean and standard deviation of the collaborative rights scores of all entities within the infectious disease emergency collaborative responsibility unit.

[0010] Furthermore, the priority of resource allocation during the demand response phase is determined by quantitative matching of the entity's collaborative rights points and the resource demand urgency coefficient.

[0011] Furthermore, the distributed ledger uses blockchain light nodes to complete distributed evidence storage, without using a centralized database.

[0012] Furthermore, when the cross-border transmission path of infectious diseases changes, the link anchor point coding parameters are automatically updated and the scope of the infectious disease emergency coordination responsibility unit is adjusted.

[0013] Furthermore, the parameter self-iterative optimization is based on the actual scheduling effect score and the standard scheduling effect score, and the parameter is corrected by a fixed iterative correction coefficient.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. Construct collaborative responsibility units for the entire cross-border transmission of infectious diseases and generate link anchor point codes, along with supporting dynamic link update rules, to break through the barriers of administrative jurisdiction and departmental functions, completely eliminate collaborative gaps, and provide a fixed execution carrier for top-level coordination of emergency resources.

[0015] 2. Maintain the independent ownership of resources for each entity, transform resource ownership into collaborative rights points, and combine this with blockchain light node distributed rights ledger and rights symbiotic compensation rules to turn the fragmented ownership defects of governance into endogenous collaborative power, completely eliminate information barriers, realize full-domain interconnection of resource data, and dispel the concerns of entities about the interests of collaborative cooperation.

[0016] 3. Driven by link anchor point coding, the allocation priority is determined by quantifying and matching collaborative rights points and urgent needs. Self-driven scheduling replaces centralized approval and complex algorithms, achieving zero-level resource coordination, significantly reducing scheduling response time, and solving the problems of scheduling lag and regional configuration imbalance.

[0017] 4. Based on the self-iterative optimization of execution parameters of the whole process scheduling data, a theoretical and practical closed loop is constructed, which deeply integrates the holistic governance theory with the port prevention and control scenario, fills the special research gap in the collaborative scheduling of port infectious disease emergency resources, and provides a practical sample for the field.

[0018] 5. Rapidly form cross-regional collaborative forces, efficiently complete resource matching and linkage support, significantly improve the efficiency of port epidemic emergency response, reduce the risk of cross-border spread of the epidemic, and ensure national public health security and stable economic and social operation. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the principle of a multi-regional collaborative planning and scheduling method for infectious disease emergency resources according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 The present invention provides a technical solution: See Figure 1 The following is an embodiment of a multi-regional collaborative planning and scheduling method for infectious disease emergency resources: First, this method retains the existing resource ownership framework and does not implement centralized data control. Instead, it leverages the fragmented governance resulting in dispersed ownership and independent entities to build an innovative mechanism for self-driven collaboration based on bound ownership and symbiotic interests. Using the cross-border transmission chain of infectious diseases at ports as the collaborative anchor point, it transforms holistic governance theory into a quantifiable and implementable rule system. Simultaneously, it abandons conventional technologies such as big data and artificial intelligence algorithms, adopting a distributed rights ledger and chain-based responsibility anchoring approach to fundamentally address the issues of the collaborative system's driving force, anchor point, and theoretical support. This achieves an overall solution where the core contradictions are resolved, and the superficial problems automatically disappear.

[0022] Specific implementation steps: Step 1: Construction of Propagation Link Anchoring Unit: 1. Abandoning the conventional approach of dividing the scope of prevention and control according to administrative jurisdiction and departmental functions, we take the entire chain of cross-border transmission of infectious diseases as the core basis and include the port departure area, transit area, destination receiving area, cross-border related area, as well as the corresponding customs, health, disease control, medical institutions, and port operators into the same infectious disease emergency collaborative responsibility unit.

[0023] 2. Generate a unique link anchor code for each infectious disease emergency response coordination responsibility unit. This code is generated based on the quantification of transmission link characteristics, and the core formula is: ; in: Link anchor point code; a unique identifier, a 16-bit character quantization value; : The number of nodes in the transmission chain; such as the total number of nodes such as port inspection points, transfer stations, and designated hospitals; : No. The risk weight of each node in the transmission chain; the value ranges from 0 to 1, and is determined by the node's personnel flow, cross-border correlation, and prevention and control capabilities. The higher the flow / correlation / prevention and control capabilities, the higher the weight value. : No. Functional characteristic values ​​of each node in the transmission link; the values ​​are integers from 1 to 10, and are assigned according to the node's functional type: inspection node = 8, transfer node = 6, medical node = 10, logistics node = 4; Custom bitwise operation rules; unconventional addition, subtraction, multiplication, and division are performed as bit-weighted XOR, and the summation result of the preceding order is bound to the unit identifier and risk level to avoid coding duplication and retain link characteristics; Basic identifier for infectious disease emergency response coordination responsibility unit; 8-digit number consisting of port number + area code; : Risk level value of transmission link; the value is an integer from 1 to 5, which is determined by a combination of infectious disease transmissibility, mortality rate and probability of cross-border transmission.

[0024] The code binds four types of information: transmission link nodes, main prevention and control responsibilities, resource ownership scope, and emergency response level. The code is kept in real-time bound to the identity information and resource information of all entities within the unit.

[0025] 3. Execute dynamic update rules for transmission links. When the cross-border transmission path of infectious diseases changes, the system automatically updates the formula. , , The value of is recalculated. Furthermore, the scope of the emergency response coordination responsibility units for infectious diseases has been adjusted to ensure that the coordinating entities always work around the transmission chain, completely eliminating the coordination gaps caused by administrative divisions and providing a fixed execution platform for top-level coordination.

[0026] It should be noted that this step does not employ an administrative-level anchoring approach. Instead, it establishes collaborative anchors based on the objective links of epidemic transmission. By quantifying the characteristics of the transmission links through a link anchor coding formula, it breaks through the existing mindset that prioritizes administrative barriers over transmission links in epidemic prevention and control. This step binds dispersed and unrelated regions and departments into a community of shared responsibility and interests through the transmission links. Collaborative work spontaneously arises based on the objective needs of epidemic transmission, providing a fixed core anchor for subsequent collaborative scheduling.

[0027] It should also be noted that this invention, by establishing a real-time monitoring module for cross-border transmission links of infectious diseases, automatically collects data on personnel movement, traffic trajectories, and epidemic spread in ports of departure, transit, destination, and cross-border related areas, accurately counting the number of nodes in the transmission link. Based on the prevention and control capability assessment submodule, data on personnel flow, cross-border correlation, and prevention and control resource allocation at each node are collected, and the risk weight of each node is automatically calculated according to preset quantitative rules. Automatically assign function feature values ​​according to node function type Retrieve port number and area code to generate 8-digit basic identifier for responsibility unit. Risk level values ​​are determined by combining the transmissibility, mortality rate, and probability of cross-border transmission of infectious diseases. The system follows the bitwise XOR operation rules. Automatically calculate and generate a 16-bit unique link anchor code. After the code is generated, it is bound in real time to the identity information and resource ledger information of each entity within the unit. When a change in the propagation path is detected, the module automatically triggers an update command and re-collects data. , , The parameters are updated and the encoding is refreshed. The scope of the responsible unit is adjusted synchronously, and the updated data is pushed to each main terminal in real time.

[0028] Step 2: Transforming fragmented ownership into collaborative rights: 1. Maintain the independent ownership of resources for each entity, and establish rules for the conversion between resource ownership and collaborative rights. Collaborative rights points are quantified and converted based on three dimensions: resource reserves, information reporting, and allocation response. The core formula is: ; in: : Entity collaborative rights points; values ​​range from 0 to 1000, with higher points indicating higher collaborative priority; Resource reserve compliance rate; value ranges from 0 to 100, calculated as actual reserve quantity / minimum reserve quantity required by the link anchor point code × 100; Resource reserve weight coefficient; with a value of 0.5, it is the core weight, reflecting the fundamental status of resource reserves; : Information reporting timeliness score; value ranges from 0 to 100, calculated as 100 - actual reporting time / standard reporting time × 100, the shorter the reporting time, the higher the score; Information reporting weight coefficient; value 0.2, which is an auxiliary weight; Cross-regional deployment response speed score; value ranges from 0 to 100, calculated as 100 - actual response time / standard response time × 100, with shorter response times resulting in higher scores; : Adjust the response weight coefficient; the value is 0.3, which is the core auxiliary weight.

[0029] Collaborative rights and interests points directly determine the priority of resource allocation, eligibility for policy support, and emergency assessment results.

[0030] 2. A distributed equity ledger model is adopted. Each entity independently records its own resource information and collaborative behavior. The ledger data is distributed and stored through blockchain light nodes. A centralized database is not used, and resource ownership information is not forcibly shared. Value exchange between entities is achieved by relying on collaborative equity points.

[0031] 3. Implement the principle of shared rights and interests. When the collaborative rights and interests score of any entity within the infectious disease emergency response collaborative responsibility unit falls below a threshold, the system triggers the collaborative compensation mechanism for all entities within the unit. The formula for the rights and interests compensation trigger threshold is as follows: ; in: : Threshold for triggering rights and interests compensation; Lower limit of points required for entities within a unit to initiate compensation; The average of the collaborative rights and interests scores of all entities within the infectious disease emergency response collaborative responsibility unit; Standard deviation of the collaborative rights and interests scores of all entities within the infectious disease emergency response collaborative responsibility unit.

[0032] When the subject At that time, within the unit High-scoring entities need to allocate resources to this entity, and the entity's resource losses are compensated through the sharing of rights and interests within the unit, eliminating the entity's concerns about refusing to share resources or cooperate.

[0033] It is important to note here that this step transforms resource control rights into resource rights without altering resource ownership. It quantifies the relationship of rights through a collaborative rights point conversion formula and a compensation threshold formula, generating collaborative motivation solely through rights binding, thus avoiding the administrative resistance associated with centralized resource control. This step addresses the fragmented and dispersed ownership deficiencies of governance by transforming them into a collaborative, endogenous driving force for shared rights. Entities proactively report resource information and share emergency resources to increase their own collaborative rights points, completely eliminating information barriers and achieving full interconnectivity of resource reserves, demand, and allocation information.

[0034] It should also be noted that this invention, through a real-time equity point calculation submodule, automatically collects the actual resource reserves of each entity, information reporting time, and cross-regional allocation response time, and automatically calculates points according to the collaborative equity point formula. The scoring results are uploaded to the blockchain light nodes in real time; the blockchain light nodes are deployed on the local servers of each entity, and each entity independently records resource information and collaborative behavior. Data is transmitted peer-to-peer to complete distributed notarization, without relying on a centralized database; the system calculates the average score within the unit in real time. with standard deviation Determine the threshold for rights and interests compensation. -1.5 When the system detects that a subject's points are below the threshold, it automatically generates a resource compensation instruction and pushes it to subjects with high points. After the high-point subjects complete the allocation, the system automatically updates the equity points and the distributed ledger, completing the compensation loop.

[0035] Step 3: Link Anchor-Driven Cooperative Planning and Scheduling: 1. Resource Reserve Phase. Each entity independently completes emergency resource reserves based on the responsibility scope corresponding to the link anchor point code. Reserve data is synchronized to the link anchor point through distributed rights ledger, without needing to be reported to a unified platform. When an entity's resource reserves fall below the minimum standard set by the link anchor point, the system triggers the rights compensation mechanism within the infectious disease emergency collaborative responsibility unit. Entities with high collaborative rights scores allocate resources to entities with low standards, ensuring a balanced resource reserve across the entire region.

[0036] 2. Demand Response Phase. When an infectious disease outbreak occurs at a port, the system uses the link anchor code as the triggering basis to directly identify the resource-demanding entity, demand type, and demand quantity within the infectious disease emergency coordination responsibility unit, without executing the hierarchical administrative approval process. The system matches resources based on coordination rights points and demand urgency metrics. The resource allocation priority formula is as follows: ; in: : Resource allocation priority value; the value is an integer ranging from 0 to 5000, and the higher the value, the higher the allocation priority; : Subject collaborative rights and interests score; using the calculation results from step 2; Resource urgency coefficient; an integer ranging from 1 to 5, determined by the speed of epidemic spread, the number of infections, and the shortage of medical resources. The faster the spread, the more infections, or the greater the shortage, the higher the coefficient value.

[0037] System basis Match emergency resources to key stakeholders from high to low levels to complete the overall resource planning.

[0038] 3. Cross-regional allocation phase. The system directly pushes resource allocation instructions to the corresponding entities through link anchor point coding. Entities do not need to conduct cross-departmental communication and perform allocation obligations according to the rights and interests symbiosis rules. The allocation progress is updated synchronously to the link anchor point, forming a complete distributed scheduling closed loop.

[0039] It's important to note here that this step abandons the conventional logic of centralized platform command issuance and algorithm-based resource matching. Instead, it quantifies allocation rules through resource allocation priority formulas and employs a self-driven scheduling mechanism driven by link anchors. It avoids complex algorithms, relying solely on established rules to complete resource planning and allocation. This step achieves zero-level coordination in resource planning, significantly reducing scheduling response time and completely resolving issues of delayed response and regional resource imbalance. It achieves comprehensive collaborative planning across the entire domain based on link anchors and equity rules, eliminating the need for top-level administrative intervention.

[0040] It should also be noted that: this invention uses an automatic resource reserve monitoring module to compare the actual reserve quantity of the entity with the minimum reserve quantity set by the link anchor code in real time. If the quantity is not met, a rights compensation mechanism is directly triggered. After the outbreak of an epidemic, the link anchor code directly activates the demand identification module, quickly identifying the demand entity, resource type, and quantity, bypassing administrative level approvals. The system allocates resources according to a priority formula. The system automatically calculates priority values ​​and matches supply entities from high to low. It directly pushes allocation instructions through link anchor point coding. After the entity executes the allocation, the progress data is transmitted back to the link anchor point in real time and synchronized with the entire unit, thus completing distributed scheduling.

[0041] Step 4: Closed-loop feedback between theory and practice: 1. After resource allocation is completed, the system automatically calculates the collaborative rights and interests points of each entity, updates the distributed rights and interests ledger, and transforms the entire process of scheduling data, including the transmission chain, resource allocation, and entity response status, into holistic governance practice data, filling the gap in special research on collaborative planning and scheduling of emergency resources for port infectious diseases.

[0042] 2. Implement a self-iterative feedback mechanism. The system associates practical data with link anchor point codes and rights conversion rules, automatically optimizing the scope of infectious disease emergency collaborative responsibility units, collaborative rights point standards, and resource reserve thresholds. The self-iterative optimization formula is as follows: ; in: : Optimized parameter values; can refer to , , , Weights / coefficients that need to be iterated over; : Parameter values ​​before optimization; : Iterative correction coefficient; the value is 0.05, which is a fixed correction ratio to avoid excessive parameter fluctuations; : Actual scheduling effect score; value ranges from 0 to 100, calculated as actual dispatch completion rate × 50 + actual response time target achievement rate × 50; Standard scheduling performance score; fixed value of 100, representing ideal scheduling performance.

[0043] The system uses this formula to complete parameter iteration, forming a complete closed loop of practical feedback optimization, enabling the holistic governance theory to continuously adapt to port control scenarios and achieve a deep integration of theory and practice.

[0044] It's important to note here that this step deeply integrates holistic governance theory with link anchors and rights rules. It quantifies the logic of practical data refining the theory's implementation through a self-iterative feedback optimization formula. Theoretical implementation is achieved through self-iteration of practical data, rather than purely theoretical exposition. This step, while addressing emergency resource allocation issues, establishes a sustainable and optimized collaborative system, filling a research gap in the application of holistic governance theory in port scenarios and providing directly applicable practical samples for subsequent research in the field.

[0045] It should also be noted that this invention uses an automatic scheduling performance evaluation module to statistically analyze the actual allocation completion rate and response time target achievement rate, and calculates the actual scheduling performance score. The optimized parameters are automatically calculated according to the self-iterative optimization formula. The new parameters are synchronized in real time to the calculation rules of link anchor point codes, rights and interests points, and resource reserve thresholds and take effect immediately. The system archives the full-process scheduling data and parameter iteration data to the practice database, continuously providing a basis for rule optimization for the collaborative scheduling of port infectious disease emergency resources.

[0046] Summarize: By constructing collaborative responsibility units across the entire cross-border transmission chain of infectious diseases and generating link anchor point codes, along with supporting dynamic link update rules, we can break through the barriers of administrative jurisdiction and departmental functions, completely eliminate collaborative gaps, and provide a fixed execution platform for top-level coordination of emergency resources.

[0047] By maintaining the independent ownership of resources for each entity, resource ownership is transformed into collaborative rights points. This is combined with distributed rights ledgers and rights symbiosis compensation rules using blockchain light nodes. This transforms the fragmented ownership and decentralized nature of governance into an endogenous driving force for collaboration, completely eliminating information barriers, achieving full interconnection of resource data, and dispelling concerns about the interests of entities in collaborative cooperation.

[0048] Driven by link anchor point coding, the allocation priority is determined by quantifying and matching collaborative rights points and urgency of demand. Self-driven scheduling replaces centralized approval and complex algorithms, achieving zero-level resource coordination, significantly reducing scheduling response time, and solving the problems of scheduling lag and regional configuration imbalance.

[0049] Based on the self-iterative optimization of execution parameters of the whole process scheduling data, a theoretical and practical closed loop is constructed, which deeply integrates the holistic governance theory with the port prevention and control scenario, fills the special research gap in the collaborative scheduling of emergency resources for port infectious diseases, and provides a practical sample for the field.

[0050] Rapidly forming cross-regional collaborative forces, efficiently completing resource matching and joint support, significantly improving the efficiency of emergency response to the epidemic at ports, reducing the risk of cross-border transmission and spread of the epidemic, and ensuring national public health security and stable economic and social operation.

Claims

1. A multi-regional collaborative planning and scheduling method for infectious disease emergency resources, characterized in that, Includes the following steps: Based on the entire chain of cross-border transmission of infectious diseases, an emergency collaborative responsibility unit for infectious diseases is constructed, and a unique link anchor code is generated for the responsibility unit and the link dynamic update rule is executed. Maintain the independent ownership of emergency resources for each entity, establish rules for the transformation of resource ownership and collaborative rights, adopt a distributed rights ledger model and implement rules for rights symbiosis compensation; Driven by link anchor point coding, emergency resource reserves, demand response, and cross-regional collaborative planning and scheduling are completed; After resource scheduling is completed, the parameters are iteratively optimized based on the full-process scheduling data, forming a theoretical-practice closed loop.

2. The multi-regional collaborative planning and scheduling method for infectious disease emergency resources as described in claim 1, characterized in that: The link anchor point code is generated by quantifying the number of nodes in the propagation link, node risk weight, node functional characteristic value, bit operation rules, basic identifier of the responsible unit, and risk level of the propagation link.

3. The multi-regional collaborative planning and scheduling method for infectious disease emergency resources as described in claim 1, characterized in that: Collaborative rights and interests are quantified and converted into collaborative rights and interests points by means of resource reserve compliance rate, information reporting timeliness score, and cross-regional allocation response speed score, combined with corresponding weight coefficients.

4. The multi-regional collaborative planning and scheduling method for infectious disease emergency resources as described in claim 3, characterized in that: The trigger threshold for the rights-sharing compensation rule is determined by the mean and standard deviation of the collaborative rights scores of all entities within the infectious disease emergency collaborative responsibility unit.

5. The multi-regional collaborative planning and scheduling method for infectious disease emergency resources as described in claim 1, characterized in that: The priority of resource allocation during the demand response phase is determined by quantitative matching of the entity's collaborative rights points and the resource demand urgency coefficient.

6. The multi-regional collaborative planning and scheduling method for infectious disease emergency resources as described in claim 1, characterized in that: Distributed equity ledger uses light blockchain nodes to complete distributed evidence storage, without using a centralized database.

7. The multi-regional collaborative planning and scheduling method for infectious disease emergency resources as described in claim 1, characterized in that: When the cross-border transmission route of infectious diseases changes, the link anchor point coding parameters are automatically updated and the scope of the infectious disease emergency coordination responsibility unit is adjusted.

8. The multi-regional collaborative planning and scheduling method for infectious disease emergency resources as described in claim 1, characterized in that: The parameter self-iterative optimization is based on the actual scheduling effect score and the standard scheduling effect score, and the parameter is corrected by a fixed iterative correction coefficient.

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