County general hospital key node supervision system

Abstract

The invention discloses a county general hospital key node supervision system, particularly relates to the field of hospital key node data transmission supervision, and comprises a node identification module, a request triggering module, a path evolution module, a system adaptation module and a closed-loop optimization module. The request triggering module calculates a resource demand expression capability index based on a node behavior perception map, activates a resource self-pickup channel, generates a resource request data block, and establishes a resource active expression and scheduling entry mechanism. A node behavior perception map is formed by constructing a feedback information set, a resource self-picking channel is activated, a scheduling request data block is generated, a scheduling structure is optimized in combination with path response consistency, and an evolution closed loop is formed through system adaptation and a periodic feedback mechanism. The key problems that resource scheduling blind area recognition is difficult, non-core resource configuration is out of control, and the supervision ability of a general hospital to a lower-level institution is weakened in the background technology are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission supervision of key nodes in hospitals, and more specifically, to a key node supervision system for county general hospitals. Background Art

[0002] In county-level medical communities, the general hospital, as the "leader", can assume overall management responsibilities, but when it comes to grassroots health centers, township health service centers and other institutions, there are often problems of information gaps and "resource blind spots";

[0003] Due to the lack of an effective data transmission or data feedback mechanism, as well as the lack of perception of resource scheduling at key nodes, the General Hospital has lost precise control over "non-emergency and non-core" resource scheduling matters such as consumables allocation, personnel scheduling, and improvement of diagnosis and treatment capabilities of subordinate institutions, resulting in the coexistence of "hot resource accumulation and cold resource depletion", causing serious unevenness in service quality. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a key node supervision system for county general hospitals, which forms a node behavior perception map by constructing a feedback information set, activates the resource self-collection channel and generates a scheduling request data block, optimizes the scheduling structure in combination with the path response consistency, and forms an evolutionary closed loop through system adaptation and periodic feedback mechanism to solve the key problems raised in the above background technology, such as "difficulty in identifying blind spots in resource scheduling, out-of-control allocation of non-core resources, and weakened supervision ability of general hospitals over subordinate institutions".

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a key node supervision system for county general hospitals, comprising a node identification module, a request triggering module, a path evolution module, a system adaptation module, and a closed-loop optimization module;

[0006] The node identification module is used to construct feedback information groups and extract abnormal data fragments, forming a node behavior perception map through data transmission, providing a basic identification basis for resource scheduling;

[0007] The request trigger module calculates the resource demand expression capability index based on the node behavior perception graph, activates the resource self-collection channel and generates the resource request data block, establishing a resource active expression and scheduling entry mechanism;

[0008] The path evolution module is used to construct all resource request data blocks into a scheduling node intersection graph and perform path deduction, identify scheduling bottlenecks and generate structural adjustment suggestions, realizing dynamic evolution modeling of resource scheduling;

[0009] The system adaptation module is used to couple the resource response log with the system execution structure to analyze the adaptation factor, generate a local system recommendation module and bind the scheduling path to ensure that the system matches the resource allocation;

[0010] The closed-loop optimization module performs feature clustering and strategy backtracking based on the periodic closed-loop structure, extracts the evolutionary governance map and solidifies the target path strategy, achieving continuous optimization and evolutionary support for key node supervision.

[0011] In a preferred embodiment, the node identification module further comprises dividing the original service data from each grassroots service unit into four types of feedback information groups according to the governance dimension. The four types of feedback information groups include medical records, nursing logs, satisfaction feedback, and work style dynamics, and uniformly constructing a feedback information set;

[0012] Perform continuity check on each piece of information in the feedback information set according to the time sequence and reporting path, and extract three types of abnormal data fragments: missing fragments, upload delays and path breaks;

[0013] Identify the service unit with abnormal data fragments as the initial key node and generate the corresponding feedback response integrity indicator;

[0014] The feedback response integrity indicators are executed through the data transmission mechanism to execute the superior supervision processing process, forming a node behavior perception map.

[0015] In a preferred embodiment, the request triggering module further comprises calculating a resource demand index for each node in the node behavior perception graph, where the resource demand index is composed of a reporting frequency, a response interval, and an active behavior flag;

[0016] If the resource demand index is lower than the preset benchmark value for three consecutive cycles, the resource scheduling request permission of the node will be suspended and feedback will be sent to the general hospital task supervision channel; if the resource demand index remains within the preset range, the resource self-collection channel permission will be activated;

[0017] In the activated resource self-collection channel, the scheduling request data submitted by the node is bound to the task dimensions, historical completion rate, and delay fluctuation range in the information group to construct a resource request data block;

[0018] All resource request data blocks are aggregated into the scheduling entry queue, and a priority map is constructed within the same governance dimension. The priority map serves as the basis for sorting into the decision processing area.

[0019] In a preferred embodiment, the path evolution module further comprises constructing a scheduling node intersection graph based on all resource request data blocks in the scheduling entry queue according to the governance dimension to which they belong, the current level of the node, and the task overlap;

[0020] Identify the subgraph area with target overlap in the scheduling node intersection graph, and output the scheduling bottleneck set based on the path response consistency coefficient and path saturation ratio;

[0021] If the path response consistency coefficient in the scheduling bottleneck set is greater than or equal to the preset threshold, the scheduling path structure remains unchanged and the scheduling trajectory is recorded. If the scheduling bottleneck set includes a path with a path response consistency coefficient lower than the preset threshold, the path optimization mechanism is triggered and scheduling structure adjustment suggestions are generated.

[0022] All scheduling structure adjustment suggestions are compared with the historical resource configuration log execution path response consistency. If the comparison results show that the path structure remains highly consistent, they are included in the scheduling trajectory reuse process; if the comparison results include structural mismatches, the path correction process is output and the allocation adaptability re-determination process is initiated.

[0023] In a preferred embodiment, the system adaptation module further includes extracting the corresponding resource response logs and the executed system structure for the key nodes of successful allocation, and constructing a corresponding relationship table between resources and systems; in the corresponding relationship table, the execution deviation degree between each system structure and resource response result is calculated, and the system adaptation factor under each governance dimension is extracted;

[0024] If the system adaptation factor is lower than the set threshold, the original system structure will be split and reorganized to generate a local system recommendation module adapted to the current key node; if the adaptation factor reaches the set standard, the original system structure will be retained and marked with the corresponding adaptation number;

[0025] The local system recommendation module is bound to the subsequent resource allocation path of the key node to form a linkage configuration table of system and resource scheduling, which is used to support the system consistency verification of subsequent scheduling decisions.

[0026] In a preferred embodiment, the closed-loop optimization module further includes, after each governance cycle, aggregating the feedback information set of key nodes, resource request data blocks, scheduling response logs, and the system adaptation module to form a cycle closed-loop structure; subjecting the cycle closed-loop structure to a continuous governance learning process, performing cross-cycle feature clustering and difference annotation analysis in the continuous governance learning process to extract governance stability indicators and a set of scheduling recurrence patterns;

[0027] If a policy path fails for two consecutive cycles in the scheduling repetition pattern set, the system triggers the governance structure freeze flag for the path, freezes the path, and suspends its system update plan. If it succeeds for more than three consecutive cycles, the path strategy is solidified as a governance template and written into the system iteration database.

[0028] All governance templates and frozen paths are used to form an evolutionary governance map for reference in the general hospital's long-term resource scheduling planning and strategy inversion optimization.

[0029] In a preferred embodiment, the node identification module includes executing the upper-level supervision processing flow through the data transmission mechanism to form a node behavior perception map, describing the node behavior perception map through the feedback response integrity index, and formulating I n It represents the feedback response completeness index of the nth service unit, and the unit is ratio;

[0030]

[0031] Where T d,n is the upload delay time in seconds; C l,n The number of consecutive upload interruptions, in times; T r,n Specifies the feedback cycle in seconds; L m,n L is the total length of the missing content, in bytes; e,n The total length of the content to be uploaded, in bytes; R p,n is the path interruption ratio, unit is dimensionless; A s,n is the path structure correction factor, unit is dimensionless;

[0032] The request trigger module includes the scheduling request data submitted by the node in the activated resource self-collection channel, which is bound to the task dimensions, historical completion rate and delay fluctuation range in the information group to construct the resource request data block;

[0033]

[0034] where Q n The scheduling priority strength of the resource request data block, the unit is ratio score; D t,n is the task dimension coverage, the unit is the number of task items; F h,n is the historical completion rate, unit is dimensionless; V d,n is the delay fluctuation amplitude, in hours; T e,n is the upper limit of the reference task response time, in hours; W e,n is the node behavior response elasticity factor, and its unit is dimensionless.

[0035] In a preferred embodiment, the path evolution module includes identifying subgraph regions of target overlap in the scheduling node intersection graph, and outputting a scheduling bottleneck set based on the path response consistency coefficient and the path saturation ratio;

[0036]

[0037] Among them B ris the scheduling bottleneck index of the rth path area, and the unit is ratio score; R s,r is the path response consistency coefficient, the unit is ratio; H o,r is the path overlap saturation ratio, in units of ratio; F l,r is the historical failure frequency of the path, in times;

[0038] The system adaptation module is included in the execution correspondence table, which calculates the degree of execution deviation between each system structure and resource response results, and extracts the system adaptation factor under each governance dimension. The derivation formula is:

[0039] Among them S k is the institutional adaptation factor of the kth governance dimension, with the unit being the deviation score; F r,k is the resource response intensity, the unit is ratio; S c,k is the system completion rate, the unit is proportion; Δ m,k is the response tolerance range, in percentage.

[0040] In a preferred embodiment, the closed-loop optimization module further includes subjecting the periodic closed-loop structure to a continuous governance learning process, performing cross-period feature clustering and difference annotation analysis in the continuous governance learning process, and extracting a set of governance stability indicators and scheduling recurrence patterns;

[0041]

[0042] Among them G c is the governance stability index, with the unit being dimensionless cycle score; δ r,t is the scheduling strategy repetition deviation of the t-th cycle, the unit is proportion; δ0 is the reference deviation standard, the unit is proportion; T is the total number of governance cycles, the unit is cycle; t is the current cycle position, the unit is cycle number.

[0043] Technical effects and advantages of the present invention:

[0044] 1. The node identification module constructs a feedback information set and extracts abnormal fragments such as upload delays and path breaks. A node behavior perception map is formed based on the feedback response integrity indicator. Combined with the data transmission link, a scheduling perception mechanism based on the behavior map is established, thus solving the resource scheduling blind spots and data gaps that exist between county general hospitals and grassroots units.

[0045] 2. The request trigger module calculates the resource demand expression capability index based on the perception graph and constructs a scheduling request data block. Under the condition of activating the resource self-provisioning channel, a priority sorting mechanism is established based on task dimensions, historical completion rates, and fluctuations. This constructs a bottom-up request-driven scheduling channel, effectively improving proactive responsiveness and distribution efficiency during resource transfer.

[0046] 3. The path evolution module establishes a scheduling node intersection graph, integrates the path response consistency coefficient and the path saturation ratio to form a scheduling bottleneck set, and combines historical scheduling logs to perform path consistency comparison and generate structural adjustment suggestions. This promotes the dynamic optimization and evolution of the scheduling path structure and enhances the adaptive capabilities of the scheduling strategy in multi-source and high-density scenarios.

[0047] 4. This solution couples the resource response log with the system execution structure through the system adaptation module, extracts the system adaptation factor of the governance dimension based on the degree of deviation between the system completion rate and the response intensity, reconstructs the system structure and generates a local recommendation module when the adaptability is insufficient, effectively avoiding the execution mismatch between resource allocation and management system, and enhancing the stability of the system's constraints on the resource behavior chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0050] Refer to the instruction manual Figure 1 , a key node supervision system of a county general hospital in one embodiment of the present invention includes a node identification module, a request trigger module, a path evolution module, a system adaptation module, and a closed-loop optimization module;

[0051] The node identification module is used to construct feedback information groups and extract abnormal data fragments, forming a node behavior perception map through data transmission, providing a basic identification basis for resource scheduling;

[0052] The request trigger module calculates the resource demand expression capability index based on the node behavior perception graph, activates the resource self-collection channel and generates the resource request data block, establishing a resource active expression and scheduling entry mechanism;

[0053] The path evolution module is used to construct all resource request data blocks into a scheduling node intersection graph and perform path deduction, identify scheduling bottlenecks and generate structural adjustment suggestions, realizing dynamic evolution modeling of resource scheduling;

[0054] The system adaptation module is used to couple the resource response log with the system execution structure to analyze the adaptation factor, generate a local system recommendation module and bind the scheduling path to ensure that the system matches the resource allocation;

[0055] The closed-loop optimization module performs feature clustering and strategy backtracking based on the periodic closed-loop structure, extracts the evolutionary governance map and solidifies the target path strategy, achieving continuous optimization and evolutionary support for key node supervision.

[0056] The node identification module also includes dividing the original service data from each grassroots service unit into four types of feedback information groups based on the governance dimension. The four types of feedback information groups include medical records, nursing logs, satisfaction feedback, and work style dynamics, and uniformly constructing feedback information sets;

[0057] Perform continuity check on each piece of information in the feedback information set according to the time sequence and reporting path, and extract three types of abnormal data fragments: missing fragments, upload delays and path breaks;

[0058] Identify the service unit with abnormal data fragments as the initial key node and generate the corresponding feedback response integrity indicator;

[0059] The feedback response integrity indicators are executed through the data transmission mechanism to execute the superior supervision processing process, forming a node behavior perception map.

[0060] The request trigger module also includes calculating the resource demand index of each node in the node behavior perception graph. The resource demand index is composed of the reporting frequency, response interval and active behavior mark.

[0061] If the resource demand index is lower than the preset benchmark value for three consecutive cycles, the resource scheduling request permission of the node will be suspended and feedback will be sent to the general hospital task supervision channel; if the resource demand index remains within the preset range, the resource self-collection channel permission will be activated;

[0062] In the activated resource self-collection channel, the scheduling request data submitted by the node is bound to the task dimensions, historical completion rate, and delay fluctuation range in the information group to construct a resource request data block;

[0063] All resource request data blocks are aggregated into the scheduling entry queue, and a priority map is constructed within the same governance dimension. The priority map serves as the basis for sorting into the decision processing area.

[0064] The path evolution module also includes constructing a scheduling node cross-graph based on the governance dimension, current level of the node and task overlap of all resource request data blocks in the scheduling entry queue;

[0065] Identify the subgraph area with target overlap in the scheduling node intersection graph, and output the scheduling bottleneck set based on the path response consistency coefficient and path saturation ratio;

[0066] If the path response consistency coefficient in the scheduling bottleneck set is greater than or equal to the preset threshold, the scheduling path structure remains unchanged and the scheduling trajectory is recorded. If the scheduling bottleneck set includes a path with a path response consistency coefficient lower than the preset threshold, the path optimization mechanism is triggered and scheduling structure adjustment suggestions are generated.

[0067] All scheduling structure adjustment suggestions are compared with the historical resource configuration log execution path response consistency. If the comparison results show that the path structure remains highly consistent, they are included in the scheduling trajectory reuse process; if the comparison results include structural mismatches, the path correction process is output and the allocation adaptability re-determination process is initiated.

[0068] The system adaptation module also includes extracting the corresponding resource response logs and implemented system structures for key nodes of successful allocation, and constructing a table of resource and system execution correspondences. In this table, the degree of execution deviation between each system structure and resource response result is calculated, and the system adaptation factor under each governance dimension is extracted.

[0069] If the system adaptation factor is lower than the set threshold, the original system structure will be split and reorganized to generate a local system recommendation module adapted to the current key node; if the adaptation factor reaches the set standard, the original system structure will be retained and marked with the corresponding adaptation number;

[0070] The local system recommendation module is bound to the subsequent resource allocation path of the key node to form a linkage configuration table of system and resource scheduling, which is used to support the system consistency verification of subsequent scheduling decisions.

[0071] The closed-loop optimization module also includes aggregating the feedback information sets of key nodes, resource request data blocks, scheduling response logs, and system adaptation modules at the end of each governance cycle to form a cycle closed-loop structure; subjecting the cycle closed-loop structure to a continuous governance learning process, performing cross-cycle feature clustering and difference annotation analysis in the continuous governance learning process to extract governance stability indicators and a set of scheduling recurrence patterns;

[0072] If a policy path fails for two consecutive cycles in the scheduling repetition pattern set, the system triggers the governance structure freeze flag for the path, freezes the path, and suspends its system update plan. If it succeeds for more than three consecutive cycles, the path strategy is solidified as a governance template and written into the system iteration database.

[0073] All governance templates and frozen paths are used to form an evolutionary governance map for reference in the general hospital's long-term resource scheduling planning and strategy inversion optimization.

[0074] In the formula structure involved in this solution, dimensionless terms can serve as proportionality or structural adjustment factors. When combined with quantities with units, they only have a numerical scaling effect and do not introduce new physical dimensions. Therefore, they do not change or confuse the overall unit system of expression. This combination of "dimensionless terms and units" can be understood as a composite structural expression commonly used in mathematical and physical modeling, conforming to the principle of dimensional consistency and having a clear physical interpretation basis.

[0075] Secondly, in the formula structure of this scheme, if multiple variables with different physical units are involved, including but not limited to time, mass, or energy variables, their joint appearance is to express the collaborative modeling relationship of multiple physical mechanisms. Each variable is formed into a unified structure through function mapping, ratio combination, or normalization adjustment. The units and meanings are clear, and the overall expression conforms to the principle of dimensional consistency and the common formula of engineering modeling.

[0076] The node identification module includes executing the upper supervision processing flow through the data transmission mechanism to form the node behavior perception map, describing the node behavior perception map through the feedback response integrity index, and formulating I n It represents the feedback response completeness index of the nth service unit, and the unit is ratio (dimensionless, %);

[0077]

[0078] Where T d,n is the upload delay time in seconds; C l,n The number of consecutive upload interruptions, in times; T r,n Specifies the feedback cycle in seconds; L m,n The total length of the missing content, in bytes (B); L e,n The total length of the content to be uploaded, in bytes; R p,n is the path interruption ratio, unit is dimensionless; A s,n is the path structure correction factor, unit is dimensionless (%);

[0079] The first of these Indicates the cumulative impact of upload interruption per unit time, with the dimension of (dimensionless); the second term Represents the product of missing measurement information and path instability, dimensionless; multiplied by A s,n The overall output is then kept dimensionless, and the units are unified into a ratio-type response index;

[0080] I nThe formula is used to quantify the data feedback deviation of key nodes, integrating three types of abnormal factors: time delay, content loss, and path interruption, to construct a "feedback response integrity index" as an evaluation entry for whether a node should be included in the supervision map;

[0081] The request trigger module includes the scheduling request data submitted by the node in the activated resource self-collection channel, which is bound to the task dimensions, historical completion rate and delay fluctuation range in the information group to construct the resource request data block;

[0082]

[0083] where Q n is the scheduling priority strength of the resource request data block, the unit is ratio score (dimensionless, %); D t,n is the task dimension coverage, the unit is the number of task items; F h,n is the historical completion rate, unit is dimensionless (%); V d,n is the delay fluctuation amplitude, in hours; T e,n is the upper limit of the reference task response time, in hours; W e,n is the node behavior response elasticity factor, unit is dimensionless (%);

[0084] The first of these It represents the weight adjustment of task density on completion ability, dimensionless; the second Indicates the normalized fluctuation amplitude, dimensionless; and W e,n After multiplication, the overall output is a ratio weight value, maintaining a dimensionless score structure;

[0085] Among them in Q n In the formula, the strength expression used to construct the resource request data block is used to regulate the request priority through the linkage of task volume coverage, historical completion performance and fluctuation stability. It is the core weight function of the "generating resource request data block" process in the request trigger module.

[0086] The path evolution module includes identifying subgraph regions with target overlap in the scheduling node intersection graph and outputting the scheduling bottleneck set based on the path response consistency coefficient and the path saturation ratio;

[0087]

[0088] Among them B r is the scheduling bottleneck index of the rth path area, the unit is ratio score (dimensionless, %); R s,r is the path response consistency coefficient, the unit is ratio (%); H o,r is the path overlap saturation ratio, in percentage (%); F l,r is the historical failure frequency of the path, in times;

[0089] in To amplify the high response path, the amplified high response path is used to suppress the high saturation path, dimensionless; Indicates exponential penalty for failed paths, dimensionless; the output result is a dimensionless bottleneck index, which meets the requirements of scheduling intensity comparison;

[0090] The above formula is used to characterize the bottleneck intensity of the path, reflecting the structural problems caused by uneven response and historical failures under high request density. It is the key numerical driving basis for the "output scheduling bottleneck set" link in the path evolution module.

[0091] The system adaptation module is included in the execution correspondence table, which calculates the degree of execution deviation between each system structure and resource response results, and extracts the system adaptation factor under each governance dimension. The derivation formula is:

[0092] Among them S k is the institutional adaptation factor of the kth governance dimension, with the unit being the deviation score (dimensionless); F r,k is the resource response intensity, the unit is proportion (%); S c,k is the system completion rate, in percentage (%); Δ m,k is the response tolerance range, the unit is percentage (%);

[0093] in is the deviation normalization operation, dimensionless; log(1+F r,k ) is the weight for increasing the high responsiveness of the system, dimensionless; the overall output is maintained as the dimensionless system adaptation deviation score;

[0094] The above formula is used to measure the degree of fit between the system content and the actual response of the node. It is the standard judgment criterion for "generating a recommended module or retaining the original structure" in the system adaptation module, ensuring that the scheduling strategy is compatible with the actual governance capabilities.

[0095] The closed-loop optimization module also includes a continuous governance learning process for the periodic closed-loop structure, performing cross-period feature clustering and difference annotation analysis in the continuous governance learning process, and extracting governance stability indicators and scheduling recurrence pattern sets;

[0096]

[0097] Among them G c is the governance stability index, the unit is dimensionless cycle score (%); δ r,t is the scheduling strategy repetition deviation of the t-th cycle, in units of proportion (%); δ0 is the reference deviation standard, in units of proportion (%); T is the total number of governance cycles, in units of cycles; t is the current cycle position, in units of cycle number;

[0098] in It represents the normalized scheduling deviation, which is still dimensionless after being squared; Used to simulate the impact of periodic rhythms, dimensionless; the sum is averaged to maintain a dimensionless score, which is used for coupling analysis of governance rhythmicity and structural stability;

[0099] The above formula is used to analyze whether the governance structure is stably executed over multiple scheduling cycles. It combines the deviation fluctuation trend with the cycle rhythm pattern to construct a stability index. It is a key standard numerical model for "judging whether the strategy is frozen or the template is solidified" in the closed-loop optimization module.

[0100] It should also be further explained that this plan is designed around the scheduling scenario of "key node supervision of county general hospitals." This plan addresses the widespread "blind spots in resource scheduling" problem in the current county-level medical system. Through strict data transmission links, multi-level node governance mechanisms, and evolutionary feedback logic, it transforms the traditional "static, centralized" resource allocation method into a four-dimensional closed-loop supervision process of "structural perception, dynamic response, institutional integration, and governance evolution," thereby effectively supporting the general hospital's precise governance of grassroots institutions and building its resource coordination capabilities.

[0101] The starting point of the entire system design is the "node identification module"; the proposal of this module is based on the practical problems of feedback chain interruption, upload delay, and serious data missing in various grassroots service units in the county medical service system. To this end, we first divided the original service data from different service units into four types of feedback information groups according to the governance dimension: medical records, nursing logs, satisfaction feedback, and work style dynamics, and uniformly constructed a feedback information set; then, we performed continuity verification on the information set according to chronological order and path logic, identified three types of abnormal fragments: missing fragments, upload lags, and path breaks, and extracted feedback response integrity indicators based on this, as a standard value for measuring the quality of data feedback; these indicators were then transmitted to the superior supervision process of the general hospital through the data transmission mechanism, forming a "node behavior perception map"; this map has basic behavior distribution visibility and is the entry benchmark for the resource scheduling process. Its core function is to identify "governable objects" through structured feedback, thereby solving the problem of "general hospitals not being able to see";

[0102] Based on the perception graph, the system realizes the transformation of resource scheduling from "passive allocation" to "active expression" through the "request trigger module"; first, for each service node in the graph, the resource demand index is calculated based on its reporting frequency, response interval and active behavior mark, which serves as a constraint threshold for whether the node has the right to apply for resources; once the resource demand index is continuously low, the scheduling authority of the node will be frozen and fed back to the general hospital supervision channel for intervention; and when the node maintains good behavior performance within the cycle, its self-collection channel will be activated; the resource request data submitted by the node will be bound to three types of indicators: task dimension, historical completion rate and delay fluctuation amplitude to construct a resource request data block; all data blocks are collected in the scheduling entry queue, and a priority graph is constructed within the same governance dimension to form a scheduling input array for sorting decisions; the design purpose of this part is to solve the problem of "the general hospital cannot adjust accurately", that is, to let "what to adjust" and "who to adjust" be driven by structured behavioral logic, so as to comprehensively improve the sensitivity and rationality of resource allocation;

[0103] To address the scheduling structure reconstruction problem after request data aggregation, the system has set up a "path evolution module" to solve the path rigidity and efficiency bottlenecks existing in traditional scheduling strategies. This module constructs a scheduling node cross-graph based on the governance dimension, node level and task overlap of resource request data blocks, and identifies high-overlap areas and their subgraph structures. On this basis, the system constructs a scheduling bottleneck set using two indicators, the path response consistency coefficient and the path saturation ratio, to identify highly repeated but low-response paths as nodes with structural degradation risk. If the overall consistency within a path area is good, the system will record the scheduling trajectory for reuse. If a structure with low response consistency is detected, the path optimization mechanism will be immediately triggered and structural adjustment suggestions will be generated. All adjustment suggestions will then be compared with the path responses of the historical scheduling log. If the structure is highly consistent, the original path structure will be retained. If there is a mismatch, the path correction process will be entered and the allocation adaptability judgment mechanism will be restarted. The role of this part is to achieve "evolvable scheduling strategy" and avoid the scheduling model from becoming static over time.

[0104] To address the risk of long-term disconnection between scheduling results and management systems, the system proposes a "system adaptation module"; this module focuses on whether resource allocation truly matches the current system execution structure; specifically, the system extracts resource response logs and their corresponding system execution status from key nodes of successful allocation, constructs a resource-system execution relationship table, and extracts the system adaptation factor under each governance dimension through deviation analysis between response intensity and system completion rate; if the adaptation factor is found to be significantly low, indicating that the current system design cannot support the resource allocation target, the system reconstruction process is executed, and the atomic system units are split and reorganized to generate a localized recommended system module; if the adaptability is high, the existing structure is retained and its adaptation number is marked; then, the newly generated system module will be bound to the resource allocation path to form a linkage configuration structure between the system and scheduling, ensuring that "how to allocate" and "how to manage the system" are updated synchronously, which is a key process for achieving "system linkage scheduling";

[0105] The outermost layer of the entire system is a closed-loop optimization module, which is used to improve the long-term governance efficiency of key node supervision; after the end of each governance cycle, the feedback information set, resource request data block, scheduling response log and system adaptation module of the key nodes are unified and summarized to form a periodic closed-loop structure; the system then performs continuous governance learning processing on the structure, including cross-cycle feature clustering, difference analysis and scheduling pattern recognition, and finally extracts the governance stability index and the set of scheduling path repetition patterns; if a scheduling path is found to have failed for two consecutive cycles, the system triggers the freeze mark and suspends the relevant system plan of the path; on the contrary, if the path shows a trend of continuous successful execution for more than three cycles, its strategy is solidified and written into the system iteration database for future use in the evolution of the governance map; the purpose of the closed-loop optimization module is to "be able to evolve after being controlled", so that the entire system can respond to short-term mutations and steadily adapt to long-term governance goal adjustments.

[0106] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A key node supervision system for county-level general hospitals, including a node identification module, a request triggering module, a path evolution module, a system adaptation module, and a closed-loop optimization module, characterized by: The node identification module is used to construct feedback information groups and extract abnormal data fragments, forming a node behavior perception map through data transmission, providing a basic identification basis for resource scheduling; The request trigger module calculates the resource demand expression capability index based on the node behavior perception graph, activates the resource self-collection channel and generates the resource request data block, establishing a resource active expression and scheduling entry mechanism; The path evolution module is used to construct all resource request data blocks into a scheduling node intersection graph and perform path deduction, identify scheduling bottlenecks and generate structural adjustment suggestions, realizing dynamic evolution modeling of resource scheduling; The system adaptation module is used to couple the resource response log with the system execution structure to analyze the adaptation factor, generate a local system recommendation module and bind the scheduling path to ensure that the system matches the resource allocation; The closed-loop optimization module performs feature clustering and strategy backtracking based on the periodic closed-loop structure, extracts the evolutionary governance map and solidifies the target path strategy, achieving continuous optimization and evolutionary support for key node supervision.

2. A county-level general hospital key node supervision system according to claim 1, characterized in that: The node identification module also includes dividing the original service data from each grassroots service unit into four types of feedback information groups based on the governance dimension. The four types of feedback information groups include medical records, nursing logs, satisfaction feedback, and work style dynamics, and uniformly constructing feedback information sets; Perform continuity check on each piece of information in the feedback information set according to the time sequence and reporting path, and extract three types of abnormal data fragments: missing fragments, upload delays and path breaks; Identify the service unit with abnormal data fragments as the initial key node and generate the corresponding feedback response integrity indicator; The feedback response integrity indicators are executed through the data transmission mechanism to execute the superior supervision processing process, forming a node behavior perception map.

3. A county-level general hospital key node supervision system according to claim 2, characterized in that: The request trigger module also includes calculating the resource demand index of each node in the node behavior perception graph. The resource demand index is composed of the reporting frequency, response interval and active behavior mark. If the resource demand index is lower than the preset benchmark value for three consecutive cycles, the resource scheduling request authority of the node will be suspended and feedback will be sent to the general hospital task supervision channel; If the resource demand index remains within the preset range, the resource self-withdrawal channel permission will be activated; In the activated resource self-collection channel, the scheduling request data submitted by the node is bound to the task dimensions, historical completion rate, and delay fluctuation range in the information group to construct a resource request data block; All resource request data blocks are aggregated into the scheduling entry queue, and a priority map is constructed within the same governance dimension. The priority map serves as the basis for sorting into the decision processing area.

4. A county-level general hospital key node supervision system according to claim 3, characterized in that: The path evolution module also includes constructing a scheduling node cross-graph based on the governance dimension, current level of the node and task overlap of all resource request data blocks in the scheduling entry queue; Identify the subgraph area with target overlap in the scheduling node intersection graph, and output the scheduling bottleneck set based on the path response consistency coefficient and path saturation ratio; If the path response consistency coefficient in the scheduling bottleneck set is greater than or equal to the preset threshold, the scheduling path structure remains unchanged and the scheduling trajectory is recorded. If the scheduling bottleneck set includes a path with a path response consistency coefficient lower than the preset threshold, the path optimization mechanism is triggered and scheduling structure adjustment suggestions are generated. All scheduling structure adjustment suggestions are compared with the historical resource configuration log execution path response consistency. If the comparison results show that the path structure remains highly consistent, they are included in the scheduling trajectory reuse process; if the comparison results include structural mismatches, the path correction process is output and the allocation adaptability re-determination process is initiated.

5. A county-level general hospital key node supervision system according to claim 4, characterized in that: The system adaptation module also includes extracting the corresponding resource response logs and implemented system structures for key nodes of successful allocation, and constructing a table of resource and system execution correspondences. In this table, the degree of execution deviation between each system structure and resource response result is calculated, and the system adaptation factor under each governance dimension is extracted. If the system adaptation factor is lower than the set threshold, the original system structure will be split and reorganized to generate a local system recommendation module adapted to the current key node; If the adaptation factor meets the set standard, the original system structure will be retained and marked with the corresponding adaptation number; The local system recommendation module is bound to the subsequent resource allocation path of the key node to form a linkage configuration table of system and resource scheduling, which is used to support the system consistency verification of subsequent scheduling decisions.

6. A county-level general hospital key node supervision system according to claim 5, characterized in that: The closed-loop optimization module also includes aggregating the feedback information sets of key nodes, resource request data blocks, scheduling response logs, and system adaptation modules at the end of each governance cycle to form a cycle closed-loop structure; subjecting the cycle closed-loop structure to a continuous governance learning process, performing cross-cycle feature clustering and difference annotation analysis in the continuous governance learning process to extract governance stability indicators and a set of scheduling recurrence patterns; If a policy path fails repeatedly for two consecutive cycles in the scheduling repetition pattern set, the system triggers the governance structure freeze flag on the path, obtains the frozen path, and suspends its system update plan; If the strategy is successfully executed for more than three consecutive cycles, the path strategy will be solidified into a governance template and written into the system iteration database; All governance templates and frozen paths are used to form an evolutionary governance map for reference in the general hospital's long-term resource scheduling planning and strategy inversion optimization.

7. A county-level general hospital key node supervision system according to claim 6, characterized in that: The node identification module includes executing the upper supervision processing flow through the data transmission mechanism to form the node behavior perception map, describing the node behavior perception map through the feedback response integrity index, and formulating I n It represents the feedback response completeness index of the nth service unit, and the unit is ratio; Where T d,n is the upload delay time in seconds; C l,n The number of consecutive upload interruptions, in times; T r,n Specifies the feedback cycle in seconds; L m,n L is the total length of the missing content, in bytes; e,n The total length of the content to be uploaded, in bytes; R p,n is the path interruption ratio, unit is dimensionless; A s,n is the path structure correction factor, unit is dimensionless; The request trigger module includes the scheduling request data submitted by the node in the activated resource self-collection channel, which is bound to the task dimensions, historical completion rate and delay fluctuation range in the information group to construct the resource request data block; where Q n The scheduling priority strength of the resource request data block, the unit is ratio score; D t,n is the task dimension coverage, the unit is the number of task items; F h,n is the historical completion rate, unit is dimensionless; V d,n is the delay fluctuation amplitude, in hours; T e,n is the upper limit of the reference task response time, in hours; W e,n is the node behavior response elasticity factor, and its unit is dimensionless.

8. A county-level general hospital key node supervision system according to claim 7, characterized in that: The path evolution module includes identifying subgraph regions with target overlap in the scheduling node intersection graph and outputting the scheduling bottleneck set based on the path response consistency coefficient and the path saturation ratio; Among them B r is the scheduling bottleneck index of the rth path area, and the unit is ratio score; R s,r is the path response consistency coefficient, the unit is ratio; H o,r is the path overlap saturation ratio, in units of ratio; F l,r is the historical failure frequency of the path, in times; The system adaptation module is included in the execution correspondence table, which calculates the degree of execution deviation between each system structure and resource response results, and extracts the system adaptation factor under each governance dimension. The derivation formula is: Among them S k is the institutional adaptation factor of the kth governance dimension, with the unit being the deviation score; F r,k is the resource response intensity, the unit is ratio; S c,k is the system completion rate, the unit is proportion; Δ m,k is the response tolerance range, in percentage.

9. A county-level general hospital key node supervision system according to claim 8, characterized in that: The closed-loop optimization module also includes a continuous governance learning process for the periodic closed-loop structure, performing cross-period feature clustering and difference annotation analysis in the continuous governance learning process, and extracting governance stability indicators and scheduling recurrence pattern sets; Among them G c is the governance stability index, with the unit being dimensionless cycle score; δ r,t is the scheduling strategy repetition deviation of the t-th period, the unit is proportion; δ0 is the reference deviation standard, the unit is proportion; T is the total number of governance cycles, the unit is cycle; t is the current cycle position, the unit is cycle number.

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