A priority scheduling method and system for a registration system

Abstract

This invention discloses a priority scheduling method and system for online appointment registration systems. Belonging to the field of internet healthcare technology, it includes the following steps: S1: Receiving user appointment registration request data, parsing the request data, and extracting feature information; S2: Calculating a comprehensive priority index for each request based on the feature information using a priority evaluation model; S3: Allocating requests to processing queues of different priorities according to the comprehensive priority index, and distributing system resources for processing according to priority; S4: Monitoring system resource usage and user behavior in real time, and identifying abnormal users based on an anomaly detection algorithm; S5: Forcibly releasing the resources occupied by identified abnormal users; S6: Dynamically adjusting the resource pool according to system load, performing tidal reclamation of idle resources in low-priority queues, and reallocating them to high-priority queues. This invention achieves efficient and fair appointment registration services.

Description

Technical Field

[0001] This invention relates to the field of internet healthcare technology, and more specifically to a priority scheduling method and system for registration systems. Background Technology

[0002] Currently, with the development of internet healthcare, online registration systems are facing an increasing number of user visits, especially during peak hours, when the system needs to handle massive concurrent requests.

[0003] However, existing registration systems typically employ a first-come, first-served approach or simple polling scheduling, failing to provide differentiated services based on the user's actual urgency or identity characteristics. This results in genuinely urgent patients potentially experiencing delays due to long queues. Furthermore, scalpers use scripts to monopolize appointment slots, severely disrupting normal order and wasting resources.

[0004] Therefore, how to provide a priority scheduling method and system for registration systems to ensure high availability and fairness is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a priority scheduling method and system for registration systems to solve the technical problems existing in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A priority scheduling method for a registration system includes: S1: Receive user registration request data, parse the request data, and extract feature information; S2: Based on the aforementioned feature information, calculate the comprehensive priority index for each request using a priority evaluation model; S3: Based on the comprehensive priority index, the requests are assigned to processing queues of different priorities, and system resources are allocated for processing according to the priority level; S4: Real-time monitoring of system resource usage and user behavior, and identification of abnormal users based on anomaly detection algorithms; S5: For identified abnormal users, forcibly release the resources they occupy; S6: Dynamically adjust the resource pool based on system load, and perform tidal reclamation of idle resources in low-priority queues and reallocate them to high-priority queues.

[0007] Furthermore, the feature information includes user identity attributes, disease description, and historical behavioral data.

[0008] Furthermore, the priority evaluation model described in step S2 uses a weighted summation formula to calculate the comprehensive priority index:

[0009] Where E is the quantitative value of the urgency of the illness, C is the quantitative value of the complexity of the illness, R is the weight value of the user role, B is the user behavior factor, and α, β, γ, and δ are weight coefficients, and α+β+γ+δ=1.

[0010] Furthermore, the urgency of the condition is obtained by analyzing keywords in the condition description through natural language processing or by analyzing medical images through image recognition, and is mapped to a value between 0 and 1.

[0011] Furthermore, the user role weight is determined according to a preset priority table, which contains weight values ​​corresponding to different user roles.

[0012] Furthermore, the user behavior factor is calculated based on the user's historical reservation records:

[0013] in To cancel the number of reservations, Let N be the number of late arrivals and N be the total number of appointments.

[0014] Furthermore, the anomaly detection algorithm described in step S4 uses a machine learning classifier, takes user features as input, outputs anomaly probability, and determines it as an anomaly when the probability exceeds a preset threshold.

[0015] Furthermore, the tidal recycling described in step S6 includes: Set the maximum and minimum number of connections for the resource pool; The number of available connections for low-priority queues is dynamically adjusted based on the length of high-priority queues. When the length of the high-priority queue exceeds the first threshold, resources are reclaimed from the low-priority queue according to the resource reclamation ratio. When the length of the high-priority queue falls below the second threshold, some of the reclaimed resources are released to the low-priority queue.

[0016] Furthermore, the formula for calculating the resource recovery ratio is as follows:

[0017] in This represents the current length of the high-priority queue. The first threshold for triggering garbage collection is set, MaxLen is the maximum allowed length of the high-priority queue, and K is an adjustment coefficient, where 0 is the maximum value of the queue. <K≤1。

[0018] A priority scheduling system for registration systems includes: Data acquisition module: Receives user registration request data, parses the request data, and extracts feature information; Evaluation module: Based on the aforementioned feature information, calculates the comprehensive priority index for each request using a priority evaluation model; Allocation module: Based on the comprehensive priority index, the requests are allocated to processing queues of different priorities, and system resources are allocated for processing according to the priority level; Monitoring module: Monitors system resource usage and user behavior in real time, and identifies abnormal users based on anomaly detection algorithms; Resource release module: For identified abnormal users, forcibly release the resources they occupy; The computing module dynamically adjusts the resource pool based on system load, and performs tidal reclamation of idle resources in low-priority queues, reallocating them to high-priority queues.

[0019] As can be seen from the above technical solutions, compared with the prior art, the present invention provides a priority scheduling method and system for registration systems. Through multi-factor priority evaluation, abnormal behavior detection and adaptive resource reclamation, it intelligently identifies user priorities, dynamically reclaims resources and forcibly releases abnormal occupancy, thereby achieving efficient and fair registration services. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0021] Figure 1 This is a schematic diagram of the method flow provided by the present invention; Figure 2 This is a schematic diagram of the system structure provided by the present invention. Detailed Implementation

[0022] 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.

[0023] Example 1: See Figure 1 Embodiment 1 of the present invention discloses a priority scheduling method for a registration system, comprising: S1: Receive user registration request data, parse the request data, and extract feature information; S2: Based on the aforementioned feature information, calculate the comprehensive priority index for each request using a priority evaluation model; S3: Based on the comprehensive priority index, the requests are assigned to processing queues of different priorities, and system resources are allocated for processing according to the priority level; S4: Real-time monitoring of system resource usage and user behavior, and identification of abnormal users based on anomaly detection algorithms; S5: For identified abnormal users, forcibly release the resources they occupy; S6: Dynamically adjust the resource pool based on system load, and perform tidal reclamation of idle resources in low-priority queues and reallocate them to high-priority queues.

[0024] In one specific embodiment, the feature information includes user identity attributes, disease description, and historical behavior data.

[0025] In one specific embodiment, the priority evaluation model in step S2 uses a weighted summation formula to calculate the comprehensive priority index:

[0026] Where E is the quantitative value of the urgency of the illness, C is the quantitative value of the complexity of the illness, R is the weight value of the user role, B is the user behavior factor, and α, β, γ, and δ are weight coefficients, and α+β+γ+δ=1.

[0027] In one specific embodiment, the urgency of the illness is obtained by analyzing keywords in the description of the illness through natural language processing, or by analyzing medical images through image recognition, and mapped to a value between 0 and 1.

[0028] In one specific embodiment, the user role weight is determined according to a preset priority table, which contains weight values ​​corresponding to different user roles.

[0029] In one specific embodiment, the user behavior factor is calculated based on the user's historical appointment records:

[0030] in To cancel the number of reservations, Let N be the number of late arrivals and N be the total number of appointments.

[0031] In one specific embodiment, the anomaly detection algorithm in step S4 uses a machine learning classifier, takes user features as input, outputs anomaly probability, and determines it as an anomaly when the probability exceeds a preset threshold.

[0032] In one specific embodiment, the tidal recycling in step S6 includes: Set the maximum and minimum number of connections for the resource pool; The number of available connections for low-priority queues is dynamically adjusted based on the length of high-priority queues. When the length of the high-priority queue exceeds the first threshold, resources are reclaimed from the low-priority queue according to the resource reclamation ratio. When the length of the high-priority queue falls below the second threshold, some of the reclaimed resources are released to the low-priority queue.

[0033] In one specific embodiment, the formula for calculating the resource recovery ratio is:

[0034] Where L_high is the current length of the high-priority queue, Thresh1 is the first threshold for triggering reclamation, MaxLen is the maximum allowed length of the high-priority queue, and K is an adjustment coefficient, and 0 <K≤1。

[0035] See Figure 2 A priority scheduling system for registration systems, comprising: Data acquisition module: Receives user registration request data, parses the request data, and extracts feature information; Evaluation module: Based on the aforementioned feature information, calculates the comprehensive priority index for each request using a priority evaluation model; Allocation module: Based on the comprehensive priority index, the requests are allocated to processing queues of different priorities, and system resources are allocated for processing according to the priority level; Monitoring module: Monitors system resource usage and user behavior in real time, and identifies abnormal users based on anomaly detection algorithms; Resource release module: For identified abnormal users, forcibly release the resources they occupy; The computing module dynamically adjusts the resource pool based on system load, and performs tidal reclamation of idle resources in low-priority queues, reallocating them to high-priority queues.

[0036] Example 2: Embodiment 2 of the present invention discloses a priority scheduling method for a registration system, comprising the following steps: S1: Receive user registration request, parse the request data, and extract feature information, including user identity attributes (age, occupation, medical history), description of illness (text or image), and historical behavior data (appointment frequency, number of cancellations, and late arrival records). S2: Based on the aforementioned feature information, calculate the comprehensive priority index for each request using a priority evaluation model; S3: Based on the comprehensive priority index, the requests are assigned to processing queues of different priorities, and system resources (such as threads and database connections) are allocated for processing according to the priority. S4: Monitor system resource usage and user behavior in real time, and identify abnormal users based on anomaly detection algorithms. Abnormal users include scalpers (high-frequency requests from the same IP / MAC address) and low-value users (long-term reservations but frequent cancellations). S5: For identified abnormal users, forcibly release the resources they occupy and add them to the blacklist to prohibit subsequent access; S6: Dynamically adjust the resource pool based on system load, and perform tidal reclamation of idle resources in low-priority queues and reallocate them to high-priority queues.

[0037] Specifically, the priority evaluation model described in step S2 uses a weighted summation formula to calculate the comprehensive priority index P:

[0038] Where E is the quantitative value of the urgency of the illness, with a value range of [0,1]; C is the quantitative value of the complexity of the illness, with a value range of [0,1]; R is the user role weight value, determined according to the preset priority table, with a value range of [0,1]; B is the user behavior factor, reflecting the user's historical integrity, with a value range of [0,1]; α, β, γ, and δ are weight coefficients, satisfying α+β+γ+δ=1, and can be obtained through machine learning training.

[0039] In one specific embodiment, the severity level E of the illness is obtained by analyzing keywords in the description of the illness through natural language processing, which in this embodiment can be chest pain or difficulty breathing; or it is obtained by analyzing medical images through image recognition, which in this embodiment can be CT scans or X-rays; and mapped to a value between 0 and 1.

[0040] In one specific embodiment, the user role weight R is determined according to a preset priority table. In this embodiment, the weights are: child 0.9, elderly 0.8, military personnel 0.7, teacher 0.6, disabled person 0.9, and ordinary adult 0.5.

[0041] In one specific embodiment, the user behavior factor B is calculated based on the user's historical reservation records:

[0042] in To cancel the number of reservations, B represents the number of late arrivals, and N represents the total number of appointments. When N=0, B takes the default value of 0.5.

[0043] In one specific embodiment, the anomaly detection algorithm in step S4 includes detection based on a statistical threshold: setting an IP request frequency threshold T. IP (10 times / minute) and MAC request frequency threshold TMAC(5 times / minute). When the number of requests from a certain IP or MAC within a unit of time exceeds the corresponding threshold, it is determined as an abnormal user. At the same time, a machine learning classifier can also be used, with user features (request interval, device fingerprint, historical behavior) as input, outputting an abnormal probability, and when the probability exceeds the preset threshold, it is determined as abnormal.

[0044] In a specific embodiment, the tidal recovery described in step S6 specifically includes: setting the maximum connection number MaxConn and the minimum connection number MinConn of the system resource pool, and maintaining the high-priority queue length L high and the low-priority queue length L low ; when L high exceeds the first threshold Thresh1, trigger resource recovery, and recover a portion of the connection resources proportionally from the low-priority queue. The recovery ratio R_reclaim is calculated as follows:

[0045] where MaxLen is the maximum allowable length of the high-priority queue, and K is an adjustment coefficient (0 < K ≤ 1); the recovered resources are immediately allocated to the high-priority queue; when is lower than the second threshold Thresh2, release some of the recovered resources to the low-priority queue to restore to the normal level.

[0046] Specifically, the present invention dynamically calculates priorities by integrating multiple factors to ensure that users in urgent need are preferentially connected; combines abnormal behavior detection and forced release to effectively combat scalpers; the tidal resource recovery mechanism adaptively adjusts resource allocation to improve system throughput. The entire solution integrates technical means such as natural language processing, image recognition, machine learning, and queue scheduling, has technological innovation, and meets the requirements of the protection object of the patent law.

[0047] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0048] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A priority scheduling method for a registration system, characterized in that, include: S1: Receive user registration request data, parse the request data, and extract feature information; S2: Based on the aforementioned feature information, calculate the comprehensive priority index for each request using a priority evaluation model; S3: Based on the comprehensive priority index, the requests are assigned to processing queues of different priorities, and system resources are allocated for processing according to the priority level; S4: Real-time monitoring of system resource usage and user behavior, and identification of abnormal users based on anomaly detection algorithms; S5: For identified abnormal users, forcibly release the resources they occupy; S6: Dynamically adjust the resource pool based on system load, and perform tidal reclamation of idle resources in low-priority queues and reallocate them to high-priority queues.

2. The priority scheduling method for a registration system according to claim 1, characterized in that, The feature information includes user identity attributes, disease description, and historical behavior data.

3. The priority scheduling method for a registration system according to claim 1, characterized in that, The priority evaluation model described in step S2 uses a weighted summation formula to calculate the comprehensive priority index: Where E is the quantitative value of the urgency of the illness, C is the quantitative value of the complexity of the illness, R is the weight value of the user role, B is the user behavior factor, and α, β, γ, and δ are weight coefficients, and α+β+γ+δ=1.

4. The priority scheduling method for a registration system according to claim 3, characterized in that, The severity of the illness is obtained by analyzing keywords in the illness description through natural language processing or by analyzing medical images through image recognition, and is mapped to a value between 0 and 1.

5. A priority scheduling method for a registration system according to claim 3, characterized in that, The user role weight is determined according to a preset priority table, which contains weight values ​​corresponding to different user roles.

6. A priority scheduling method for a registration system according to claim 3, characterized in that, The user behavior factors are calculated based on the user's historical reservation records: in To cancel the number of reservations, Let N be the number of late arrivals and N be the total number of appointments.

7. A priority scheduling method for a registration system according to claim 1, characterized in that, The anomaly detection algorithm described in step S4 uses a machine learning classifier, takes user features as input, and outputs anomaly probability. When the probability exceeds a preset threshold, it is determined to be an anomaly.

8. A priority scheduling method for a registration system according to claim 1, characterized in that, The tidal recycling described in step S6 includes: Set the maximum and minimum number of connections for the resource pool; The number of available connections for low-priority queues is dynamically adjusted based on the length of high-priority queues. When the length of the high-priority queue exceeds the first threshold, resources are reclaimed from the low-priority queue according to the resource reclamation ratio. When the length of the high-priority queue falls below the second threshold, some of the reclaimed resources are released to the low-priority queue.

9. A priority scheduling method for a registration system according to claim 8, characterized in that, The formula for calculating the resource recovery ratio is as follows: in This represents the current length of the high-priority queue. The first threshold for triggering garbage collection is set, MaxLen is the maximum allowed length of the high-priority queue, and K is an adjustment coefficient, where 0 is the maximum value of the queue. <K≤1。 10. A priority scheduling system for a registration system, characterized in that, include: Data acquisition module: Receives user registration request data, parses the request data, and extracts feature information; Evaluation module: Based on the aforementioned feature information, calculates the comprehensive priority index for each request using a priority evaluation model; Allocation module: Based on the comprehensive priority index, the requests are allocated to processing queues of different priorities, and system resources are allocated for processing according to the priority level; Monitoring module: Monitors system resource usage and user behavior in real time, and identifies abnormal users based on anomaly detection algorithms; Resource release module: For identified abnormal users, forcibly release the resources they occupy; The computing module dynamically adjusts the resource pool based on system load, and performs tidal reclamation of idle resources in low-priority queues, reallocating them to high-priority queues.

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