Intelligent scheduling platform, method, device and equipment and storage medium

Abstract

The embodiment of the invention provides an intelligent scheduling platform, an intelligent scheduling method and device, equipment and a storage medium. The intelligent scheduling platform comprises a rule configuration module used for configuring corresponding scheduling rules based on a plurality of scenes and defining task labels and resource labels, the task labels at least comprise task types and task priorities, and the resource labels at least comprise available human resources and hospital resources; the scheduling matching module is connected with the rule configuration module, comprises a scheduling matching model and a weight integral model, and is used for determining a corresponding target scheduling rule and a target task label based on the target scene; and determining the target scheduling rule and a resource label score corresponding to the target task label through the scheduling matching model and the weight integral model, and performing resource scheduling according to the maximum resource label score. According to the invention, the medical service is accurately and efficiently scheduled through the intelligent scheduling platform, and the systematic scheduling cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to an intelligent scheduling platform, an intelligent scheduling method, an intelligent scheduling device, a computer device, and a computer-readable storage medium. Background Art

[0002] With the aging of the population and changes in the disease spectrum, the demand for medical services continues to increase. However, problems such as limited medical resources, insufficient supply of medical services, and unreasonable scheduling of medical services are becoming increasingly prominent. In order to solve problems such as uneven distribution of medical resources and waste of medical resources, it is necessary to fully analyze medical resources in order to carry out intelligent scheduling of medical services.

[0003] The existing medical service scheduling is to randomly judge through medical resources to schedule medical services. In actual applications, there will be problems such as uneven distribution of medical resources, waste of medical resources, and unreasonable medical service processes, which will lead to reduced satisfaction with medical service scheduling, and thus lower quality and accuracy of medical service scheduling. Summary of the invention

[0004] The present application provides an intelligent scheduling platform, an intelligent scheduling method, an intelligent scheduling device, a computer device and a computer-readable storage medium, aiming to accurately and efficiently schedule medical services through the intelligent scheduling platform and reduce systematic scheduling costs.

[0005] To achieve the above objectives, the present application provides an intelligent scheduling platform, including:

[0006] A rule configuration module, used to configure corresponding scheduling rules based on several scenarios, and define task tags and resource tags, wherein the task tags at least include task type and task priority, and the resource tags at least include available human resources and hospital resources;

[0007] The scheduling matching module is connected to the rule configuration module, and includes a scheduling matching model and a weight integration model. After determining the corresponding target scheduling rules and target task labels based on the target scenario, the resource label scores corresponding to the target scheduling rules and the target task labels are determined by the scheduling matching model and the weight integration model respectively, and resource scheduling is performed according to the maximum resource label score.

[0008] To achieve the above objectives, the present application also provides an intelligent scheduling method, including:

[0009] Determine the corresponding target scheduling rules and target task labels based on the target scenario;

[0010] Determine the target scheduling rule and the resource tag score corresponding to the target task tag through a scheduling matching model and a weighted integral model;

[0011] Resources are scheduled based on the resource tag score with the maximum value.

[0012] To achieve the above objectives, the present application also provides an intelligent scheduling device, comprising:

[0013] A determination module, the determination module is used to determine a corresponding target scheduling rule and a target task label based on a target scenario;

[0014] The determination module is also used to determine the target scheduling rule and the resource tag score corresponding to the target task tag through a scheduling matching model and a weighted integral model;

[0015] An intelligent scheduling module is used to schedule resources according to the maximum resource label score.

[0016] In addition, to achieve the above-mentioned purpose, the present application also provides a computer device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the steps of any one of the intelligent scheduling methods provided in the embodiments of the present application when executing the computer program.

[0017] In addition, to achieve the above-mentioned purpose, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the steps of the intelligent scheduling method described in any one of the embodiments of the present application.

[0018] The intelligent scheduling platform, intelligent scheduling method, intelligent scheduling device, computer equipment and computer-readable storage medium disclosed in the embodiment of the present application. Among them, the intelligent scheduling platform includes a rule configuration module for configuring corresponding scheduling rules based on several scenarios, and defining task tags and resource tags, wherein the task tags at least include task types and task priorities, and the resource tags at least include available human resources and hospital resources; the scheduling matching module is connected to the rule configuration module, including a scheduling matching model and a weighted integral model, which is used to determine the corresponding target scheduling rules and target task tags based on the target scenario, and respectively determine the resource tag scores corresponding to the target scheduling rules and target task tags through the scheduling matching model and the weighted integral model, and perform resource scheduling according to the maximum resource tag score. By defining task tags (including task types and task priorities) and resource tags (including human resources and hospital resources), the present application can accurately match task requirements with available resources, ensure the rationality and efficiency of resource scheduling, and thus maximize resource utilization. In addition, by combining the scheduling matching model and the weighted integral model, the scheduling rules and task tags can be comprehensively scored to ensure the optimality of resource allocation decisions, reduce resource waste and task delays, and improve overall scheduling efficiency. In addition, using automated intelligent scheduling methods instead of manual scheduling can reduce human intervention, reduce error rates, and improve the accuracy and timeliness of scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 It is a scenario diagram of an intelligent scheduling method provided in an embodiment of the present application;

[0021] Figure 2 It is a structural diagram of an intelligent scheduling platform provided in an embodiment of the present application;

[0022] Figure 3 It is a flowchart of an intelligent scheduling method provided in an embodiment of the present application;

[0023] Figure 4 is a schematic block diagram of an intelligent scheduling device provided in an embodiment of the present application;

[0024] Figure 5 It is a schematic block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor do they have to be executed in the order described. For example, some operations / steps can also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions. In addition, although the functional modules are divided in the device schematic, in some cases, the module division can be different from that in the device schematic.

[0027] The term "and / or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0028] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0029] like Figure 1 As shown, the intelligent scheduling method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The application environment includes a terminal device 110 and a server 120, wherein the terminal device 110 can communicate with the server 120 through a network. Specifically, the server 120 can determine the corresponding target scheduling rules and target task tags based on the target scenario; determine the resource tag scores corresponding to the target scheduling rules and the target task tags through a scheduling matching model and a weight integral model; perform resource scheduling according to the maximum resource tag score, and send the resource scheduling result to the terminal device 110. Among them, the server 120 can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms. The terminal device 110 can be a smart phone, a tablet computer, a laptop, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected via wired or wireless communication, and this application is not limited here.

[0030] See also Figure 2 , Figure 2 Schematic diagram of the structure of an intelligent scheduling platform provided in the embodiment of the present application. Figure 2 As shown, the intelligent scheduling platform includes a rule configuration module 12 and a scheduling matching module 13. The rule configuration module 12 is used to configure corresponding scheduling rules based on a number of scenarios, and to define task tags and resource tags, wherein the task tags at least include the task type and the task priority, and the resource tags at least include available human resources and hospital resources; the scheduling matching module 13 is connected to the rule configuration module 12, and includes a scheduling matching model and a weighted integral model, which is used to determine the corresponding target scheduling rules and target task tags based on the target scenario, and then determine the resource tag scores corresponding to the target scheduling rules and the target task tags through the scheduling matching model and the weighted integral model, and perform resource scheduling according to the maximum resource tag score.

[0031] For example, the above-mentioned task types are used to indicate specific categories of tasks, such as medical tasks, logistics tasks, etc.; task priority is used to indicate the urgency or importance of the task, and tasks with high priority will obtain higher resource allocation weights during the scheduling process; available human resources are used to indicate the number of human resources currently available for scheduling and related information (such as skills, idle status, etc.); hospital resources are used to indicate medical-related resources, such as wards, operating rooms, equipment, etc.

[0032] It should be understood that the intelligent scheduling platform can flexibly adapt to different scenarios and formulate personalized scheduling strategies for specific situations through the rule configuration module 12. For example, a certain scenario may require more human resources, while another scenario may focus on the scheduling of medical equipment.

[0033] For example, when determining the target scenario, the scheduling matching module 13 can evaluate the scheduling rules and task labels through the matching model and the integral model, and the corresponding scores between each task label (such as task type, task priority) and resource label (such as available human resources, hospital resources) will be calculated according to the model. Among them, the scheduling matching model is used to analyze the adaptability of task labels and resource labels, that is, the scheduling matching model can match the most suitable resources according to the requirements of the task, such as matching high-priority tasks with human resources with high idle rates. The weighted integral model can calculate the comprehensive score of the resource label by assigning different weights to different tasks and resources, thereby ensuring that the allocation of resources follows the highest priority rules.

[0034] The intelligent scheduling platform proposed in this application can combine the flexibility of scheduling rules with the intelligence of matching models, and achieve optimal allocation of resources through a weighted integral model. It can not only adapt to complex and changeable scheduling scenarios, but also maximize resource utilization and ensure that high-priority tasks receive timely responses.

[0035] The intelligent scheduling platform disclosed in the embodiment of the present application includes a rule configuration module for configuring corresponding scheduling rules based on several scenarios, and defining task tags and resource tags, wherein the task tags include at least task types and task priorities, and the resource tags include at least available human resources and hospital resources; a scheduling matching module, connected to the rule configuration module, includes a scheduling matching model and a weighted integral model, for determining the corresponding target scheduling rules and target task tags based on the target scenario, respectively determining the resource tag scores corresponding to the target scheduling rules and target task tags by the scheduling matching model and the weighted integral model, and performing resource scheduling according to the maximum resource tag score. The present application can accurately match task requirements with available resources by defining task tags (including task types, task priorities) and resource tags (including human resources and hospital resources), ensuring the rationality and efficiency of resource scheduling, thereby maximizing resource utilization. In addition, by combining the scheduling matching model with the weighted integral model, it is possible to comprehensively score scheduling rules and task tags, ensuring the optimality of resource allocation decisions, reducing resource waste and task delays, and improving overall scheduling efficiency. In addition, using automated intelligent scheduling methods instead of manual scheduling can reduce human intervention, reduce error rates, and improve the accuracy and timeliness of scheduling.

[0036] Optionally, the intelligent scheduling platform further includes a monitoring module 11, which is connected to the rule configuration module 12 and is used to monitor target task labels, hospital resources and available human resources.

[0037] For example, the monitoring module 11 can track the execution status of the task in real time, including whether the task has started, how the task is progressing, and whether the task has changed (such as priority increase or decrease). When certain elements of the task label change (such as urgent task insertion, task priority adjustment, etc.), it can ensure that the intelligent scheduling platform can respond quickly and recalculate resource allocation.

[0038] It should be understood that the monitoring module 11 can provide real-time monitoring of tasks and resources, ensuring that the intelligent scheduling platform always makes decisions based on the latest status information, thereby improving the accuracy of scheduling. Since the status of resources and tasks may change frequently in a short period of time, the introduction of the monitoring module 11 can ensure that the intelligent scheduling platform can still respond flexibly in a dynamic environment and achieve accurate scheduling.

[0039] Optionally, the target scheduling rule and the resource tag score corresponding to the target task tag are determined through a weighted integral model, including: determining the corresponding scheduling factor based on the target task tag, and predefining the weight, grade and score of each scheduling factor; calculating the score of each scheduling factor according to the target scheduling rule, and determining the score of the scheduling factor as the resource tag score, wherein the score of the scheduling factor is the product of the weight of the scheduling factor, the grade of the scheduling factor and the score of the scheduling factor.

[0040] Among them, the scheduling factor is a key factor used to evaluate the matching degree between tasks and resources, which can reflect the relationship between task requirements and resource characteristics. For example, the scheduling factors may include but are not limited to the following: resource availability (such as free beds, equipment availability time, etc.), task priority (such as emergency tasks vs. ordinary tasks), personnel skills (whether there are suitable professional medical staff), etc. The above scheduling factors can affect the resource allocation decision.

[0041] It should be understood that each scheduling factor is assigned a weight to indicate its importance. The weight determines the proportion of the factor in the resource scheduling decision. For example, the priority factor of an urgent task may have a higher weight, while the factor weight of resource distance is lower. The level of a scheduling factor represents its current actual situation or status. The above level can be a discrete multiple value, such as: high, medium, low, or expressed as a numerical value (such as 1 to 5). The score of each scheduling factor is pre-defined according to its level. For example, a high level of resource availability may correspond to a higher score, while a low level corresponds to a lower score. The above score can be regarded as a quantitative evaluation of the current status of the factor.

[0042] For example, in the actual scheduling process, the intelligent scheduling platform can determine the corresponding scheduling factor based on the target task label. For example, for emergency medical tasks, the intelligent scheduling platform can select resource availability, task priority, personnel skills, etc. as scheduling factors. Furthermore, the intelligent scheduling platform can calculate the score of each scheduling factor according to the target scheduling rules. For example, the score of the scheduling factor can be determined by calculating the product of its weight, grade, and score, and the score of each scheduling factor will be accumulated or weighted to form a resource label score. The higher the resource label score, the more the resource meets the needs of the current task.

[0043] In the above way, through the weighted integral model, the intelligent scheduling platform can dynamically make resource scheduling decisions in a complex and changing task and resource environment. The model comprehensively considers the priority of tasks, the availability of resources, and other scheduling factors to ensure that the system makes the best decision during the scheduling process, thereby improving overall scheduling efficiency and resource utilization.

[0044] Optionally, the scheduling matching model includes at least one of a grid model, a cost model, a capacity model, a quality model, and a tilt / correction model.

[0045] It should be noted that the grid model can divide resources and tasks into multiple grids (areas) to perform resource scheduling within each grid and across grids. This model is suitable for scenarios where resources need to be scheduled based on geographic location or physical location. Scenarios where the grid model can be applied include: medical emergency, logistics distribution, etc. By dividing the entire scheduling area into several small areas, resources and tasks in each area can be quickly matched. The outdoor scheduling method of the grid model can improve scheduling efficiency and reduce task response time.

[0046] The cost model is a scheduling model based on multiple aspects such as resource usage cost and task execution cost. The intelligent scheduling system will comprehensively consider the level of various costs, select a resource scheduling plan, and finally select the plan with the lowest total cost. Among them, the above costs can include time costs, economic costs (such as labor costs, equipment usage fees), etc. This model is widely used in cost-sensitive scenarios, such as scenarios with limited resource allocation budgets. Through the cost optimization scheduling process, the total cost can be reduced, which is suitable for scheduling scenarios with limited budgets.

[0047] The capability model can be used to schedule resources by analyzing the matching degree between resource capabilities and task requirements. When scheduling, the intelligent scheduling platform can give priority to resources whose capabilities best match the task requirements. For example, a medical task may require doctors or medical staff with specific skills, and a logistics task may require a vehicle with specific equipment. The capability model focuses on the matching degree between resource capabilities and task requirements, ensuring that resources are competent for the task, thereby improving the success rate and quality of task completion.

[0048] The quality model can focus on the quality or service level of resources. When scheduling, not only the availability of resources is considered, but also the service quality, historical performance, evaluation and other information of resources. The intelligent scheduling platform can give priority to resources with high quality and good service. Therefore, the quality model is suitable for scenarios with high service quality requirements, ensuring that high-quality resources are allocated first and improving user satisfaction.

[0049] The tilt / correction model can be used to optimize and adjust when the distribution of resources or tasks is unbalanced. The intelligent scheduling platform will perform tilt or correction operations based on the current status of resources or tasks to avoid excessive concentration of resources on certain tasks or certain resources being idle for a long time. The tilt / correction model is usually used when the resource load is unbalanced in the intelligent scheduling platform to ensure the maximum utilization of the resources of the intelligent scheduling platform.

[0050] It should be understood that the scheduling matching model is not limited to using a single model. In actual applications, multiple models can be combined and used according to the needs of different scenarios. For example, in the hospital resource scheduling scenario, the capability model and the cost model can be used at the same time, first matching according to the professional skills and task requirements of the doctor, and then optimizing the selection based on the cost.

[0051] In the above embodiments, by introducing different scheduling matching models, the intelligent scheduling platform can flexibly adapt to various complex scheduling scenarios. Each model has its specific application scenarios and advantages. When making a specific selection, the model can be combined and adjusted according to the priority of the task, the characteristics of the resource, and the current needs of the system, so as to achieve the optimal allocation of resources and maximize the scheduling efficiency.

[0052] Optionally, the intelligent scheduling platform also includes an auxiliary scheduling module (not shown), which is used to perform resource scheduling according to the input values ​​received from the user after determining the target scheduling rules and the target task tags.

[0053] For example, the auxiliary scheduling module is used to enhance the scheduling flexibility of the intelligent scheduling platform in specific scenarios. After the scheduling platform determines the target scheduling rules and target task labels through the main scheduling module (such as scheduling matching model, weighted integral model, etc.), the auxiliary scheduling module can receive input values ​​from the user and perform secondary scheduling of resources based on these input values.

[0054] For example, the auxiliary scheduling module allows users to enter customized scheduling parameters or preferences based on their specific needs or actual conditions after the system determines the preliminary scheduling results. The intelligent scheduling platform can recalculate and match the resource tag scores according to the user input values, perform secondary scheduling of resources, and ensure that the scheduling results are more in line with the user's needs. For example, in medical resource scheduling, the system may initially assign a team of doctors. However, if the user inputs that the doctor team should include certain specific professional skills or certain specific doctors, the auxiliary scheduling module will adjust the scheduling results according to the user's input and reallocate resources.

[0055] Optionally, the auxiliary scheduling module also includes an automatic scheduling model, and the automatic scheduling model is used to provide a reference basis for user input.

[0056] For example, the auxiliary scheduling module also includes an automatic scheduling model, which is used to provide a reference for users to help them make better input decisions. When users need to input certain scheduling parameters, the intelligent scheduling platform can generate intelligent suggestions or preset options through the automatic scheduling model, thereby providing guidance and optimization solutions for user input.

[0057] In the above embodiments, by introducing auxiliary scheduling modules and automatic scheduling models, the intelligent scheduling platform can increase flexibility and personalized customization capabilities on the basis of standardized scheduling, thereby improving resource allocation efficiency. Users can intervene in the scheduling process by inputting input to achieve more accurate and reasonable scheduling arrangements.

[0058] Please continue reading Figure 3 , Figure 3 Schematic diagram of a flow chart of an intelligent scheduling method provided by an embodiment of the present application. Figure 3 As shown, the intelligent scheduling method can be implemented through steps S11 to S13.

[0059] Step S11: Determine the corresponding target scheduling rules and target task labels based on the target scenario.

[0060] Among them, the target scenario is used to characterize the actual application scenario that currently needs to be scheduled. For example, patient allocation in hospitals, human resource scheduling for enterprise projects, cargo transportation arrangements in logistics, etc. The target scheduling rules may include the priority of resource allocation, the order of task processing, the principle of maximizing resource utilization, etc. For example, in a hospital scenario, the target scheduling rule may prioritize the allocation of medical resources required for critically ill patients. The target task label is used to characterize the key features of the task, including the task type (such as emergency tasks, regular tasks, etc.) and the task priority (high, medium, low, etc.). These labels are used to define the nature of the task and help the system make decisions when scheduling.

[0061] This step can lay the foundation for subsequent resource scheduling and clarify the specific requirements and scheduling rules of the task.

[0062] Step S12: Determine the target scheduling rule and the resource tag score corresponding to the target task tag through the scheduling matching model and the weighted integral model.

[0063] The scheduling matching model is used to match resources according to the target scheduling rules and task tags. Different scenarios may use different models, such as grid model, cost model, capacity model, quality model, etc. These models are scheduled based on resource availability, cost-effectiveness, capacity level, etc.

[0064] The weighted integral model is used to score each resource based on different scheduling factors of the task (such as priority, task type, resource characteristics, etc.).

[0065] Through this process, each resource can be assigned a corresponding score reflecting its suitability for the current task.

[0066] Step S13: Perform resource scheduling according to the resource label score with the maximum value.

[0067] The resource label score is used to represent that each resource is assigned a score based on its capabilities, availability, cost, etc. The higher the score, the more suitable the resource is for the current task. The maximum value selection is used to indicate that the resource with the highest score is selected for allocation. In other words, under the current scenario and task requirements, the scheduling of this resource is optimal and can meet the task requirements to the greatest extent.

[0068] For example, in a hospital scenario, if a patient requires emergency treatment, calculations may reveal that a certain doctor and equipment have the highest resource score, meaning that these resources are best suited for the current task, and therefore can be allocated with priority.

[0069] The intelligent scheduling method proposed in this application, through the comprehensive calculation of the scheduling matching model and the weight integral model, the system can accurately allocate the most appropriate resources to the most needed tasks, ensuring the maximization of resource utilization and the efficiency of task completion. The scheduling process has a high degree of automation and can dynamically adjust the scheduling rules according to task requirements in different scenarios, and flexibly respond to changing resource and task requirements. In addition, by scheduling resources according to the maximum score, it is ensured that when resources are limited, these resources can be used reasonably and efficiently to avoid waste and redundant allocation.

[0070] See also Figure 4 , Figure 4 1 is a schematic block diagram of an intelligent scheduling device provided in an embodiment of the present application. The intelligent scheduling device can be configured in a server to execute the aforementioned intelligent scheduling method.

[0071] like Figure 4 As shown, the intelligent scheduling device 200 includes: a determination module 201 and an intelligent scheduling module 202.

[0072] Determination module 201, used to determine the corresponding target scheduling rules and target task labels based on the target scenario;

[0073] The determination module 201 is further used to determine the target scheduling rule and the resource tag score corresponding to the target task tag through a scheduling matching model and a weighted integral model;

[0074] The intelligent scheduling module 202 is used to schedule resources according to the maximum resource label score.

[0075] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0076] The method and apparatus of the present application can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer terminal devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices.

[0077] Exemplarily, the above method and apparatus may be implemented in the form of a computer program. The computer program may be implemented in the form of a computer program. Figure 5 Runs on the computer device shown.

[0078] See also Figure 5 , Figure 5 1 is a schematic diagram of a computer device provided in an embodiment of the present application. The computer device may be a server.

[0079] like Figure 5 As shown, the computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory may include a volatile storage medium, a non-volatile storage medium and an internal memory.

[0080] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any intelligent scheduling method.

[0081] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0082] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any intelligent scheduling method.

[0083] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that the structure of the computer device is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0084] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0085] Among them, in some embodiments, the processor is used to determine the corresponding target scheduling rules and target task labels based on the target scenario; determine the target scheduling rules and the resource label scores corresponding to the target task labels through a scheduling matching model and a weight integral model; and perform resource scheduling according to the maximum resource label score.

[0086] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions, and when the program instructions are executed, any one of the intelligent scheduling methods provided in the embodiments of the present application is implemented.

[0087] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the computer device.

[0088] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like.

[0089] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. An intelligent scheduling platform, characterized in that: The intelligent scheduling platform includes: A rule configuration module, used to configure corresponding scheduling rules based on several scenarios, and define task tags and resource tags, wherein the task tags at least include task type and task priority, and the resource tags at least include available human resources and hospital resources; The scheduling matching module is connected to the rule configuration module, and includes a scheduling matching model and a weight integration model. After determining the corresponding target scheduling rules and target task labels based on the target scenario, the resource label scores corresponding to the target scheduling rules and the target task labels are determined by the scheduling matching model and the weight integration model respectively, and resource scheduling is performed according to the maximum resource label score.

2. The intelligent scheduling platform according to claim 1, characterized in that: The intelligent scheduling platform also includes a monitoring module, which is connected to the rule configuration module and is used to monitor the target task label, the hospital resources and the available human resources.

3. The intelligent scheduling platform according to claim 1, characterized in that: Determining the target scheduling rule and the resource tag score corresponding to the target task tag through the weighted integral model includes: Determine a corresponding scheduling factor based on the target task tag, and predefine a weight, a level, and a score for each scheduling factor; The score of each scheduling factor is calculated according to the target scheduling rule, and the score of the scheduling factor is determined as the resource label score, wherein the score of the scheduling factor is the product of the weight of the scheduling factor, the level of the scheduling factor and the score of the scheduling factor.

4. The intelligent scheduling platform according to claim 1, characterized in that: The scheduling matching model includes at least one of a grid model, a cost model, a capability model, a quality model, and a tilt / correction model.

5. The intelligent scheduling platform according to claim 1, characterized in that: The intelligent scheduling platform also includes an auxiliary scheduling module, which is used to perform resource scheduling according to the input value received by the user after determining the target scheduling rule and the target task label.

6. The intelligent scheduling platform according to claim 5, characterized in that: The auxiliary scheduling module also includes an automatic scheduling model, and the automatic scheduling model is used to provide a reference basis for the user input.

7. An intelligent scheduling method, characterized in that: The method comprises: Determine the corresponding target scheduling rules and target task labels based on the target scenario; Determine the target scheduling rule and the resource tag score corresponding to the target task tag through a scheduling matching model and a weighted integral model; Resources are scheduled based on the resource tag score with the maximum value.

8. An intelligent scheduling device, characterized in that: The intelligent scheduling device comprises: A determination module, the determination module is used to determine a corresponding target scheduling rule and a target task label based on a target scenario; The determination module is also used to determine the target scheduling rule and the resource tag score corresponding to the target task tag through a scheduling matching model and a weighted integral model; An intelligent scheduling module is used to schedule resources according to the maximum resource label score.

9. A computer device, characterized in that: include: A memory and a processor; wherein the memory is connected to the processor for storing programs; the processor is used to implement the steps of the intelligent scheduling method as described in claim 7 by running the programs stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the processor to implement the steps of the intelligent scheduling method as described in claim 7.

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