Digital human resource scheduling method and device supporting high concurrency and storage medium

Abstract

The embodiment of the invention provides a digital human resource scheduling method and device supporting high concurrency and a storage medium, and the method comprises the steps: monitoring the resource use data of a cloud computing center and an edge computing node in real time; dynamically adjusting resource quotas in the cloud computing center and the edge computing node according to resource use data on the basis of a containerization technology; determining different user requests and task types according to interaction behaviors of different users and the digital human; and based on a load balancing technology, automatically distributing the user request to the cloud computing center or the edge computing node for processing according to the task type. The dynamic allocation and scheduling of resources can be realized by utilizing cloud computing and edge computing technologies so as to meet the requirements of digital people in different scenes, and the concurrent processing capability and the resource utilization rate of the system are improved.

Description

Technical Field

[0001] This document relates to the technical field of digital humans, and particularly to a digital human resource scheduling method, device, and storage medium that support high concurrency. Background Art

[0002] In complex AI digital human business scenarios, efficient resource scheduling can ensure the reasonable allocation and use of computing power resources, avoid application crashes or performance degradation caused by insufficient resources, and thus ensure the stable operation of digital human applications.

[0003] Traditional digital human resource scheduling mainly realizes digital human services through static resource allocation and centralized cloud computing, relying on pre-fixed virtual machines, storage resources, and cloud data centers to process all user requests. Polling or random load balancing algorithms are used to distribute traffic, and vertical scaling (such as upgrading the hardware of a single server) is used to handle traffic growth. Resource allocation is based on manual experience or historical averages, and edge devices only act as data collection terminals, and computing tasks are completed centrally in the cloud.

[0004] However, when traditional digital humans perform resource scheduling, using static allocation will result in a high resource idle rate, making it difficult to handle sudden traffic; and relying on manual rules or simple algorithms, it is impossible to predict future resource requirements, resulting in a rigid scheduling strategy. At the same time, due to ignoring the edge computing ability, all data needs to be uploaded to the cloud, resulting in high bandwidth pressure and significant response delays in real-time interaction scenarios (such as the metaverse, AR / VR). In addition, the fault tolerance mechanism is weak, unable to support high concurrency and low service availability. Summary of the Invention

[0005] In view of the above solutions, the present application aims to propose a digital human resource scheduling method, device, and storage medium that support high concurrency to solve at least one of the above technical problems.

[0006] In a first aspect, one or more embodiments of this specification provide a digital human resource scheduling method that supports high concurrency, including:

[0007] Real-time monitoring of resource usage data of the cloud computing center and edge computing nodes;

[0008] Based on containerization technology, dynamically adjust the resource quotas in the cloud computing center and the edge computing nodes according to the resource usage data;

[0009] Determine different user requests and task types according to the interaction behaviors of different users and digital humans; and

[0010] Based on load balancing technology, automatically allocate the user requests to the cloud computing center or the edge computing node for processing according to the task type.

[0011] Further, it further includes:

[0012] Collect historical resource usage data;

[0013] Based on a machine learning algorithm, extract user behavior feature data according to the historical resource usage data; and

[0014] Predict the resource quotas of the cloud computing center and the edge computing nodes according to the user behavior feature data.

[0015] Further, the dynamically adjusting the resource quotas in the cloud computing center and the edge computing nodes based on containerization technology includes:

[0016] Based on containerization technology, when the resource usage data of the edge computing node is less than a preset first threshold, schedule the cold resource pool in the cloud computing center; and

[0017] When the resource usage data of the cloud computing center is less than a preset second threshold, dynamically expand the container units required by the cloud computing center.

[0018] Further, the automatically allocating the user requests to the cloud computing center or the edge computing node for processing based on load balancing technology includes:

[0019] The task types are divided into real-time tasks and non-real-time tasks;

[0020] Based on load balancing technology, schedule real-time tasks to the edge computing node; and

[0021] Migrate non-real-time tasks to the cloud computing center.

[0022] In a second aspect, an embodiment of the present application provides a digital human resource scheduling device supporting high concurrency, including:

[0023] A monitoring module for real-time monitoring of the resource usage data of the cloud computing center and the edge computing nodes;

[0024] A scheduling module for dynamically adjusting the resource quotas in the cloud computing center and the edge computing nodes based on containerization technology according to the resource usage data;

[0025] A determination module for determining different user requests and task types according to the interaction behaviors of different users with digital humans; and

[0026] A processing module for automatically allocating the user requests to the cloud computing center or the edge computing node for processing based on load balancing technology according to the task type.

[0027] Further, it further includes a training module,

[0028] Collect historical resource usage data;

[0029] Based on machine learning algorithms, extract user behavior feature data according to the historical resource usage data; and

[0030] Predict the resource quotas of the cloud computing center and the edge computing nodes according to the user behavior feature data.

[0031] Further, the scheduling module is configured to:

[0032] Based on containerization technology, when the resource usage data of the edge computing node is less than a preset first threshold, schedule the cold resource pool in the cloud computing center; and

[0033] When the resource usage data of the cloud computing center is less than a preset second threshold, dynamically expand the container units required by the cloud computing center.

[0034] Further, the processing module is configured to:

[0035] The task types are divided into real-time tasks and non-real-time tasks;

[0036] Based on load balancing technology, schedule real-time tasks to the edge computing node; and

[0037] Migrate non-real-time tasks to the cloud computing center.

[0038] In a third aspect, an embodiment of the present application provides a computing device, including a memory, a processor, and computer instructions stored on the memory and executable on the processor, characterized in that when the processor executes the computer instructions, the steps of the digital human resource scheduling method described in any item of the first aspect are implemented.

[0039] In a fourth aspect, an embodiment of the present application provides a storage medium for storing computer-executable instructions, characterized in that when the computer-executable instructions are executed, the steps of the digital human resource scheduling method described in any item of the first aspect are implemented.

[0040] Compared with the prior art, the present application can at least achieve the following technical effects:

[0041] The present application can monitor the resource usage situation, continuously optimize the resource configuration situation by using containerization technology, so that the system runs efficiently; and through load balancing, realize dynamic allocation and scheduling of resources according to the task requirements of users, to meet the needs of digital humans in different scenarios, improve the concurrent processing ability and resource utilization rate of the system, and thus improve the overall service quality and user experience. Description of the Drawings

[0042] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0043] Figure 1 A flowchart of a digital human resource scheduling method supporting high concurrency provided for one or more embodiments of this specification;

[0044] Figure 2 A schematic structural diagram of a digital human resource scheduling device supporting high concurrency provided for one or more embodiments of this specification. Detailed implementation manners

[0045] In order to enable those skilled in the art of this technology to better understand the technical solutions in one or more embodiments of this specification, the following will clearly and completely describe the technical solutions in one or more embodiments of this specification in conjunction with the accompanying drawings in one or more embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this document.

[0046] With the application of digital human technology in multiple scenarios, such as the metaverse, virtual assistants, etc., higher requirements are placed on the concurrency processing ability of the system. In traditional resource scheduling methods, it is usually necessary to pre-define the resource requirements of each service or application in advance, and these configurations are usually static. When the actual load changes, the static configuration cannot dynamically adjust the resource quota, resulting in some nodes being overloaded while other nodes' resources are idle, causing resource waste and a decline in service quality. Moreover, the resource scheduling strategy is single and cannot be flexibly adjusted according to different application scenarios, easily leading to unreasonable resource allocation and affecting the overall efficiency. At the same time, traditional resource scheduling often focuses on the cloud computing center and ignores the resource utilization of edge computing nodes. Edge computing nodes can provide lower latency and better local data processing capabilities. If the resources of edge nodes are not effectively utilized, it will lead to unnecessary data transmission latency and bandwidth consumption, reducing the user experience. In addition, traditional resource scheduling does not have the ability of automatic scaling and is difficult to cope with sudden traffic peaks or load fluctuations. When facing sudden traffic, the system cannot quickly increase resources to meet the demand, which will lead to service interruption or a significant increase in response time, affecting the user experience.

[0047] In view of the above problems, the present application proposes a digital human resource scheduling method supporting high concurrency, as Figure 1 shown, which specifically includes:

[0048] Step S1, real-time monitor the resource usage data of the cloud computing center and the edge computing nodes.

[0049] In the embodiment of the present application, monitoring tools are deployed on the cloud computing center and the edge computing nodes. For example, Prometheus, cAdvisor, and Node Exporter are deployed on the cloud computing center nodes, and then Prometheus is configured to regularly scrape the resource usage data from the cloud computing center nodes. Among them, Prometheus is used to scrape and store the resource usage data; cAdvisor is used to collect the resource usage information at the container level; Node Exporter is used to collect the resource usage information at the node level. Node Exporter and cAdvisor are deployed on the edge computing nodes, and then Prometheus is configured to regularly scrape the resource usage data from each edge computing node (such as once per minute). The usage data of cloud computing and edge computing resources (such as data on CPU, memory, disk I / O, network bandwidth, etc.) are collected in real time.

[0050] By collecting the real-time usage data of cloud computing and edge computing nodes in real time, it can ensure that problems are timely discovered while the system is running efficiently, thereby optimizing resource utilization.

[0051] Step S2, based on containerization technology, dynamically adjust the resource quotas in the cloud computing center and the edge computing nodes according to the resource usage data.

[0052] Specifically, first, resource quotas and priority allocation policies are set for the cloud computing nodes and the edge computing nodes. A container orchestration platform Kubernetes (application software) is selected, and resource requests (requests) and resource limits (limits) are defined for each namespace or specific application service in Kubernetes. For example, the maximum CPU usage of a certain service is set to 2 cores, and the maximum memory usage is set to 4GB. Then, the PriorityClass mechanism of Kubernetes is used to set priorities for different services to ensure that critical tasks can obtain sufficient resources. And the Pod preemption function (Pod Priority and Preemption) is enabled to allow high-priority tasks to preempt the resources of low-priority tasks.

[0053] Secondly, dynamically adjust resource quotas according to different resource request situations in the cloud computing center and edge computing nodes, which are divided into three cases: First, configure Horizontal Pod Autoscaler (HPA) to automatically scale the number of Pods up or down according to the actual load. For example, when the CPU utilization rate of a certain service exceeds the set threshold, automatically increase the number of Pods of this service to share the load. Second, configure Vertical Pod Autoscaler (VPA) to automatically adjust the resource requests and limits of Pods according to the actual resource usage. For example, when the memory usage rate of a certain service remains high, automatically increase the memory requests and limits of this service. Third, use Kubernetes Federation or other multi-cluster management tools to migrate some computing tasks to the edge computing nodes for execution, reducing the load on the cloud computing center. For example, deploy lightweight service instances on edge devices to process local user requests and reduce the amount of data transmitted back to the cloud.

[0054] Finally, continuously monitor the performance of the system after the policy adjustment, collect new performance data and analyze it to confirm that the CPU and memory utilization rates return to normal levels, evaluate the effect of resource allocation, and the response time gradually shortens. And further optimize the resource allocation policy according to the feedback information until the allocated resources can meet all task responses and achieve the best effect. For example, the priority of certain services can be adjusted or the model can be retrained to improve the accuracy of the results.

[0055] Dynamically allocating resources through containerization technology can effectively handle the load changes of the system, ensure the stability and efficiency of the digital human in various application scenarios, thereby improving the utilization rate of resources, while enhancing the elasticity and flexibility of the system.

[0056] Step S3: Determine different user requests and task types according to the interaction behaviors of different users with the digital human.

[0057] In the embodiment of this application, collect the data of the interaction between the user and the digital human, analyze the user behavior, and the data includes session frequency, interaction content complexity, response time requirements, etc. Classify user requests according to this information, such as real-time interaction type, batch processing type, etc.

[0058] Identify different types of task requirements by analyzing user behavior, so as to better plan the resource allocation strategy.

[0059] Step S4: Based on the load balancing technology, automatically allocate the user requests to the cloud computing center or the edge computing node for processing according to the task type.

[0060] In the embodiments of the present application, task types are divided into real-time tasks and non-real-time tasks; based on load balancing technology, real-time tasks are scheduled to edge computing nodes; non-real-time tasks are migrated to the cloud computing center.

[0061] Specifically, load balancing rules are configured according to task types. For example: for tasks that require low-latency response (video streaming media), they are preferentially allocated to edge computing nodes for timely processing; while for tasks intensive in machine loss, they are allocated to the cloud computing center.

[0062] The load balancing strategy is dynamically adjusted, changing with the change of resource usage conditions, which can ensure the efficient utilization of available resources.

[0063] Furthermore, based on containerization technology, when the resource usage data of the edge computing node is less than a preset first threshold, the cold resource pool in the cloud computing center is scheduled; and when the resource usage data of the cloud computing center is less than a preset second threshold, the container units required by the cloud computing center are dynamically expanded.

[0064] For example: the scheduling system includes scheduling the cold resource pool and expanding container units, and a monitoring tool is used to collect the resource usage conditions such as CPU, memory, storage, and network bandwidth in the edge computing node or the cloud computing center. If the resource utilization rate (CPU / memory / network) of the edge computing node continuously drops below the first threshold (such as CPU < 20%), some container tasks of the edge node are migrated to the cold resource pool in the cloud computing center. If the container instances in the cold resource pool are in the dormant state (such as marked as Standby by kubelet), they are woken up and bound to tasks, and the scheduler policy is updated. Subsequently, new tasks are preferentially allocated to the cold resource pool in the cloud center. When the resource utilization rate of the cloud computing center continuously drops below the second threshold (such as CPU < 30%), the container units are expanded as needed.

[0065] Through the dynamic scheduling method, the resource quotas of the cloud computing center and the edge computing nodes can be effectively dynamically adjusted according to the resource usage conditions, thereby improving resource utilization and service quality.

[0066] The embodiments of the present application provide a digital human resource scheduling device supporting high concurrency, such as Figure 2 shown, including:

[0067] A monitoring module, configured to monitor the resource usage data of the cloud computing center and the edge computing nodes in real time;

[0068] A scheduling module, configured to dynamically adjust the resource quotas in the cloud computing center and the edge computing nodes based on containerization technology according to the resource usage data;

[0069] A determination module, configured to determine different user requests and task types according to the interaction behaviors of different users with the digital human; and

[0070] A processing module, configured to automatically allocate the user requests to the cloud computing center or the edge computing node for processing based on the load balancing technology according to the task types.

[0071] Further, it further includes a training module,

[0072] Collect historical resource usage data;

[0073] Based on machine learning algorithms, extract user behavior feature data according to the historical resource usage data; and

[0074] Predict the resource quotas of the cloud computing center and the edge computing node according to the user behavior feature data.

[0075] Further, the scheduling module is configured to:

[0076] Based on containerization technology, when the resource usage data of the edge computing node is less than a preset first threshold, schedule the cold resource pool in the cloud computing center; and

[0077] When the resource usage data of the cloud computing center is less than a preset second threshold, dynamically expand the container units required by the cloud computing center.

[0078] Further, the processing module is configured to:

[0079] The task types are divided into real-time tasks and non-real-time tasks;

[0080] Based on the load balancing technology, schedule real-time tasks to the edge computing node; and

[0081] Migrate non-real-time tasks to the cloud computing center.

[0082] An embodiment of the present application provides a computing device, including a memory, a processor, and computer instructions stored on the memory and executable on the processor. The processor, when executing the computer instructions, implements the steps of the digital human resource scheduling method described in any one of the above embodiments.

[0083] An embodiment of the present application provides a storage medium for storing computer-executable instructions, characterized in that the computer-executable instructions, when executed, implement the steps of the digital human resource scheduling method described in any one of the above embodiments.

[0084] It should be noted that the embodiments of the storage medium in this specification and the embodiments of the blockchain-based service providing method in this specification are based on the same inventive concept. Therefore, for the specific implementation of this embodiment, reference may be made to the corresponding implementation of the blockchain-based service providing method described above, and repeated parts will not be elaborated.

[0085] The specific embodiments of this specification have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0086] In the 1930s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements to method flows). However, with the development of technology, many method flow improvements today can be regarded as direct improvements to hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement to a method flow cannot be implemented using a hardware entity module. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logical function is determined by the user programming the device. Designers can program themselves to "integrate" a digital system onto a single PLD, without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a Hardware Description Language (HDL). There is not just one type of HDL, but many types, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones currently are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that by simply performing a little logical programming on the method flow using the above-mentioned several hardware description languages and programming it into the integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0087] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to logically program the method steps to enable the controller to be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. to achieve the same function. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0088] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0089] For the convenience of description, when describing the above devices, they are described separately as various units according to their functions. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0090] Those skilled in the art should understand that one or more embodiments of this specification can be provided as a method, a system, or a computer program product. Therefore, one or more embodiments of this specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0091] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the specification. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or in multiple blocks, the functions specified in the blocks.

[0092] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or in multiple blocks, the functions specified in the blocks.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or in multiple blocks, the functions specified in the blocks.

[0094] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0095] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0096] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0097] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0098] One or more embodiments of this specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. One or more embodiments of this specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0099] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0100] The above are only examples of this document and are not intended to limit this document. For those skilled in the art, this document may have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document shall be included within the scope of the claims of this document.

Claims

1. A digital human resource scheduling method supporting high concurrency, characterized in that Including: Real-time monitoring of the resource usage data of the cloud computing center and the edge computing nodes; Based on containerization technology, dynamically adjusting the resource quotas in the cloud computing center and the edge computing nodes according to the resource usage data; Determining different user requests and task types according to the interaction behaviors of different users and the digital human; And Based on load balancing technology, automatically allocating the user requests to the cloud computing center or the edge computing nodes for processing according to the task types.

2. The method according to claim 1, characterized in that The method further includes: Collecting historical resource usage data; Based on machine learning algorithms, extracting user behavior feature data according to the historical resource usage data; and Predicting the resource quotas of the cloud computing center and the edge computing nodes according to the user behavior feature data.

3. The method according to claim 1, wherein The dynamically adjusting the resource quotas in the cloud computing center and the edge computing nodes based on containerization technology according to the resource usage data includes: Based on containerization technology, when the resource usage data of the edge computing node is less than a preset first threshold, scheduling the cold resource pool in the cloud computing center; and When the resource usage data of the cloud computing center is less than a preset second threshold, dynamically expanding the container units required by the cloud computing center.

4. The method according to claim 1, wherein The automatically allocating the user requests to the cloud computing center or the edge computing nodes for processing based on load balancing technology according to the task types includes: The task types are divided into real-time tasks and non-real-time tasks; Based on load balancing technology, scheduling real-time tasks to the edge computing nodes; and Migrating non-real-time tasks to the cloud computing center.

5. A digital human resource scheduling device supporting high concurrency, characterized in that Including: A monitoring module for real-time monitoring of the resource usage data of the cloud computing center and the edge computing nodes; A scheduling module for dynamically adjusting the resource quotas in the cloud computing center and the edge computing nodes based on containerization technology according to the resource usage data; A determining module for determining different user requests and task types according to the interaction behaviors of different users and the digital human; And A processing module for automatically allocating the user requests to the cloud computing center or the edge computing nodes for processing based on load balancing technology according to the task types.

6. The device according to claim 5, characterized in that It further includes a training module, Collecting historical resource usage data; Based on machine learning algorithms, extracting user behavior feature data according to the historical resource usage data; And Predicting the resource quotas of the cloud computing center and the edge computing nodes according to the user behavior feature data.

7. The device according to claim 5, characterized in that The scheduling module is configured to: Based on containerization technology, when the resource usage data of the edge computing node is less than a preset first threshold, schedule the cold resource pool in the cloud computing center; and When the resource usage data of the cloud computing center is less than a preset second threshold, dynamically expand the container units required by the cloud computing center.

8. The device according to claim 5, characterized in that The processing module is configured to: The task types are divided into real-time tasks and non-real-time tasks; Based on load balancing technology, schedule real-time tasks to the edge computing nodes; and Migrate non-real-time tasks to the cloud computing center.

9. A computing device, comprising a memory, a processor, and computer instructions stored on the memory and executable on the processor, characterized in that, When the processor executes the computer instructions, the steps of the digital human resource scheduling method described in any one of claims 1-4 are implemented.

10. A storage medium for storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed, the steps of the digital human resource scheduling method described in any one of claims 1-4 are implemented.

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