Computer resource allocation method and device for differentiated teaching scenes

Abstract

The invention provides a computer resource allocation method and device for differentiated teaching scenes, which are specially suitable for teaching management. The method comprises the following steps: collecting resource requirements of a plurality of teaching scenes; dividing the plurality of teaching scenes into at least two scene groups according to the resource requirements; analyzing priorities among the resource demands in the scene group, and calculating resource weights of the resource demands in the scene group according to the priorities; key resources of the scene group are obtained through analysis according to the resource weights; dividing the scene group into a high-priority scene group or a low-priority scene group according to the key resources; reserving the lowest resource quantity of each high-priority scene group; calculating according to the total resource quantity and the minimum resource quantity to obtain the residual resource quantity; configuring a resource allocation proportion of each low-priority scene group; and allocating resources to each low-priority scene group according to the residual resource quantity and the resource allocation proportion.

Description

Technical Field

[0001] The present invention relates to the technical field of computer technologies specifically applicable to teaching management, and particularly to a computer resource allocation method and device for differentiated teaching scenarios. Background Art

[0002] In the scenario of educational informatization, high-computing-power demand application scenarios such as virtual simulation laboratories and multimedia teaching platforms coexist with low-priority technical task scenarios such as ordinary computer rooms and online teaching platforms. How to establish a computer resource allocation strategy for numerous differentiated teaching scenarios and effectively improve the computer resource allocation ability and flexibility will be a technical problem that needs to be urgently solved by those skilled in the art.

[0003] In the prior art, the resource allocation methods for different technical fields are becoming increasingly mature, but the resource allocation methods for each technical field in the prior art are not applicable to the teaching field.

[0004] Document 1 with the publication number CN116402318A proposes a multi-level computing power resource allocation method for a distribution network. In the allocation method, the computing power resources are determined according to the task requirements of different end-side devices (computer terminals), and it is judged whether there is a surplus of the resource quota of the end-side device according to the computing power resources, and then the remaining resource quota is redistributed. Although Document 1 avoids the waste of computing power resources by each end-side device, the resource quota of the end-side device in Document 1 is fixed and determined by its hardware, which makes Document 1 inapplicable to the teaching field. In the teaching field, the support of computer devices for different teaching scenarios is different. For some teaching scenarios, computer devices are necessary supports for completing teaching tasks in these teaching scenarios, such as virtual simulation laboratories and multimedia teaching platforms. When the computer devices operate inefficiently in these teaching scenarios, the teaching tasks cannot be completed smoothly; for some other teaching scenarios, computer devices are non-necessary aids for completing teaching tasks in these teaching scenarios, such as ordinary computer rooms and online teaching platforms. When the computer devices operate inefficiently in these teaching scenarios, only the process experience of the teaching tasks will be affected, and the teaching tasks will not be unable to proceed. Then, for the commonly used cloud server deployment and local computer execution devices, how to allocate resources according to different teaching scenarios, on the one hand, ensure the minimum operation requirements of some teaching scenarios, and on the other hand, improve the resource allocation efficiency for all teaching scenarios as much as possible will be the key problem in applying the resource allocation technology to teaching scenarios.

[0005] Document 2 with the publication number CN116012067A proposes a resource allocation method. The allocation method is used in the field of advertising business, which efficiently allocates computing power resources according to the value of different service output information, improving the resource allocation utilization rate. However, Document 2 only focuses on the allocation of computing power resources among multiple service output information (equivalent to the teaching scenario), without considering ensuring the minimum resource requirements of each service output information, nor considering how to more adaptively and quickly and effectively allocate resources among all service output information when there is too much service output information. Summary of the Invention

[0006] In view of this, a first aspect of the present invention provides a computer resource configuration method for a differentiated teaching scenario.

[0007] The method includes the following steps: Collect the resource requirements of multiple teaching scenarios; Divide the multiple teaching scenarios into at least two scenario groups according to the resource requirements; Analyze the priority levels among the resource requirements in the scenario group, and calculate the resource weights of the resource requirements in the scenario group according to the priority levels; Analyze the key resources of the scenario group according to the resource weights; Divide the scenario group into a high-priority scenario group or a low-priority scenario group according to the key resources; Reserve the minimum resource amount for each high-priority scenario group; Calculate the remaining resource amount according to the total resource amount and the minimum resource amount; Configure the resource allocation ratio for each low-priority scenario group; Allocate resources to each low-priority scenario group according to the remaining resource amount and the resource allocation ratio.

[0008] Moreover, a second aspect of the present invention provides a computer resource configuration method for a differentiated teaching scenario.

[0009] The method includes the following steps: Collect the resource requirements of multiple teaching scenarios; Divide the multiple teaching scenarios into at least two scenario groups according to the resource requirements; Analyze the combination characteristics among the resource requirements in the scenario group, and configure at least two resource requirements as combined resource requirements; Analyze the priority levels among the resource requirements and / or combined resource requirements in the scenario group, and calculate the resource weights of the resource requirements and / or combined resource requirements in the scenario group according to the priority levels; Obtain the key resources and / or key combined resources of the scenario group according to the resource weight analysis; Divide the scenario group into a high-priority scenario group or a low-priority scenario group according to the key resources and / or key combined resources; Reserve the minimum resource amount for each high-priority scenario group; Calculate the remaining resource amount according to the total resource amount and the minimum resource amount; Configure the resource allocation ratio for each low-priority scenario group; Allocate resources to each low-priority scenario group according to the remaining resource amount and the resource allocation ratio.

[0010] Moreover, a third aspect of the present invention provides a computer resource configuration device for a differentiated teaching scenario.

[0011] The device includes a demand acquisition module, a scenario group configuration module, a scenario group division module, and a resource allocation module.

[0012] The demand acquisition module is used to acquire the resource requirements of multiple teaching scenarios; The scenario group configuration module is used to divide multiple teaching scenarios into at least two scenario groups according to the resource requirements; The scenario group division module is used to analyze the priority levels among the resource requirements in the scenario group, calculate the resource weights of the resource requirements in the scenario group according to the priority levels; obtain the key resources of the scenario group according to the resource weights; divide the scenario group into a high-priority scenario group or a low-priority scenario group according to the key resources; The resource allocation module is used to reserve the minimum resource amount for each high-priority scenario group; calculate the remaining resource amount according to the total resource amount and the minimum resource amount; configure the resource allocation ratio for each low-priority scenario group; allocate resources to each low-priority scenario group according to the remaining resource amount and the resource allocation ratio.

[0013] Compared with the prior art, the present invention divides multiple teaching scenarios into different scenario groups according to their resource requirements, and the resource requirements of each teaching scenario in the scenario group are similar; then establish resource weights according to the relative relationship of different resource requirements in the scenario group; finally, use the resource weights to determine the priority of the scenario group and establish resource allocation rules for high- and low-priority scenario groups. The present invention uses the technical idea of "seeking common ground while reserving differences". On the one hand, it performs unified resource configuration for similar teaching scenarios, and on the other hand, it establishes a resource allocation strategy based on the priority level for different teaching scenarios, improving the efficiency and robustness of computer resource configuration for differentiated teaching scenarios. Description of the Drawings

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a schematic flowchart of the computer resource allocation method of the present invention for the differentiated teaching scenario; Figure 2 It is a schematic flowchart of the present invention for dividing scenario groups; Figure 3 It is a schematic structural diagram of the computer resource allocation device of the present invention for the differentiated teaching scenario. Detailed implementation manners

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in combination with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0017] An embodiment of the present invention provides a computer resource allocation method for a differentiated teaching scenario.

[0018] Figure 1 It is a schematic flowchart of the computer resource allocation method of the present invention for the differentiated teaching scenario.

[0019] Figure 1 It shows that the method of the present invention includes steps 10 to 90.

[0020] 10 Collect the resource requirements of multiple teaching scenarios.

[0021] Among them, the teaching scenario refers to a teaching scenario with different requirements for computer resources, such as a multimedia classroom, an online teaching platform, a programming laboratory, a virtual simulation laboratory, an ordinary computer room, etc. These teaching scenarios have their own typical resource requirement characteristics. For example, a multimedia classroom has high requirements for GPU performance, large memory requirements, and requires real-time high-speed storage.

[0022] Among them, there are various ways to collect the resource requirements of teaching scenarios. For example, use a monitoring system (such as Prometheus) to collect the historical resource data of each teaching scenario. Or, conduct an interview survey of the teaching staff in charge of each teaching scenario in the form of a questionnaire to judge and analyze the resource requirements of each teaching scenario.

[0023] 20 Divide the multiple teaching scenarios into at least two scenario groups according to the resource requirements.

[0024] Among them, a scenario group is a set of teaching scenarios with similar resource requirements. In the present invention, teaching scenarios with similar resource requirements are assigned to the same scenario group, and then resource allocation is performed with the scenario group as a unit. This can reduce the overly refined and unnecessary computer resource allocation process for individual teaching scenarios, reduce the complexity of resource allocation for multiple teaching scenarios, and improve robustness.

[0025] In the present invention, K-means is used to divide multiple teaching scenarios into different scenario groups.

[0026] Figure 2 It is a schematic flowchart of dividing scenario groups in step 20 of the present invention.

[0027] Figure 2 It shows that dividing scenario groups in step 20 includes steps 21 to 25.

[0028] 21 Define the resource requirement of the teaching scenario as an n-dimensional vector, , where is the demand for resource k by teaching scenario i.

[0029] For example, the vector of the programming laboratory is [4 cores, 0.8 GB (memory), 100 GB (hard disk), 100 Mbps].

[0030] 22 Randomly initialize K teaching scenarios as group centers, ; 23 Calculate the Euclidean distance from each teaching scenario to each group center, , where is the Euclidean distance between the standardized resource requirement of teaching scenario i and group center j, is after the standardized value, is the resource requirement of resource k of group center j, is the dimensional distance between teaching scenario i and group center j only in the dimension of resource k.

[0031] Among them, in order to avoid the influence of dimensional differences on the clustering structure, the present invention adopts Z-score standardization, , where is the average demand of all scenarios for k, is the standard deviation of the demand for resource k.

[0032] 24 Allocate each teaching scenario to the group center with the minimum Euclidean distance and form the scenario group with it. , is the scenario group belonging to group center j after the t-th iteration calculation, is the minimum distance from teaching scenario i to all group centers (i.e., assigning teaching scenario i to the group center with the minimum distance), and K is the number of initialized group centers.

[0033] 25 Iteratively calculate the group centers of each of the said scenario groups according to at least one stop condition and return the calculated Euclidean distance; , The stop condition is or the current iteration calculation times reach an iteration calculation threshold, is the number of teaching scenarios in scenario group j, is the vector sum of the resource requirements of all teaching scenarios within scenario group j, is the current group center of scenario group j of group center j after the (t + 1)-th iteration calculation, means that the group center in scenario group j is the same in the t-th iteration calculation and the (t + 1)-th iteration calculation. Then, when the stop condition is not satisfied after performing step 25, step 23 is re-executed. When the stop condition is satisfied after performing step 25, the process ends and the current each scenario group is saved.

[0034] Therefore, the present invention divides multiple teaching scenarios into K scenario groups, minimizing the sum of the squares of the distances from the teaching scenarios within the group to the group center (WCSS), . Then all teaching scenarios can be clustered into several scenario groups according to the characteristics of their n-dimensional resource requirements, so as to facilitate resource allocation.

[0035] Preferably, each resource requirement in the scenario group is the mean or median of the corresponding resource requirements of the teaching scenarios within the group.

[0036] 30 Analyze the priority levels among the resource requirements in the said scenario group, and calculate the resource weights of the resource requirements in the said scenario group according to the said priority levels.

[0037] Among them, the priority levels can be established by using a scaling rule, that is, using a 1-9 scale to define the relative importance between each resource requirement.

[0038] For example, the scale value 1 means that resource requirement i is equally important as resource requirement j; the scale value 3 means that resource requirement i is slightly more important than resource requirement j; the scale value 5 means that resource requirement i is significantly more important than resource requirement j; the scale value 7 means that resource requirement i is strongly more important than resource requirement j; the scale value 9 means that resource requirement i is absolutely more important than resource requirement j, etc.

[0039] 40 Obtain the key resources of the scenario group based on the analysis of the resource weights.

[0040] Among them, in step 40, a judgment matrix of each scenario group is established according to the priority level. , is the scale value between the nth resource requirement and the nth resource requirement in the scenario group.

[0041] Normalize the judgment matrix and obtain the resource weights of each resource. , is the resource weight of scenario group i for resource k.

[0042] 50 Divide the scenario group into a high-priority scenario group or a low-priority scenario group according to the key resources.

[0043] Among them, in step 50, the key resources of the scenario group are obtained based on the analysis of the resource weights. . The scenario group not less than a resource threshold is the high-priority scenario group; The scenario group less than a resource threshold is the low-priority scenario group.

[0044] Preferably, among all scenario groups, there can be and is configured as one high-priority scenario group. By adjusting the resource threshold of a certain resource weight, only 1 group of high-priority scenario groups will be divided in the scenario group.

[0045] 60 Reserve the minimum resource amount for each high-priority scenario group.

[0046] Preferably, reserving the minimum resource amount in step 60 includes reserving the minimum resource amount of the key resources. , is the minimum resource amount reserved for key resource k in the high-priority scenario group. is the total resource amount of key resource k. is the resource threshold. is the resource requirement of teaching scenario i for key resource k. is the sum of the resource requirements of each teaching scenario in the high-priority scenario group for key resource k.

[0047] Preferably, considering that the minimum resource amount requirements of some scenario groups are extremely high while the total resource amount corresponding to this resource is limited, when reserving the minimum resource amount of each key resource, it is related to the upper limit of the minimum resource amount reserved for necessary configuration.

[0048] For example, , is the maximum reserved resource amount for the critical resource k, is the global upper limit coefficient.

[0049] 70 Calculate the remaining resource amount based on the total resource amount and the minimum resource amount.

[0050] Preferably, the remaining resource amount calculated in step 70 includes the remaining resource amount of the reserved critical resource, , is the remaining resource amount of the critical resource k, and m1 is the number of teaching scenarios in the high-priority scenario group.

[0051] 80 Configure the resource allocation ratio for each of the low-priority scenario groups.

[0052] Among them, configuring the resource allocation ratio in step 80 includes, , is the resource allocation ratio of resource i in the jth low-priority scenario group, is the resource demand weight of resource i in the low-priority scenario group; is the sum of the resource demand weights of resource i in all the low-priority scenario groups.

[0053] Preferably, the resource allocation ratio configured in step 80 is configurable, , K is the resource allocation ratio of a certain resource, is the resource demand of a certain resource in the low-priority scenario group; is the sum of the resource demands of a certain resource in all the low-priority scenario groups.

[0054] 90 Allocate resources to each of the low-priority scenario groups according to the remaining resource amount and the resource allocation ratio.

[0055] Preferably, allocating the resources of the low-priority scenario groups in step 90 includes, , is the allocation amount of the critical resource of the teaching scenario in the low-priority scenario group, and m2 is the number of teaching scenarios in the low-priority scenario group.

[0056] Preferably, after step 90, monitor the resource utilization rate of all the current teaching scenarios. When the utilization rate of the critical resource of one or more teaching scenarios in the high-priority scenario group is lower than a utilization rate threshold, it is defaulted that the teaching scenario has a low activity state, and these teaching scenarios with low activity states are separated from the high-priority scenario group where they are located and temporarily formed into a low-priority scenario group and returned to step 60 to perform the reservation of the minimum resource amount in the high-priority scenario group and the subsequent allocation of the remaining resource amount in the low-priority scenario group.

[0057] Based on this, the computer resource allocation method for differentiated teaching scenarios in the present invention divides multiple teaching scenarios into different scenario groups according to their resource requirements, and the resource requirements of each teaching scenario in the scenario group are similar. The present invention then establishes resource weights according to the relative relationships of different resource requirements in the scenario group, determines the scenario group priorities using the resource weights, and establishes resource (preferably key resource) allocation rules for high- and low-priority scenario groups. Using the technical idea of "seeking common ground while reserving differences", on the one hand, the present invention performs unified resource allocation for similar teaching scenarios, and on the other hand, establishes a resource (preferably key resource) allocation strategy based on the priority level for different teaching scenarios, improving the efficiency and robustness of computer resource allocation for differentiated teaching scenarios.

[0058] Furthermore, the present invention provides a computer resource allocation method for differentiated teaching scenarios. The method includes the following steps 10 to 100.

[0059] 10 Collect the resource requirements of multiple teaching scenarios.

[0060] 20 Divide the multiple teaching scenarios into at least two scenario groups according to the resource requirements; 30 Analyze the combination characteristics among the resource requirements in the scenario group, and configure at least two resource requirements as combined resource requirements.

[0061] 40 Analyze the priority levels among the resource requirements and / or combined resource requirements in the scenario group, and calculate the resource weights of the resource requirements and / or combined resource requirements in the scenario group according to the priority levels.

[0062] 50 Analyze and obtain the key resources and / or key combined resources of the scenario group according to the resource weights.

[0063] 60 Divide the scenario group into high-priority scenario groups or low-priority scenario groups according to the key resources and / or key combined resources.

[0064] 70 Reserve the minimum resource amount for each high-priority scenario group.

[0065] 80 Calculate the remaining resource amount according to the total resource amount and the minimum resource amount.

[0066] 90 Configure the resource allocation ratios for each low-priority scenario group.

[0067] 100 Allocate resources to each low-priority scenario group according to the remaining resource amount and the resource allocation ratios.

[0068] Therefore, the present invention integrates relevant resource requirements to obtain combined required resources, and applies the combined resource requirements to the computer resource allocation of the present invention for the differentiated teaching scenario.

[0069] In addition, the present invention provides a computer resource allocation device for the differentiated teaching scenario.

[0070] Figure 3 It is a schematic structural diagram of the computer resource allocation device for the differentiated teaching scenario of the present invention. Figure 3 It shows that the device includes a requirement collection module, a scenario group configuration module, a scenario group division module, and a resource allocation module.

[0071] Among them, the requirement collection module is used to collect the resource requirements of multiple teaching scenarios.

[0072] The scenario group configuration module is used to divide multiple teaching scenarios into at least two scenario groups according to the resource requirements.

[0073] Among them, the scenario group division module is used to analyze the priority levels among the resource requirements in the scenario group, calculate the resource weights of the resource requirements in the scenario group according to the priority levels; analyze the key resources of the scenario group according to the resource weights; and divide the scenario group into a high-priority scenario group or a low-priority scenario group according to the key resources.

[0074] Among them, the resource allocation module is used to reserve the minimum resource amount for each high-priority scenario group; calculate the remaining resource amount according to the total resource amount and the minimum resource amount; configure the resource allocation ratio for each low-priority scenario group; and allocate resources to each low-priority scenario group according to the remaining resource amount and the resource allocation ratio.

[0075] Therefore, the computer resource allocation device for the differentiated teaching scenario of the present invention can execute the process steps of the computer resource allocation method for the differentiated teaching scenario, and realizes the computer resource allocation of the teaching scenario in an embodied manner.

[0076] In addition, the present invention provides a computer device.

[0077] The computer device includes: at least one processor, at least one memory, a power supply, a communication interface, an input / output interface, and a communication bus. Among them, the memory is used to store a computer program, and the computer program is loaded and executed by the processor to implement the relevant steps in the method provided by any one of the foregoing inventions.

[0078] In the present invention, the power supply is used to provide operating voltages for various hardware devices on the electronic device; the communication interface can create a data transmission channel between the electronic device and external devices, and the communication protocol it follows can be any communication protocol applicable to the present invention, and no specific limitation is imposed thereon herein; the input / output interface is used to obtain external input data or output data to the outside, and the specific interface type can be selected according to specific application requirements, and no specific limitation is imposed herein. The memory, as a carrier for resource storage, can be a read-only memory, a random access memory, a disk, an optical disc, etc., and the resources stored thereon can include an operating system, a computer program, etc., and the storage method can be transient storage or permanent storage. The operating system is used to manage and control various hardware devices and computer programs on the electronic device, and it can be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to implement the method executed by the electronic device provided in any of the foregoing embodiments, the computer program can further include a computer program that can be used to complete other specific tasks.

[0079] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A computer resource allocation method for differentiated teaching scenarios, characterized in that, the method comprises the following steps, collecting resource requirements of multiple teaching scenarios; dividing the multiple teaching scenarios into at least two scenario groups according to the resource requirements; analyzing the priority levels among the resource requirements in the scenario groups, and calculating the resource weights of the resource requirements in the scenario groups according to the priority levels; analyzing the key resources of the scenario groups according to the resource weights; dividing the scenario groups into high-priority scenario groups or low-priority scenario groups according to the key resources; reserving the minimum resource amounts for each of the high-priority scenario groups; calculating the remaining resource amount according to an overall resource amount and the minimum resource amounts; configuring the resource allocation ratios for each of the low-priority scenario groups; allocating resources to each of the low-priority scenario groups according to the remaining resource amount and the resource allocation ratios.

2. The computer resource allocation method for differentiated teaching scenarios according to claim 1, characterized in that, dividing the scenario groups includes, using K-means to divide the multiple teaching scenarios into at least two scenario groups.

3. The computer resource allocation method for differentiated teaching scenarios according to claim 2, characterized in that, dividing the scenario groups includes, defining the resource requirements of the teaching scenarios as n-dimensional vectors, , r ik Demand for resource k for teaching scenario i; Initialize K of the teaching scenarios as group centers, ; calculating the Euclidean distances from each of the teaching scenarios to the group centers, ， d ji is the Euclidean distance between the standardized resource requirements for teaching scenario i and group center j ， μ jk is the resource requirement of resource k for group center j. ； allocating each of the teaching scenarios to the group center with the minimum Euclidean distance and forming the scenario groups, ， is the scene group belonging to group center j after the t-th iteration calculation, is the minimum distance from the teaching scenario i to all group centers, and K is the number of initialized group centers; iteratively calculating the group centers of each of the scenario groups according to at least one stop condition and returning to recalculate the Euclidean distances; ， The stopping condition is or the current iteration count reaches an iteration count threshold, is the number of teaching scenarios for scenario group j, is the vector sum of all teaching scenario resource requirements within scenario group j. Calculate the current group center of scenario group j for the (t + 1)-th iteration, It means that the group center in scenario group j remains the same during the t-th iteration calculation and the (t + 1)-th iteration calculation.

4. The computer resource allocation method for differentiated teaching scenarios according to claim 1, characterized in that, calculating the resource weights includes, analyzing the priority levels among the resource requirements in the scenario groups, where the priority levels are relative importance values among the resource requirements; establishing a judgment matrix for each of the scenario groups according to the priority levels, ， is the relative importance value of the nth resource requirement in the scene group to the nth resource requirement; normalizing the judgment matrix and obtaining the resource weights, ， w ik is the resource weight of resource k for scenario group i.

5. The computer resource allocation method for differentiated teaching scenarios according to claim 4, characterized in that, The key resources of the scenario group are obtained according to the analysis of the resource weights. ; Filter out The scenario groups not less than a resource threshold are the high-priority scenario groups; Filter out The scenario groups smaller than a resource threshold are the low-priority scenario groups.

6. The computer resource allocation method for differentiated teaching scenarios according to claim 5, characterized in that, reserving the minimum resource amounts includes reserving the minimum resource amounts of the key resources, ， is the minimum amount of resources reserved for the critical resource k in the high-priority scenario group, C k is the total resource amount of the key resource k is the resource threshold, is the resource requirement of the teaching scenario i for the key resource k, is the sum of the resource requirements of each of the teaching scenarios in the high-priority scenario group for the key resource k; calculating the remaining resource amount includes calculating the remaining resource amount of the key resources, is the remaining resource amount of the key resource k, and m1 is the number of the teaching scenarios in each of the high-priority scenario groups; configuring the resource allocation ratios for each of the low-priority scenario groups includes, The resource allocation ratio of resource i in a low-priority scenario group; configuring the resources for the low-priority scenario groups includes, m1 is the allocation amount of the key resources for each teaching scenario in the low-priority scenario group, and m2 is the number of the teaching scenarios in the low-priority scenario group.

7. The computer resource allocation method for differentiated teaching scenarios according to claim 6, characterized in that, reserving the minimum resource amounts includes, configuring the upper limit of the minimum resource amounts of the reserved key resources, , is the maximum reserved resource amount for the key resource k, is the global upper limit coefficient.

8. The computer resource allocation method for differentiated teaching scenarios according to claim 6, characterized in that, configuring the resources for the low-priority scenario groups includes, monitoring the resource utilization rates of each of the current teaching scenarios; When the utilization rate of the critical resources of one or more teaching scenarios in the high-priority scenario group is lower than a utilization rate threshold, one or more of the teaching scenarios are temporarily grouped into a low-priority scenario group and the reservation of the minimum amount of resources is re-executed.

9. A computer resource allocation method for differentiated teaching scenarios, characterized in that the method includes the following steps collecting the resource requirements of multiple teaching scenarios; dividing the multiple teaching scenarios into at least two scenario groups according to the resource requirements; analyzing the combination characteristics among the resource requirements in the scenario group and configuring at least two resource requirements as combined resource requirements; analyzing the priority levels among the resource requirements and / or combined resource requirements in the scenario group and calculating the resource weights of the resource requirements and / or combined resource requirements in the scenario group according to the priority levels; analyzing the critical resources and / or critical combined resources of the scenario group according to the resource weights; dividing the scenario group into a high-priority scenario group or a low-priority scenario group according to the critical resources and / or critical combined resources; reserving the minimum amount of resources for each high-priority scenario group; calculating the remaining amount of resources according to the total amount of resources and the minimum amount of resources; configuring the resource allocation ratios for each low-priority scenario group; allocating resources to each low-priority scenario group according to the remaining amount of resources and the resource allocation ratios.

10. A computer resource allocation device for differentiated teaching scenarios, characterized in that the device includes a requirement collection module, a scenario group configuration module, a scenario group division module and a resource allocation module; the requirement collection module is used to collect the resource requirements of multiple teaching scenarios; the scenario group configuration module is used to divide the multiple teaching scenarios into at least two scenario groups according to the resource requirements; the scenario group division module is used to analyze the priority levels among the resource requirements in the scenario group and calculate the resource weights of the resource requirements in the scenario group according to the priority levels; analyzing the critical resources of the scenario group according to the resource weights; dividing the scenario group into a high-priority scenario group or a low-priority scenario group according to the critical resources; the resource allocation module is used to reserve the minimum amount of resources for each high-priority scenario group; calculate the remaining amount of resources according to the total amount of resources and the minimum amount of resources; configure the resource allocation ratios for each low-priority scenario group; and allocate resources to each low-priority scenario group according to the remaining amount of resources and the resource allocation ratios.

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