A learning resource intelligent scheduling method and system

Abstract

The application discloses a learning resource intelligent scheduling method and system, relates to the technical field of resource scheduling, determines a plurality of target student terminals, establishes a scheduling graph between the target student terminals and a learning resource database, marks the user portrait of each target student terminal in the corresponding subgraph, and establishes the correlation between the subgraph of each target student terminal and the learning resource database; learning resource data in the learning resource database is actively provided to the target student terminal through a crawling community based on the correlation; the subgraph of the target student terminal is updated in real time, and when the subgraph of the target student terminal changes, the crawling community in which the target student terminal is located is replaced. The application can schedule multiple types of data for multiple target student terminals, the student can switch between multiple crawling communities, has good flexibility, and the data provided is more flexible and accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of resource scheduling, and particularly relates to a learning resource intelligent scheduling method and system. BACKGROUND

[0002] The traditional education mode is mainly based on "standardized supply" (such as unified textbooks, unified progress), but with the increasing demand for talent diversification in society, the individual differences of learners are increasingly valued, for example, the learning foundation, interest direction, goal planning, etc. of some learners are different; in the same group, some learners need to consolidate basic knowledge, some learners want to learn advanced content in advance, and some learners have different professional preferences, which cannot be differentiated by the traditional education mode. Resource scheduling, and because the number of learners is large, the complex data exchange is easy to cause system collapse, which cannot guarantee the stability of the system to schedule resources. SUMMARY

[0003] The purpose of the present application is to provide a learning resource intelligent scheduling method and system to solve the problems in the background art.

[0004] In order to achieve the above purpose, the present application provides the following technical scheme: a learning resource intelligent scheduling method, comprising the following steps:

[0005] Determine a plurality of target student terminals, and establish a scheduling graph between the plurality of target student terminals and a learning resource database, wherein the scheduling graph comprises a main graph, a plurality of sub-graphs corresponding to the target student terminals, and a plurality of crawling communities;

[0006] Mark the user portrait of each target student terminal in the corresponding sub-graph, and establish a correlation relationship between the sub-graph of each target student terminal and the learning resource database;

[0007] Based on the correlation relationship, learning resource data in the learning resource database is actively provided to the target student terminal through the crawling community;

[0008] Real-time update the sub-graph of the target student terminal, and when the sub-graph of the target student terminal changes, replace the crawling community where the target student terminal is located.

[0009] In a preferred embodiment, the step of determining a plurality of target student terminals and establishing a scheduling graph between the plurality of target student terminals and a learning resource database comprises:

[0010] Divide the learning resource data in the learning resource database according to types to obtain a plurality of resource pools, and take the type as a label of the corresponding resource pool.

[0011] According to the resource pool configuration, a plurality of resource points are connected with corresponding resource pools, and the plurality of resource points are connected to obtain a main graph;

[0012] A plurality of type points are set corresponding to each target student terminal, and the plurality of type points are connected to obtain a sub-graph;

[0013] A grabbing community is set corresponding to each resource point.

[0014] In a preferred embodiment, the step of setting a grabbing community corresponding to each resource point comprises:

[0015] A distribution body is set corresponding to each resource point, wherein the distribution body includes a main resource and a plurality of mapping surfaces, and the distribution body is connected with the corresponding resource point;

[0016] A plurality of tentacles are set corresponding to the distribution body, wherein the tentacles include touch points and connection lines between the touch points and the distribution body, and a one-to-one correspondence is established between the touch points and the mapping surfaces;

[0017] A distribution body and a plurality of corresponding tentacles are set as a grabbing community.

[0018] In a preferred embodiment, the step of setting a distribution body corresponding to each resource point comprises:

[0019] A storage space is set corresponding to each resource point, and the storage space is annularly divided to obtain a plurality of virtual spaces;

[0020] After annular division, data surfaces are set in the internal space of the storage space as main resources, and data surfaces are set in the edge space of the storage space as mapping surfaces;

[0021] A carrying surface is set between the mapping surfaces and the main resources, wherein the carrying surface is composed of a plurality of carrying points, the carrying points include nodes and virtual machines, and adjacent carrying points are connected with each other;

[0022] The main resources are respectively connected with a plurality of mapping surfaces in data range, the main resources and the plurality of mapping surfaces are divided in corresponding data range according to the number of carrying points, a plurality of main data range surfaces in the main resources and a plurality of mapping data range surfaces in the mapping surfaces are obtained, and the number of the plurality of mapping data range surfaces is the same as that of the plurality of main data range surfaces;

[0023] The carrying points are connected with the mapping data range surfaces in the corresponding mapping surfaces and the main data range surfaces in the main resources in one-to-one manner to obtain the distribution body.

[0024] In a preferred embodiment, the step of marking the user portrait of each target student terminal in the corresponding sub-graph, and establishing the correlation between the sub-graph of each target student terminal and the learning resource database, comprises:

[0025] Corresponding to the collection of the user portrait of each target student terminal, the user portrait is converted into a type, and is marked in the type point of the corresponding type;

[0026] After marking the type point of each target student terminal, the type point is triggered to connect with the resource pool of the corresponding type, and the correlation is obtained.

[0027] In a preferred embodiment, the step of providing the learning resource data in the learning resource database to the target student terminal based on the correlation, comprises:

[0028] According to the correlation, the tentacle of the resource pool is connected with the corresponding target student terminal, wherein the touch point of the tentacle is connected with the target student terminal;

[0029] The learning resource data in the resource pool is transmitted to the main resource of the distribution point through the resource point, and is laid on the data surface in the main resource;

[0030] The learning resource data in the main data range surface of the corresponding main resource is copied and provided to the mapping data range surface in the corresponding mapping surface through the carrying point;

[0031] When the carrying point cannot copy the learning resource data in the corresponding main data range surface, as an abnormal carrying point, a virtual machine of an adjacent carrying point is connected with the node of the abnormal carrying point, and the learning resource data in the main data range surface of the abnormal carrying point is copied and provided to the mapping data range surface in the corresponding mapping surface through the virtual machine of the adjacent carrying point;

[0032] The learning resource data in the mapping data range surface is provided to the corresponding target student terminal through the touch point.

[0033] In a preferred embodiment, the step of updating the sub-graph of the target student terminal in real time, and replacing the grabbing community where the target student terminal is located when the sub-graph of the target student terminal changes, comprises:

[0034] The user portrait of the target student terminal is collected in real time, and the type point of the sub-graph is marked and updated according to the user portrait collected in real time;

[0035] When the updated sub-graph has a label change, the correlation between the type point of the changed and marked label and the corresponding resource pool is established;

[0036] According to the target student end corresponding change and the type point of the marked type point community, the tentacles of the change and the type point of the corresponding community are connected with the target student end.

[0037] The application further provides a learning resource intelligent scheduling system, comprising:

[0038] A construction module is configured to determine a plurality of target student ends and establish a scheduling graph between the target student ends and a learning resource database, wherein the scheduling graph comprises a main graph, a plurality of sub-graphs corresponding to the target student ends, and a plurality of communities;

[0039] A connection module is connected with the construction module and configured to mark a user portrait of each target student end in a corresponding sub-graph and establish a correlation between the sub-graph of each target student end and the learning resource database;

[0040] A scheduling module is connected with the connection module and configured to actively provide learning resource data in the learning resource database to the target student end through the community based on the correlation;

[0041] An updating module is connected with the scheduling module and configured to update the sub-graph of the target student end in real time and replace the community in which the target student end is located when the sub-graph of the target student end changes.

[0042] In the above technical solution, the application provides the following technical effects and advantages:

[0043] 1. The application can schedule multiple types of data for a plurality of target student ends, and the student can switch between multiple communities, which has good flexibility and provides more flexible and accurate data.

[0044] 2. The application is not prone to system access busy and crash when a plurality of target student ends use the system, and has good data stability. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0046] Figure 1 The method flowchart of the present application.

[0047] Figure 2 The system block diagram of the present application. DETAILED DESCRIPTION

[0048] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0049] Embodiment 1, please refer to Figure 1 The learning resource intelligent scheduling method described in the embodiment includes the following steps:

[0050] S1, a plurality of target student terminals are determined, and a scheduling graph between the plurality of target student terminals and a learning resource database is established, wherein the scheduling graph includes a main graph, a plurality of sub-graphs corresponding to the target student terminals, and a plurality of crawling communities;

[0051] S2, a user portrait of each target student terminal is marked in the corresponding sub-graph, and a correlation relationship between the sub-graph of each target student terminal and the learning resource database is established;

[0052] S3, based on the correlation relationship, learning resource data in the learning resource database is actively provided to the target student terminal through the crawling community;

[0053] S4, the sub-graph of the target student terminal is updated in real time, and when the sub-graph of the target student terminal changes, the crawling community in which the target student terminal is located is replaced.

[0054] As described above in steps S1-S4, multiple target student terminals can be allocated with multiple types of data, and students can switch between multiple crawling communities, which has good flexibility, and data is provided more flexibly. Moreover, when multiple target student terminals use the system, system access is not easy to be busy and crash, and has good data stability.

[0055] In one embodiment, the step S1 of determining a plurality of target student terminals and establishing a scheduling graph between the plurality of target student terminals and a learning resource database includes:

[0056] S11, the learning resource data in the learning resource database is divided into a plurality of resource pools according to types, and the types are taken as tags of the corresponding resource pools (the learning resource database is a cloud database, and the resource pool is a storage space divided in the cloud database, and each storage space is used for storing learning resource data of a corresponding type).

[0057] S12, a plurality of resource points are configured according to the resource pool, the resource points and the corresponding resource pool are connected to each other, the plurality of resource points are connected to obtain a main graph (the resource points corresponding to the resource pool are network ports, one resource pool corresponds to one connected network port, a plurality of network ports are connected to each other, the resource points can represent the data type of the resource pool in the learning resource database, and can also be used for subsequent acquisition of data in the resource pool, and then a main graph is obtained);

[0058] S13, a plurality of type points are set for each target student terminal, the plurality of type points are connected to obtain a sub-graph (the number of type points is the same as the number of types of learning resource data used for division, the type points here are network nodes, which can be used for the pointing of the target student terminal to the resource pool, and subsequent connection between the network nodes and the resource pool cannot transmit data, and is only used to determine the corresponding resource pool of the target student terminal, the judgment of the corresponding resource pool is through the type of the target student terminal, which includes the knowledge level, learning progress, interest preference and wrong question type of the learner, and the above data of the learner is divided by type, for example, the knowledge level can be described by the education level type, the interest preference can include data that the learner likes a tool class course type or data of a technology type, and different types are used to classify the data in the learning resource database, and the type of the type point is the same as the type used for dividing the learning resource data);

[0059] S14, a grabbing community is set for each resource point.

[0060] In one embodiment, the step S14 of setting a grabbing community for each resource point comprises:

[0061] S141, a distribution body is set for each resource point, wherein the distribution body includes a main resource and a plurality of mapping surfaces, and the distribution body is connected to the corresponding resource point;

[0062] S142, a plurality of tentacles are set for the distribution body, wherein the tentacles include touch points and connection lines between the touch points and the distribution body, and a one-to-one correspondence is established between the touch points and the mapping surfaces;

[0063] S143, a distribution body and a plurality of tentacles connected thereto are taken as a grabbing community.

[0064] In one embodiment, the step S141 of setting a distribution body for each resource point comprises:

[0065] S1411, a storage space is set for each resource point, and the storage space is annularly divided to obtain a plurality of virtual spaces;

[0066] S1412, set the data plane in the internal space in the storage space after ring division as a main resource, and set the data plane in the edge space in the storage space after ring division as a mapping plane;

[0067] S1413, set a carrying plane between the mapping plane and the main resource and correspond, wherein the carrying plane is composed of a plurality of carrying points, the carrying points include nodes and virtual machines, and adjacent carrying points are connected to each other;

[0068] S1414, correspond the main resource with a plurality of mapping planes respectively in data range, divide the main resource and the plurality of mapping planes respectively in corresponding data range according to the number of carrying points, obtain a plurality of main data range planes in the main resource and a plurality of mapping data range planes in the mapping plane which are the same in number and corresponding;

[0069] S1415, connect the carrying points with the mapping data range planes in the corresponding mapping planes and the main data range planes in the main resource one by one, and obtain a distribution body.

[0070] As described in steps S11-S14, the learning resource database collects a plurality of types of learning resource data about the corresponding student group based on big data, and divides them according to the types of learning resource data, for example, humanistic and social science, natural science and tool knowledge, each major category includes a plurality of detailed sub-categories, for example, humanistic and social science includes college Chinese, ideological and political theory, history, philosophy, art appreciation and other related knowledge data, the data is differentiated according to various sub-categories, and a plurality of resource pools are obtained, each resource pool stores learning resource data of the corresponding type, then in order to better recommend the type of data according to the user portrait of the student, the corresponding resource point is set for the corresponding resource pool, one resource point corresponds to one resource pool, as an output network port for transmitting data in the resource pool, the resource points of a plurality of resource pools are connected to each other as a fixed network, which is called a main graph here, the type of the resource pool is marked on the resource point, and then in order to better target the scheduling recommendation of learning resource data.

[0071] A storage space is arranged for each resource point, and the storage space is divided in a ring shape. A first storage space is divided in the middle of the storage space to receive learning resource data transmitted from the corresponding resource point. A plurality of small storage spaces are divided outside the storage space, which are virtual spaces. After the ring division, a data plane is arranged in the inner space of the storage space as a main resource. After the ring division, a data plane is arranged in the edge space of the storage space as a mapping plane. The main resource is distributed according to the data plane. The mapping plane also has the same operation, and the difference is that there are different virtual spaces. A carrying plane is arranged between the mapping plane and the main resource. The storage space position of the carrying plane is in the carrying plane. The carrying plane is composed of a plurality of carrying points. The carrying points include nodes and virtual machines. The adjacent carrying points are connected to obtain the carrying plane. After the data in the resource pool is transmitted to the main resource of the distribution body, the resource needs to be allocated to a plurality of target student terminals. In order to avoid data collapse and instability in the process of data being acquired by a plurality of target student terminals at the same time, the learning resource data on the main resource is copied according to the target student terminal to be allocated. Specifically, the main resource is respectively matched with a plurality of mapping planes in terms of data range. For example, the main resource is divided into 8 main data range planes. Since the data plane of the main resource is the same as the data plane of the plurality of mapping planes, the plurality of mapping planes are also divided into 8 mapping data range planes according to the same division method. In order to copy the learning resource data of the main resource later, the 8 mapping data range planes after division are one-to-one matched with the 8 main data range planes. The number of carrying points is also 8. First, the number of carrying points is determined to divide the mapping plane and the main resource. The main resource and the plurality of mapping planes are divided according to the number of carrying points in terms of data range to obtain a plurality of main data range planes in the main resource and a plurality of mapping data range planes in the mapping plane. The number of mapping data range planes is the same as that of the main data range planes. The carrying point includes a node and a virtual machine. The node is a network node arranged in the storage space between the main resource and the mapping plane, which is a network placement place of the virtual machine. Through the network node, the plurality of virtual machines can be connected. After the learning resource data in the main resource is in place, the carrying point is one-to-one connected and matched with the 8 mapping data range planes and the 8 main data range planes. The virtual machine on the carrying point copies the learning resource data in the corresponding main data range plane, and then provides the learning resource data to the corresponding mapping data range plane. Since the number of carrying points is large, when some of the carrying points cannot be used, the adjacent carrying points can be moved to the node of the carrying point that cannot be used to replace the virtual machine that cannot be used to copy the learning resource data in the corresponding main data range plane. Until the copying of the learning resource data in all main resources is completed, the mapping plane is a data providing space of the tentacle. The learning resource data in the corresponding mapping plane is provided to the target student terminal through the tentacle to complete the scheduling of the resource for the student user portrait.

[0072] A plurality of tentacles are arranged on the distribution body, the tentacles include contact points and connecting lines between the contact points and the distribution body, a one-to-one correspondence between the contact points and the mapping surfaces is established, one tentacle is used to connect one target student terminal, the mapping surface corresponding to the tentacle not connected to the target student terminal does not copy the learning resource data in the main resource; only the mapping surface corresponding to the tentacle connected to the target student terminal can copy the learning resource data in the main resource, so that the stability and bias of the learning resource data recommendation can be ensured, and the learning resource data can be stably distributed when more students use the system, and the situation of being busy and collapsing does not exist.

[0073] In one embodiment, the step S2 of marking the user portrait of each target student terminal in the corresponding sub-graph respectively and establishing the correlation relationship between the sub-graph of each target student terminal and the learning resource database comprises:

[0074] S21, the user portrait of each target student terminal is collected, the user portrait is converted into a type, and the type point of the corresponding type is marked;

[0075] S22, the type point after each target student terminal is marked is triggered and connected with the resource pool of the corresponding type respectively, and the correlation relationship is obtained.

[0076] In one embodiment, the step S3 of actively providing the learning resource data in the learning resource database to the target student terminal based on the correlation relationship comprises:

[0077] S31, the tentacle of the resource pool is connected with the corresponding target student terminal according to the correlation relationship, wherein the contact point of the tentacle is connected with the target student terminal (the contact point is a network node, which can be connected with the target student terminal, and a communication relationship between the distribution body and the contact point and the target student terminal is established through the connecting line);

[0078] S32, the learning resource data in the resource pool is transmitted to the main resource of the distribution point through the resource point, and is laid on the data surface in the main resource;

[0079] S33, the learning resource data in the main data range surface of the corresponding main resource is copied and provided to the mapping data range surface in the corresponding mapping surface through the carrying point;

[0080] S34, when the carrying point exists and cannot copy the learning resource data in the corresponding main data range surface, a virtual machine of a carrying point adjacent to the abnormal carrying point is connected with the node of the abnormal carrying point, and the learning resource data in the main data range surface corresponding to the abnormal carrying point is copied through the virtual machine of the adjacent carrying point and provided to the mapping data range surface in the corresponding mapping surface (the learning resource data in the main data range surface corresponding to the abnormal carrying point is copied through the virtual machine of the adjacent carrying point and provided to the mapping data range surface in the corresponding mapping surface, after the adjacent carrying point virtual machine completes the copying task, the virtual machine returns to the original position, and a new virtual machine is configured to replace the virtual machine of the abnormal carrying point, since the data in the resource library is updated and changed in the subsequent process, the carrying point needs to timely carry the data to the mapping data range surface and recommend it to the target student end);

[0081] S35, the learning resource data in the mapping data range surface is provided to the corresponding target student end through the touch point.

[0082] As described in the above steps S31-S35, when each target student end actively acquires data in each resource pool, and the historical use, there are preferences and weak points of the target student end, according to the user portrait obtained from these data, the type of the corresponding data is judged according to the preferences and weak points, the type and the division type of the resource pool are the same type judgment rule, for example, the knowledge of humanities and social sciences, natural science and tool class. Then, the resource pool is related according to the user portrait through the sub-graph, which is a pointing relationship, used to understand the corresponding resource pool of the target student end, so as to facilitate the subsequent data in the resource pool to be recommended to the target student end through the distribution body, so that the student end can learn, multiple target student ends can be deployed with multiple types of data, and the student can switch between multiple grabbing communities, which has good flexibility, the data is provided more flexibly, and when multiple target student ends use the system, system access is not easy to be busy and crash, and has good data stability.

[0083] In one embodiment, the step S4 of replacing the grabbing community where the target student end is located when the sub-graph of the target student end changes includes:

[0084] S41, real-time collection of the user portrait of the target student end, and type point marking update of the sub-graph according to the real-time collected user portrait;

[0085] S42, if the updated sub-graph has a label change, a related relationship between the corresponding resource pool and the type point according to the change and the label is established;

[0086] S43, according to the target student end corresponding to the change of the relationship and the type of the marked point of the community, the tentacles of the community corresponding to the changed and marked type point are connected with the target student end.

[0087] As described in steps S41-S43, since the learning progress and direction of the student are changed, for example, the current student has a weak direction on the basic knowledge, the learning resource classified for the current target student end will be biased towards the technical knowledge data, and after strengthening, the weak direction is no longer the learning problem of the student. Therefore, the target student end and the corresponding community of the type resource pool can be disconnected, and the learning resource data of this type is no longer focused. If the student generates a new type according to the user portrait during learning, such as liking to understand the data of a certain research direction, then the connection between the target student end and the corresponding resource point distribution body is established according to this type. The target student end can also actively obtain data in different other type resource pools. This belongs to active acquisition. When not actively acquired, the platform provides active provision of biased data for different types of target student ends according to the corresponding communities. A target student end can have multiple types, and the user portrait is complex and variable. A target student end can be connected with multiple communities for actively receiving learning resource data provided by the distribution body.

[0088] Embodiment 2, please refer to Figure 2 As shown in the figure, the learning resource intelligent scheduling system described in the embodiment comprises:

[0089] The construction module is configured to determine a plurality of target student ends, and establish a scheduling graph between the plurality of target student ends and a learning resource database, wherein the scheduling graph comprises a main graph, a plurality of sub-graphs corresponding to the target student ends, and a plurality of communities;

[0090] The connection module is connected with the construction module, and is configured to mark the user portrait of each target student end in the corresponding sub-graph, and establish a relationship between the sub-graph of each target student end and the learning resource database;

[0091] The scheduling module is connected with the connection module, and is configured to actively provide learning resource data in the learning resource database to the target student end through the community based on the relationship.

[0092] The updating module is connected with the scheduling module, and is configured to update the sub-graph of the target student end in real time, and replace the community where the target student end is located when the sub-graph of the target student end changes.

[0093] It should be noted that the multiple target student terminals can be arranged with multiple types of data, and the students can switch between multiple grabbing communities, which has good flexibility, the data is provided more flexibly, and when multiple target student terminals use the system, the system access is not easy to be busy and crash, and has good data stability.

[0094] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for intelligent scheduling of learning resources, characterized in that, Includes the following steps: Multiple target student clients are identified, and a scheduling graph is established between these target student clients and the learning resource database. The scheduling graph includes a main graph, multiple sub-graphs corresponding to the target student clients, and multiple crawling communities. The steps of identifying multiple target student terminals and establishing a scheduling graph between the multiple target student terminals and the learning resource database include: The learning resource data in the learning resource database is divided into multiple resource pools according to type, and the type is used as the label for the corresponding resource pool. Multiple resource points are configured according to resource pools, and the resource points are interconnected with their corresponding resource pools. By connecting multiple resource points, the main graph is obtained. Multiple type points are set for each target student, and the multiple type points are connected to obtain a sub-map; A crawling community is set up for each resource point; The step of setting up community crawling for each resource point includes: A distribution body is set up for each resource point. The distribution body includes the main resource and multiple mapping surfaces. The distribution body is connected to the corresponding resource point. The step of setting up a distribution body for each resource point includes: A storage space is set up for each resource point, and the storage space is divided into multiple virtual spaces in a ring. After the ring partitioning, the data plane is set as the primary resource in the internal space of the storage space, and the data plane is set as the mapping plane in the edge space of the storage space after the ring partitioning. Set up a transport plane between the mapping plane and the main resource and make it correspond. The transport plane consists of multiple transport points, which include nodes and virtual machines. Adjacent transport points are connected to each other. The main resource is mapped to multiple mapping surfaces for data range. The main resource and multiple mapping surfaces are divided into corresponding data ranges according to the number of transfer points, resulting in multiple main data range surfaces in the main resource and the same number of corresponding mapping data range surfaces in the mapping surfaces. Connect the transport point to the corresponding mapping data range in the mapping surface and the main data range in the main resource one-to-one to obtain the distribution body; Multiple tentacles are set for the corresponding distribution body. Each tentacle includes a contact point and a connecting line between the contact point and the distribution body, establishing a one-to-one correspondence between the contact point and the mapping surface. Treat a distribution entity and its corresponding connected tentacles as a single grabbing community; Each target student's user profile is marked in the corresponding sub-graph, and the connection between each target student's sub-graph and the learning resource database is established. Based on the relationship, learning resource data from the learning resource database is proactively provided to target students by crawling the community. The subgraph of the target student is updated in real time. When the subgraph of the target student changes, the crawling community of the target student is changed.

2. The intelligent scheduling method for learning resources according to claim 1, characterized in that, The step of marking the user profile of each target student in the corresponding sub-graph and establishing the connection between the sub-graph of each target student and the learning resource database includes: For each target student, a user profile is collected, converted into a type, and marked in the corresponding type point; Each target student's marked type point is triggered and connected to the corresponding type's resource pool to obtain the relationship.

3. The intelligent scheduling method for learning resources according to claim 2, characterized in that, The step of proactively providing learning resource data from the learning resource database to target students by crawling the community based on the relationship includes: Based on the relationship, the tentacles corresponding to the resource pool are connected to the corresponding target student terminals, wherein the contact points of the tentacles are connected to the target student terminals; The learning resource data in the resource pool is transmitted to the main resource of the distribution point through the resource point and laid on the data surface of the main resource; By using the transfer point, the learning resource data of the master data range in the corresponding master resource is copied and provided to the mapping data range in the corresponding mapping surface; When a transfer point is unable to copy the learning resource data in the corresponding master data scope, it is designated as an abnormal transfer point. A virtual machine of a transfer point adjacent to the abnormal transfer point is randomly selected and connected to the node of the abnormal transfer point. The virtual machine of the adjacent transfer point copies the learning resource data of the master data scope corresponding to the abnormal transfer point and provides it to the mapping data scope in the corresponding mapping surface. The learning resource data in the mapped data range is provided to the corresponding target student through touchpoints.

4. The intelligent scheduling method for learning resources according to claim 1, characterized in that, The step of updating the subgraph of the target student's terminal in real time, and changing the crawling community of the target student's terminal when the subgraph of the target student's terminal changes, includes: Real-time collection of user profiles from the target student's end; and updating the sub-graph by marking type points based on the real-time collected user profiles. If the updated subgraph has changed labels, establish a connection between the changed and labeled type points and the corresponding resource pools. Based on the relationship, connect the crawling communities corresponding to the changes and marked type points on the target student's end, and connect the tentacles of the crawling communities corresponding to the changes and marked type points to the target student's end.

5. A learning resource intelligent scheduling system, used to implement the learning resource intelligent scheduling method according to any one of claims 1-4, characterized in that, include: The module is used to identify multiple target student terminals and establish a scheduling graph between the multiple target student terminals and the learning resource database. The scheduling graph includes a main graph, multiple sub-graphs corresponding to the target student terminals, and multiple crawling communities. The connection module, connected to the construction module, is used to mark the user profile of each target student in the corresponding sub-graph and establish the connection between the sub-graph of each target student and the learning resource database. The scheduling module, connected to the connection module, is used to proactively provide learning resource data from the learning resource database to the target students based on the relationship between them and the community. The update module, connected to the scheduling module, is used to update the subgraph of the target student in real time. When the subgraph of the target student changes, the crawling community in which the target student is located is changed.

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