A novel aesthetic education resource intelligent sharing optimization method and system based on a cloud platform

Abstract

This invention discloses a novel intelligent sharing and optimization method and system for aesthetic education resources based on a cloud platform, belonging to the field of resource sharing technology. By deploying multiple types of sensors in the classroom to collect teaching environment data and construct a classroom environment feature vector, a resource environment demand vector is established, and the environmental adaptability between the classroom environment and various aesthetic education resources is calculated. The network link status between the classroom terminal and the cloud platform is detected in real time, and parameters such as bandwidth, latency, and packet loss rate are obtained. Resource network matching coefficients are calculated, and the environmental adaptability and network matching coefficients are further integrated to construct a comprehensive resource availability. A resource scheduling priority sequence is generated by combining course matching degree and concurrent call status. This achieves intelligent sharing and dynamic optimization scheduling of aesthetic education resources in multi-classroom scenarios, improving the environmental adaptability and transmission stability of resource sharing, and enhancing the utilization efficiency and teaching implementation reliability of aesthetic education resources on the cloud platform.

Description

Technical Field

[0001] This invention relates to the field of resource sharing technology, specifically to a novel intelligent sharing and optimization method and system for aesthetic education resources based on a cloud platform. Background Technology

[0002] With the continuous advancement of smart education, Education Informatization 2.0, and the "cloud-device-edge" collaborative architecture, centralized management and remote sharing of teaching resources based on cloud platforms have become an important development direction in the field of educational technology. In the context of aesthetic education, the form of resources is gradually shifting from traditional physical teaching aids to digital forms such as high-definition audio and video, interactive digital art content, immersive multimedia materials, and virtual simulation art courses. This places higher demands on the ability to perceive the teaching environment, network transmission capabilities, and the level of intelligent resource scheduling. Existing technologies typically rely on cloud resource libraries and multimedia teaching terminals to achieve on-demand playback of courseware or distribution of streaming media. However, their resource calling logic is mostly based on static timetables or manual selection, lacking a collaborative perception mechanism for the physical environment of the classroom (such as lighting, spatial activity conditions, and acoustic characteristics) and the real-time transmission quality of the network. This makes it difficult to support the refined adaptation of differentiated aesthetic education resources such as music, dance, drama, and fine arts, resulting in a significant disconnect between the "accessibility" and "effective usability" of resources.

[0003] On the other hand, existing educational resource scheduling technologies often employ single bandwidth thresholds or empirical QoS judgment strategies on the network side, which lack sufficient comprehensive constraints on multi-dimensional link indicators such as latency and packet loss rate, and fail to incorporate the transmission demand characteristics of the resources themselves into unified modeling. On the environmental side, they rarely quantify and incorporate physical parameters highly relevant to art education, such as acoustic reverberation and effective activity area, into the scheduling decision-making process. This scheduling mode, characterized by "lack of environmental dimension + coarse-grained network dimension," easily leads to problems such as stuttering in high-bitrate art videos, amplified acoustic distortion in music resources, and reduced effectiveness of courses requiring large spatial activities in limited spaces. Furthermore, existing solutions often employ simple polling or static priority configuration in multi-classroom concurrent scenarios, lacking a mechanism to couple environmental adaptability, network matching degree, and course-related attributes to form a unified scheduling indicator, thus limiting resource utilization efficiency and the stability of the teaching experience. Summary of the Invention

[0004] The purpose of this invention is to provide a novel intelligent sharing and optimization method and system for aesthetic education resources based on a cloud platform, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform is proposed. This method includes the following steps: Step S1: Construct a sensor acquisition module to collect data on the light intensity, effective activity area, and environmental reverberation time of the target classroom; construct a classroom environment feature vector for the target classroom at the data sampling time point; Step S2: Preset corresponding environmental requirement parameters for each aesthetic education resource, constructing an environmental requirement vector for the aesthetic education resource; calculate the environmental adaptability between the target classroom and the aesthetic education resource at the data sampling time point based on the classroom environment feature vector of the target classroom at the data sampling time point; Step S3: Construct a candidate shared resource set for the target classroom at the data sampling time point; detect the network link transmission data between the terminal of the target classroom and the cloud platform in real time, calculate the network matching coefficient of the aesthetic education resource in the target classroom at the data sampling time point, and determine the set of available network resources; Step S4: Calculate the comprehensive availability and resource scheduling priority index of the aesthetic education resource in the target classroom at the data sampling time point, and arrange them in descending order according to the resource scheduling priority index to construct a scheduling priority sequence for intelligent sharing of aesthetic education resources.

[0007] As a preferred embodiment of the novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform as described in this invention, a sensor acquisition module is constructed. The sensor acquisition module is used to collect teaching environment data of the target classroom. The sensor acquisition module includes an ambient illuminance sensor, a depth vision sensor, a millimeter-wave radar sensor, and an acoustic acquisition unit. The teaching environment data includes light intensity data, effective activity area data, and environmental reverberation time data.

[0008] The ambient light sensor is installed on the ceiling of the target classroom to collect light intensity data. The depth vision sensor is used to collect the area occupied by fixed obstacles in the target classroom, and the millimeter-wave radar sensor is used to collect the real-time area of ​​densely populated areas in the target classroom. The effective activity area data is calculated by subtracting the area occupied by fixed obstacles and the real-time area of ​​densely populated areas from the total floor area of ​​the classroom. The acoustic acquisition unit includes a microphone array. By playing a standard short-pulse sound signal in the target classroom, the microphone array records the sound pressure attenuation process (calculating the sound energy attenuation to 1 / 10 of the initial value). 6 (Required time) to obtain environmental reverberation time data.

[0009] As a preferred embodiment of the novel cloud-based intelligent sharing and optimization method for aesthetic education resources described in this invention, a data sampling time period is constructed, denoted as... ,in, Let A represent the a-th data sampling time point, and A represent the total number of data sampling time points; the data sampling time points are respectively... The collected light intensity data, effective activity area data, and environmental reverberation time data are denoted as follows: , and ;

[0010] Construct a set of target teaching classrooms, denoted as . ,in, Let B represent the b-th target classroom and B represent the total number of target classrooms; the data sampling time points are respectively... The target classroom for the next collection Illumination intensity data, effective activity area data, and environmental reverberation time data are denoted as , and ;

[0011] Constructing data sampling time points Next target classroom The classroom environment feature vector, denoted as .

[0012] As a preferred embodiment of the novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform as described in this invention, all aesthetic education resources are obtained from the cloud platform, and corresponding environmental requirement parameters are preset for each aesthetic education resource to construct an aesthetic education resource environmental requirement vector. The aesthetic education resource environmental requirement vector of the i-th aesthetic education resource is denoted as... ,in, This represents the appropriate light intensity requirement for the i-th aesthetic education resource. This represents the minimum effective activity area requirement for the i-th aesthetic education resource. This represents the environmental reverberation time requirement value for the i-th aesthetic education resource;

[0013] Based on data sampling time points Next target classroom Classroom environment feature vector Calculate the data sampling time point Next target classroom The environmental suitability of the i-th aesthetic education resource is calculated using the following formula:

[0014] ;

[0015] in, Indicates the data sampling time point Next target classroom Environmental adaptability with the i-th aesthetic education resource This represents a preset constant.

[0016] As a preferred embodiment of the novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform as described in this invention, a preset environmental adaptability threshold is used, if the data sampling time point Next target classroom Environmental adaptability with the i-th aesthetic education resource If the environmental adaptability threshold is greater than or equal to the i-th aesthetic education resource at the data sampling time point, then the i-th aesthetic education resource is determined to be within the range of the data sampling time point. The target teaching classroom is below The conditions for using the teaching environment;

[0017] Get at the data sampling time point The target teaching classroom is below The teaching environment and conditions for using all aesthetic education resources, and the construction of data sampling time points. Next target classroom A set of candidate shared resources;

[0018] At the data sampling time point Below, real-time detection of target classrooms The data transmitted via the network link between the terminal and the cloud platform includes the actual available transmission bandwidth, network round-trip time, and packet loss rate, which are respectively denoted as... , and ;

[0019] Data sampling time points Next target classroom For each candidate shared resource in the candidate shared resource set, a standard network link transmission data is preset, and a standard network link transmission dataset for the i-th aesthetic education resource is constructed, denoted as . ,in, This represents the standard bandwidth required for the i-th aesthetic education resource to play normally. This represents the maximum acceptable network latency for the i-th aesthetic education resource. This represents the maximum acceptable packet loss rate for the i-th aesthetic education resource;

[0020] Calculate data sampling time points Next target classroom The network matching coefficient of the i-th aesthetic education resource is calculated using the following formula:

[0021] ;

[0022] in, Indicates the data sampling time point Next target classroom The network matching coefficient of the i-th aesthetic education resource This represents a preset constant;

[0023] Preset network matching coefficient threshold, if data sampling time point Next target classroom The network matching coefficient of the i-th aesthetic education resource If the data sampling time point is greater than or equal to the network matching coefficient threshold, then the data sampling time point is determined. Next target classroom The network conditions meet the transmission requirements of the i-th aesthetic education resource;

[0024] From the data sampling time point Next target classroom From the candidate shared resource set, obtain the data sampling time point. Next target classroom The network conditions meet the transmission requirements of all aesthetic education resources, and data sampling time points are constructed. Next target classroom The collection of available network resources.

[0025] As a preferred embodiment of the novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform as described in this invention, starting from the data sampling time point... Next target classroom From the set of available network resources, obtain the environmental adaptability and network matching coefficient of each art education resource, and calculate the data sampling time points. Next target classroom The comprehensive availability of the i-th aesthetic education resource is calculated using the following formula:

[0026] ;

[0027] in, Indicates the data sampling time point Next target classroom The overall availability of the i-th aesthetic education resource, Indicates the preset environmental adaptability Influence factors Represents the preset network matching coefficients Influence factors;

[0028] Based on data sampling time points Next target classroom The overall availability of the i-th aesthetic education resource Calculate the resource allocation priority index for the i-th aesthetic education resource using the following formula:

[0029] ;

[0030] in, Indicates the data sampling time point Next target classroom The resource allocation priority index of the i-th aesthetic education resource. This represents the course matching coefficient of the i-th aesthetic education resource (the degree of consistency with the current teaching theme tag, ensuring that the resource content is appropriate). Indicates the data sampling time point The number of concurrent calls to the i-th aesthetic education resource (to prevent overload of popular resources);

[0031] Data sampling time points Next target classroom All aesthetic education resources in the available network resources are sorted in descending order according to the resource scheduling priority index to construct a scheduling priority sequence;

[0032] The scheduling priority sequence of different classrooms at the current data sampling time point is obtained in real time to enable intelligent sharing of aesthetic education resources.

[0033] A novel intelligent sharing and optimization system for aesthetic education resources based on a cloud platform. This system includes: a data acquisition and vector construction module, a fitness calculation module, a set construction and coefficient calculation module, and an index calculation, analysis, and scheduling module.

[0034] The data acquisition and vector construction module includes: constructing a sensor acquisition module to collect light intensity data, effective activity area data, and environmental reverberation time data of the target classroom; and constructing a classroom environment feature vector of the target classroom at the data sampling time point.

[0035] The adaptation calculation module: presets corresponding environmental requirement parameters for each aesthetic education resource, constructs an environmental requirement vector for aesthetic education resources; and calculates the environmental adaptation degree between the target teaching classroom and the aesthetic education resource at the data sampling time point based on the classroom environment feature vector of the target teaching classroom at the data sampling time point.

[0036] The set construction and coefficient calculation module: constructs a candidate shared resource set for the target teaching classroom at the data sampling time point; detects the network link transmission data between the terminal of the target teaching classroom and the cloud platform in real time, calculates the network matching coefficient of the aesthetic education resources of the target teaching classroom at the data sampling time point, and sets the available network resources.

[0037] The index calculation and analysis scheduling module calculates the comprehensive availability of aesthetic education resources and the resource scheduling priority index of the target teaching classroom at the data sampling time point, and arranges them in descending order according to the resource scheduling priority index to construct a scheduling priority sequence for intelligent sharing of aesthetic education resources.

[0038] Furthermore, the adaptation calculation module includes an adaptation calculation unit;

[0039] The adaptation calculation unit: obtains all aesthetic education resources from the cloud platform, and presets corresponding environmental requirement parameters for each aesthetic education resource to construct an environmental requirement vector for aesthetic education resources; based on the classroom environment feature vector of the target teaching classroom at the data sampling time point, it calculates the environmental adaptation degree between the target teaching classroom and the i-th aesthetic education resource at the data sampling time point.

[0040] Furthermore, the set construction and coefficient calculation module includes a set construction unit and a coefficient calculation unit;

[0041] The set construction unit: presets an environment adaptability threshold. If the environment adaptability between the target classroom and the i-th aesthetic education resource at the data sampling time point is greater than or equal to the environment adaptability threshold, then it is determined that the i-th aesthetic education resource meets the teaching environment usage conditions of the target classroom at the data sampling time point; obtains all aesthetic education resources that meet the teaching environment usage conditions of the target classroom at the data sampling time point, and constructs a candidate shared resource set for the target classroom at the data sampling time point;

[0042] The coefficient calculation unit: at the data sampling time point, it detects the network link transmission data between the terminal of the target classroom and the cloud platform in real time. The network link transmission data includes the actual available transmission bandwidth value, network round-trip delay value, and packet loss rate. It presets standard network link transmission data for each candidate shared resource in the candidate shared resource set of the target classroom at the data sampling time point, constructing a standard network link transmission dataset for the i-th aesthetic education resource. It calculates the network matching coefficient of the i-th aesthetic education resource in the target classroom at the data sampling time point. It presets a network matching coefficient threshold. If the network matching coefficient of the i-th aesthetic education resource in the target classroom at the data sampling time point is greater than or equal to the network matching coefficient threshold, it determines that the network conditions of the target classroom at the data sampling time point meet the transmission requirements of the i-th aesthetic education resource. From the candidate shared resource set of the target classroom at the data sampling time point, it obtains all aesthetic education resources whose network conditions meet the transmission requirements of the target classroom at the data sampling time point, and constructs a set of available network resources for the target classroom at the data sampling time point.

[0043] Furthermore, the index calculation and analysis scheduling module includes an index calculation unit and an analysis scheduling unit;

[0044] The index calculation unit: obtains the environmental adaptability and network matching coefficient of each aesthetic education resource from the set of network available resources of the target teaching classroom at the data sampling time point, and calculates the comprehensive availability of the i-th aesthetic education resource of the target teaching classroom at the data sampling time point; based on the comprehensive availability of the i-th aesthetic education resource of the target teaching classroom at the data sampling time point, calculates the resource scheduling priority index of the i-th aesthetic education resource.

[0045] The analysis and scheduling unit: sorts all aesthetic education resources in the network available resource set of the target teaching classroom in descending order according to the resource scheduling priority index at the data sampling time point, and constructs a scheduling priority sequence; it obtains the scheduling priority sequence of different teaching classrooms in real time at the current data sampling time point, and performs intelligent sharing of aesthetic education resources.

[0046] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention provides a novel intelligent sharing and optimization method and system for aesthetic education resources based on a cloud platform. By using multi-sensor fusion to collect data on classroom lighting intensity, effective activity area, and environmental reverberation time, and constructing a classroom environment feature vector, it transforms the physical space of teaching from experience-based judgment to a quantifiable environmental model. This enables the teaching venue conditions to establish an objective correspondence with the visual presentation requirements, physical activity requirements, and acoustic propagation requirements of different types of aesthetic education resources, ensuring that resources have a foundation for practical teaching from the outset. Furthermore, by constructing an environmental demand vector for aesthetic education resources and calculating environmental adaptability, it transforms resource selection from content-level matching to a dual-constraint matching of "content + physical environment," preemptively eliminating resources that do not meet environmental conditions and reducing the probability of ineffective scheduling and classroom implementation failure. Finally, it introduces a real-time network... Link status establishes a correlation between bandwidth, latency, and packet loss rate and the transmission requirements of the resource itself, enabling a two-level screening from "environmental availability" to "transmission availability." This makes network capability a dynamic criterion for resource feasibility, avoiding issues such as stuttering and interruptions in resource playback under high load or network fluctuation conditions, and improving the stability of remote sharing. A comprehensive availability index is constructed based on environmental adaptability and network matching coefficients, and course matching degree and concurrent call count are superimposed to form a scheduling priority index. This achieves a unified quantitative ranking of classroom adaptability, teaching content relevance, and cloud resource load status, enabling resource scheduling to simultaneously possess teaching rationality and system-level load balancing capabilities. Ultimately, a multi-level decision-making link is formed, encompassing "physical environment constraints—teaching adaptability assessment—network transmission verification—global scheduling optimization," significantly improving the sharing success rate, transmission stability, and overall resource utilization efficiency of cloud platform aesthetic education resources in multi-classroom concurrent scenarios. Attached Figure Description

[0047] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0048] Figure 1 This is a schematic diagram illustrating the steps of a novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform according to the present invention.

[0049] Figure 2 This is a schematic diagram of the structure of a novel intelligent sharing and optimization system for aesthetic education resources based on a cloud platform, according to the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Please see Figure 1 In this first embodiment: a novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform is provided, which includes the following steps:

[0052] Step S1: Construct a sensor acquisition module to collect data on the light intensity, effective activity area, and environmental reverberation time of the target classroom; construct a classroom environment feature vector of the target classroom at the data sampling time point.

[0053] Specifically, a sensor acquisition module is constructed to collect teaching environment data of the target classroom. The sensor acquisition module includes an ambient illuminance sensor, a depth vision sensor, a millimeter-wave radar sensor, and an acoustic acquisition unit. The teaching environment data includes light intensity data, effective activity area data, and ambient reverberation time data.

[0054] The ambient light sensor is installed on the ceiling of the target classroom to collect light intensity data. The depth vision sensor is used to collect the area occupied by fixed obstacles in the target classroom, and the millimeter-wave radar sensor is used to collect the real-time area of ​​densely populated areas in the target classroom. The effective activity area data is calculated by subtracting the area occupied by fixed obstacles and the real-time area of ​​densely populated areas from the total floor area of ​​the classroom. The acoustic acquisition unit includes a microphone array. By playing a standard short-pulse sound signal in the target classroom, the microphone array records the sound pressure attenuation process (calculating the sound energy attenuation to 1 / 10 of the initial value). 6 (Required time) to obtain environmental reverberation time data.

[0055] Furthermore, the data sampling time period is constructed, denoted as . ,in, Let A represent the a-th data sampling time point, and A represent the total number of data sampling time points; the data sampling time points are respectively... The collected light intensity data, effective activity area data, and environmental reverberation time data are denoted as follows: , and ;

[0056] Construct a set of target teaching classrooms, denoted as . ,in, Let B represent the b-th target classroom and B represent the total number of target classrooms; the data sampling time points are respectively... The target classroom for the next collection Illumination intensity data, effective activity area data, and environmental reverberation time data are denoted as , and ;

[0057] Constructing data sampling time points Next target classroom The classroom environment feature vector, denoted as .

[0058] In this invention, this step, by constructing a multi-source environmental perception module composed of an illumination sensor, a depth vision sensor, a millimeter-wave radar, and an acoustic acquisition unit, achieves real-time quantitative modeling of the physical teaching environment in the classroom. This transforms classroom environmental conditions, which originally relied on human experience for judgment, into a computable environmental feature vector, wherein:

[0059] Light intensity reflects the visual presentation conditions of visual art education resources (painting, color teaching, video appreciation);

[0060] The effective activity area of ​​a space directly constrains the feasibility of resources involving physical participation such as dance, physical exercise, and drama;

[0061] Reverberation time determines the acoustic fit of auditory resources such as music, recitation, and vocal performance.

[0062] By structurally expressing the above environmental elements, an objective mapping basis is established between the physical attributes of teaching spaces and the needs of aesthetic education resource types. This provides real environmental constraints for subsequent resource selection, avoids the mismatch between advanced resource content and physical environment, and improves the actual usability of aesthetic education resources and the success rate of classroom implementation.

[0063] Step S2: Preset corresponding environmental requirement parameters for each aesthetic education resource and construct an environmental requirement vector for aesthetic education resources; based on the classroom environment feature vector of the target teaching classroom at the data sampling time point, calculate the environmental adaptability between the target teaching classroom and the aesthetic education resource at the data sampling time point.

[0064] Specifically, all aesthetic education resources are retrieved from the cloud platform, and corresponding environmental requirement parameters are preset for each resource to construct an environmental requirement vector for aesthetic education resources. The environmental requirement vector for the i-th aesthetic education resource is denoted as... ,in, This represents the appropriate light intensity requirement for the i-th aesthetic education resource. This represents the minimum effective activity area requirement for the i-th aesthetic education resource. This represents the environmental reverberation time requirement value for the i-th aesthetic education resource; the aesthetic education resource refers to a digital or digitizable teaching content unit that can be used to cultivate aesthetic perception, artistic expression ability and artistic understanding ability, and can be called, displayed or interacted with in classroom teaching.

[0065] Based on data sampling time points Next target classroom Classroom environment feature vector Calculate the data sampling time point Next target classroom The environmental suitability of the i-th aesthetic education resource is calculated using the following formula:

[0066] ;

[0067] in, Indicates the data sampling time point Next target classroom Environmental adaptability with the i-th aesthetic education resource This represents a preset constant.

[0068] It should be noted that, This is used to determine whether resources are suitable for the classroom's physical environment; the closer the value is to 1, the better the suitability. For example, art classrooms need sufficient lighting, while dance classrooms may need soft lighting, which needs to be matched with resource requirements; This represents the appropriate light intensity requirement for the i-th art education resource, such as ≥500 lux for high-definition art appreciation videos and ≥300 lux for calligraphy teaching resources, preset according to the resource type; The system utilizes a combination of depth vision sensors (for detecting fixed obstacles) and millimeter-wave radar sensors (for detecting densely populated areas) for calculations. For example, dance resources require a relatively large activity space. This represents the minimum effective activity area required for the i-th aesthetic education resource. For example, group dance teaching resources require ≥20㎡, and painting group activity resources require ≥10㎡.

[0069] This formula quantifies the degree of fit between the actual classroom environmental parameters and resource requirement parameters by calculating the relative deviation between the two. The smaller the deviation, the higher the fit. It selects three core dimensions—light intensity, effective activity area, and environmental reverberation time—to correspond to the core environmental requirements of different aesthetic education resources, such as art (visual needs), dance (physical activity needs), and music (acoustic needs), thus avoiding the limitations of single-dimensional assessment.

[0070] Step S3: Construct a candidate shared resource set for the target classroom at the data sampling time point; detect the network link data transmission between the terminal of the target classroom and the cloud platform in real time, calculate the network matching coefficient of the aesthetic education resources of the target classroom at the data sampling time point, and the set of available network resources.

[0071] Specifically, a preset environment adaptability threshold is set if the data sampling time point Next target classroom Environmental adaptability with the i-th aesthetic education resource If the environmental adaptability threshold is greater than or equal to the i-th aesthetic education resource at the data sampling time point, then the i-th aesthetic education resource is determined to be within the range of the data sampling time point. The target teaching classroom is below The conditions for using the teaching environment;

[0072] Get at the data sampling time point The target teaching classroom is below The teaching environment and conditions for using all aesthetic education resources, and the construction of data sampling time points. Next target classroom A set of candidate shared resources;

[0073] At the data sampling time point Below, real-time detection of target classrooms The data transmitted via the network link between the terminal and the cloud platform includes the actual available transmission bandwidth, network round-trip time, and packet loss rate, which are respectively denoted as... , and ;

[0074] Data sampling time points Next target classroom For each candidate shared resource in the candidate shared resource set, a standard network link transmission data is preset, and a standard network link transmission dataset for the i-th aesthetic education resource is constructed, denoted as . ,in, This represents the standard bandwidth required for the i-th aesthetic education resource to play normally. This represents the maximum acceptable network latency for the i-th aesthetic education resource. This represents the maximum acceptable packet loss rate for the i-th aesthetic education resource;

[0075] Calculate data sampling time points Next target classroom The network matching coefficient of the i-th aesthetic education resource is calculated using the following formula:

[0076] ;

[0077] in, Indicates the data sampling time point Next target classroom The network matching coefficient of the i-th aesthetic education resource This represents a preset constant;

[0078] It should be noted that this formula quantifies the matching degree between classroom network capabilities and resource transmission needs using three key network indicators: bandwidth, latency, and packet loss rate, ensuring that resource transmission is accessible and stable. Based on candidate resources that are environmentally compatible, a preset network matching coefficient threshold (e.g., 0.6) is used. Only resources with a network matching coefficient ≥ the threshold are included in the set of available network resources, avoiding playback failures due to environmental compatibility but network limitations (e.g., pushing 4K video resources to a classroom with insufficient bandwidth). By sampling network data (bandwidth, latency, packet loss rate) in real time, the network matching coefficient is dynamically calculated to cope with changes in network load (e.g., bandwidth drops during peak periods), ensuring the stability of resource transmission. For the different network requirements of different types of art education resources (e.g., real-time interactive resources are sensitive to latency, while high-definition video resources are sensitive to bandwidth), precise matching of network resources is achieved through multi-dimensional parameter calculations.

[0079] Preset network matching coefficient threshold, if data sampling time point Next target classroom The network matching coefficient of the i-th aesthetic education resource If the data sampling time point is greater than or equal to the network matching coefficient threshold, then the data sampling time point is determined. Next target classroom The network conditions meet the transmission requirements of the i-th aesthetic education resource;

[0080] From the data sampling time point Next target classroom From the candidate shared resource set, obtain the data sampling time point. Next target classroom The network conditions meet the transmission requirements of all aesthetic education resources, and data sampling time points are constructed. Next target classroom The collection of available network resources.

[0081] Step S4: Calculate the comprehensive availability of aesthetic education resources and the resource scheduling priority index of the target classroom at the data sampling time point, and sort them in descending order according to the resource scheduling priority index to construct a scheduling priority sequence for intelligent sharing of aesthetic education resources.

[0082] Specifically, from the data sampling time point Next target classroom From the set of available network resources, obtain the environmental adaptability and network matching coefficient of each art education resource, and calculate the data sampling time points. Next target classroom The comprehensive availability of the i-th aesthetic education resource is calculated using the following formula:

[0083] ;

[0084] in, Indicates the data sampling time point Next target classroom The overall availability of the i-th aesthetic education resource, Indicates the preset environmental adaptability Influence factors Represents the preset network matching coefficients Influence factors;

[0085] It should be noted that the following is adopted: ×Environmental adaptability) × ( The overall availability is expressed as a product of environmental adaptability and network matching coefficient, rather than additively. This means that overall availability will only significantly improve when both environmental adaptability and network matching coefficient are high. If either dimension is weak (e.g., environmental adaptability 0.9, network matching coefficient 0.3), overall availability will be significantly reduced, preventing resources that excel in one dimension but fail in both dimensions from being mis-scheduled; a preset impact factor is used. and It can be flexibly adjusted according to the teaching scenario (e.g., offline art classes focus on environmental adaptation). Take 0.6; online music live streaming prioritizes network stability. (Take 0.6) to improve the universality of the formula.

[0086] Based on data sampling time points Next target classroom The overall availability of the i-th aesthetic education resource Calculate the resource allocation priority index for the i-th aesthetic education resource using the following formula:

[0087] ;

[0088] in, Indicates the data sampling time point Next target classroom The resource allocation priority index of the i-th aesthetic education resource. This represents the course matching coefficient of the i-th aesthetic education resource (the degree of consistency with the current teaching theme tag, ensuring that the resource content is appropriate). Indicates the data sampling time point The number of concurrent calls to the i-th aesthetic education resource (to prevent overload of popular resources);

[0089] It should be noted that the introduction of a course matching coefficient... (Consistency with teaching theme tags), avoiding bandwidth consumption by resources that are compatible with the environment and network but irrelevant to the content (such as using art resources in a Chinese language class), and ensuring that the scheduling results match the actual teaching needs; through ( A reverse proportional adjustment mechanism is constructed for the number of concurrent calls. The more times a popular resource is called, the lower its priority. This avoids the cloud platform from crashing due to the overload of a single resource and solves the resource contention problem in multi-classroom concurrent scenarios. The formula is based on comprehensive availability, superimposed with content relevance and system load constraints, forming a four-level screening logic of environment feasibility → network feasibility → content relevance → system stability. This ensures that the scheduling results meet both teaching needs and platform operation stability.

[0090] Data sampling time points Next target classroom All aesthetic education resources in the available network resources are sorted in descending order according to the resource scheduling priority index to construct a scheduling priority sequence;

[0091] The scheduling priority sequence of different classrooms at the current data sampling time point is obtained in real time to enable intelligent sharing of aesthetic education resources.

[0092] In this invention, this step takes into account four major objectives: environmental adaptation, network stability, content relevance, and load balancing. It addresses the shortcomings of traditional scheduling methods that are single-objective-oriented (such as scheduling based solely on content matching, which may lead to unsupported environments or networks; or scheduling based solely on popularity, which may lead to system overload). All available network resources are arranged in descending order of scheduling priority index to form a scheduling priority sequence. The cloud platform allocates resources according to the sequence, ensuring that each classroom can prioritize accessing the most suitable and system-supported art education resources. By concurrently calling adjustment items, the resource needs of different classrooms are balanced, avoiding service degradation caused by multiple classrooms competing for the same resources, and improving the overall teaching reliability in multi-classroom scenarios.

[0093] Please see Figure 2 In this second embodiment: a novel intelligent sharing and optimization system for aesthetic education resources based on a cloud platform is provided. The system includes: a data acquisition and vector construction module, an adaptation calculation module, a set construction and coefficient calculation module, and an index calculation, analysis, and scheduling module.

[0094] The data acquisition and vector construction module includes: constructing a sensor acquisition module to collect light intensity data, effective activity area data, and environmental reverberation time data of the target classroom; and constructing a classroom environment feature vector of the target classroom at the data sampling time point.

[0095] The adaptation calculation module: presets corresponding environmental requirement parameters for each aesthetic education resource, constructs an environmental requirement vector for aesthetic education resources; and calculates the environmental adaptation degree between the target teaching classroom and the aesthetic education resource at the data sampling time point based on the classroom environment feature vector of the target teaching classroom at the data sampling time point.

[0096] The set construction and coefficient calculation module: constructs a candidate shared resource set for the target teaching classroom at the data sampling time point; detects the network link transmission data between the terminal of the target teaching classroom and the cloud platform in real time, calculates the network matching coefficient of the aesthetic education resources of the target teaching classroom at the data sampling time point, and sets the available network resources.

[0097] The index calculation and analysis scheduling module calculates the comprehensive availability of aesthetic education resources and the resource scheduling priority index of the target teaching classroom at the data sampling time point, and arranges them in descending order according to the resource scheduling priority index to construct a scheduling priority sequence for intelligent sharing of aesthetic education resources.

[0098] Furthermore, the adaptation calculation module includes an adaptation calculation unit;

[0099] The adaptation calculation unit: obtains all aesthetic education resources from the cloud platform, and presets corresponding environmental requirement parameters for each aesthetic education resource to construct an environmental requirement vector for aesthetic education resources; based on the classroom environment feature vector of the target teaching classroom at the data sampling time point, it calculates the environmental adaptation degree between the target teaching classroom and the i-th aesthetic education resource at the data sampling time point.

[0100] Furthermore, the set construction and coefficient calculation module includes a set construction unit and a coefficient calculation unit;

[0101] The set construction unit: presets an environment adaptability threshold. If the environment adaptability between the target classroom and the i-th aesthetic education resource at the data sampling time point is greater than or equal to the environment adaptability threshold, then it is determined that the i-th aesthetic education resource meets the teaching environment usage conditions of the target classroom at the data sampling time point; obtains all aesthetic education resources that meet the teaching environment usage conditions of the target classroom at the data sampling time point, and constructs a candidate shared resource set for the target classroom at the data sampling time point;

[0102] The coefficient calculation unit: at the data sampling time point, it detects the network link transmission data between the terminal of the target classroom and the cloud platform in real time. The network link transmission data includes the actual available transmission bandwidth value, network round-trip delay value, and packet loss rate. It presets standard network link transmission data for each candidate shared resource in the candidate shared resource set of the target classroom at the data sampling time point, constructing a standard network link transmission dataset for the i-th aesthetic education resource. It calculates the network matching coefficient of the i-th aesthetic education resource in the target classroom at the data sampling time point. It presets a network matching coefficient threshold. If the network matching coefficient of the i-th aesthetic education resource in the target classroom at the data sampling time point is greater than or equal to the network matching coefficient threshold, it determines that the network conditions of the target classroom at the data sampling time point meet the transmission requirements of the i-th aesthetic education resource. From the candidate shared resource set of the target classroom at the data sampling time point, it obtains all aesthetic education resources whose network conditions meet the transmission requirements of the target classroom at the data sampling time point, and constructs a set of available network resources for the target classroom at the data sampling time point.

[0103] Furthermore, the index calculation and analysis scheduling module includes an index calculation unit and an analysis scheduling unit;

[0104] The index calculation unit: obtains the environmental adaptability and network matching coefficient of each aesthetic education resource from the set of network available resources of the target teaching classroom at the data sampling time point, and calculates the comprehensive availability of the i-th aesthetic education resource of the target teaching classroom at the data sampling time point; based on the comprehensive availability of the i-th aesthetic education resource of the target teaching classroom at the data sampling time point, calculates the resource scheduling priority index of the i-th aesthetic education resource.

[0105] The analysis and scheduling unit: sorts all aesthetic education resources in the network available resource set of the target teaching classroom in descending order according to the resource scheduling priority index at the data sampling time point, and constructs a scheduling priority sequence; it obtains the scheduling priority sequence of different teaching classrooms in real time at the current data sampling time point, and performs intelligent sharing of aesthetic education resources.

[0106] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0107] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform, characterized in that, The method includes the following steps: Step S1: Construct a sensor acquisition module to collect data on the light intensity, effective activity area, and environmental reverberation time of the target classroom; construct a classroom environment feature vector of the target classroom at the data sampling time point; Step S2: Preset corresponding environmental requirement parameters for each aesthetic education resource and construct an environmental requirement vector for aesthetic education resources; based on the classroom environment feature vector of the target classroom at the data sampling time point, calculate the environmental adaptability between the target classroom and the aesthetic education resource at the data sampling time point. Step S3: Construct a candidate shared resource set for the target classroom at the data sampling time point; detect the network link data transmission between the terminal of the target classroom and the cloud platform in real time, calculate the network matching coefficient of the aesthetic education resources of the target classroom at the data sampling time point, and the set of available network resources; Step S4: Calculate the comprehensive availability of aesthetic education resources and the resource scheduling priority index of the target classroom at the data sampling time point, and sort them in descending order according to the resource scheduling priority index to construct a scheduling priority sequence for intelligent sharing of aesthetic education resources.

2. The novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform according to claim 1, characterized in that, The specific implementation process of step S1 includes: A sensor acquisition module is constructed to collect teaching environment data of the target classroom. The sensor acquisition module includes an ambient illuminance sensor, a depth vision sensor, a millimeter-wave radar sensor, and an acoustic acquisition unit. The teaching environment data includes light intensity data, effective activity area data, and ambient reverberation time data. The ambient light sensor is installed on the top of the target classroom to collect light intensity data. The depth vision sensor is used to collect the area occupied by fixed obstacles in the target classroom. The millimeter-wave radar sensor is used to collect the real-time area of ​​densely populated areas in the target classroom. The effective activity area data is calculated by subtracting the area occupied by fixed obstacles and the real-time area of ​​densely populated areas from the total floor area of ​​the classroom. The acoustic acquisition unit includes a microphone array. By playing a standard short-pulse sound signal in the target classroom, the microphone array records the sound pressure attenuation process to obtain environmental reverberation time data.

3. The novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform according to claim 2, characterized in that, The specific implementation process of step S1 also includes: The data sampling time period is defined as follows: ,in, Let A represent the a-th data sampling time point, and A represent the total number of data sampling time points; the data sampling time points are respectively... The collected light intensity data, effective activity area data, and environmental reverberation time data are denoted as follows: , and ; Construct a set of target teaching classrooms, denoted as . ,in, Let B represent the b-th target classroom and B represent the total number of target classrooms; the data sampling time points are respectively... The target classroom for the next collection Illumination intensity data, effective activity area data, and environmental reverberation time data are denoted as , and ; Constructing data sampling time points Next target classroom The classroom environment feature vector, denoted as .

4. The novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform according to claim 3, characterized in that, The specific implementation process of step S2 includes: All aesthetic education resources are retrieved from the cloud platform, and corresponding environmental requirement parameters are preset for each resource. An environmental requirement vector for aesthetic education resources is constructed, and the environmental requirement vector for the i-th aesthetic education resource is denoted as […]. ,in, This represents the appropriate light intensity requirement for the i-th aesthetic education resource. This represents the minimum effective activity area requirement for the i-th aesthetic education resource. This represents the environmental reverberation time requirement value for the i-th aesthetic education resource; Based on data sampling time points Next target classroom Classroom environment feature vector Calculate the data sampling time point Next target classroom The environmental suitability of the i-th aesthetic education resource is calculated using the following formula: ； in, Indicates the data sampling time point Next target classroom Environmental adaptability with the i-th aesthetic education resource This represents a preset constant.

5. A novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform according to claim 4, characterized in that, The specific implementation process of step S3 includes: Preset environment adaptability threshold, if the data sampling time point Next target classroom Environmental adaptability with the i-th aesthetic education resource If the environmental adaptability threshold is greater than or equal to the i-th aesthetic education resource at the data sampling time point, then the i-th aesthetic education resource is determined to be within the range of the data sampling time point. The target teaching classroom is below The conditions for using the teaching environment; Get at the data sampling time point The target teaching classroom is below The teaching environment and conditions for using all aesthetic education resources, and the construction of data sampling time points. Next target classroom A set of candidate shared resources; At the data sampling time point Below, real-time detection of target classrooms The network link between the terminal and the cloud platform transmits data, including the actual available transmission bandwidth, network round-trip time, and packet loss rate, which are respectively denoted as... , and ; Data sampling time points Next target classroom For each candidate shared resource in the candidate shared resource set, a standard network link transmission data is preset, and a standard network link transmission dataset for the i-th aesthetic education resource is constructed, denoted as . ,in, This represents the standard bandwidth required for the i-th aesthetic education resource to play normally. This represents the maximum acceptable network latency for the i-th aesthetic education resource. This represents the maximum acceptable packet loss rate for the i-th aesthetic education resource; Calculate data sampling time points Next target classroom The network matching coefficient of the i-th aesthetic education resource is calculated using the following formula: ； in, Indicates the data sampling time point Next target classroom The network matching coefficient of the i-th aesthetic education resource This represents a preset constant; Preset network matching coefficient threshold, if data sampling time point Next target classroom The network matching coefficient of the i-th aesthetic education resource If the data sampling time point is greater than or equal to the network matching coefficient threshold, then the data sampling time point is determined. Next target classroom The network conditions meet the transmission requirements of the i-th aesthetic education resource; From the data sampling time point Next target classroom From the candidate shared resource set, obtain the data sampling time point. Next target classroom The network conditions meet the transmission requirements of all aesthetic education resources, and data sampling time points are constructed. Next target classroom The collection of available network resources.

6. The novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform according to claim 5, characterized in that, The specific implementation process of step S4 includes: From the data sampling time point Next target classroom From the set of available network resources, obtain the environmental adaptability and network matching coefficient of each art education resource, and calculate the data sampling time points. Next target classroom The comprehensive availability of the i-th aesthetic education resource is calculated using the following formula: ； in, Indicates the data sampling time point Next target classroom The overall availability of the i-th aesthetic education resource, Indicates the preset environmental adaptability Influence factors Represents the preset network matching coefficients Influence factors; Based on data sampling time points Next target classroom The overall availability of the i-th aesthetic education resource Calculate the resource allocation priority index for the i-th aesthetic education resource using the following formula: ； in, Indicates the data sampling time point Next target classroom The resource allocation priority index of the i-th aesthetic education resource. This represents the course matching coefficient of the i-th aesthetic education resource. Indicates the data sampling time point The number of concurrent calls to the i-th aesthetic education resource; Data sampling time points Next target classroom All art education resources in the available network resource set are sorted in descending order according to the resource scheduling priority index to construct a scheduling priority sequence; the scheduling priority sequence of different teaching classrooms at the current data sampling time point is obtained in real time to achieve intelligent sharing of art education resources.

7. A novel intelligent sharing and optimization system for aesthetic education resources based on a cloud platform, executing the novel intelligent sharing and optimization method for aesthetic education resources based on a cloud platform as described in any one of claims 1-6, characterized in that, The system includes: a data acquisition and vector construction module, a fitness calculation module, a set construction and coefficient calculation module, and an exponent calculation, analysis, and scheduling module. The data acquisition and vector construction module includes: constructing a sensor acquisition module to collect light intensity data, effective activity area data, and environmental reverberation time data of the target classroom; and constructing a classroom environment feature vector of the target classroom at the data sampling time point. The adaptation calculation module: presets corresponding environmental requirement parameters for each aesthetic education resource, constructs an environmental requirement vector for aesthetic education resources; and calculates the environmental adaptation degree between the target teaching classroom and the aesthetic education resource at the data sampling time point based on the classroom environment feature vector of the target teaching classroom at the data sampling time point. The set construction and coefficient calculation module: constructs a candidate shared resource set for the target teaching classroom at the data sampling time point; detects the network link transmission data between the terminal of the target teaching classroom and the cloud platform in real time, calculates the network matching coefficient of the aesthetic education resources of the target teaching classroom at the data sampling time point, and sets the available network resources. The index calculation and analysis scheduling module calculates the comprehensive availability of aesthetic education resources and the resource scheduling priority index of the target teaching classroom at the data sampling time point, and arranges them in descending order according to the resource scheduling priority index to construct a scheduling priority sequence for intelligent sharing of aesthetic education resources.

8. A novel intelligent sharing and optimization system for aesthetic education resources based on a cloud platform as described in claim 7, characterized in that: The adaptability calculation module includes an adaptability calculation unit; The adaptation calculation unit: obtains all aesthetic education resources from the cloud platform, and presets corresponding environmental requirement parameters for each aesthetic education resource to construct an environmental requirement vector for aesthetic education resources; based on the classroom environment feature vector of the target teaching classroom at the data sampling time point, it calculates the environmental adaptation degree between the target teaching classroom and the i-th aesthetic education resource at the data sampling time point.

9. A novel intelligent sharing and optimization system for aesthetic education resources based on a cloud platform as described in claim 8, characterized in that: The set construction and coefficient calculation module includes a set construction unit and a coefficient calculation unit; The set construction unit: presets an environment adaptability threshold. If the environment adaptability between the target classroom and the i-th aesthetic education resource at the data sampling time point is greater than or equal to the environment adaptability threshold, then it is determined that the i-th aesthetic education resource meets the teaching environment usage conditions of the target classroom at the data sampling time point; obtains all aesthetic education resources that meet the teaching environment usage conditions of the target classroom at the data sampling time point, and constructs a candidate shared resource set for the target classroom at the data sampling time point; The coefficient calculation unit: at the data sampling time point, detects in real time the network link transmission data between the terminal of the target teaching classroom and the cloud platform. The network link transmission data includes the actual available transmission bandwidth value, the network round-trip delay value, and the packet loss rate. For each candidate shared resource in the candidate shared resource set of the target teaching classroom at the data sampling time point, a standard network link transmission data is preset for each candidate shared resource, and a standard network link transmission dataset for the i-th aesthetic education resource is constructed. Calculate the network matching coefficient of the i-th aesthetic education resource in the target classroom at the data sampling time point; A preset network matching coefficient threshold is set. If the network matching coefficient of the i-th aesthetic education resource in the target classroom at the data sampling time point is greater than or equal to the network matching coefficient threshold, it is determined that the network conditions of the target classroom at the data sampling time point meet the transmission requirements of the i-th aesthetic education resource. From the candidate shared resource set of the target classroom at the data sampling time point, all aesthetic education resources whose network conditions meet the transmission requirements of the target classroom at the data sampling time point are obtained, and a set of available network resources of the target classroom at the data sampling time point is constructed.

10. A novel intelligent sharing and optimization system for aesthetic education resources based on a cloud platform as described in claim 9, characterized in that: The index calculation and analysis scheduling module includes an index calculation unit and an analysis scheduling unit; The index calculation unit: obtains the environmental adaptability and network matching coefficient of each aesthetic education resource from the set of network available resources of the target teaching classroom at the data sampling time point, and calculates the comprehensive availability of the i-th aesthetic education resource of the target teaching classroom at the data sampling time point; based on the comprehensive availability of the i-th aesthetic education resource of the target teaching classroom at the data sampling time point, calculates the resource scheduling priority index of the i-th aesthetic education resource. The analysis and scheduling unit: sorts all aesthetic education resources in the network available resource set of the target teaching classroom in descending order according to the resource scheduling priority index at the data sampling time point, and constructs a scheduling priority sequence; it obtains the scheduling priority sequence of different teaching classrooms in real time at the current data sampling time point, and performs intelligent sharing of aesthetic education resources.

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