CN-120930858-B - Multi-school zone linkage teaching resource block chain dynamic scheduling system

CN120930858BCN 120930858 BCN120930858 BCN 120930858BCN-120930858-B

Abstract

The invention provides a multi-school zone linkage teaching resource block chain dynamic scheduling system, relates to the technical field of block chains, and solves the problem of inaccurate resource demand estimation; the method comprises the steps of constructing a classroom type preference matrix through a principal component analysis method, combining expert experience and user feedback, generating type demand coefficients, achieving accurate matching of resource allocation and actual demands, utilizing collection and processing of real-time data points in edge nodes, enabling a multi-chain framework to keep consistency of classroom metadata and intelligent event triggering, reducing influence of scheduling misjudgment and frequent compensation on stability, adopting a federal learning dynamic optimization resource prediction model, protecting data privacy, improving performance, improving data optimization and adjustment accuracy through updating of energy consumption smoothing factors, enabling a block chain management mechanism to ensure optimization data encryption and real-time synchronization of edge modules, reducing misjudgment rate, improving resource management and equipment stability, and optimizing user experience.

Inventors

Zhang Tuoliang
ZHANG ZHILIANG
ZHANG WENLIANG
YANG JINQIU

Assignees

北京漂洋过海科技有限责任公司

Dates

Publication Date: 20260508
Application Date: 20250717

Claims (9)

1. The utility model provides a many school district linkage teaching resource block chain dynamic scheduling system, is applied to adjacent school district sharing high demand resource, its characterized in that includes: The system comprises a classroom type identification module, a classroom type preference matrix, a characteristic weight generation module and a characteristic analysis module, wherein the classroom type identification module is used for collecting historical multidimensional state data in each classroom, extracting characteristics including acoustic requirements, equipment requirements and space requirements from the historical multidimensional state data by using a principal component analysis method, constructing a classroom type preference matrix according to the extracted characteristics, taking different types of classrooms as rows and taking each characteristic requirement as a column, and generating specific characteristic weights to form a classroom type preference matrix, wherein the classroom type preference matrix consists of acoustic requirements, connectivity requirements and environmental stability of each class of classrooms; the system comprises a data acquisition processing module, a classification and homomorphic encryption processing module and a classification and homomorphic encryption processing module, wherein the data acquisition processing module acquires environmental state data, operation calculation data and power demand data of classrooms in real time at edge nodes of all school areas; The system comprises a block chain storage consensus module, a calculation power demand fluctuation model and a storage unit, wherein the block chain storage consensus module is used for storing data blocks of each classroom type mark; The intelligent scheduling execution module reads the resource allocation parameters and the latest calculation force demand fluctuation of each classroom in the block chain after receiving the data ready signal, calculates the calculation force priority allocation value P and generates a smoothed resource allocation scheme; Writing a resource allocation scheme into a block chain, triggering calculation power and electric power execution, and simultaneously monitoring switching of calculation power resources and electric power in real time; The feedback optimization and federal learning module is used for setting an optimization period, locally and parallelly utilizing federal learning training 'resource prediction and authentication performance model' in each school zone, calculating an updated energy consumption smoothing factor S, and distributing the updated energy consumption smoothing factor S to the edge acquisition and scheduling execution module through on-chain treatment so as to further improve the estimation precision, reduce the misjudgment frequency and the power supply switching times.
2. The multi-school district linked teaching resource block chain dynamic scheduling system of claim 1, wherein the data acquisition processing module comprises a data acquisition unit: the data acquisition unit is used for acquiring environmental state data, operation calculation power data and power demand data; wherein the environmental status data includes temperature, humidity, air quality, and illumination intensity of the classroom; the operation calculation force data comprises the processor utilization rate, the memory occupancy rate and the network bandwidth of the computer equipment; the power demand data includes power consumption and peak load of classrooms; and transmitting the collected environmental state data, operation calculation power data and power demand data through an encryption transmission protocol.
3. The multi-school district linked teaching resource block chain dynamic scheduling system of claim 2, wherein the data acquisition processing module further comprises a threshold layering processing unit; the threshold layering processing unit is used for receiving environmental state data, operation calculation power data and power demand data, and processing the environmental state data, the operation calculation power data and the power demand data through the funnel filtering window to filter noise data; according to different classroom types, a layering judgment standard threshold is set, wherein the layering judgment standard threshold comprises a first threshold and a second threshold, layering processing is carried out on data, and the method is specifically as follows: Calculating a baseline threshold value based on historical data, wherein the baseline threshold value comprises an arithmetic mean value of data in a statistical period and a standard deviation of the data in the statistical period; Generating a threshold value of each classroom type, updating the threshold value in real time, calculating a new arithmetic mean value and a standard deviation by rolling by a threshold value layering processing unit, and smoothing the threshold value by applying an exponential weighted moving average; And carrying out layering judgment on the real-time data points subjected to funnel filtration, and carrying out comparison evaluation on the real-time data points and layering judgment standard thresholds to respectively mark the real-time data points, wherein the real-time data points comprise a first load layer, a second load layer and a third load layer, so that data blocks with hierarchical labels are generated.
4. The multi-school district linked teaching resource block chain dynamic scheduling system of claim 3, wherein the block chain storage consensus module comprises a multi-chain architecture unit: the multi-chain architecture unit is used for being arranged at a school zone level edge node, configuring independent side chains for each classroom type, and writing a classroom type label in an creation block of each side chain; For each writing operation of the classroom type tag, automatically generating an associated intelligent event based on the characteristics of the written data, wherein the intelligent event comprises meta information of the data, namely a metadata type, a time stamp, a data block identifier and a cumulative signature; The method comprises the steps of presetting a threshold value M for confirming the number of nodes, triggering a data ready signal when the number of confirmed nodes reaches or exceeds the threshold value M, encrypting the written data and the related intelligent events thereof, and storing the encrypted written data and the encrypted intelligent events into corresponding side chains.
5. The multi-school district linked teaching resource block chain dynamic scheduling system of claim 4, wherein the block chain storage consensus module further comprises a demand fluctuation model construction unit; The demand fluctuation model construction unit collects resource use history data including the frequency and time point of writing and accessing each type of classroom data, and inputs the data into the computational power demand fluctuation model; predicting future data access and writing requirements of each type of classroom by using a time sequence analysis method, wherein a time sequence model takes the periodicity and trend change factors of history into consideration to generate a prediction report for each type of classroom; According to the prediction result, dynamically adjusting the resource allocation of each node in the blockchain network, specifically, automatically increasing the support of the node for the classroom type predicted in the peak period of the demand, and reducing the resource allocation in the valley period of the demand; in addition, the demand fluctuation model is used for monitoring in real time, the difference between the use condition of actual data and the prediction is used for automatically adjusting the periodicity and trend change factors of the history of the next monitoring time period.
6. The multi-school district linked teaching resource block chain dynamic scheduling system of claim 5, wherein the intelligent scheduling execution module comprises a resource scheduling unit; The resource scheduling unit accesses the resource allocation parameters of the current classrooms and the latest calculation power demand fluctuation information thereof from the blockchain database in real time after receiving a data ready signal transmitted in the blockchain network; according to the acquired resource configuration parameters and the latest calculation force demand fluctuation information, calculating the resource demand priority of each type of classroom by using a calculation force priority distribution algorithm; a smoothed resource allocation scheme based on the calculated priority allocation value P is generated to adjust the allocation of resources for various classes of classrooms.
7. The multi-school district linked teaching resource block chain dynamic scheduling system of claim 6, wherein the intelligent scheduling execution module further comprises a resource management unit; The resource management unit records the resource allocation scheme reconfigured according to the priority allocation value P to a blockchain network, and triggers the automatic execution of calculation power and electric power resources; In the process, the switching condition of the computing power resource and the electric power is monitored and adjusted in real time under the control of a preset intelligent contract.
8. The multi-school district linked teaching resource block chain dynamic scheduling system of claim 7, wherein the feedback optimization and federal learning module comprises: configuration and starting of federal learning, which are used for jointly optimizing and training a resource prediction and authentication performance model under the condition of not sharing school zone data, protecting data privacy and improving global performance of the model; calculating and updating an energy consumption smoothing factor S by using a federal learning result, and adopting an updating formula: ; Wherein, the Represents a smoothing parameter, and 0< alpha <1, sold is the previous energy consumption smoothing factor, slear is the updated value derived from the current federal learning result.
9. The multi-school district linked teaching resource block chain dynamic scheduling system of claim 8, wherein the feedback optimization and federal learning module specifically further comprises: The encryption and distribution of the energy consumption smoothing factor S obtained by optimization in the federal learning process are realized through a treatment mechanism on a blockchain, and the updated energy consumption smoothing factor S is sent to all edge acquisition and scheduling execution modules in the system so as to ensure that each module uses the latest parameters in resource scheduling and energy management; The on-chain treatment mechanism is not only responsible for distributing the energy consumption smoothing factors S, but also for monitoring the real-time synchronization and execution of the energy consumption smoothing factors S by each edge acquisition and scheduling execution module, and automatically checking the receiving and executing states of the updated data through intelligent contracts.

Description

Multi-school zone linkage teaching resource block chain dynamic scheduling system Technical Field The invention relates to the technical field of blockchains, in particular to a dynamic scheduling system of a multi-school zone linkage teaching resource blockchain. Background In the prior art, the bulletin number is CN116582704B, the name is a virtual simulation teaching resource sharing management platform, the invention discloses a virtual simulation teaching resource sharing management platform, which relates to the technical field of virtual simulation teaching and is used for solving the problem that teaching resource difference exists in practice in the current virtual simulation training room, and comprises a comprehensive monitoring module, a scheduling analysis module, a practical training management system, a resource scheduling module and a practical training feedback module, wherein the scheduling analysis module is used for analyzing the teaching resource scheduling requirement of the virtual simulation training room to obtain the resource scheduling of the virtual simulation training room, the comprehensive monitoring module is used for monitoring the use condition of the virtual simulation training room, the resource scheduling module is used for analyzing the scheduling condition of the computing resource in each virtual simulation training room to obtain the resource scheduling compensation grade of the virtual simulation training room, and the practical training feedback module is used for analyzing the use feedback of the virtual simulation training room to obtain the practical training feedback grade of the virtual simulation training room. However, in practical application, when the virtual simulation teaching resource sharing management platform is combined with the virtual simulation teaching resource sharing management platform to be used in classrooms of multiple school areas, namely, piano rooms, laboratories and library study rooms, the following technical defects often exist: 1. The lack of refinement of training configuration data for different classroom types results in inaccurate resource demand predictions. 2. Frequent compensation is caused by misjudgment of resource scheduling, so that computational power resource overload and frequent switching of power supply lines are caused, and equipment stability and user experience are affected. The technical defects form a chain reaction, and the resource management and the use effect of classrooms in multiple school areas are affected; the above information disclosed in the above background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to those of ordinary skill in the art. Disclosure of Invention The invention aims to provide a multi-school zone linkage teaching resource block chain dynamic scheduling system so as to solve the problems in the background technology. In order to achieve the above purpose, the present invention provides the following technical solutions: a dynamic scheduling system of multi-school district linkage teaching resource block chain is applied to adjacent school districts to share high-demand resources, and comprises: The classroom type identification module is used for creating a classroom type preference matrix, identifying the use characteristics of classrooms of all school areas through the classroom type preference matrix when the system is initialized and updated regularly, and calculating a corresponding type demand coefficient Dxq; the system comprises a data acquisition processing module, a classification and homomorphic encryption processing module and a classification and homomorphic encryption processing module, wherein the data acquisition processing module acquires environmental state data, operation calculation data and power demand data of classrooms in real time at edge nodes of all school areas; The system comprises a block chain storage consensus module, a calculation power demand fluctuation model and a storage unit, wherein the block chain storage consensus module is used for storing data blocks of each classroom type mark; the intelligent scheduling execution module reads the resource allocation parameters and the latest calculation force demand fluctuation of each classroom in the block chain after receiving the data ready, calculates the calculation force priority allocation value P and generates a smoothed resource allocation scheme; Writing a resource allocation scheme into a block chain, triggering calculation power and electric power execution, and simultaneously monitoring switching of calculation power resources and electric power in real time; The feedback optimization and federal learning module is used for setting an optimization period, locally and parallelly utilizing federal learning training 'resource prediction and authentication perform