# Exploration of Practical Strategies of School Labor Education under the Information Environment

Authors: Jianhai Liu; Weidong Zhao; Guanqi Gao
Journal: Advances in Multimedia
 (2022)

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## Abstract

In order to improve the practical effect of modern school labor education, this paper applies information technology to the analysis of school labor education practice strategy and proposes a load equivalent model to measure the load distribution of parallel crawler system nodes. According to the load distribution of system nodes, this paper uses the hypergraph repartitioning model to model the dynamic load scheduling problem and introduces a hierarchical strategy to solve the hypergraph repartitioning problem. In addition, this paper uses distributed crawlers to utilize network resources to effectively reduce the operating cost of the crawler system. Finally, this paper constructs a system based on the current situation of school labor education. The experimental research results show that the practical strategies of labor education in the information environment proposed in this paper have certain effects.


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## Body

## 1. Introduction

Labor is an important means for human survival and development, and it is also an important way to hone the quality of one’s own will and realize the value of life. In the process of human growth, labor has a profound influence and important role in human development. However, with the rapid economic development, the values and social thoughts are diversified, and labor education is faced with more challenges and problems in this environment. How to find practical and effective methods and approaches to strengthen students’ labor education, and how to make schools, society, and families form a correct understanding of labor values, labor spirit, and labor morality to better help students develop better is an important problem that labor education research needs to solve in this period.After entering the new period, with the continuous increase of the state and society’s attention to labor education, the exploration of the development strategies and practical approaches of secondary vocational labor education under the new situation has become the key to effectively highlighting the function of secondary vocational education. At the same time, the development of labor education can effectively change the shortcomings of educating people such as the weakening of labor education awareness, the single form of labor education, and the lack of labor education strategies in the field of secondary vocational education and teaching at this stage. Moreover, it provides a guarantee for promoting the internalization of labor consciousness and strengthening of labor skills of secondary vocational students. More importantly, realizing the deep connection and organic integration of labor education and secondary vocational education can effectively broaden the teaching path of secondary vocational education and is more conducive to the improvement of students’ professional skills and labor literacy. Based on this, teachers should be closely related to school-based reality, students’ reality, and teaching needs when implementing the teaching strategies and practical approaches of secondary vocational labor under the new situation. At the same time, it is necessary to gradually transfer the development forms, implementation methods, and implementation strategies of labor education to the fields of professional skills training and cultural theory teaching. This enables students to gradually gain cognitive transformation in close contact with labor, deep perception of labor, and all-round development of labor. In addition, it is necessary to cultivate students’ labor awareness, labor skills, and labor literacy in a subtle and step-by-step process so that students can gradually establish the ideological understanding of the glory of labor, the greatness of creation, and the valuable practice in the experience and practice, and promote their cognitive sublimation and self-realization.Labor education is an educational activity formed to liberate the free nature of students [1]. People change nature through labor, and their natural nature will also change in response to changes in the environment. This change is the expression of human freedom and initiative [2]. In the current education, the natural nature of human beings is missing, and the contact with the natural environment in the teaching process is broken, and the freedom of students cannot be effectively developed. At this time, labor education came into being [3]. Labor education enables students not to be bound and restricted by the external natural environment but to obtain the material basis for their own growth from nature so as to connect with nature, return to natural characteristics, stimulate their own potential, and finally achieve their own goals. From the perspective of the history of labor development, Marx’s theory of labor liberation advocates the realization of the liberation of the proletariat and all human beings, and the great goal of achieving the union of free people [4]. From this perspective, labor is the bridge and link between the objective world and the subjective world and the only way to liberate and free human nature [5]. Therefore, labor education promotes the combination of students’ intelligence and physical strength to achieve the liberation of individuality and freedom so as to realize the comprehensive development and free development of students. Second, labor education is an educational activity for the free development of students [6]. In order to correctly view the importance of labor education to the free development of students, it is necessary to consider the freedom of students’ thoughts and actions. Because labor education is the practical basis for students’ free development, it makes students’ freedom of thought and freedom of action both indispensable [7]. In school life, labor is an activity that is closely integrated with the actual life of modern society, that is, while students are doing labor on their own, they are also enlightening students in their critical, selective, and reflective labor thinking ability [8]. Of course, when students have labor interests and hobbies, students will be free to stimulate their own internal needs, accumulate labor experience, and then freely experience labor activities. Through labor education, students have the freedom to follow their inner choices and can actively and actively develop their individuality and advocate their own unique value and meaning [9]. That is to say, students have the feasible space for free labor, which makes the freedom of labor rights of students have an operable path and gives students the potential and possibility of free development [10].Literature [11] believes that “labor education is the cultivation of human labor quality” and emphasizes that labor education as a moral category is an important part of the communist social education system. Literature [12] believes that “labor education is the practical training for the younger generation to participate in social production, and it is also an important factor in moral, intellectual, and esthetic education.” In western developed countries, the subject name or course name of “labor education” rarely appears, but the courses are carried out in the form of career technical education (Career Technical Education), home economics education (Home Economics Education), handicraft education (Handicraft Course), etc. [13]. For example, career technical education is the key direction of American education reform, which emphasizes on cultivating students to master employment-related knowledge and skills around 16 career clusters and overall career preparation [14]. Literature [15] emphasizes on teaching students to become members of society with higher value through life-centered career education courses. Literature [16] believes that an important feature of career technical education is to strengthen the links between courses, projects, and industry outputs. The understanding of the connotation of labor education is more complicated. The first thing is to emphasize the social production attributes of school labor education, positioning labor education as labor skills education [17]. Labor education is gradually equivalent to “(basic) production technology education” or “vocational guidance education.” It is proposed that “labor education is to disseminate the basic knowledge and skills of modern production to the educated” [18]. It is emphasized that labor education is “education to train students with basic knowledge and basic skills of modern industry and agriculture.” The second one is to emphasize the moral or ideological education attributes of school labor education and regard labor education as an integral part of the content of moral or ideological education [19].“A theory is nothing more than a systematic representation of a rational understanding of the world using concepts and logic.” School labor education is a complex practical activity involving many research areas. We must not only focus on grasping the upper concept of labor education, comprehensively analyzing the rich connotation of labor education, but also on refining the essential characteristics of school labor education on this basis. The perspective of labor, labor education, and school labor education is the starting point for exploring the problems of school labor education, and it is also the fulcrum for deepening the understanding of school labor education. We need to strengthen our understanding of these categories in the dialectical unity of theoretical logic, historical logic, and realistic logic. It is necessary not only to systematically sort out the development context of these categories based on the depth of history but also to fully understand the era connotation of these categories from the height of reality.Analyzing labor education from a synchronic perspective, the value orientation affects the definition of the connotation of labor education. The social value orientation in a specific time and space affects the definition of the connotation of labor education. In general, influenced by the tool value orientation and the humanistic value orientation, there are two types of definitions of the connotation of labor education: “tool-oriented” and “human-oriented.” The definition of “tool orientation” mainly focuses on the tool value of labor education, positioning labor education as a practical activity that meets the needs of external political situation, economic construction, or social development. In the early days of Nu Skin China, labor education was a practical activity to cultivate students’ basic production knowledge and skills to promote social and economic recovery. During the Cultural Revolution, labor education was a political tool for ideological transformation. The definition of “people-oriented orientation” mainly focuses on the development of people themselves and positions labor education as a practical activity that meets the inherent needs of people in the labor process. Since Nu Skin’s curriculum reform, the education reform aimed at developing students’ core literacy that has directly affected the development of school labor education. Labor education not only pays attention to cultivating students’ labor knowledge and skills but also pays more attention to the cultivation of students’ labor habits and quality. It aims to comprehensively improve students’ labor literacy and guide them to establish a scientific Marxist labor view.This paper applies information technology to the analysis of school labor education practice strategy, improves the educational effect and safety of school labor education practice, and promotes the effective implementation of school labor education practice.


## 2. Informatization Data Processing Technology

### 2.1. Web Crawler Technology

The usual distributed crawler includes a coordinator and multiple crawlers. The coordinator is responsible for logically dividing the searched network, assigning the divided logical partitions to the crawlers, and coordinating task scheduling among the crawlers. The crawler is responsible for downloading the web pages in the logical partition that it is responsible for and exchanges download tasks with each other through the coordinator. It uses the local storage space to store downloaded web pages and communicates with each other through a high-speed local area network, and the processed results are centrally stored for retrieval programs. The overall system framework is shown in Figure1.Figure 1
System framework of a distributed crawler.The use of distributed crawler can make better use of network resources and effectively reduce the operating cost of the crawler system. The current major search engines all use distributed crawler systems without exception. However, many new problems have arisen in the design of distributed crawlers:


#### 2.1.1. Task Segmentation Problem

The crawlers should to be able to work together accurately, and it will not generate a URL that is repeatedly downloaded in multiple crawlers and will not generate a URL that is not allocated to the download space of any crawler. If it is assumed that the number of crawlers isN and the set of web pages to be downloaded is P, the task can be divided into n subsets Pl,P2,…,Pn, which need to satisfy(1)∪i=1nPi=PAndPi∩Pj=∅amongi≠j,1≤i<j≤n.


#### 2.1.2. Load Balancing Problem

The amount of download space of each crawler is roughly equal, and there will not be a situation where a certain crawler has far more download tasks than another crawler. This is the key to the performance improvement of distributed crawlers, and the model can be expressed as(2)Pi≈Pjamongi≠j,1≤i<j≤n.


#### 2.1.3. Page Quality Problems

When the downloaded pages need to be evaluated, a single crawler can adjust the search strategy according to the obtained global information to obtain the globally optimal page. However, if the distributed structure is adopted, each crawler can only collect locally optimal pages, which affects the global collection quality.


#### 2.1.4. Communication Cost Problem

The dynamic load balancing problem has been studied extensively and deeply, and a large number of excellent load balancing algorithms have been proposed. On the whole, various load balancing algorithms are weighed in the following factors so as to properly solve applications in different fields.(1)
After dynamic load scheduling, the system needs to form a new balance state.(2)
In the new equilibrium state, the communication overhead of system operation should be as little as possible.(3)
In the process of load adjustment, the less the amount of data migration, the better.(4)
The load adjustment process should take as little time as possible.According to the above four design indicators, the overall running time consumption of the task is usually formally modeled as(3)ttotal=αtcomp+tcomm+tmig+trepart.Among them,tcomp and tcomm represent the time used for task job and system communication in an iterative operation of the parallel crawler system, tmig represents the time spent migrating data during a load adjustment process, and trepart represents the time spent in order to calculate the new load balancing state. α represents the number of iterations that the system runs from this load adjustment to the next load adjustment.In all kinds of dynamic load scheduling algorithms, the goal is to minimize the overall running time of the task according to the actual situation. Therefore, we measure the pros and cons of a dynamic load scheduling algorithm by the size of the overall running timettotal of the task. Next, we will further analyze the various components of the overall running time of the task and strive to simplify the analytical model of dynamic load scheduling.If the load is evenly distributed among the cluster nodes,tcomp will be minimized. At the same time, since load balancing is the primary goal of all dynamic load adjustment algorithms, we can safely assume that tcomp is minimized in all dynamic load scheduling algorithms. Furthermore, in the running process of the parallel crawler system, the time trepart spent to calculate the new load balance state should be much smaller than the time αtcomp spent by the task job. From the above analysis, we ignore the role of tcomp and trepart in measuring the pros and cons of a dynamic load adjustment algorithm and simplify the time consumption caused by dynamic load scheduling as(4)tcost=αtcomm+tmig.Since the communication overheadtcomm of the system is closely related to the structural characteristics of the cluster system (such as the network topology and message delay), we directly measure the load balancing algorithm by the communication time and introduce irrelevant structural factors. Generally, we believe that the communication overhead of the system is proportional to the amount of data transferred during the communication process, so the dynamic load balancing consumption defined in the form of time can be further transformed into the form of the amount of transferred data as(5)costvol=αbcomm+bmig.Among them,bcomm and bmig, respectively, represent the amount of data communicated by the system in one iteration process and the amount of data transferred in the dynamic load scheduling process.SetN=Nl,N2,…,Ni is used to represent all nodes in the cluster environment where the parallel crawler system is located. Among them, the carrying capacity of Node Ni is cci (carrying capacity). At the same time, the set T=T1,T2,…,Ti represents the J tasks running on the cluster at a certain time, and the weight of task Tj is represented by wj. The number of logical nodes assigned by task Tj to node Ni is nij. From this, we define the load equivalent lwi of cluster node Ni at this moment as(6)lwi=∑j=1Jnij×wjcci.The load equivalent combines the task weight, the number of logical nodes, and the node carrying capacity, which can more accurately describe the load size of each cluster node than the simple number of tasks.Definition 1.
A hypergraph can be viewed as a generalization of a graph. Each edge in the hypergraph links a nonempty subset of the vertex set and not just two vertices. Formally, a hypergraph is defined asH=V,N. Among them, V=v1,v2,…,vi is the set of vertices and N=n1,n2,…,nj is the set of hyperedges. For any nj∈N, it is a nonempty subset of the vertex set V. Each vertex vi has a nonnegative weight wi. Correspondingly, each hyperedge nj also has a nonnegative weight cj, which is called consumption.Definition 2.
P=V1,V2,…Vk is called a K partition of the hypergraph H; among them, Vpp=1,2,…,k is a nonempty subset of the vertex set V, and there is Vi∩Vj=∅1≤i<j≤k,∪p=1kVp=V.Definition 3.
A K partitionP=V1,V2,…Vk of a hypergraph H is said to be balanced if and only if Wp≤Wavg1+ε,p=1,2,…,k. Among them, Wp=∑vi∈Vpwi,Wavg=∑vi∈Vwi/k,ε>0 is the predetermined maximum tolerable imbalance factor.Definition 4.
P=V1,V2,…Vk is a K partition of hypergraph H=V,N. If there is a vertex vi in the hypergraph that satisfies vi∈Vk and there is vi∈nj, the hyperedge nj is said to be associated with the partition Vk. The number of partitions associated with a hyperedge nj is called the connectivity of that hyperedge, denoted by λj.Definition 5.
CutCostH,P represents the partition cost brought by a partition P on the hypergraph H, which is formally defined as CutCostH,P=∑nj∈Ncjλj−1.
For example, Figure2 is an instance of a hypergraph H with the vertex set V=v1,v2,v3,v4,v5,v6 and the hyperedge set N=n1,n2,n3,n4,n5,n6. Among them, the hyperedge is n1=v1,v2,n2=v1,v3,n3=v1,v4,n4=v1,v4,v5,v6,n5=v1,v5,n6=v1,v6. P=v1,v2,v3,v4,v5,v6,∅ is a 4-partition of the hypergraph H.
In fact, balanced partitioning is an important partitioning method in hypergraphs. Therefore, we often need to convert the unbalanced partition of the hypergraph into a balanced partition, and this conversion process is called the repartition of the hypergraph.
The load equivalent combines the task weight, the number of logical nodes, and the node carrying capacity, which can more accurately describe the load size of each cluster node than the simple number of tasks.
Each epoch starts from the end of a load balancing action and performs several iterative operations. During the iterative operation, the load structure of the system changes. When the load imbalance reaches the maximum tolerance limit, the iterative operation ends, and dynamic load scheduling is performed again. Between epochs, the task structure and load structure on the parallel crawler system are changing. Therefore, we create a hypergraph model for each epoch in the task runtime, formally denote the hypergraph model of thej-th epoch as Hj=Vj,Nj, and denote the number of iteration operations performed during the period as αj. The relationship between the two adjacent epochs is denoted as Hj−1⟶RjHj.
From formula (5), we know that the cost of dynamic load adjustment is proportional to the sum of the amount of data transferred in the process of dynamic load scheduling and the amount of data communicated by the system during the iterative operation after adjustment. In the process of Hj−1⟶RjHj, in order to measure the pros and cons of Rj, we must seek a way to characterize the communication amount in the iterative operation process and the load migration amount in the load transfer process in the hypergraph model. It can be seen from Table 1 that the hyperedge in the hypergraph model represents the set of load units that need to communicate, and the weight of the hyperedge represents the traffic between load units. Then, under the premise that the load unit traffic in the same node is zero, the system traffic bcomm in the iterative operation process can be expressed as(7)bcomm=αj×CutCostHj,Pj.
In order to establish a model for measuring the amount of load transfer in the process of load adjustment in the hypergraph, we add a virtual vertexui with zero weight to each partition Pi of hypergraph Hj−1 and add a virtual hyperedge between all vertices v and ui belonging to Pi. The cost of the virtual hyperedge is the weight of the vertex v. Figures 3 and 4 demonstrate this process. Figure 3 shows a simple hypergraph partition model, and Figure 4 shows the state after adding virtual vertices to each partition in Figure 3.
If we stipulate that in the process of dynamic load scheduling, virtual vertices are not allowed to be migrated, any vertexv migrated from Pi will increase the partition cost due to the existence of virtual hyperedges between ui and v. In this way, we unify both load migration and system traffic into the hypergraph partition cost. It should be noted that the partition cost introduced by the virtual hyperedge does not need to be weighted by the iteration number α.
The hypergraph repartitioning process by adding virtual vertices is represented asHj−1⟶Add virtual vertexH¯j−1⟶RjH¯j⟶Delete virtual vertexHj, then the cost of repartitioning Rj can be represented as(8)costRj=Cut CostH¯j,P¯j.
In view of the relationship betweenH¯j and Hj, another expression of the above formula is(9)costRj=αjCutCostHj,Pj+∑nv∈N¯j−Njcvλv−1.
After establishing the measurement method of the hypergraph repartitioning cost, we illustrate the influence of the iteration numberα on the repartitioning strategy. Figures 5 and 6 show the results of repartitioning the hypergraph in Figure 4 in order to pursue the minimum repartitioning cost when the number of iterations is 1 and 10, respectively. The difference between the results after division is very obvious, so the correct estimation of the number of operation iterations in an epoch has a profound effect on dynamic load scheduling.Figure 2
An example of a hypergraph.Table 1
Verification of the effect of practical strategy formulation of school labor education in the information environment.
NumPractice strategyNumPractice strategyNumPractice strategy186.711872.063571.47283.801971.383676.63385.102083.183774.25478.222182.493879.84572.092279.173975.65678.362381.744085.31772.152482.204176.39885.112580.664270.21971.722682.974387.801079.442777.554474.181181.312879.004570.091279.422978.124685.491379.483077.404782.981483.543170.214882.401581.103273.384987.351671.843376.325070.241777.233482.105177.26Figure 3
A simple hypergraph partitioning model.Figure 4
Hypergraph partition model after adding virtual vertices.Figure 5
Results of hypergraph repartitioning (α = 1).Figure 6
Results of hypergraph repartitioning (α = 10).


### 2.2. Hypergraph Multilevel Repartitioning Strategy

The hypergraph repartitioning strategy consists of three stages, which are the coarsening stage, the original partitioning stage, and the refining stage. As shown in Figure7, in the coarsening stage, we perform a series of coarsening on the original hypergraph by merging vertices and their associated hyperedges in the hypergraph until the coarsened hypergraph is small to a certain size. In the original partition stage, we perform an appropriate repartition on the smallest hypergraph obtained in the coarsening stage. In the refinement phase, a reverse process of the roughening phase is performed. In each refinement process, we project the partitions on the coarser hypergraph onto the finer hypergraph, and we perform certain optimizations where possible. The following is a detailed description of each stage:Figure 7
Demonstration of the hypergraph repartitioning strategy.


#### 2.2.1. Roughening Stage

The coarsening stage consists of a series of steps. In eachi step, a coarser approximate hypergraph Hi+1Vi+1,Ni+1 is constructed with the hypergraph HiVi,Ni as the prototype. The construction process is done by merging a set of vertices in Hi to form the vertices in Hi+1. We characterize this process by the mapping gi:Vi⟶Vi+1, where(10)ViVi+1=ri,ri>1.In order to ensure that the repartitioning of the coarser hypergraphHi+1Vi+1,Ni+1 is projected to the finer hypergraph HiVi,Ni and still maintain a good division effect, we try to merge the vertices with high correlation degree first. Among them, the degree of association cu,v of two vertices u,v in Hi is defined as(11)cu,v=B,B⊂NiAnd∀n∈B,u∈n∧v∈n.Among them,B is the largest subset of Ni that satisfies the condition of ∀n∈B,u∈n∧v∈n, and |B| is the number of elements in the set B. If vertex A corresponds to a vertex v under hypergraph y under the mapping relation gi, then there must be(12)∃n∈Ni,Vp∈P,bring∀v∈A,v∈n∧v∈Vp.In this mapping relationship, the weight of vertexv is set to the sum of the weights of all vertices in vertex set A. Further, if A∈Ni, the hyperedge in Hi+1 contains only one vertex. Then, such a hyperedge will be discarded in Hi+1. The reason for this is that it can be in at most one partition. Under the relationship of mapping gi, if a set of hyperedges B⊂Ni generates a set of identical hyperedges B′⊂Ni+1, then B′ will be replaced by a hyperedge b, and the weight of hyperedge b is set to the sum of the weights of all hyperedges in B.


#### 2.2.2. The Original Division Stage

The goal of the original partitioning stage is to calculate an appropriate repartitioning scheme on the roughest hypergraphHcVc,Nc. Since the scale of Hc is much smaller than H1, we think that a scheme that satisfies the requirements can be found in a reasonable time.In order to make full use of system resources, we calculate a repartitioning scheme with random greedy algorithm on each node of the cluster system. The repartitioning costs of these schemes are aggregated to the master control node in the system, and the master control node selects the lowest cost as the final solution.


#### 2.2.3. Refinement Stage

The refinement phase is the inverse of the coarsening phase. In the process of each step of refinement, the divisionPi+1 on the coarser hypergraph Hi+1 is projected onto the finer hypergraph Hi, and some appropriate adjustments are made to form the division Pi on Hi.Intuitively speaking, it is to guide the repartitioning of the approximate finer hypergraph with the excellent repartitioning scheme on the coarser hypergraph and make reasonable optimizations.


## 2.1. Web Crawler Technology

The usual distributed crawler includes a coordinator and multiple crawlers. The coordinator is responsible for logically dividing the searched network, assigning the divided logical partitions to the crawlers, and coordinating task scheduling among the crawlers. The crawler is responsible for downloading the web pages in the logical partition that it is responsible for and exchanges download tasks with each other through the coordinator. It uses the local storage space to store downloaded web pages and communicates with each other through a high-speed local area network, and the processed results are centrally stored for retrieval programs. The overall system framework is shown in Figure1.Figure 1
System framework of a distributed crawler.The use of distributed crawler can make better use of network resources and effectively reduce the operating cost of the crawler system. The current major search engines all use distributed crawler systems without exception. However, many new problems have arisen in the design of distributed crawlers:


### 2.1.1. Task Segmentation Problem

The crawlers should to be able to work together accurately, and it will not generate a URL that is repeatedly downloaded in multiple crawlers and will not generate a URL that is not allocated to the download space of any crawler. If it is assumed that the number of crawlers isN and the set of web pages to be downloaded is P, the task can be divided into n subsets Pl,P2,…,Pn, which need to satisfy(1)∪i=1nPi=PAndPi∩Pj=∅amongi≠j,1≤i<j≤n.


### 2.1.2. Load Balancing Problem

The amount of download space of each crawler is roughly equal, and there will not be a situation where a certain crawler has far more download tasks than another crawler. This is the key to the performance improvement of distributed crawlers, and the model can be expressed as(2)Pi≈Pjamongi≠j,1≤i<j≤n.


### 2.1.3. Page Quality Problems

When the downloaded pages need to be evaluated, a single crawler can adjust the search strategy according to the obtained global information to obtain the globally optimal page. However, if the distributed structure is adopted, each crawler can only collect locally optimal pages, which affects the global collection quality.


### 2.1.4. Communication Cost Problem

The dynamic load balancing problem has been studied extensively and deeply, and a large number of excellent load balancing algorithms have been proposed. On the whole, various load balancing algorithms are weighed in the following factors so as to properly solve applications in different fields.(1)
After dynamic load scheduling, the system needs to form a new balance state.(2)
In the new equilibrium state, the communication overhead of system operation should be as little as possible.(3)
In the process of load adjustment, the less the amount of data migration, the better.(4)
The load adjustment process should take as little time as possible.According to the above four design indicators, the overall running time consumption of the task is usually formally modeled as(3)ttotal=αtcomp+tcomm+tmig+trepart.Among them,tcomp and tcomm represent the time used for task job and system communication in an iterative operation of the parallel crawler system, tmig represents the time spent migrating data during a load adjustment process, and trepart represents the time spent in order to calculate the new load balancing state. α represents the number of iterations that the system runs from this load adjustment to the next load adjustment.In all kinds of dynamic load scheduling algorithms, the goal is to minimize the overall running time of the task according to the actual situation. Therefore, we measure the pros and cons of a dynamic load scheduling algorithm by the size of the overall running timettotal of the task. Next, we will further analyze the various components of the overall running time of the task and strive to simplify the analytical model of dynamic load scheduling.If the load is evenly distributed among the cluster nodes,tcomp will be minimized. At the same time, since load balancing is the primary goal of all dynamic load adjustment algorithms, we can safely assume that tcomp is minimized in all dynamic load scheduling algorithms. Furthermore, in the running process of the parallel crawler system, the time trepart spent to calculate the new load balance state should be much smaller than the time αtcomp spent by the task job. From the above analysis, we ignore the role of tcomp and trepart in measuring the pros and cons of a dynamic load adjustment algorithm and simplify the time consumption caused by dynamic load scheduling as(4)tcost=αtcomm+tmig.Since the communication overheadtcomm of the system is closely related to the structural characteristics of the cluster system (such as the network topology and message delay), we directly measure the load balancing algorithm by the communication time and introduce irrelevant structural factors. Generally, we believe that the communication overhead of the system is proportional to the amount of data transferred during the communication process, so the dynamic load balancing consumption defined in the form of time can be further transformed into the form of the amount of transferred data as(5)costvol=αbcomm+bmig.Among them,bcomm and bmig, respectively, represent the amount of data communicated by the system in one iteration process and the amount of data transferred in the dynamic load scheduling process.SetN=Nl,N2,…,Ni is used to represent all nodes in the cluster environment where the parallel crawler system is located. Among them, the carrying capacity of Node Ni is cci (carrying capacity). At the same time, the set T=T1,T2,…,Ti represents the J tasks running on the cluster at a certain time, and the weight of task Tj is represented by wj. The number of logical nodes assigned by task Tj to node Ni is nij. From this, we define the load equivalent lwi of cluster node Ni at this moment as(6)lwi=∑j=1Jnij×wjcci.The load equivalent combines the task weight, the number of logical nodes, and the node carrying capacity, which can more accurately describe the load size of each cluster node than the simple number of tasks.Definition 1.
A hypergraph can be viewed as a generalization of a graph. Each edge in the hypergraph links a nonempty subset of the vertex set and not just two vertices. Formally, a hypergraph is defined asH=V,N. Among them, V=v1,v2,…,vi is the set of vertices and N=n1,n2,…,nj is the set of hyperedges. For any nj∈N, it is a nonempty subset of the vertex set V. Each vertex vi has a nonnegative weight wi. Correspondingly, each hyperedge nj also has a nonnegative weight cj, which is called consumption.Definition 2.
P=V1,V2,…Vk is called a K partition of the hypergraph H; among them, Vpp=1,2,…,k is a nonempty subset of the vertex set V, and there is Vi∩Vj=∅1≤i<j≤k,∪p=1kVp=V.Definition 3.
A K partitionP=V1,V2,…Vk of a hypergraph H is said to be balanced if and only if Wp≤Wavg1+ε,p=1,2,…,k. Among them, Wp=∑vi∈Vpwi,Wavg=∑vi∈Vwi/k,ε>0 is the predetermined maximum tolerable imbalance factor.Definition 4.
P=V1,V2,…Vk is a K partition of hypergraph H=V,N. If there is a vertex vi in the hypergraph that satisfies vi∈Vk and there is vi∈nj, the hyperedge nj is said to be associated with the partition Vk. The number of partitions associated with a hyperedge nj is called the connectivity of that hyperedge, denoted by λj.Definition 5.
CutCostH,P represents the partition cost brought by a partition P on the hypergraph H, which is formally defined as CutCostH,P=∑nj∈Ncjλj−1.
For example, Figure2 is an instance of a hypergraph H with the vertex set V=v1,v2,v3,v4,v5,v6 and the hyperedge set N=n1,n2,n3,n4,n5,n6. Among them, the hyperedge is n1=v1,v2,n2=v1,v3,n3=v1,v4,n4=v1,v4,v5,v6,n5=v1,v5,n6=v1,v6. P=v1,v2,v3,v4,v5,v6,∅ is a 4-partition of the hypergraph H.
In fact, balanced partitioning is an important partitioning method in hypergraphs. Therefore, we often need to convert the unbalanced partition of the hypergraph into a balanced partition, and this conversion process is called the repartition of the hypergraph.
The load equivalent combines the task weight, the number of logical nodes, and the node carrying capacity, which can more accurately describe the load size of each cluster node than the simple number of tasks.
Each epoch starts from the end of a load balancing action and performs several iterative operations. During the iterative operation, the load structure of the system changes. When the load imbalance reaches the maximum tolerance limit, the iterative operation ends, and dynamic load scheduling is performed again. Between epochs, the task structure and load structure on the parallel crawler system are changing. Therefore, we create a hypergraph model for each epoch in the task runtime, formally denote the hypergraph model of thej-th epoch as Hj=Vj,Nj, and denote the number of iteration operations performed during the period as αj. The relationship between the two adjacent epochs is denoted as Hj−1⟶RjHj.
From formula (5), we know that the cost of dynamic load adjustment is proportional to the sum of the amount of data transferred in the process of dynamic load scheduling and the amount of data communicated by the system during the iterative operation after adjustment. In the process of Hj−1⟶RjHj, in order to measure the pros and cons of Rj, we must seek a way to characterize the communication amount in the iterative operation process and the load migration amount in the load transfer process in the hypergraph model. It can be seen from Table 1 that the hyperedge in the hypergraph model represents the set of load units that need to communicate, and the weight of the hyperedge represents the traffic between load units. Then, under the premise that the load unit traffic in the same node is zero, the system traffic bcomm in the iterative operation process can be expressed as(7)bcomm=αj×CutCostHj,Pj.
In order to establish a model for measuring the amount of load transfer in the process of load adjustment in the hypergraph, we add a virtual vertexui with zero weight to each partition Pi of hypergraph Hj−1 and add a virtual hyperedge between all vertices v and ui belonging to Pi. The cost of the virtual hyperedge is the weight of the vertex v. Figures 3 and 4 demonstrate this process. Figure 3 shows a simple hypergraph partition model, and Figure 4 shows the state after adding virtual vertices to each partition in Figure 3.
If we stipulate that in the process of dynamic load scheduling, virtual vertices are not allowed to be migrated, any vertexv migrated from Pi will increase the partition cost due to the existence of virtual hyperedges between ui and v. In this way, we unify both load migration and system traffic into the hypergraph partition cost. It should be noted that the partition cost introduced by the virtual hyperedge does not need to be weighted by the iteration number α.
The hypergraph repartitioning process by adding virtual vertices is represented asHj−1⟶Add virtual vertexH¯j−1⟶RjH¯j⟶Delete virtual vertexHj, then the cost of repartitioning Rj can be represented as(8)costRj=Cut CostH¯j,P¯j.
In view of the relationship betweenH¯j and Hj, another expression of the above formula is(9)costRj=αjCutCostHj,Pj+∑nv∈N¯j−Njcvλv−1.
After establishing the measurement method of the hypergraph repartitioning cost, we illustrate the influence of the iteration numberα on the repartitioning strategy. Figures 5 and 6 show the results of repartitioning the hypergraph in Figure 4 in order to pursue the minimum repartitioning cost when the number of iterations is 1 and 10, respectively. The difference between the results after division is very obvious, so the correct estimation of the number of operation iterations in an epoch has a profound effect on dynamic load scheduling.Figure 2
An example of a hypergraph.Table 1
Verification of the effect of practical strategy formulation of school labor education in the information environment.
NumPractice strategyNumPractice strategyNumPractice strategy186.711872.063571.47283.801971.383676.63385.102083.183774.25478.222182.493879.84572.092279.173975.65678.362381.744085.31772.152482.204176.39885.112580.664270.21971.722682.974387.801079.442777.554474.181181.312879.004570.091279.422978.124685.491379.483077.404782.981483.543170.214882.401581.103273.384987.351671.843376.325070.241777.233482.105177.26Figure 3
A simple hypergraph partitioning model.Figure 4
Hypergraph partition model after adding virtual vertices.Figure 5
Results of hypergraph repartitioning (α = 1).Figure 6
Results of hypergraph repartitioning (α = 10).


## 2.1.1. Task Segmentation Problem

The crawlers should to be able to work together accurately, and it will not generate a URL that is repeatedly downloaded in multiple crawlers and will not generate a URL that is not allocated to the download space of any crawler. If it is assumed that the number of crawlers isN and the set of web pages to be downloaded is P, the task can be divided into n subsets Pl,P2,…,Pn, which need to satisfy(1)∪i=1nPi=PAndPi∩Pj=∅amongi≠j,1≤i<j≤n.


## 2.1.2. Load Balancing Problem

The amount of download space of each crawler is roughly equal, and there will not be a situation where a certain crawler has far more download tasks than another crawler. This is the key to the performance improvement of distributed crawlers, and the model can be expressed as(2)Pi≈Pjamongi≠j,1≤i<j≤n.


## 2.1.3. Page Quality Problems

When the downloaded pages need to be evaluated, a single crawler can adjust the search strategy according to the obtained global information to obtain the globally optimal page. However, if the distributed structure is adopted, each crawler can only collect locally optimal pages, which affects the global collection quality.


## 2.1.4. Communication Cost Problem

The dynamic load balancing problem has been studied extensively and deeply, and a large number of excellent load balancing algorithms have been proposed. On the whole, various load balancing algorithms are weighed in the following factors so as to properly solve applications in different fields.(1)
After dynamic load scheduling, the system needs to form a new balance state.(2)
In the new equilibrium state, the communication overhead of system operation should be as little as possible.(3)
In the process of load adjustment, the less the amount of data migration, the better.(4)
The load adjustment process should take as little time as possible.According to the above four design indicators, the overall running time consumption of the task is usually formally modeled as(3)ttotal=αtcomp+tcomm+tmig+trepart.Among them,tcomp and tcomm represent the time used for task job and system communication in an iterative operation of the parallel crawler system, tmig represents the time spent migrating data during a load adjustment process, and trepart represents the time spent in order to calculate the new load balancing state. α represents the number of iterations that the system runs from this load adjustment to the next load adjustment.In all kinds of dynamic load scheduling algorithms, the goal is to minimize the overall running time of the task according to the actual situation. Therefore, we measure the pros and cons of a dynamic load scheduling algorithm by the size of the overall running timettotal of the task. Next, we will further analyze the various components of the overall running time of the task and strive to simplify the analytical model of dynamic load scheduling.If the load is evenly distributed among the cluster nodes,tcomp will be minimized. At the same time, since load balancing is the primary goal of all dynamic load adjustment algorithms, we can safely assume that tcomp is minimized in all dynamic load scheduling algorithms. Furthermore, in the running process of the parallel crawler system, the time trepart spent to calculate the new load balance state should be much smaller than the time αtcomp spent by the task job. From the above analysis, we ignore the role of tcomp and trepart in measuring the pros and cons of a dynamic load adjustment algorithm and simplify the time consumption caused by dynamic load scheduling as(4)tcost=αtcomm+tmig.Since the communication overheadtcomm of the system is closely related to the structural characteristics of the cluster system (such as the network topology and message delay), we directly measure the load balancing algorithm by the communication time and introduce irrelevant structural factors. Generally, we believe that the communication overhead of the system is proportional to the amount of data transferred during the communication process, so the dynamic load balancing consumption defined in the form of time can be further transformed into the form of the amount of transferred data as(5)costvol=αbcomm+bmig.Among them,bcomm and bmig, respectively, represent the amount of data communicated by the system in one iteration process and the amount of data transferred in the dynamic load scheduling process.SetN=Nl,N2,…,Ni is used to represent all nodes in the cluster environment where the parallel crawler system is located. Among them, the carrying capacity of Node Ni is cci (carrying capacity). At the same time, the set T=T1,T2,…,Ti represents the J tasks running on the cluster at a certain time, and the weight of task Tj is represented by wj. The number of logical nodes assigned by task Tj to node Ni is nij. From this, we define the load equivalent lwi of cluster node Ni at this moment as(6)lwi=∑j=1Jnij×wjcci.The load equivalent combines the task weight, the number of logical nodes, and the node carrying capacity, which can more accurately describe the load size of each cluster node than the simple number of tasks.Definition 1.
A hypergraph can be viewed as a generalization of a graph. Each edge in the hypergraph links a nonempty subset of the vertex set and not just two vertices. Formally, a hypergraph is defined asH=V,N. Among them, V=v1,v2,…,vi is the set of vertices and N=n1,n2,…,nj is the set of hyperedges. For any nj∈N, it is a nonempty subset of the vertex set V. Each vertex vi has a nonnegative weight wi. Correspondingly, each hyperedge nj also has a nonnegative weight cj, which is called consumption.Definition 2.
P=V1,V2,…Vk is called a K partition of the hypergraph H; among them, Vpp=1,2,…,k is a nonempty subset of the vertex set V, and there is Vi∩Vj=∅1≤i<j≤k,∪p=1kVp=V.Definition 3.
A K partitionP=V1,V2,…Vk of a hypergraph H is said to be balanced if and only if Wp≤Wavg1+ε,p=1,2,…,k. Among them, Wp=∑vi∈Vpwi,Wavg=∑vi∈Vwi/k,ε>0 is the predetermined maximum tolerable imbalance factor.Definition 4.
P=V1,V2,…Vk is a K partition of hypergraph H=V,N. If there is a vertex vi in the hypergraph that satisfies vi∈Vk and there is vi∈nj, the hyperedge nj is said to be associated with the partition Vk. The number of partitions associated with a hyperedge nj is called the connectivity of that hyperedge, denoted by λj.Definition 5.
CutCostH,P represents the partition cost brought by a partition P on the hypergraph H, which is formally defined as CutCostH,P=∑nj∈Ncjλj−1.
For example, Figure2 is an instance of a hypergraph H with the vertex set V=v1,v2,v3,v4,v5,v6 and the hyperedge set N=n1,n2,n3,n4,n5,n6. Among them, the hyperedge is n1=v1,v2,n2=v1,v3,n3=v1,v4,n4=v1,v4,v5,v6,n5=v1,v5,n6=v1,v6. P=v1,v2,v3,v4,v5,v6,∅ is a 4-partition of the hypergraph H.
In fact, balanced partitioning is an important partitioning method in hypergraphs. Therefore, we often need to convert the unbalanced partition of the hypergraph into a balanced partition, and this conversion process is called the repartition of the hypergraph.
The load equivalent combines the task weight, the number of logical nodes, and the node carrying capacity, which can more accurately describe the load size of each cluster node than the simple number of tasks.
Each epoch starts from the end of a load balancing action and performs several iterative operations. During the iterative operation, the load structure of the system changes. When the load imbalance reaches the maximum tolerance limit, the iterative operation ends, and dynamic load scheduling is performed again. Between epochs, the task structure and load structure on the parallel crawler system are changing. Therefore, we create a hypergraph model for each epoch in the task runtime, formally denote the hypergraph model of thej-th epoch as Hj=Vj,Nj, and denote the number of iteration operations performed during the period as αj. The relationship between the two adjacent epochs is denoted as Hj−1⟶RjHj.
From formula (5), we know that the cost of dynamic load adjustment is proportional to the sum of the amount of data transferred in the process of dynamic load scheduling and the amount of data communicated by the system during the iterative operation after adjustment. In the process of Hj−1⟶RjHj, in order to measure the pros and cons of Rj, we must seek a way to characterize the communication amount in the iterative operation process and the load migration amount in the load transfer process in the hypergraph model. It can be seen from Table 1 that the hyperedge in the hypergraph model represents the set of load units that need to communicate, and the weight of the hyperedge represents the traffic between load units. Then, under the premise that the load unit traffic in the same node is zero, the system traffic bcomm in the iterative operation process can be expressed as(7)bcomm=αj×CutCostHj,Pj.
In order to establish a model for measuring the amount of load transfer in the process of load adjustment in the hypergraph, we add a virtual vertexui with zero weight to each partition Pi of hypergraph Hj−1 and add a virtual hyperedge between all vertices v and ui belonging to Pi. The cost of the virtual hyperedge is the weight of the vertex v. Figures 3 and 4 demonstrate this process. Figure 3 shows a simple hypergraph partition model, and Figure 4 shows the state after adding virtual vertices to each partition in Figure 3.
If we stipulate that in the process of dynamic load scheduling, virtual vertices are not allowed to be migrated, any vertexv migrated from Pi will increase the partition cost due to the existence of virtual hyperedges between ui and v. In this way, we unify both load migration and system traffic into the hypergraph partition cost. It should be noted that the partition cost introduced by the virtual hyperedge does not need to be weighted by the iteration number α.
The hypergraph repartitioning process by adding virtual vertices is represented asHj−1⟶Add virtual vertexH¯j−1⟶RjH¯j⟶Delete virtual vertexHj, then the cost of repartitioning Rj can be represented as(8)costRj=Cut CostH¯j,P¯j.
In view of the relationship betweenH¯j and Hj, another expression of the above formula is(9)costRj=αjCutCostHj,Pj+∑nv∈N¯j−Njcvλv−1.
After establishing the measurement method of the hypergraph repartitioning cost, we illustrate the influence of the iteration numberα on the repartitioning strategy. Figures 5 and 6 show the results of repartitioning the hypergraph in Figure 4 in order to pursue the minimum repartitioning cost when the number of iterations is 1 and 10, respectively. The difference between the results after division is very obvious, so the correct estimation of the number of operation iterations in an epoch has a profound effect on dynamic load scheduling.Figure 2
An example of a hypergraph.Table 1
Verification of the effect of practical strategy formulation of school labor education in the information environment.
NumPractice strategyNumPractice strategyNumPractice strategy186.711872.063571.47283.801971.383676.63385.102083.183774.25478.222182.493879.84572.092279.173975.65678.362381.744085.31772.152482.204176.39885.112580.664270.21971.722682.974387.801079.442777.554474.181181.312879.004570.091279.422978.124685.491379.483077.404782.981483.543170.214882.401581.103273.384987.351671.843376.325070.241777.233482.105177.26Figure 3
A simple hypergraph partitioning model.Figure 4
Hypergraph partition model after adding virtual vertices.Figure 5
Results of hypergraph repartitioning (α = 1).Figure 6
Results of hypergraph repartitioning (α = 10).


## 2.2. Hypergraph Multilevel Repartitioning Strategy

The hypergraph repartitioning strategy consists of three stages, which are the coarsening stage, the original partitioning stage, and the refining stage. As shown in Figure7, in the coarsening stage, we perform a series of coarsening on the original hypergraph by merging vertices and their associated hyperedges in the hypergraph until the coarsened hypergraph is small to a certain size. In the original partition stage, we perform an appropriate repartition on the smallest hypergraph obtained in the coarsening stage. In the refinement phase, a reverse process of the roughening phase is performed. In each refinement process, we project the partitions on the coarser hypergraph onto the finer hypergraph, and we perform certain optimizations where possible. The following is a detailed description of each stage:Figure 7
Demonstration of the hypergraph repartitioning strategy.


### 2.2.1. Roughening Stage

The coarsening stage consists of a series of steps. In eachi step, a coarser approximate hypergraph Hi+1Vi+1,Ni+1 is constructed with the hypergraph HiVi,Ni as the prototype. The construction process is done by merging a set of vertices in Hi to form the vertices in Hi+1. We characterize this process by the mapping gi:Vi⟶Vi+1, where(10)ViVi+1=ri,ri>1.In order to ensure that the repartitioning of the coarser hypergraphHi+1Vi+1,Ni+1 is projected to the finer hypergraph HiVi,Ni and still maintain a good division effect, we try to merge the vertices with high correlation degree first. Among them, the degree of association cu,v of two vertices u,v in Hi is defined as(11)cu,v=B,B⊂NiAnd∀n∈B,u∈n∧v∈n.Among them,B is the largest subset of Ni that satisfies the condition of ∀n∈B,u∈n∧v∈n, and |B| is the number of elements in the set B. If vertex A corresponds to a vertex v under hypergraph y under the mapping relation gi, then there must be(12)∃n∈Ni,Vp∈P,bring∀v∈A,v∈n∧v∈Vp.In this mapping relationship, the weight of vertexv is set to the sum of the weights of all vertices in vertex set A. Further, if A∈Ni, the hyperedge in Hi+1 contains only one vertex. Then, such a hyperedge will be discarded in Hi+1. The reason for this is that it can be in at most one partition. Under the relationship of mapping gi, if a set of hyperedges B⊂Ni generates a set of identical hyperedges B′⊂Ni+1, then B′ will be replaced by a hyperedge b, and the weight of hyperedge b is set to the sum of the weights of all hyperedges in B.


### 2.2.2. The Original Division Stage

The goal of the original partitioning stage is to calculate an appropriate repartitioning scheme on the roughest hypergraphHcVc,Nc. Since the scale of Hc is much smaller than H1, we think that a scheme that satisfies the requirements can be found in a reasonable time.In order to make full use of system resources, we calculate a repartitioning scheme with random greedy algorithm on each node of the cluster system. The repartitioning costs of these schemes are aggregated to the master control node in the system, and the master control node selects the lowest cost as the final solution.


### 2.2.3. Refinement Stage

The refinement phase is the inverse of the coarsening phase. In the process of each step of refinement, the divisionPi+1 on the coarser hypergraph Hi+1 is projected onto the finer hypergraph Hi, and some appropriate adjustments are made to form the division Pi on Hi.Intuitively speaking, it is to guide the repartitioning of the approximate finer hypergraph with the excellent repartitioning scheme on the coarser hypergraph and make reasonable optimizations.


## 2.2.1. Roughening Stage

The coarsening stage consists of a series of steps. In eachi step, a coarser approximate hypergraph Hi+1Vi+1,Ni+1 is constructed with the hypergraph HiVi,Ni as the prototype. The construction process is done by merging a set of vertices in Hi to form the vertices in Hi+1. We characterize this process by the mapping gi:Vi⟶Vi+1, where(10)ViVi+1=ri,ri>1.In order to ensure that the repartitioning of the coarser hypergraphHi+1Vi+1,Ni+1 is projected to the finer hypergraph HiVi,Ni and still maintain a good division effect, we try to merge the vertices with high correlation degree first. Among them, the degree of association cu,v of two vertices u,v in Hi is defined as(11)cu,v=B,B⊂NiAnd∀n∈B,u∈n∧v∈n.Among them,B is the largest subset of Ni that satisfies the condition of ∀n∈B,u∈n∧v∈n, and |B| is the number of elements in the set B. If vertex A corresponds to a vertex v under hypergraph y under the mapping relation gi, then there must be(12)∃n∈Ni,Vp∈P,bring∀v∈A,v∈n∧v∈Vp.In this mapping relationship, the weight of vertexv is set to the sum of the weights of all vertices in vertex set A. Further, if A∈Ni, the hyperedge in Hi+1 contains only one vertex. Then, such a hyperedge will be discarded in Hi+1. The reason for this is that it can be in at most one partition. Under the relationship of mapping gi, if a set of hyperedges B⊂Ni generates a set of identical hyperedges B′⊂Ni+1, then B′ will be replaced by a hyperedge b, and the weight of hyperedge b is set to the sum of the weights of all hyperedges in B.


## 2.2.2. The Original Division Stage

The goal of the original partitioning stage is to calculate an appropriate repartitioning scheme on the roughest hypergraphHcVc,Nc. Since the scale of Hc is much smaller than H1, we think that a scheme that satisfies the requirements can be found in a reasonable time.In order to make full use of system resources, we calculate a repartitioning scheme with random greedy algorithm on each node of the cluster system. The repartitioning costs of these schemes are aggregated to the master control node in the system, and the master control node selects the lowest cost as the final solution.


## 2.2.3. Refinement Stage

The refinement phase is the inverse of the coarsening phase. In the process of each step of refinement, the divisionPi+1 on the coarser hypergraph Hi+1 is projected onto the finer hypergraph Hi, and some appropriate adjustments are made to form the division Pi on Hi.Intuitively speaking, it is to guide the repartitioning of the approximate finer hypergraph with the excellent repartitioning scheme on the coarser hypergraph and make reasonable optimizations.


## 3. Experimental Research on the Effect of Practical Strategies of School Labor Education in the Information Environment

The school labor education system structure based on the information environment is divided into three layers, as shown in Figure8.Figure 8
The structure of the school labor education system based on the information environment.The smart education data analysis system extracts hot topic content and user relationship from basic data such as information content, user behavior, and dynamic release and has diversified side linkages, as shown in Figure9.Figure 9
Informationalized labor education practice system.The effect of the model proposed in this paper is verified, and the practical effect of the model in this paper is counted. The statistical data are shown in Table1.Through the above research, we can see that the practical strategies of labor education in the information environment proposed in this paper have certain effects, and on this basis, we can analyze the actual situation to improve the practical effect of school labor education.


## 4. Conclusion

In the new era, school labor education should pay special attention to students’ learning, mastery and application of scientific knowledge and technology, as well as the cultivation of students’ scientific thinking methods and spirit. The reason is that under the background of modernization and globalization, the competitiveness of social development mainly depends on science and technology. It is directly related to the productivity of society and the level of material and economic development and is also related to people living a high-quality life and health with abundant food and clothing, convenient travel, rich entertainment, and longevity. Moreover, this point will become more and more prominent in the future, and modern science and technology will be more widely integrated into people’s study, work, and daily life. This paper applies information technology to the analysis of school labor education practice strategy to improve the educational effect and safety of school labor education practice. The experimental research shows that the practice strategy of labor education in the information environment proposed in this paper has certain effects.



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*Source: 8025036-2022-07-12.xml*