# The Construction and Exploration of University English Translation Teaching Mode Based on the Integration of Multimedia Network Technology

Authors: Liying Yuan
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The teaching mode of English translation in universities has been changed greatly, so the design of teaching mode is needed to be optimized. Besides, the network teaching mode should be studied. Specifically, in this article, the advantages of English translation teaching in network environment are analyzed. Finally, we focus on the effective countermeasures to solve the problems of translation teaching mode and provide the reference values for teaching staff. This paper summarizes the integration of information technology and interpretation teaching according to the evolutionary route of technology application from three aspects: (1) general field, (2) interpretation professional field, and (3) education field. Furthermore, we propose a correlation analysis method of English translation teaching based on data fusion technology. Considering the time-related characteristics of English translation teaching, a perturbation time period division method based on moving time window is proposed based on the actual data and the development results of cutting-edge technologies. Finally, interpretation teaching is foreseen in four aspects: intelligence, simulation, precision, and collaboration.


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## Body

## 1. Introduction

Led by a series of cutting-edge information technologies such as big data, virtual reality, and artificial intelligence, interpretation education is undergoing unprecedented and profound changes. Among them, the triggering factors of the changes involve the updating and iteration of technical tools, the continuous evolution of professional interpretation and translation forms, and the introduction of new educational technology concepts; the influence of the changes covers various fields such as interpretation teaching concepts, teaching modes, teaching strategies, and teaching management; the manifestations of the changes are reflected in various elements such as interpretation teaching environment, contents, methods, tools, and evaluation [1–3].In this context, the application of information technology is no longer limited to the fragmented application of local and side aspects of interpretation teaching but gradually forms a closer connection with teaching objectives, curriculum, teaching environment, teaching content, and teaching process. Nowadays, the informatization of interpretation education is becoming a hot spot for the interpretation community [4, 5]. The Ministry of Education of China has proposed “Information Technology and Education.” The Chinese Ministry of Education has proposed the core concept of “deep integration of information technology and education teaching.” In terms of interpretation teaching, we should consider the path of deep integration of information technology and interpretation teaching in the new era and technology wave and explore the innovative mechanism of interpretation teaching and talent cultivation [6, 7].It has also triggered many changes in the working methods (such as remote working mode and online working mode) and work contents (such as machine-assisted human interpretation based on speech recognition) of the interpreting profession [8, 9]. At the same time, the above changes in the interpretation profession will be transmitted to the interpretation teaching field, thus causing a series of changes in interpretation curriculum, teaching contents, and teaching methods. Secondly, information technology is applied to interpretation teaching, which is mainly manifested as general education technology tools, language education technology tools, and special interpretation education technology tools [10]. Again, the technical changes in the field of interpretation profession and interpretation teaching are counteracted by information technology in two ways: the specialized interpretation profession technical tools and interpretation education technical tools enrich the types and expressions of information technology, and at the same time, the above two types of technical tools will bring direct or indirect effects to other fields [11–13].The factors that lead to the change of information technology in interpretation teaching involve many aspects such as information technology itself, interpretation profession, and teaching factors. In view of this, the author divides the information technology related to interpretation teaching into three major fields, each of which is subdivided into several technological manifestations. Among them, the information technology in the general field refers to the basic information technology tools that serve various industries, such as computers, networks, communications, and optoelectronics [14]. Information technology in the field of interpretation can be divided into human interpretation technology, machine-assisted human interpretation technology, and machine interpretation technology according to the human-machine intervention method. Among them, human interpretation technology mainly provides basic technical support for human interpretation activities; machine-assisted human interpretation technology aims to reduce the workload of human interpreters before, during, and after translation with the help of technical means and improve their work efficiency and quality; machine interpretation technology, on the other hand, is based on the inherent translation corpus of human interpreters, and through the comprehensive use of speech recognition, machine translation, speech synthesis, and other technologies, it completes the interpretation tasks in certain scenarios. The above three forms of technology bring pioneering effects on the objectives, methods, contents, and processes of interpretation teaching in the workplace. According to the relationship between interpretation and other related disciplines, the technologies in the field of education can be divided into general education technology, language education technology, and interpretation education technology. Among them, non-interpreter education technologies mainly provide peripheral support for interpretation teaching, while interpreter education technologies focus on providing specialized technical solutions for enriching interpretation teaching contents, optimizing interpretation teaching processes, and improving interpretation teaching effects in terms of interpretation skills, interpretation cognition, interpretation literacy, and interpretation strategies [15].English translation teaching in colleges and universities needs to be carried out in the context of language and culture, making the teaching process more scientific and rational and improving translation skills. Optimizing the teaching mode by using network technology can improve translation teaching and provide reliable help for students’ development. In practical teaching, teachers should focus on analyzing the advantages of teaching English translation in the network environment and, on this basis, study effective measures to consolidate the translation teaching achievements.


## 2. Advantages of Teaching English Translation in Online Environment

### 2.1. Rich Learning Resources

Compared with offline translation teaching resources, English translation teaching resources in the online environment are richer and can meet the practical needs of students with different learning bases, making the translation process more efficient and in line with the teaching concept of the new era. English translation teaching in colleges and universities has certain complexity and rigor, and teaching resources need to be enriched to provide guarantee for students’ efficient translation [16]. Taking translation teaching of literary topics as an example, the specific teaching process should not only consider relevant knowledge points such as lexicality and grammar but also need to understand the relevant words in specific contexts and combine them with relevant linguistic and cultural backgrounds to make translation teaching more scientific and reasonable [17].


### 2.2. Providing Networking Opportunities

Teaching English translation in an online environment provides certain opportunities for communication. Through the network, students and teachers can communicate and share through social software. English translation teaching should be good at using advanced technological means and adjusting teaching mode so that network teaching forms can be efficiently applied. In practice, it is found that teachers’ teaching techniques and network teaching awareness are directly related to the teaching effect, and they need to create an atmosphere of progress and growth with students in cooperation and communication, so that the traditional teaching mode can be improved [18].


### 2.3. Innovative Translation Tools

In the past, various translation tools were often used in English translation teaching, and students wasted a lot of time in consulting dictionaries and related literature, which affected the high quality of translation teaching work. At the present stage, with the development and progress of science and technology, various English translation teaching software programs have emerged, which have brought some convenience to English translation work. In practical teaching, it is found that through the application of translation tools and software technology, students’ frequency of consulting English vocabulary is significantly increased [19].


## 2.1. Rich Learning Resources

Compared with offline translation teaching resources, English translation teaching resources in the online environment are richer and can meet the practical needs of students with different learning bases, making the translation process more efficient and in line with the teaching concept of the new era. English translation teaching in colleges and universities has certain complexity and rigor, and teaching resources need to be enriched to provide guarantee for students’ efficient translation [16]. Taking translation teaching of literary topics as an example, the specific teaching process should not only consider relevant knowledge points such as lexicality and grammar but also need to understand the relevant words in specific contexts and combine them with relevant linguistic and cultural backgrounds to make translation teaching more scientific and reasonable [17].


## 2.2. Providing Networking Opportunities

Teaching English translation in an online environment provides certain opportunities for communication. Through the network, students and teachers can communicate and share through social software. English translation teaching should be good at using advanced technological means and adjusting teaching mode so that network teaching forms can be efficiently applied. In practice, it is found that teachers’ teaching techniques and network teaching awareness are directly related to the teaching effect, and they need to create an atmosphere of progress and growth with students in cooperation and communication, so that the traditional teaching mode can be improved [18].


## 2.3. Innovative Translation Tools

In the past, various translation tools were often used in English translation teaching, and students wasted a lot of time in consulting dictionaries and related literature, which affected the high quality of translation teaching work. At the present stage, with the development and progress of science and technology, various English translation teaching software programs have emerged, which have brought some convenience to English translation work. In practical teaching, it is found that through the application of translation tools and software technology, students’ frequency of consulting English vocabulary is significantly increased [19].


## 3. English Translation Teaching Data Integration

### 3.1. Data Related to English Translation Teaching

The English translation teaching and English translation teaching action events within the collection have certain correlation relationships [20].Based on the above analysis, this paper analyzes the development and evolution of English translation teaching events and determines the causes of events by fusing English translation teaching data and English translation teaching action event data and combining English translation teaching connection data. The analysis framework of multisource data fusion is shown in Figure1.Figure 1
Multisource data fusion and association analysis framework.Among them, the data sources of each type contain the following data types:(1)
English translation teaching data: recording time, recording substation, event type, duration, characteristic amplitude, transient voltage, and current waveforms.(2)
English translation teaching action event data: record time, event type, substation to which the equipment belongs, protection action signal, equipment action signal, fault alarm signal, and field checking line information.(3)
English translation teaching wiring data [21].The disturbance data in Figure1 are taken from PQMS; the English translation teaching action event data are mainly from energy management system, wide area monitoring system, and production management system; the English translation teaching wiring diagram type data are taken from SCADA, energy management system.


### 3.2. Multisource Data Fusion

Given that the number of English translation teaching events is much smaller than the number of English translation teaching action events, this paper takes English translation teaching as the entity object and adopts the entity matching method to achieve multisource data fusion [22].


#### 3.2.1. Definition of the Entity Matching Problem

Entity matching is used to identify multiple records describing the same object in a database. Entity matching across data sources is the classical entity matching problem, and its formal definition is given below.Given two data tablesRA and RB for English translation teaching records and English translation teaching action event records, record rA∈RA,rB∈RB. Then, the goal of entity matching is to determine the sequential even subset rA,rB∈SM. In this paper, one basic perturbation or complex English translation instruction is used as the matching entity.


#### 3.2.2. Entity Matching Process

(1)
Determine the matching keys. Given a common set of attributes P, the combination of attributes consisting of non-empty subsets in P is called a matching key. In this paper, the time and location information of each record is used as the matching key. Considering that a basic perturbation or complex English translation teaching may contain multiple transient event records, the position variation station lc of the first transient event record in the perturbation period and the start and end time (tc, ts) of the perturbation period are taken as the matching keys [23].(2)
Set matching constraints. The matching constraint describes the allowed matching methods between records across data sources: one-to-one matching and one-to-many matching or many-to-many matching. Considering the perturbation energy propagation and the continuous action of protection devices, this paper mainly considers the many-to-many matching method.(3)
Calculate the similarity between records. The similarity between records is calculated using the set matching keys as follows:Step 1: select the set of recordsRc that satisfy tc−φ,ts+φ, where φ is the set time threshold to correct the recording time deviation of each monitoring system.Step 2: select the set of recordsRc′ in the set of records Rc whose record position matches the key of lc; if Rc′=∅, then go to step 3; otherwise, go to step 4.Step 3: setlN to represent the set of nearby substations directly connected to substation lc by line. In the record set Rc, select the record set Rc′ whose record location matching key is consistent with lN; if Rc′=∅, determine that there is no matching English translation teaching action data; otherwise, execute step 4.Step 4: output the set of English translation teaching action records matching the English translation teaching recordsRc′.(4)
Analysis of data fusion results: matching English translation teaching event data and English translation teaching action data, analyzing the formation mechanism of English translation teaching, and extracting the correlation characteristics between disturbance features and device actions and event causes.


## 3.1. Data Related to English Translation Teaching

The English translation teaching and English translation teaching action events within the collection have certain correlation relationships [20].Based on the above analysis, this paper analyzes the development and evolution of English translation teaching events and determines the causes of events by fusing English translation teaching data and English translation teaching action event data and combining English translation teaching connection data. The analysis framework of multisource data fusion is shown in Figure1.Figure 1
Multisource data fusion and association analysis framework.Among them, the data sources of each type contain the following data types:(1)
English translation teaching data: recording time, recording substation, event type, duration, characteristic amplitude, transient voltage, and current waveforms.(2)
English translation teaching action event data: record time, event type, substation to which the equipment belongs, protection action signal, equipment action signal, fault alarm signal, and field checking line information.(3)
English translation teaching wiring data [21].The disturbance data in Figure1 are taken from PQMS; the English translation teaching action event data are mainly from energy management system, wide area monitoring system, and production management system; the English translation teaching wiring diagram type data are taken from SCADA, energy management system.


## 3.2. Multisource Data Fusion

Given that the number of English translation teaching events is much smaller than the number of English translation teaching action events, this paper takes English translation teaching as the entity object and adopts the entity matching method to achieve multisource data fusion [22].


### 3.2.1. Definition of the Entity Matching Problem

Entity matching is used to identify multiple records describing the same object in a database. Entity matching across data sources is the classical entity matching problem, and its formal definition is given below.Given two data tablesRA and RB for English translation teaching records and English translation teaching action event records, record rA∈RA,rB∈RB. Then, the goal of entity matching is to determine the sequential even subset rA,rB∈SM. In this paper, one basic perturbation or complex English translation instruction is used as the matching entity.


### 3.2.2. Entity Matching Process

(1)
Determine the matching keys. Given a common set of attributes P, the combination of attributes consisting of non-empty subsets in P is called a matching key. In this paper, the time and location information of each record is used as the matching key. Considering that a basic perturbation or complex English translation teaching may contain multiple transient event records, the position variation station lc of the first transient event record in the perturbation period and the start and end time (tc, ts) of the perturbation period are taken as the matching keys [23].(2)
Set matching constraints. The matching constraint describes the allowed matching methods between records across data sources: one-to-one matching and one-to-many matching or many-to-many matching. Considering the perturbation energy propagation and the continuous action of protection devices, this paper mainly considers the many-to-many matching method.(3)
Calculate the similarity between records. The similarity between records is calculated using the set matching keys as follows:Step 1: select the set of recordsRc that satisfy tc−φ,ts+φ, where φ is the set time threshold to correct the recording time deviation of each monitoring system.Step 2: select the set of recordsRc′ in the set of records Rc whose record position matches the key of lc; if Rc′=∅, then go to step 3; otherwise, go to step 4.Step 3: setlN to represent the set of nearby substations directly connected to substation lc by line. In the record set Rc, select the record set Rc′ whose record location matching key is consistent with lN; if Rc′=∅, determine that there is no matching English translation teaching action data; otherwise, execute step 4.Step 4: output the set of English translation teaching action records matching the English translation teaching recordsRc′.(4)
Analysis of data fusion results: matching English translation teaching event data and English translation teaching action data, analyzing the formation mechanism of English translation teaching, and extracting the correlation characteristics between disturbance features and device actions and event causes.


## 3.2.1. Definition of the Entity Matching Problem

Entity matching is used to identify multiple records describing the same object in a database. Entity matching across data sources is the classical entity matching problem, and its formal definition is given below.Given two data tablesRA and RB for English translation teaching records and English translation teaching action event records, record rA∈RA,rB∈RB. Then, the goal of entity matching is to determine the sequential even subset rA,rB∈SM. In this paper, one basic perturbation or complex English translation instruction is used as the matching entity.


## 3.2.2. Entity Matching Process

(1)
Determine the matching keys. Given a common set of attributes P, the combination of attributes consisting of non-empty subsets in P is called a matching key. In this paper, the time and location information of each record is used as the matching key. Considering that a basic perturbation or complex English translation teaching may contain multiple transient event records, the position variation station lc of the first transient event record in the perturbation period and the start and end time (tc, ts) of the perturbation period are taken as the matching keys [23].(2)
Set matching constraints. The matching constraint describes the allowed matching methods between records across data sources: one-to-one matching and one-to-many matching or many-to-many matching. Considering the perturbation energy propagation and the continuous action of protection devices, this paper mainly considers the many-to-many matching method.(3)
Calculate the similarity between records. The similarity between records is calculated using the set matching keys as follows:Step 1: select the set of recordsRc that satisfy tc−φ,ts+φ, where φ is the set time threshold to correct the recording time deviation of each monitoring system.Step 2: select the set of recordsRc′ in the set of records Rc whose record position matches the key of lc; if Rc′=∅, then go to step 3; otherwise, go to step 4.Step 3: setlN to represent the set of nearby substations directly connected to substation lc by line. In the record set Rc, select the record set Rc′ whose record location matching key is consistent with lN; if Rc′=∅, determine that there is no matching English translation teaching action data; otherwise, execute step 4.Step 4: output the set of English translation teaching action records matching the English translation teaching recordsRc′.(4)
Analysis of data fusion results: matching English translation teaching event data and English translation teaching action data, analyzing the formation mechanism of English translation teaching, and extracting the correlation characteristics between disturbance features and device actions and event causes.


## 4. Methods and Steps of Correlation Analysis for English Translation Teaching

The matching of power quality data and English translation teaching action data in each disturbance time period is realized by the entity matching method, but due to the temporal and spatial differences in the various types of English translation teaching and the diversity of action devices, the universality of the analysis results is not strong, and it is difficult to provide guidance for future power quality causes and governance; therefore, this paper uses the association rule method to analyze the English translation teaching characteristics and English translation teaching actions strong association rules between them. Considering the similarity of fused data in spatial location and avoiding over-congestion in the process of processing fused data, this paper adopts the distributed association rule analysis method [22].


### 4.1. Distributed Association Rule Analysis

#### 4.1.1. Data Discretization Processing

To facilitate the analysis of the correlation properties between the data, the matched perturbation data and English translation teaching action data need to be transformed into a transaction datasetU = { u1, u2,…, uv}, and V is the number of the set of items to be studied. The transaction items studied in this paper are U = { { monitoring node voltage level}, { English translation teaching type}, { English translation teaching amplitude}, { English translation teaching duration}, { action device type}, { English translation teaching cause}.(1)
Voltage classification of monitoring nodes.(2)
Classification of English translation teaching types. The types of English translation teaching are divided into three types of basic English translation teaching, namely, voltage transient drop, voltage transient rise, and voltage interruption, and the corresponding feature codes are 3 to 5. When the action equipment type in the matching data is not unique, the event is judged to be complex English translation teaching, and the corresponding feature code is 6.(3)
English translation teaching features division. According to the transient voltage waveform recorded by the power quality monitoring device, the characteristic amplitude and duration of English translation teaching are calculated.


#### 4.1.2. Distributed Association Rule Mining and Algorithm Implementation

The distributed data mining system is shown in Figure2. It is a global-local site data analysis model based on a distributed database, which is accomplished by global and local sites in collaboration.Figure 2
Distributed data mining system.Local Site Task. Using data preprocessing and fusion techniques, we match English translation teaching data and English translation teaching action data in the monitoring area. Apriori algorithm is used to find the local frequent itemsets, which are saved in the local rule base [23].Global Site Task. Combine the frequent itemsets of each local site, calculate the global association rules, and save them in the global rule base.(1) Local Frequent Itemset Mining Algorithm. In order to ensure the timeliness of extracting association rules, the database updating process is considered in this paper. The parameters of the algorithm are defined as follows. Let Di and Di′ be the databases before and after the update of the i-th local site, where the unchanged databases are Di∗;supiX and sup′iX are the support numbers of itemsets X in Di and Di′, respectively.X is a subset of the itemset U; Li, K and Li′, K are the frequent K itemsets in Di and Di′, respectively; Hi, K and Hi′, K are the re-frequent K itemsets in Di and Di′, respectively, where re-frequent is defined as the frequent itemset contained in both local and global sites; Ci, K is the frequent K item candidate set; d −i and d+i are the sets of transactions deleted and added to the database in d-i and d+ i, respectively; sup-i(X) and sup∗i(X) are the sets of transactions in d-i and d + i, respectively. sup-i(X) and sup + i(X) are the number of supported itemsets X in d-i and d+i, respectively, and sup∗i(X) is the number of supported itemsets X in d-i and d+i, respectively.Local frequent itemset mining utilizes the apriori-gen function [23] to compute the frequent K-item candidate set Ci from the frequent K - 1 itemset, K. The local site sends the frequent K itemset and the number of supports that satisfy the minimum support threshold to the global site.(2) Global Frequent Itemset Mining Algorithm. The global site finds the global frequent itemset by combining the frequent itemset and the support number of each local site. In order to realize the distributed cooperative computation between the local site and the global site, the frequent K - 1 itemsets of the global site are sent to each local site for determining the frequent K itemset candidates. When each local database is updated, the steps of the Kth cycle operation at the global site are as follows:Step 1: the global site receives the frequent item candidate setCi, K, and its support number from each local site.Step 2: the global site merges all local frequent item candidatesCi, K, calculates the global support, determines whether the minimum support supmin is satisfied, and finds the global frequent K itemset Fk.Step 3: the global site sendsFk to each local site.Step 4: thei-th local site compares Fk with the frequent itemset of this site to obtain the frequent itemset Hi′, K of this site, and uses the apriori-gen function to calculate the frequent K + 1 item candidate set Ci,k+1.


## 4.1. Distributed Association Rule Analysis

### 4.1.1. Data Discretization Processing

To facilitate the analysis of the correlation properties between the data, the matched perturbation data and English translation teaching action data need to be transformed into a transaction datasetU = { u1, u2,…, uv}, and V is the number of the set of items to be studied. The transaction items studied in this paper are U = { { monitoring node voltage level}, { English translation teaching type}, { English translation teaching amplitude}, { English translation teaching duration}, { action device type}, { English translation teaching cause}.(1)
Voltage classification of monitoring nodes.(2)
Classification of English translation teaching types. The types of English translation teaching are divided into three types of basic English translation teaching, namely, voltage transient drop, voltage transient rise, and voltage interruption, and the corresponding feature codes are 3 to 5. When the action equipment type in the matching data is not unique, the event is judged to be complex English translation teaching, and the corresponding feature code is 6.(3)
English translation teaching features division. According to the transient voltage waveform recorded by the power quality monitoring device, the characteristic amplitude and duration of English translation teaching are calculated.


### 4.1.2. Distributed Association Rule Mining and Algorithm Implementation

The distributed data mining system is shown in Figure2. It is a global-local site data analysis model based on a distributed database, which is accomplished by global and local sites in collaboration.Figure 2
Distributed data mining system.Local Site Task. Using data preprocessing and fusion techniques, we match English translation teaching data and English translation teaching action data in the monitoring area. Apriori algorithm is used to find the local frequent itemsets, which are saved in the local rule base [23].Global Site Task. Combine the frequent itemsets of each local site, calculate the global association rules, and save them in the global rule base.(1) Local Frequent Itemset Mining Algorithm. In order to ensure the timeliness of extracting association rules, the database updating process is considered in this paper. The parameters of the algorithm are defined as follows. Let Di and Di′ be the databases before and after the update of the i-th local site, where the unchanged databases are Di∗;supiX and sup′iX are the support numbers of itemsets X in Di and Di′, respectively.X is a subset of the itemset U; Li, K and Li′, K are the frequent K itemsets in Di and Di′, respectively; Hi, K and Hi′, K are the re-frequent K itemsets in Di and Di′, respectively, where re-frequent is defined as the frequent itemset contained in both local and global sites; Ci, K is the frequent K item candidate set; d −i and d+i are the sets of transactions deleted and added to the database in d-i and d+ i, respectively; sup-i(X) and sup∗i(X) are the sets of transactions in d-i and d + i, respectively. sup-i(X) and sup + i(X) are the number of supported itemsets X in d-i and d+i, respectively, and sup∗i(X) is the number of supported itemsets X in d-i and d+i, respectively.Local frequent itemset mining utilizes the apriori-gen function [23] to compute the frequent K-item candidate set Ci from the frequent K - 1 itemset, K. The local site sends the frequent K itemset and the number of supports that satisfy the minimum support threshold to the global site.(2) Global Frequent Itemset Mining Algorithm. The global site finds the global frequent itemset by combining the frequent itemset and the support number of each local site. In order to realize the distributed cooperative computation between the local site and the global site, the frequent K - 1 itemsets of the global site are sent to each local site for determining the frequent K itemset candidates. When each local database is updated, the steps of the Kth cycle operation at the global site are as follows:Step 1: the global site receives the frequent item candidate setCi, K, and its support number from each local site.Step 2: the global site merges all local frequent item candidatesCi, K, calculates the global support, determines whether the minimum support supmin is satisfied, and finds the global frequent K itemset Fk.Step 3: the global site sendsFk to each local site.Step 4: thei-th local site compares Fk with the frequent itemset of this site to obtain the frequent itemset Hi′, K of this site, and uses the apriori-gen function to calculate the frequent K + 1 item candidate set Ci,k+1.


## 4.1.1. Data Discretization Processing

To facilitate the analysis of the correlation properties between the data, the matched perturbation data and English translation teaching action data need to be transformed into a transaction datasetU = { u1, u2,…, uv}, and V is the number of the set of items to be studied. The transaction items studied in this paper are U = { { monitoring node voltage level}, { English translation teaching type}, { English translation teaching amplitude}, { English translation teaching duration}, { action device type}, { English translation teaching cause}.(1)
Voltage classification of monitoring nodes.(2)
Classification of English translation teaching types. The types of English translation teaching are divided into three types of basic English translation teaching, namely, voltage transient drop, voltage transient rise, and voltage interruption, and the corresponding feature codes are 3 to 5. When the action equipment type in the matching data is not unique, the event is judged to be complex English translation teaching, and the corresponding feature code is 6.(3)
English translation teaching features division. According to the transient voltage waveform recorded by the power quality monitoring device, the characteristic amplitude and duration of English translation teaching are calculated.


## 4.1.2. Distributed Association Rule Mining and Algorithm Implementation

The distributed data mining system is shown in Figure2. It is a global-local site data analysis model based on a distributed database, which is accomplished by global and local sites in collaboration.Figure 2
Distributed data mining system.Local Site Task. Using data preprocessing and fusion techniques, we match English translation teaching data and English translation teaching action data in the monitoring area. Apriori algorithm is used to find the local frequent itemsets, which are saved in the local rule base [23].Global Site Task. Combine the frequent itemsets of each local site, calculate the global association rules, and save them in the global rule base.(1) Local Frequent Itemset Mining Algorithm. In order to ensure the timeliness of extracting association rules, the database updating process is considered in this paper. The parameters of the algorithm are defined as follows. Let Di and Di′ be the databases before and after the update of the i-th local site, where the unchanged databases are Di∗;supiX and sup′iX are the support numbers of itemsets X in Di and Di′, respectively.X is a subset of the itemset U; Li, K and Li′, K are the frequent K itemsets in Di and Di′, respectively; Hi, K and Hi′, K are the re-frequent K itemsets in Di and Di′, respectively, where re-frequent is defined as the frequent itemset contained in both local and global sites; Ci, K is the frequent K item candidate set; d −i and d+i are the sets of transactions deleted and added to the database in d-i and d+ i, respectively; sup-i(X) and sup∗i(X) are the sets of transactions in d-i and d + i, respectively. sup-i(X) and sup + i(X) are the number of supported itemsets X in d-i and d+i, respectively, and sup∗i(X) is the number of supported itemsets X in d-i and d+i, respectively.Local frequent itemset mining utilizes the apriori-gen function [23] to compute the frequent K-item candidate set Ci from the frequent K - 1 itemset, K. The local site sends the frequent K itemset and the number of supports that satisfy the minimum support threshold to the global site.(2) Global Frequent Itemset Mining Algorithm. The global site finds the global frequent itemset by combining the frequent itemset and the support number of each local site. In order to realize the distributed cooperative computation between the local site and the global site, the frequent K - 1 itemsets of the global site are sent to each local site for determining the frequent K itemset candidates. When each local database is updated, the steps of the Kth cycle operation at the global site are as follows:Step 1: the global site receives the frequent item candidate setCi, K, and its support number from each local site.Step 2: the global site merges all local frequent item candidatesCi, K, calculates the global support, determines whether the minimum support supmin is satisfied, and finds the global frequent K itemset Fk.Step 3: the global site sendsFk to each local site.Step 4: thei-th local site compares Fk with the frequent itemset of this site to obtain the frequent itemset Hi′, K of this site, and uses the apriori-gen function to calculate the frequent K + 1 item candidate set Ci,k+1.


## 5. Case Study

Teaching English translation is a systematic and comprehensive process that requires students to have not only sufficient vocabulary but also knowledge of English grammar and familiarity with English idiomatic expressions and specific contexts. Teachers need to make use of networked teaching methods, improve the current translation teaching contents, focus on grammar and sentence translation teaching, and make the application of teaching methods more practical. In the teaching of English translation, students should be good at cultivating their translation ability, so as to make them understand the relevant translation contents in specific language environment. Students need to analyze and understand the relevant grammatical structures in a specific language context. In the context of networking, students’ English translations are more enriched. In practical teaching, it is found that there is a close relationship between students’ English translation ability and skills; therefore, in the application of online teaching resources, students should communicate with their teachers to share their translation skills and practical ability, so as to prompt students to get progress and improve their own English translation level, as shown in Figure3, the effect of different English translation.Figure 3
Different correct translation frequencies.The curriculum should be designed to incorporate new ideas and approaches and to place greater emphasis on networked teaching technologies. For example, in the translation of “Karsh was praised as a master portraitist, often working in black and white, influenced by great painters of the past,” this sentence is translated as “Karsh was praised as a master portraitist, often working in black and white, influenced by great painters of the past.” In the specific translation process, teachers should use the multimedia teaching platform to decompose the sentence structure and show it to students and interpret the relevant sentences, so that the teaching method can be applied efficiently. In the design of the lesson, “was praised as” is translated as “influenced by great painters of the past” as a passive voice structure in the sentence structure.As shown in Figure4, the course design process should recognize the new needs of students for translation teaching, improve the previous curriculum system with students as the center, and use the online teaching platform to carry out modular design, so as to encourage students to form an interest in the study of English translation and contribute to the improvement of their own English translation level. At the same time, the teaching process should recognize the actual impact of advanced network technology on students, combine previous teaching experience and technology, adjust the problems existing in current English translation teaching, and bring into play the value of the application of advanced teaching methods.Figure 4
Frequency of English translation vocabulary distribution.English translation teaching in colleges and universities is different from other teaching model and should be carried out within the framework of specific language environment and knowledge system. The application of networked teaching mode can meet this teaching demand. In practice, colleges and universities should provide convenient conditions for networked teaching technology, for example, increase financial investment and technical support and encourage teachers to use networked teaching mode to improve the previous teaching methods, so that the overall level of English translation teaching in colleges and universities can be improved as shown in Figure5.Figure 5
Translation and logical distribution.The application of advanced teaching equipment and teaching technology in the teaching of English translation in colleges and universities promotes the improvement of translation teaching level and provides an important guarantee for upgrading and improving the teaching mode in colleges and universities. In the practical teaching session, teachers should recognize the problem of insufficient application of current teaching technology and improve the actual teaching ability of English translation through the application of networked platform. English translation has high requirements on vocabulary accumulation, grammar proficiency, and sentence fixation, and teachers should cultivate students in relevant aspects, for example, guiding students to download word memory software and learn anytime and anywhere through cell phones and other mobile terminals.


## 6. Conclusions

English translation teaching in colleges and universities always faces the problems of backward teaching concept, unreasonable curriculum design, and unsuitable application of network teaching mode. To tackle such problems, the ideology of teachers and teaching technology is needed to improve simultaneously. Thereafter, the efficiency of English translation teaching in colleges and universities can be improved effectively.



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