An english word auxiliary learning method and system based on big data analysis

Abstract

The present application relates to the field of information technology, disclose a kind of based on big data analysis English word auxiliary learning method and system, including based on work breakdown structure node associated business document and executes text segmentation and generates target vocabulary subset;Call staff vocabulary ability matrix and generate vocabulary gap list by comparing target vocabulary subset;Based on the total time length of scheduling and the total time consumption data of production task, calculate the available quota of time window, when the estimated learning time consumption is greater than the available quota of time window, execute truncation processing and write gap residual state mark;Listen to business document execution request, when there is gap residual state mark, execute read-write permission interception and trigger to generate supplementary test task, receive evaluation result data and update staff vocabulary ability matrix and clear gap residual state mark.The present application executes truncation processing by calculating the available quota of time window, to ensure that learning load is within the scheduling flexibility margin, and establishes a forced control process by using system read-write permission interception operation.

Description

Technical Field

[0001] This invention relates to the field of information technology, specifically to a method and system for assisting in learning English vocabulary based on big data analysis. Background Technology

[0002] In an office environment, English vocabulary learning primarily relies on stand-alone learning applications. These applications lack data interfaces with business project management systems, preventing the targeting of learning content based on specific work breakdown structure nodes and associated business documents. Because the learning system is isolated from document and project management systems, the learning content is disconnected from actual business execution scenarios, making it impossible to accurately extract target vocabulary based on business needs.

[0003] Existing learning platforms lack the ability to synchronize scheduling and task progress data with office automation systems. When assigning learning tasks, the system cannot perceive the current production task duration and total shift time of employees, making it difficult to dynamically calculate employees' learning time quotas. This results in the random distribution of learning tasks, easily causing time conflicts with established production tasks, increasing employee workload, reducing the efficiency of production task execution, and failing to perform task truncation processing under resource constraints.

[0004] Regarding document read / write control during business execution, existing technologies do not incorporate employees' vocabulary mastery status into permission interception logic. Document management systems cannot automatically restrict or downgrade read / write permissions based on employees' test results for core vocabulary in business documents, resulting in a lack of enforceable constraints on business behavior from learning outcomes. The system lacks a dynamic monitoring mechanism for vocabulary retention and cannot schedule review tasks based on business priority weights and forgetting curve decay, making it difficult to establish a comprehensive management system encompassing learning, interception, retesting, and rewriting of work-hours.

[0005] Therefore, this invention proposes an English vocabulary learning aid method and system based on big data analysis to address the shortcomings of existing technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for assisting English vocabulary learning based on big data analysis. This solves the problems faced by enterprise employees in their daily work, such as the disconnect between business vocabulary learning and business documents, the time conflict between learning tasks and production tasks leading to overload and obstacles, and the lack of a mandatory constraint verification mechanism for execution progress.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of this invention provides a method for assisting English vocabulary learning based on big data analysis, comprising the following steps: S10. Generate a subset of target words, calculate business priority weights, and bind relevant nodes and lists.

[0008] S20. Execute time window trigger judgment, compare and generate a list of word gaps with weighted sorting results.

[0009] S30. Calculate the available quota for the time window, perform truncation processing, and generate a gap legacy status marker.

[0010] S40, perform read / write permission interception and downgrade bypass processing or suspension operation, generate supplementary test task and update matrix.

[0011] S50. Calculate the retention rate of vocabulary objects, change them to the forgetting decay state, and add them to the end of the review task queue.

[0012] This invention establishes a time window available quota calculation logic that associates the total scheduling time with the total production task consumption data. By comparing the available quota of the time window with the expected learning time execution truncation mechanism, scheduling overload is avoided. At the same time, unassigned vocabulary objects are converted into gap legacy status markers. Combined with the underlying read and write permission interception operation for business document execution requests, a suspension blocking architecture is constructed to convert vocabulary learning behavior into a closed loop of system underlying scheduling data flow.

[0013] After generating the target vocabulary subset, business priority weights are calculated for the vocabulary objects, which are then used to sort the vocabulary gap list according to these weights. Plain text data segments are extracted from business documents, and the frequency of each vocabulary object within these segments is counted to generate word frequency data. The text content of each paragraph within the plain text data segments is extracted using a regular expression engine, and the corresponding paragraph weight constants are obtained through logical verification and matching. The word frequency data and the paragraph weight constants are multiplied to output the business priority weights. After generating the vocabulary gap list, the vocabulary objects within the list are sorted in descending order according to their business priority weights.

[0014] Before retrieving the employee vocabulary competency matrix stored in the database, retrieve the estimated startup time bound to the work breakdown structure node whose status is marked as not started; calculate the time difference between the estimated startup time and the current system time; when the time difference is less than or equal to the preset pre-time window constant, generate a thread activation command to wake up the background scheduling thread of the corresponding work breakdown structure node.

[0015] In the step of calculating the available quota for the time window, the total scheduling time in the attendance scheduling data table is extracted, and the estimated consumption time value in the details of unfinished production tasks is added to obtain the total production task consumption data. The total scheduling time is subtracted from the total production task consumption data, and the preset overload redundancy time constant is subtracted to obtain the available quota for the time window.

[0016] In the truncation process, the result of the division between the available quota in the time window and the basic learning time constant is rounded down to determine the limit value for the number of words to be retained. Within the vocabulary gap list, a cursor index is set according to the limit value for the number of words to be retained to perform data segmentation. After extracting the cursor index, the vocabulary objects ranked lower are collected to generate the tail vocabulary set. The gap status mark is written into the corresponding record entries in the employee vocabulary ability matrix, and the current system time is obtained synchronously as the generation timestamp.

[0017] In the step of intercepting read / write permissions and triggering the generation of supplementary testing tasks for business documents, the difference between the current system time and the duration of the generated timestamp is calculated. When the difference is less than or equal to a preset grace period constant, a downgrade bypass process is triggered to allow the execution request. When a business document closure trigger event is detected, a secondary wake-up command is sent to trigger the supplementary testing task. When the difference is greater than the grace period constant, the suspension control logic is triggered to reject the execution request and suspend the work decomposition structure node, simultaneously triggering a blocking learning supplementary testing task.

[0018] In the steps of updating the employee vocabulary ability matrix and clearing gap legacy status markers, when the test score corresponding to the evaluation result data is greater than or equal to the passing threshold, the cognitive status enumeration value of the corresponding vocabulary object is overwritten as the mastered state, and the corresponding gap legacy status marker is erased; the overload compensation time is calculated based on the number of vocabulary objects with gap legacy status markers that have passed the supplementary test, and the overload compensation time is added as punitive liability working hours to the total production task time data of the corresponding employee.

[0019] The system periodically iterates through the vocabulary ability matrix of employees, collecting vocabulary objects whose cognitive state enumeration value is "mastered". It calculates the silent time span between the current system time and the timestamp of the vocabulary object's most recent test pass. It then uses the historical review count parameter index bound to the vocabulary object to obtain the corresponding memory half-life constant. Based on the silent time span value and the memory half-life constant, it performs an exponential decay function calculation to output the memory retention value of the corresponding vocabulary object. When the memory retention value is lower than the memory decay threshold, it changes the cognitive state enumeration value of the corresponding vocabulary object to the forgetting decay state and appends the extracted vocabulary object to the review task queue in the system memory.

[0020] Perform head-down extraction on the review task queue, add the extracted vocabulary objects that are in a state of forgetting decay to the vocabulary gap list, and forcibly assign the business priority weight to the system's preset maximum constant weight.

[0021] A second aspect of this invention provides an English vocabulary learning assistance system based on big data analysis, comprising: The document parsing module is used to perform text segmentation on business documents associated with work breakdown structure nodes to generate a target vocabulary subset; The quota calculation module is used to calculate the available quota for a time window based on the total shift duration and the total production task time data. The learning scheduling server is used to retrieve the employee vocabulary ability matrix, compare the target vocabulary subset with the employee vocabulary ability matrix to generate a vocabulary gap list; calculate the estimated learning time based on the vocabulary gap list, and perform truncation processing when the estimated learning time is greater than the available quota in the time window. Write a gap legacy status marker with the generation timestamp to the vocabulary objects that have been truncated in the employee vocabulary ability matrix. The document management system is used to listen for execution requests for business documents and intercept read and write permissions. When it is determined that there is a gap in the status marker, it triggers the learning scheduling server to generate a supplementary test task. The learning scheduling server updates the employee's vocabulary ability matrix and clears the gap in the status marker based on the evaluation result data returned by the employee's terminal.

[0022] This invention provides a method and system for assisting English vocabulary learning based on big data analysis. It has the following beneficial effects: 1. This invention calculates the available quota of the time window by extracting the total scheduling time and the total production task time data, and performs truncation processing when the expected learning time is greater than the available quota of the time window, so as to ensure that the vocabulary learning load is within the scheduling flexibility margin and eliminate the interference of learning tasks on the progress of production task processing.

[0023] 2. This invention establishes a rigid control process for the progress of assisted learning by listening to business document execution requests in the document management system, comparing the generated timestamp with the grace period constant to intercept read and write permissions, and triggering the suspension operation of the work breakdown structure node.

[0024] 3. This invention calculates the memory retention rate by combining the silent time span value with the memory half-life constant through an asynchronous daemon process, and converts the number of vocabulary objects in the supplementary test into overload compensation time and writes it back to the office automation system. It also uses the punitive debt working hours accumulation logic to achieve dynamic feedback on the quality of employees' vocabulary mastery. Attached Figure Description

[0025] Figure 1 This is a system block diagram of the present invention.

[0026] Figure 2 This is a flowchart of the method of the present invention.

[0027] Figure 3 This is a line graph comparing the overload protection effect of the system according to the present invention. Detailed Implementation

[0028] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] See attached document Figure 1 This invention provides an English vocabulary learning system based on big data analysis, including a project management system, an office automation system, a document management system, a learning scheduling server, and an employee terminal.

[0030] The project management system, office automation system, document management system, and employee terminal establish network communication connections with the learning scheduling server. The learning scheduling server uses the application programming interface to retrieve the work breakdown structure nodes and business documents in the project management system. The learning scheduling server connects to a relational database, which stores the employee vocabulary ability matrix. The employee vocabulary ability matrix records the mastery status of the employee identifier for the vocabulary object and records the initialization timestamp of the employee matrix.

[0031] The document management system connects to a directory server, which stores role-based access control lists (RBACs). These RBACs define the work breakdown structure nodes and the set of legal roles that are allowed to access business documents. The document management system then uses these RBACs to enforce document read / write permission blocking.

[0032] The learning scheduling server is divided into a document parsing module, a clock scanning module, a quota calculation module, a permission interception module, and a rerouting calculation module. The document parsing module performs text segmentation on business documents to generate a target vocabulary subset. The clock scanning module reads the system time and the estimated start time of the work breakdown structure nodes. The office automation system stores the total scheduling time and the total production task consumption data. The quota calculation module retrieves data from the office automation system and calculates the available quota for the time window.

[0033] ; In the formula, The available quota for the specified time window; Represents the total duration of the shift schedule; This represents the total time spent on production tasks; This represents the redundancy time constant for overload protection.

[0034] The employee terminal receives a subset of the target vocabulary and presents a vocabulary test interface. The employee terminal then sends the evaluation result data back to the learning scheduling server. The learning scheduling server receives the evaluation result data and updates the employee vocabulary ability matrix in the relational database.

[0035] See attached document Figure 2 This invention provides an English vocabulary learning system based on big data analysis, and also a method for English vocabulary learning based on big data analysis, which includes the following steps: S10, the learning scheduling server receives the work breakdown structure node and business documents in the project management system and performs text segmentation to generate a target vocabulary subset. The learning scheduling server calculates the business priority weight of the vocabulary objects in the target vocabulary subset. The learning scheduling server retrieves the role access control list in the document management system and binds the target vocabulary subset and the role access control list to the work breakdown structure node. S20, the clock scanning module reads the system time and the expected start time of the work breakdown structure node and performs time window trigger judgment. The learning scheduling server retrieves the employee vocabulary ability matrix in the relational database. The learning scheduling server compares the target vocabulary subset with the employee vocabulary ability matrix to generate a vocabulary gap list with business priority weight sorting results. S30, the quota calculation module extracts the total scheduling time and production task consumption time data in the office automation system and calculates the available quota for the time window. The learning scheduling server calculates the expected learning time based on the vocabulary gap list. The learning scheduling server compares the expected learning time with the available quota for the time window, performs truncation processing, and generates a gap legacy status marker. S40, the document management system receives a business document execution request sent by the employee terminal and performs read and write permission interception. The document management system compares the system time with the generation timestamp of the gap legacy status mark and performs downgrade bypass processing. The document management system performs a suspension operation on the work breakdown structure node and triggers the learning scheduling server to generate a supplementary test task for the currently intercepted business document. The employee terminal receives the task and sends the evaluation result data back to the learning scheduling server. The learning scheduling server receives the evaluation result data and updates the employee vocabulary ability matrix in the relational database and clears the gap legacy status mark. S50: The learning scheduling server obtains the recent test timestamps of mastered vocabulary objects based on the system's underlying clock component, and calculates the memory retention rate of vocabulary objects by combining the historical review counts. When the memory retention rate is lower than the system's preset memory decay threshold, the corresponding vocabulary object is changed to the forgetting decay state, and the extracted vocabulary object is added to the tail of the review task queue in the system memory to wait for subsequent time windows to be allocated and scheduled.

[0036] The following section provides a detailed explanation of methods for assisting English vocabulary learning based on big data analysis.

[0037] Step S10 includes the following sub-steps: S101, the learning scheduling server sends a data read request to the project management system using the application programming interface (API). Based on the data read request, the learning scheduling server extracts the business documents associated with the work breakdown structure nodes within the project management system. Regarding the API data transmission verification rules, those skilled in the art can utilize token verification methods to perform the processing; the data transmission verification rules are well-known in the field and will not be elaborated upon here.

[0038] S102, the document parsing module reads the business document and identifies its file format category. Business document file formats are categorized into portable document formats and word processing document formats. The document parsing module calls the corresponding format parser based on the business document file format category. The document parsing module uses the format parser to extract character data from the business document and generate plain text data segments. The document parsing module then transmits these plain text data segments to the learning scheduling server's memory space.

[0039] S103, the document parsing module starts its built-in natural language processing (NLP) word segmentation engine. The document parsing module uses the NLP word segmentation engine to perform lexical analysis and tokenization extraction on the plain text data segment based on spaces, punctuation marks, and newlines, and filters out meaningless strings containing non-alphabetic characters. The document parsing module converts the plain text data segment into a set of independent words and arranges them to generate an initial vocabulary sequence.

[0040] S104: The learning scheduling server database pre-stores a general stop word list. This list records basic prepositions, basic pronouns, and common conjunctions. The document parsing module reads all words from the initial vocabulary sequence. It then compares each word with the entries in the general stop word list. Finally, it removes words from the initial vocabulary sequence that match the general stop word list and performs deduplication on these identical words. The document parsing module outputs a subset of the target vocabulary.

[0041] ; In the formula, Represents a subset of the target vocabulary; Represents the initial word sequence; This represents the words recorded in the general stop word list.

[0042] S105, the learning scheduling server traverses all vocabulary objects within the target vocabulary subset. The document parsing module counts the occurrences of vocabulary objects within the plain text data segment and generates word frequency data. The specific execution steps of the string occurrence matching algorithm can be performed by those skilled in the art using hash table retrieval methods; the string occurrence matching algorithm is a well-known technology in this field and will not be elaborated upon here.

[0043] S106, the learning scheduling server's memory pre-configures a paragraph weight mapping table. This table records the correspondence between regular expressions for business terms and paragraph weight constants. The document parsing module invokes the system's regular expression engine. It scans the paragraph position structure within the plain text data segment and extracts the paragraph text content. The module then performs logical validation between the paragraph text content and the regular expressions for business terms. If the validation passes, the module retrieves the paragraph weight constant bound in the paragraph weight mapping table. This constant is then uniformly assigned to all parsed vocabulary objects within the currently matched paragraph. If the validation fails, the module assigns a value to the system's default paragraph weight constant. During the traversal of the entire plain text data segment, when the same vocabulary object matches multiple paragraphs with different paragraph weight constants, the module initiates conflict resolution logic, extracting the paragraph weight constant with the highest value as the unique final paragraph weight constant for that vocabulary object, preventing numerical ambiguity in subsequent priority calculations.

[0044] S107, the learning scheduling server extracts the word frequency data and paragraph weight constants corresponding to the vocabulary objects. The learning scheduling server uses the processor to execute multiplication logic and outputs the business priority weight of the vocabulary objects.

[0045] ; In the formula, Represents business priority weight; Representative word frequency data; Represents a paragraph weight constant; Represents the vocabulary object.

[0046] The learning scheduling server associates the business priority weight values ​​with the vocabulary objects, stores them in memory, and caches them for subsequent node binding operations.

[0047] S108, the learning scheduling server sends a permission query command to the document management system using the application programming interface. The document management system receives the permission query command and calls its internal communication protocol to connect to the directory server. The internal identity verification query logic of the directory server can be processed using a lightweight directory access protocol by those skilled in the art; the internal identity verification query logic of the directory server is well-known in the field and will not be described further here.

[0048] S109, the directory server scans the internal storage space according to the permission query command. The directory server retrieves the work breakdown structure nodes and the data of the legal roles that are allowed to access the corresponding business documents. The directory server summarizes the legal role data and generates a role access control list. The directory server returns the role access control list to the learning scheduling server's memory via the document management system.

[0049] S111, the learning scheduling server extracts the generated target vocabulary subset and business priority weights. The learning scheduling server creates a data encapsulation object in memory. The learning scheduling server then combines the target vocabulary subset, business priority weights, and role access control lists into the data encapsulation object.

[0050] S112, the learning scheduling server reads the work breakdown structure (WBS) node identifiers transmitted from the project management system. The learning scheduling server matches these WBS node identifiers as primary keys in the relational database. The learning scheduling server establishes a mapping relationship between the data encapsulation object and the WBS node identifier. The learning scheduling server then writes the data encapsulation object to the relational database for persistent storage.

[0051] ; In the formula, Represents a data encapsulation object; Represents a subset of the target vocabulary; Represents a set of business priority weights; Representative role access control list.

[0052] Step S20 includes the following sub-steps: S201, the clock scanning module starts the system's underlying timer program. The clock scanning module periodically reads the system time generated by the server motherboard's hardware clock chip according to a preset polling frequency. The scheduling and control mechanism of the system's underlying timer program can be processed using a time wheel algorithm by those skilled in the art; the scheduling and control mechanism of the system's underlying timer program is well-known in the field and will not be described in detail here.

[0053] S202, the clock scanning module accesses the relational database using an application programming interface (API). The clock scanning module retrieves work breakdown structure (WBS) nodes marked as "not started" within the relational database. The clock scanning module extracts the estimated start time bound to the "not started" WBS nodes. The estimated start time is stored in the relational database using a standard timestamp data format.

[0054] S203, the clock scanning module retrieves a pre-set leading time window constant within the memory area. This leading time window constant represents the threshold value for triggering actions on the learning and scheduling server. The clock scanning module subtracts the system time from the estimated startup time of each work decomposition structure node to obtain the time difference value. The clock scanning module inputs this time difference value into the comparator logic unit. The comparator logic unit compares the time difference value with the leading time window constant.

[0055] S204, when the comparator logic unit determines that the time difference is less than or equal to the preceding time window constant, the clock scanning module generates a trigger determination success signal. The clock scanning module converts the trigger determination success signal into a thread activation instruction. The clock scanning module sends the thread activation instruction to the learning scheduling server. The learning scheduling server receives the thread activation instruction and wakes up the background scheduling thread of the corresponding work decomposition structure node to wait for subsequent data processing.

[0056] ; In the formula, The estimated start time of the node in the work breakdown structure; Represents system time; This represents the constant of the preceding time window.

[0057] S205, the learning scheduling server receives the thread activation command sent by the clock scanning module. The learning scheduling server reads the personnel allocation data field inside the work breakdown structure node. The learning scheduling server parses the personnel allocation data field and extracts the target employee identifier. For the data field separation and parsing logic, those skilled in the art can use regular expression extraction techniques to perform the processing; the data field separation and parsing logic is a well-known technology in the field and will not be described in detail here.

[0058] S206, The learning scheduling server establishes a network connection channel with the relational database. The learning scheduling server converts the target employee identifier into structured query language instruction condition parameters. For the network connection channel establishment process, those skilled in the art can utilize database connection pooling technology to perform the processing; the network connection channel establishment process is well-known in the art and will not be described in detail here.

[0059] S207, the learning scheduler server sends a Structured Query Language (SCL) command to the relational database. The relational database uses the target employee identifier to perform an index matching retrieval operation on its internal data tables. The relational database then locates the associated data row corresponding to the target employee identifier.

[0060] In step S208, the relational database reads the stored values ​​from the associated data rows and packages them to generate an employee vocabulary competency matrix. The relational database then sends the employee vocabulary competency matrix to the learning scheduling server's memory space via a network connection. The employee vocabulary competency matrix records vocabulary object identifiers, cognitive state enumeration values, and employee matrix initialization timestamps. The cognitive state enumeration values ​​are categorized into mastered, unmastered, and forgotten / decayed states. The learning scheduling server locks the employee vocabulary competency matrix in its memory area, awaiting data comparison and calculation.

[0061] S209, the learning scheduling server reads the target vocabulary subset and the employee vocabulary ability matrix from the memory space. The learning scheduling server extracts the vocabulary objects within the target vocabulary subset.

[0062] S210, the learning scheduling server inputs the vocabulary object as an index parameter into the employee's vocabulary ability matrix to perform a search and matching. The learning scheduling server obtains the cognitive state enumeration value corresponding to the vocabulary object. The learning scheduling server executes a Boolean logic judgment to determine whether the cognitive state enumeration value is equal to the mastered state. When the cognitive state enumeration value is equal to the unmastered state or the forgetting decay state, the learning scheduling server extracts the vocabulary object and marks it as an unmastered vocabulary.

[0063] S211, the learning scheduling server aggregates unmastered vocabulary to generate an initial gap set. Simultaneously, the learning scheduling server reads the review task queue in system memory. To prevent legacy review tasks from excessively crowding out the learning time window for new vocabulary, the learning scheduling server truncates the head of the review task queue according to the system's preset upper limit for review vocabulary (e.g., extracting a maximum of 10 or no more than 30% of the total target vocabulary subset). The limited number of vocabulary objects in a state of forgetting decay obtained from the truncation are extracted and added to the initial gap set. The learning scheduling server reads the business priority weight set within the data encapsulation object. The learning scheduling server extracts the business priority weights of the unmastered vocabulary from the business priority weight set.

[0064] Meanwhile, for vocabulary objects in the forgetting decay state added from the review task queue, since they do not exist in the current document's business priority weight set, the learning scheduling server forcibly assigns their business priority weights to the system's preset maximum constant weights to fill in the missing weight parameters and ensure that they participate in the sorting first.

[0065] In step S212, the learning scheduling server uses business priority weights as the ranking comparison benchmark. For the descending order sorting logic of numerical sequences, those skilled in the art can utilize the quicksort algorithm for processing; the descending order sorting logic of numerical sequences is a well-known technique in this field and will not be elaborated upon here. The learning scheduling server sorts the unmastered words in the initial gap set in descending order according to their business priority weights. The learning scheduling server generates a vocabulary gap list with the sorting results based on business priority weights.

[0066] ; In the formula, List of representative vocabulary gaps; Symbol representing the definition of a set; Represents a condition separator (meaning "makes" or "satisfies the following condition"); The symbol represents the element belonging to a set. The logical AND symbol represents "and", meaning that both conditions must be met simultaneously. Representation is not equal to symbol; Represents the lexical object; Represents a subset of the target vocabulary; This represents a function expression that queries the cognitive state of a specific vocabulary object within the employee's vocabulary competence matrix. A matrix representing employees' vocabulary proficiency; This indicates that the situation is under control.

[0067] Step S30 includes the following sub-steps: S301, the quota calculation module sends a data query command to the office automation system via the application programming interface (API). The data query command carries the target employee identifier and the current date and timestamp. The office automation system receives the data query command and retrieves the attendance schedule data table corresponding to the target employee identifier. The office automation system extracts the total shift duration recorded in the attendance schedule data table. The office automation system returns the total shift duration to the quota calculation module's memory space.

[0068] S302, the quota calculation module sends a task detail retrieval instruction to the office automation system. The office automation system retrieves details of all incomplete production tasks assigned to the target employee identifier. The production task details include estimated time consumption values. For the task status filtering and detailed data packaging logic, those skilled in the art can utilize structured query language conditional filtering methods to perform the processing. The task status filtering and detailed data packaging logic are well-known technologies in the field and will not be elaborated upon here.

[0069] S303, the quota calculation module receives the details of incomplete production tasks and extracts all estimated time values. The quota calculation module inputs all estimated time values ​​into an accumulator to perform mathematical addition. The quota calculation module outputs the total production task time data.

[0070] S304, the quota calculation module reads the system memory's preset overload redundancy time constant. The overload redundancy time constant is set to a fixed value in minutes. The quota calculation module uses the processor to subtract the total production task time from the total scheduled shift time and then subtract the overload redundancy time constant to obtain the available quota for the time window.

[0071] ; In the formula, The available quota for the specified time window; Represents the total duration of the shift schedule; This represents the total time spent on production tasks; This represents the redundancy time constant for overload protection.

[0072] S305, the learning scheduling server reads the temporary vocabulary gap list stored in the memory area. The learning scheduling server calls the underlying array length calculation function to count the total number of unmastered words in the vocabulary gap list. For the logic of looping and accumulating the total number of set elements, those skilled in the art can use a traversal counter to perform the processing. The logic of looping and accumulating the total number of set elements is a well-known technology in this field and will not be elaborated here.

[0073] S306, The learning scheduling server retrieves the basic learning time constant set in the system database. The basic learning time constant represents the estimated average duration of standard user cognitive memory of a single vocabulary item. The basic learning time constant uses seconds or minutes as the unit of time measurement and is converted into a unified timestamp span format.

[0074] S307, the learning scheduling server transmits the total number of unmastered words to the central processing unit's data bus. Simultaneously, the learning scheduling server transmits the basic learning time constant to the central processing unit's data bus.

[0075] The S308's internal arithmetic logic unit performs a mathematical multiplication operation between the total number of unmastered words and the basic learning time constant. The CPU outputs the estimated learning time. The learning scheduler receives the estimated learning time and writes it to a memory register.

[0076] ; In the formula, This represents the estimated learning time; This represents the total number of words not yet mastered. This represents the basic learning time constant.

[0077] S309, the learning scheduler reads the estimated learning time and available time window quota from the register. The learning scheduler uses a logic comparator to compare the estimated learning time with the available time window quota. When the estimated learning time exceeds the available time window quota, the learning scheduler triggers a task truncation policy.

[0078] S310, the learning scheduling server uses the available quota of the time window as the dividend and the basic learning time constant as the divisor, and sends them to the arithmetic logic unit to perform the division calculation. The learning scheduling server performs floor function rounding on the division result to obtain the limit value for the number of words to be retained.

[0079] S311, the learning scheduling server reads the vocabulary gap list. Within the vocabulary gap list, the learning scheduling server sets a cursor index based on a limit value for the number of words to retain. The learning scheduling server performs data segmentation on the vocabulary gap list based on the cursor index. After extracting the cursor index, the learning scheduling server extracts the vocabulary objects ranked lower and aggregates them to generate a tail vocabulary set. For the cursor segmentation logic of linear list data structures, those skilled in the art can use array slicing techniques to perform the processing. The cursor segmentation logic of linear list data structures is a well-known technology in this field and will not be elaborated upon here.

[0080] S312, the learning scheduling server establishes a data interaction channel with the relational database. The learning scheduling server retrieves all corresponding record entries for vocabulary objects within the tail vocabulary set from the employee vocabulary competency matrix stored in the relational database. The learning scheduling server writes a gap / residual status marker into a specific attribute field of the corresponding record entry, binds this gap / residual status marker to the business document identifier associated with the current work breakdown structure node, and synchronously obtains the current system time as the generation timestamp of the gap / residual status marker, writing it into the attribute field as well. The learning scheduling server uses the gap / residual status marker to indicate that the corresponding vocabulary object has not been assigned to the current test task list.

[0081] ; In the formula, This represents the limit value for the number of words to retain; The available quota for the specified time window; This represents the basic learning time constant.

[0082] S313, the learning scheduling server extracts the vocabulary gap list from the top-ranked vocabulary objects within the cursor index and aggregates them to generate the current timely learning list. The learning scheduling server writes the current timely learning list into the pending task list in the relational database, marks the test status of the vocabulary objects in the list as pending test, and obtains the current system time as the generation timestamp of the pending test mark for binding, waiting for subsequent employee terminals to trigger business document execution requests to merge, distribute and process them.

[0083] Simultaneously, the learning scheduling server dequeues and removes words in the forgetting decay state that have been extracted and successfully included in the current timely learning list from the review task queue in the system memory. Words in the forgetting decay state that have not been included remain in the review task queue.

[0084] S314, the learning scheduling server counts the number of words in the current timely learning list, multiplies it by the basic learning time constant to obtain the actual estimated time. The learning scheduling server encapsulates the actual estimated time into virtual task time data, writes it back through the application programming interface, and adds it to the total production task time data corresponding to the target employee in the office automation system, completing the dynamic closed-loop deduction of the available quota for the time window.

[0085] Step S40 includes the following sub-steps: S401, the employee terminal submits a business document execution request to the document management system via the Transmission Control Protocol (TCP). The business document execution request internally carries read or write instructions for a specific document. The document management system pre-deploys a network request interception gateway at the network access layer. The document management system uses the network request interception gateway to listen for and capture business document execution requests in real time. For the network request packet listening and capture logic, those skilled in the art can use reverse proxy port listening methods to perform the processing; the network request packet listening and capture logic is well-known technology in the field and will not be described in detail here.

[0086] S402, the document management system parses the underlying data packet of the business document execution request. The document management system locates the authentication field in the packet header and extracts the target employee identifier. The document management system synchronously reads the payload area of ​​the data packet to extract the corresponding business document identifier. The document management system stores the target employee identifier and the business document identifier in its local cache.

[0087] S403, the document management system uses the application programming interface (API) to construct a backend data retrieval statement. The document management system inputs the target employee identifier as the primary key parameter into the data retrieval statement. The document management system sends the data retrieval statement to the relational database. The document management system retrieves the employee vocabulary and competency matrix bound to the target employee identifier from the corresponding data table in the relational database and loads it into runtime memory.

[0088] S404, the document management system calls the processor to extract the target vocabulary subset bound to the current business document identifier, and then iterates through only the underlying attribute fields of the vocabulary objects contained within this target vocabulary subset in the employee vocabulary ability matrix. The document management system uses a conditional comparator to verify the values ​​of each attribute field.

[0089] The document management system determines whether the cognitive state enumeration value of the corresponding vocabulary object in the employee vocabulary ability matrix is ​​in an unmastered state or a forgotten decay state, and further determines whether there is a gap in the attribute field, or whether there is a test mark generated in step S313, and whether the business document identifier bound to the mark matches the current request.

[0090] When either a gap legacy status marker or a test marker exists that matches the identifier, the document management system outputs a read / write permission interception status boolean value as true based on the verification result, in order to suspend the business document execution request and trigger the subsequent test task distribution process; if the cognitive status of the vocabulary object is not mastered or has declined due to forgetting, but there is neither a gap legacy status marker nor a test marker in its underlying attribute fields, the system will not trigger the interception and will allow it to pass.

[0091] ; In the formula, This represents a boolean value indicating the read / write permission interception status. It represents an existential quantifier (i.e., at least one exists); The symbol represents the element belonging to a set. The intersection symbol represents the common part of two sets. Represents a condition separator (meaning "makes" or "satisfies the following condition"); A matrix representing employees' vocabulary proficiency; This represents a subset of the target vocabulary bound to the current business document; Represents the lexical object; This represents a function expression that extracts the underlying attribute state markers for a specific vocabulary object. Indicates the remaining status of the gap; This represents the test tag generated from the timely learning list.

[0092] In S405, when the document management system determines that the read / write permission interception status boolean value is true, it extracts the generation timestamp of the target status marker (i.e., the gap status marker or the marker to be tested that caused the interception condition) from the employee vocabulary ability matrix loaded into the running memory. The document management system reads the system time output by the current server motherboard hardware clock through the operating system's underlying application programming interface.

[0093] S406, the document management system reads a preset grace period constant from the system configuration file. The grace period constant represents the time span threshold during which employees who newly create vocabulary competency matrices are allowed to postpone performing test tasks. The grace period constant is measured in a time span unit format of the same magnitude as the system timestamp (e.g., uniformly converted to seconds or milliseconds).

[0094] S407, the document management system calls the processor's arithmetic logic unit to subtract the generated timestamp from the system time and outputs the duration difference. The document management system then sends the duration difference and the grace period constant into a comparator to perform a logical judgment operation. For the overflow prevention logic in the timestamp difference calculation, those skilled in the art can utilize long integer data type constraints to perform the processing. The overflow prevention logic in the timestamp difference calculation is a well-known technology in this field and will not be described in detail here.

[0095] In step S408, when the comparator determines that the duration difference is less than or equal to the grace period constant, the document management system triggers a degradation bypass mechanism. The document management system performs a test and then allows the intercepted business document execution request to proceed, permitting employee terminals to read or write the document normally. Simultaneously, the document management system injects a document closure event listener script into the document browser or editing interface of the employee terminal using an application programming interface (API). When this listener script detects a user-closed business document event, the document management system converts this event into a secondary wake-up command and sends it to the learning scheduling server. Upon receiving the command, the learning scheduling server forcibly triggers step S409 and subsequent operations, completing the closed-loop recovery of the test post-task. When the comparator determines that the duration difference is greater than the grace period constant, the document management system triggers a suspension control logic. The document management system rejects the business document execution request and sends a blocking command to the project management system. The project management system changes the status attribute of the corresponding work breakdown structure node to suspended based on the blocking command.

[0096] ; In the formula, Represents a control instruction branch; This indicates that the test should be completed before the vehicle is released. This indicates that a node in the work breakdown structure is suspended. Represents system time; The timestamp representing the generation of the target status marker that triggered the interception; This represents the grace period constant.

[0097] S409: After triggering the suspension control logic, the document management system calls the network communication interface to send a blocking learning and supplementary testing task generation instruction to the learning scheduling server. The blocking learning and supplementary testing task generation instruction data packet encapsulates the target employee identifier and the business document identifier. The learning scheduling server receives the supplementary testing task generation instruction and stores it in its internal message queue for processing. At this time, blocking learning and supplementary testing tasks triggered by gap-related overdue periods are at the forced release level, and their dispatch and scheduling are not limited by the available quota in the current time window.

[0098] S410, the learning scheduling server reads the internal message queue to extract the target employee identifier. The learning scheduling server uses the target employee identifier to retrieve the corresponding employee vocabulary ability matrix from the relational database. Combining the intercepted business document identifier, the learning scheduling server precisely traverses the target vocabulary subset involved in the business document within the employee vocabulary ability matrix, extracting vocabulary objects whose cognitive state enumeration value is not in the "mastered" state, and vocabulary objects with gap / residual state markers in their underlying attribute fields. The learning scheduling server retrieves the corresponding definitions and example sentences for the above vocabulary objects, aggregates and encapsulates them, and generates a list of blocking learning and retesting tasks. For the data structure object serialization processing logic, those skilled in the art can use lightweight data exchange format conversion methods to perform the processing; its data structure object serialization processing logic is a well-known technology in the field and will not be elaborated here.

[0099] S411: The learning scheduling server uses a two-way full-duplex communication protocol to push the current test task list to the employee terminal. The employee terminal receives the current test task list and renders the test interactive interface on the display screen. The employee terminal captures the interaction signals of the user's input device and records the test scores. The employee terminal packages the test scores to generate a test result data packet and transmits it back to the learning scheduling server through the network channel.

[0100] S412, the learning scheduling server parses the test result data packet to extract the test score for each vocabulary object. The learning scheduling server compares the test score with the system's preset passing threshold. When the test score is greater than or equal to the passing threshold, the learning scheduling server overwrites the corresponding vocabulary object's cognitive state enumeration value to "mastered" in the relational database; simultaneously, it erases and nulls the target state marker (gap marker or pending test marker) bound to the vocabulary object's underlying attribute fields.

[0101] The learning scheduling server calculates the overload compensation time by multiplying the number of vocabulary objects with gaps and residual status markers that have passed the supplementary test by the basic learning time constant. Then, through the application programming interface, the overload compensation time is added back as punitive debt hours and accumulated into the total production task time data of the corresponding employee in the office automation system.

[0102] The learning scheduling server sends a node wake-up command to the project management system. Based on the node wake-up command, the project management system restores the corresponding work breakdown structure node from a suspended state to a normally active state.

[0103] ; ; In the formula, This represents the updated status label attribute; The test score corresponding to the vocabulary object; This represents the passing threshold; Indicates the remaining status of the gap; This indicates that null values ​​have been erased. Represents the updated cognitive state enumeration value; This indicates that the situation is under control.

[0104] Step S50 includes the following sub-steps: S501: The learning scheduling server has an independently running asynchronous daemon process pre-deployed internally. This asynchronous daemon process triggers a status scan task according to a preset system time span (such as a fixed time each day or every 24 hours). After being triggered, the asynchronous daemon process obtains the current system time through the underlying system clock component. The learning scheduling server accesses a relational database using an application programming interface (API). The learning scheduling server retrieves the employee vocabulary ability matrix bound to the target employee identifier from the relational database. The learning scheduling server iterates through all records within the employee vocabulary ability matrix. The learning scheduling server performs a conditional matching operation and extracts vocabulary objects whose cognitive state enumeration value is "mastered".

[0105] S502, the learning scheduling server reads the underlying data table structure of the extracted vocabulary object. The learning scheduling server parses the recent test pass timestamps of the corresponding vocabulary object from the data table structure. For the database time format parsing and conversion logic, those skilled in the art can use standard time application programming interfaces to perform the processing; its database time format parsing and conversion logic is well-known technology in the field and will not be elaborated upon here.

[0106] In S503, the learning scheduling server subtracts the recent test timestamp from the current system time to obtain the silent time span value. The learning scheduling server extracts the historical review count parameter bound to the vocabulary object from the employee's vocabulary ability matrix. The learning scheduling server uses the historical review count parameter to perform an index lookup in the preset decay parameter mapping table in system memory. The learning scheduling server obtains the corresponding memory half-life constant. The memory half-life constant increases non-linearly with the increase of the historical review count parameter.

[0107] In S504, the learning scheduler server passes the quiescent time span value and the memory half-life constant to the floating-point unit inside the central processing unit (CPU). The CPU uses the floating-point unit to execute the exponential decay function to calculate and output the current memory retention value of the vocabulary object. The learning scheduler server receives the memory retention value and temporarily stores it in the cache space for subsequent state determination.

[0108] ; In the formula, This represents the numerical value of memory retention. Represents the natural constant; This represents the duration of the silence period. Represents parameters based on the number of historical reviews The memory half-life constant retrieved by index; Represents the vocabulary object.

[0109] S505, the learning scheduler server reads the memory retention value temporarily stored in the cache space. The learning scheduler server retrieves the preset memory decay threshold from the system's underlying configuration file. The memory decay threshold is stored as a floating-point number and is used to measure the minimum acceptable limit of a user's memory clarity for a specific vocabulary object.

[0110] S506, the learning scheduler server transmits the memory retention value and the memory decay threshold to the logic comparator. The logic comparator performs a value comparison operation. When the logic comparator determines that the memory retention value is less than the memory decay threshold, the learning scheduler server generates a state change trigger signal. The learning scheduler server determines that the corresponding vocabulary object has been forgotten based on the state change trigger signal.

[0111] S507, the learning scheduling server establishes a write connection channel with the relational database. The learning scheduling server uses this write connection channel to locate the data storage row corresponding to the vocabulary object in the employee's vocabulary ability matrix. The learning scheduling server executes a data update instruction, overwriting the cognitive state enumeration value of the vocabulary object from the "mastered" state to the "forgotten / decayed" state. For the database concurrent write lock control logic, those skilled in the art can utilize a row-level exclusive lock mechanism for processing; its database concurrent write lock control logic is well-known in the field and will not be elaborated upon here.

[0112] In S508, the learning scheduler extracts the memory addresses of vocabulary objects that have been changed to the forgetting decay state. The learning scheduler then appends the extracted vocabulary objects to the tail of the review task queue in system memory using pointers. The learning scheduler performs a persistent backup operation on the review task queue, awaiting the subsequent system triggering of the available quota allocation logic within a time window for review task scheduling.

[0113] ; In the formula, Represents the updated cognitive state enumeration value; This represents the numerical value of memory retention. This represents the critical threshold for memory decline. This indicates that the situation is under control; This represents a state of forgetfulness decline.

[0114] Specific application examples: To better understand this invention, the following example illustrates the participation of an employee of a multinational engineering company in an overseas engineering project.

[0115] In step S10, the learning scheduling server receives the work breakdown structure nodes and their associated business documents from the project management system. The document parsing module performs text segmentation, filters out words that hit the general stop word list (such as "in", "the", "and"), generates an initial vocabulary sequence, and outputs a target vocabulary subset containing 80 vocabulary objects after deduplication.

[0116] For lexical objects (e.g., “Rack”), its word frequency data =6, simultaneously hitting the key technical requirement paragraph, obtaining the paragraph weight constant. =1.5. The learning scheduler outputs its business priority weights: ; In step S20, the clock scanning module determines that the difference between the estimated start time of the work breakdown structure node and the current system time is less than a preset pre-time window constant, and wakes up the thread. The learning scheduling server retrieves the employee's vocabulary ability matrix and finds that the employee has not mastered 30 of the 80 words. At the same time, the learning scheduling server adds 5 historical words in a state of forgetting decay from the review task queue. These 35 words form the initial gap set and are sorted in descending order according to business priority weight to generate a vocabulary gap list.

[0117] In step S30, the quota calculation module extracts data from the office automation system, including the total daily shift hours. =480 minutes, total time spent on unfinished production tasks =380 minutes, set overload redundancy time constant. =30 minutes. The available quota for the time window is calculated as follows: ; The system sets a basic learning time constant. =2.5 minutes / vocabulary. The system calculates the total number of vocabulary words not yet mastered. =35, therefore the estimated learning time is: ; because (87.5)> (70) Triggers the task truncation strategy. The threshold value for the number of words to retain is calculated: ; The system uses a cursor to segment the vocabulary gap list, with the first 28 items grouped into the current learning list and the last 7 items marked with the gap status.

[0118] In step S40, due to the urgency of the work task, the employee sent a business document execution request to the document management system that evening. The document management system checked and found that the underlying vocabulary of the document had a gap in the residual status marker, and the read / write permission interception status boolean value was determined to be true. Subsequently, the system calculated the difference in duration. Since the interception occurred 2 hours after the marker was generated, which is less than the grace period constant (preset to 12 hours), the system triggered a downgrade bypass process, allowing the employee to read the document and inject a listening script. If the grace period is exceeded, the node will be strictly suspended and a blocking learning and retesting task will be issued.

[0119] In step S50, the asynchronous daemon scans the status. For a given known status term, its silent time span value is calculated. =20 days, based on historical review frequency parameters ( =2) Obtain the memory half-life constant =18 days. Calculate its memory retention value: ; The system's preset memory decay threshold is 0.40. Because... If the value is less than 0.40, the system changes the cognitive state enumeration value of the word to the forgetting decay state and adds it to the review task queue.

[0120] The experimental verification and effect comparison are as follows: To verify the actual engineering effectiveness of the quota calculation and truncation mechanism in this invention in ensuring a balance between employee production tasks and learning load, we selected 100 software R&D engineers within the company for a 30-day comparative test.

[0121] The test was divided into two groups: Control group (50 people): The traditional fixed vocabulary push learning system was used (it does not have the function of calculating available quotas for time windows and truncation processing, and sends out a list of all vocabulary gaps associated with the document every day).

[0122] Experimental group (50 people): The English vocabulary learning system based on big data analysis of this invention was used.

[0123] Within a 30-day statistical period, the system records the production tasks faced by employees each day at varying levels of workload (expressed as "production task time percentage"). / When measured at ×100%, the "average daily overload time of employees" (i.e., the total time employees spend encroaching on rest time or working overtime to complete learning tasks) is caused by English-assisted learning.

[0124] Reference Appendix Figure 3 , Figure 3The differences in engineering effectiveness between the present invention and traditional solutions for managing employee time boundaries are demonstrated under different workloads.

[0125] like Figure 3 As shown, the horizontal axis represents the percentage of time spent on production tasks, and the vertical axis represents the average daily overload time of employees.

[0126] When the production task time accounts for 50% to 70% of the normal range, the quota calculation module extracts the data and substitutes it into the formula. The calculated time window available quota Relatively abundant.

[0127] At this point, the learning scheduling server uses the formula... The calculated expected learning time was generally less than the available quota within the time window, and the system did not trigger the truncation logic. Therefore, the overload situation of both the control group and the experimental group remained at a low level near 0.

[0128] However, when the time taken for production tasks exceeds 80% and approaches the extremely high load range of 95%, the physical time boundary of the system is compressed. Figure 3 The dotted line with a circular mark (control group) shows that, due to the lack of a data-based blocking mechanism, the system forcibly distributed all words in the initial gap set, causing the employee overload time to rise out of control (reaching 55 minutes at 95% capacity), breaking through the preset safety bottom line of the overload redundancy time constant.

[0129] On the contrary Figure 3 The solid line marked with a square (experimental group) indicates that, under the same high load pressure, the learning scheduling server uses a logic comparator to determine that the expected learning time exceeds the available quota within the time window, thus precisely triggering the task truncation strategy. The system calls the arithmetic logic unit to perform division to calculate the limit value for the number of words to be retained. The system utilizes a cursor index to perform strict data partitioning on the vocabulary gap list, writing gap legacy status markers to the lower-ranking vocabulary sets and performing deferred scheduling. As the solid line trend shows, this architecture design forcibly converges and suppresses the system overload time in extreme cases to within 5 minutes, successfully isolating the hard conflict between heavy production and learning tasks at the physical clock level, and realizing dynamic closed-loop scheduling.

Claims

1. A method for assisting English vocabulary learning based on big data analysis, characterized in that, Includes the following steps: Based on the work breakdown structure node associations, perform text segmentation on business documents to generate a target vocabulary subset; Retrieve the employee vocabulary competency matrix stored in the database, compare the target vocabulary subset with the employee vocabulary competency matrix, and extract the vocabulary objects that are not mastered to generate a vocabulary gap list; Obtain the total shift duration and production task time data of the target employees, calculate the available quota of the time window based on the total shift duration and production task time data, and calculate the estimated learning time based on the vocabulary gap list; When the estimated learning time exceeds the available quota of the time window, the vocabulary gap list is truncated, and the truncated vocabulary objects are written into the employee vocabulary ability matrix with a gap legacy status marker with a generation timestamp. The system monitors execution requests for the business document. When it is determined that there is a gap / residual status marker for the business document in the employee's vocabulary ability matrix, it intercepts read / write permissions and triggers the generation of a supplementary test task for the business document, which is then sent to the employee's terminal. Based on the evaluation result data received from the employee's terminal, the system updates the employee's vocabulary ability matrix and clears the gap / residual status marker.

2. The English vocabulary learning aid based on big data analysis according to claim 1, characterized in that, After generating the target vocabulary subset, the method further includes calculating business priority weights for the vocabulary objects, so that the vocabulary gap list can be sorted according to the business priority weights. Extract plain text data segments from the business documents, and count the number of times word objects appear in the plain text data segments to generate word frequency data; The paragraph text content within the plain text data segment is extracted using a regular expression engine, and the corresponding paragraph weight constant is obtained through logical verification and matching. The word frequency data is multiplied by the paragraph weight constant to output the business priority weight. After generating the vocabulary gap list, the vocabulary objects in the vocabulary gap list are sorted in descending order according to the business priority weight.

3. The English vocabulary learning aid based on big data analysis according to claim 1, characterized in that, Before retrieving the employee vocabulary competency matrix stored in the database, a background thread wake-up step is also included: The estimated start time is bound to the work breakdown structure node whose status is marked as not started; Calculate the time difference between the estimated startup time and the current system time. When the time difference is less than or equal to a preset pre-time window constant, generate a thread activation instruction to wake up the background scheduling thread of the corresponding work decomposition structure node.

4. The English vocabulary learning aid based on big data analysis according to claim 1, characterized in that, The available quota for the calculation time window includes: Extract the total shift duration from the attendance and shift schedule data table, and sum the estimated time consumption values ​​from the details of unfinished production tasks to obtain the total time consumption data of the production tasks; The available quota for the time window is obtained by subtracting the total production task time data from the total scheduled time and subtracting the preset overload redundancy time constant.

5. The English vocabulary learning aid based on big data analysis according to claim 1, characterized in that, The truncation process includes: The result of the division between the available quota and the basic learning time constant within the time window is rounded down to determine the limit value for the number of words to be retained. Within the vocabulary gap list, a cursor index is set according to the limit value of the number of words to be retained to perform data segmentation. After extracting the cursor index, the vocabulary objects ranked lower are aggregated to generate a tail vocabulary set. In the employee vocabulary ability matrix, the gap legacy status marker is written to the corresponding record entry of the vocabulary object in the tail vocabulary set, and the current system time is obtained simultaneously as the generation timestamp.

6. The English vocabulary learning aid based on big data analysis according to claim 1, characterized in that, The step of intercepting read / write permissions and triggering the generation of a supplementary testing task for the business document includes: Calculate the difference between the current system time and the duration of the generated timestamp; When the difference in the duration is less than or equal to the preset grace period constant, a downgrade bypass process is triggered to allow the execution request, and when the trigger event of the business document being closed is detected, a second wake-up command is sent to trigger the supplementary test task. When the difference in duration is greater than the grace period constant, the suspension control logic is triggered to reject the execution request and perform a suspension operation on the work decomposition structure node, and the supplementary test task of blocking learning is triggered simultaneously.

7. The English vocabulary learning aid based on big data analysis according to claim 1, characterized in that, Updating the employee vocabulary competency matrix and clearing the gap legacy status markers includes: When the test score corresponding to the evaluation result data is greater than or equal to the passing threshold, the cognitive state enumeration value of the corresponding vocabulary object is overwritten as the mastered state, and the corresponding gap left state mark is erased. The overload compensation time is calculated based on the number of vocabulary objects marked with the missing status that passed the supplementary test, and the overload compensation time is added as punitive liability hours to the total production task time data of the corresponding employee.

8. The English vocabulary learning aid based on big data analysis according to claim 1, characterized in that, It also includes a memory retention calculation step based on asynchronous daemon execution: Periodically traverse the vocabulary objects in the employee vocabulary ability matrix whose cognitive state enumeration value is "mastered", and calculate the silent time span between the current system time and the recent test pass timestamp of the vocabulary object. The corresponding memory half-life constant is obtained by using the historical review count parameter index bound to the vocabulary object; Based on the silent time span value and the memory half-life constant, the exponential decay function is executed to calculate and output the memory retention value of the corresponding vocabulary object. When the memory retention value is lower than the memory decay threshold, the cognitive state enumeration value of the corresponding vocabulary object is changed to the forgetting decay state, and the extracted vocabulary object is added to the review task queue in the system memory.

9. The English vocabulary learning aid based on big data analysis according to claim 8, characterized in that, The step of extracting unacquainted vocabulary objects to generate a vocabulary gap list also includes: The head of the review task queue is truncated, and the vocabulary objects in the forgetting decay state are added to the vocabulary gap list. The business priority weight of the vocabulary objects in the forgetting decay state is forcibly assigned to the preset maximum constant weight.

10. An English vocabulary learning system based on big data analysis, characterized in that, The method for assisting English vocabulary learning based on big data analysis, as described in any one of claims 1-9, comprises: The document parsing module is used to perform text segmentation on business documents based on the work breakdown structure nodes to generate a target vocabulary subset; The quota calculation module is used to calculate the available quota for a time window based on the total shift duration and the total production task time data. The learning scheduling server is used to retrieve the employee vocabulary ability matrix stored in the database, compare the target vocabulary subset with the employee vocabulary ability matrix, and extract the vocabulary objects that have not been mastered to generate a vocabulary gap list. Calculate the estimated learning time based on the vocabulary gap list, and perform truncation processing when the estimated learning time is greater than the available quota of the time window, and write a gap legacy status marker with a generation timestamp to the truncated vocabulary object; The document management system is used to listen for execution requests for the business documents and intercept read and write permissions. When the gap status mark exists, the learning scheduling server is triggered to generate a supplementary test task. The learning scheduling server updates the employee's vocabulary ability matrix and clears the gap status mark based on the evaluation result data received from the employee's terminal.

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