A conference project management method and system based on digitalization of the entire cycle

Abstract

The present invention relates to the field of conference information technology, and in particular to a conference project management method and system based on a digital full-cycle. The present invention determines the mean value of the semantic association between each text sentence and each instruction sample sentence, determines whether the text sentence meets the recognition standard in combination with the identity tag in the text sentence, determines the text sentence that meets the recognition standard as a minutes segment, introduces the text sentence that does not meet the recognition standard into adjacent text sentences to form a text segment, determines the subject words and feature identity tags based on the occurrence probability of keywords and identity tags, verifies whether the association standard is met based on the conditional probability of the feature identity tags and the subject words in the text information, determines the subject words that meet the recognition standard as the minutes segment, constructs the association relationship between the minutes segment and the feature identity tag, generates exclusive meeting minutes for each user terminal, and improves the efficiency and pertinence of the meeting.

Description

Technical Field

[0001] The present invention relates to the field of conference information technology, and in particular to a conference project management method and system based on digitalization and full cycle. Background Art

[0002] Online meetings are convenient and fast, and have become a common method of communication in many companies and organizations. Participants only need to connect to the Internet to hold meetings at any time without being restricted by time and place, which greatly improves meeting efficiency. In addition, online meetings reduce meeting costs. There is no need to prepare paper materials or make reservations for offline meeting rooms, which greatly reduces the flow of personnel and the waste of related items, thereby beneficially improving meeting efficiency.

[0003] Chinese patent publication number: CN118301248A, discloses a conference transcription method and system, the method includes a server connected to an audio encoder network to monitor the switch status of the microphone in real time; when the microphone is turned on, the server obtains the opening information and creates a conference process including: determining whether the engine has idle transcription channels; if so, creating a conference project, enabling the microphone and receiving the audio information of the microphone; transcribing the audio information and storing it in the conference project to form conference text. The present invention improves the efficiency and quality of conference records through real-time transcription, automated processing, multi-channel transcription support and accurate storage of conference content.

[0004] In the prior art, the method of completely transcoding the conference audio and recording all the meeting contents is often used for taking meeting minutes. However, in actual situations, meetings are long and the content is complicated. If all the meeting contents are transcoded and recorded, the content of the meeting minutes will be mixed and the key points will be unclear, making it difficult for people who are not in the meeting to quickly find the content related to themselves, affecting the efficiency and completeness of the meeting. Summary of the invention

[0005] To this end, the present invention provides a full-cycle conference project management method and system based on digitization to solve the problem that the content of meeting minutes is mixed and the key points are unclear, making it difficult for non-members to quickly find content related to themselves, thus affecting the efficiency and completeness of the meeting.

[0006] To achieve the above objectives, the present invention provides a digital full-cycle conference project management method and system, which includes:

[0007] Step S1, constructing an instruction sample database including instruction sample sentences, wherein the instruction sample sentences contain instruction keywords;

[0008] Step S2, obtaining the identity tag of the participating user terminal, synchronously converting the audio information transmitted by the user terminal during the meeting into text information, determining the mean value of the semantic association between each text sentence and each of the command sample sentences, and determining whether the text sentence meets the recognition standard in combination with the identity tag in the text sentence;

[0009] Step S3, analyzing the text sentence based on the determination result of the text sentence to determine the minutes segment and the association relationship between each of the minutes segment and the identity tag, including:

[0010] Determine the text sentence as a minutes segment, extract the identity tag in the text sentence, and establish an association relationship between the minutes segment and the identity tag;

[0011] Or, based on the text sentence, introducing the context to determine the minutes segment, including,

[0012] Determine the number of adjacent text sentences to be introduced, introduce a corresponding number of adjacent text sentences to form a text segment, determine the subject words based on the probability of occurrence of each keyword in the text segment, and determine the characteristic identity tags based on the probability of occurrence of each identity tag in the text segment, verify whether the characteristic identity tags and the subject words meet the association standard based on the conditional probability in the text information, extract the text sentences as the minutes segment based on the subject words, and construct the association relationship between the minutes segment and the characteristic identity tags;

[0013] Step S4: Generate exclusive meeting minutes for each of the user terminals based on the minutes fragments associated with the identity tags.

[0014] Furthermore, in step S2, the process of determining whether the text sentence meets the recognition standard includes:

[0015] Determining the mean value of the semantic relevance between the text sentence and each of the instruction sample sentences;

[0016] comparing the semantic relevance mean with a predetermined semantic relevance standard threshold;

[0017] Determining whether an identity tag exists in the text sentence;

[0018] If the semantic association mean is greater than or equal to the semantic association standard threshold and there is an identity tag in the text sentence, it is determined that the text sentence meets the recognition standard.

[0019] Furthermore, in step S2, a method for analyzing the text sentence is selected based on the determination result of the text sentence, including:

[0020] If it is determined that the text sentence meets the recognition standard, the text sentence is determined to be a minutes segment, the identity tag in the text sentence is extracted, and an association relationship between the minutes segment and the identity tag is established;

[0021] If it is determined that the text sentence does not meet the recognition standard, a context determination summary segment is introduced based on the text sentence.

[0022] Furthermore, the process of determining the number of adjacent text sentences to be introduced includes:

[0023] If the semantic relevance mean is less than the semantic relevance standard threshold and there is no identity tag in the text sentence, a first number of adjacent text sentences need to be introduced;

[0024] If the semantic relevance mean is less than the semantic relevance standard threshold or the identity tag does not exist in the text sentence, a second number of adjacent text sentences need to be introduced;

[0025] The first number is greater than the second number.

[0026] Furthermore, the process of determining the subject words based on the occurrence probability ranking of each keyword in the text segment includes:

[0027] Arrange the keywords in descending order according to their corresponding probability of occurrence to obtain the keyword ranking;

[0028] A predetermined number of keywords are selected in sequence from the keyword sorting head as subject words.

[0029] Furthermore, the identity tag corresponding to the maximum occurrence probability is determined as the feature identity tag.

[0030] Furthermore, the process of verifying whether the semantic association between the feature identity tag and the subject word meets the association standard includes:

[0031] Determine the conditional probability of the subject word appearing under the condition that the characteristic identity tag appears in each comparison segment of the text information;

[0032] If the conditional probability is greater than a predetermined conditional probability standard threshold, it is determined that the association standard is met;

[0033] Wherein, the comparison segments are a predetermined number of adjacent text sentences;

[0034] The pre-defined subject words that meet the association criteria are determined as subject words in the text segment.

[0035] Further, the process of extracting text sentences as minutes fragments based on the subject words includes:

[0036] Determine each text sentence where the subject word is located;

[0037] Each of the text sentences is determined as a minutes segment.

[0038] Furthermore, the process of generating the exclusive meeting minutes for each of the user terminals includes:

[0039] Determine the identity tag corresponding to the user end;

[0040] Extracting the minutes fragment associated with the identity tag;

[0041] The minutes fragments are combined to obtain a dedicated meeting minutes for the user terminal.

[0042] Furthermore, a system of a digital full-cycle conference project management method is also included, including:

[0043] one or more processors;

[0044] Memory;

[0045] and one or more programs,

[0046] The one or more programs are configured to be executed by one or more processors, and the memory includes a storage medium storing computer programs;

[0047] When the computer program is executed by a processor, it is used to implement a digital full-cycle conference project management method.

[0048] Compared with the prior art, the present invention constructs an instruction sample database, obtains identity tags of participating user terminals, synchronously converts audio information transmitted by the user terminals during the meeting into text information, determines the mean value of the semantic association between each text sentence and each of the instruction sample sentences, determines whether the text sentence meets the recognition standard in combination with the identity tag in the text sentence, determines the text sentence that meets the recognition standard as a minutes segment, extracts the identity tag in the text sentence, constructs an association relationship between the minutes segment and the identity tag, and introduces adjacent text sentences introduced by the text sentence that does not meet the recognition standard into a text segment, determines the subject word and the characteristic identity tag based on the occurrence probability of the keyword and the occurrence probability of the identity tag, verifies whether the association standard is met based on the conditional probability of the characteristic identity tag and the subject word in the text information, determines the minutes segment, constructs an association relationship between the minutes segment and the characteristic identity tag, and forms the minutes segment to generate exclusive meeting minutes for each of the user terminals, thereby improving the efficiency and pertinence of the meeting.

[0049] In particular, the present invention determines whether a text sentence meets the recognition standard by combining the mean value of the semantic correlation between each text sentence and each of the command sample sentences with the identity tag in the text sentence. The purpose is to identify text sentences in the meeting that have a command tendency and have an identity tag. In actual situations, project meetings often involve content with a strong command tendency, such as task allocation, division of labor explanation, etc. The meeting time is long and the content is complicated. The relevant meeting records in the form of transcoded records involve a large amount of text information with unclear key points. Therefore, the present invention determines text sentences that meet the recognition standard. Such text sentences have a strong command tendency and have an identity tag. Subsequently, it is convenient to determine the user-specific meeting minutes based on the identity tag, which is convenient for non-attendants or related personnel to quickly learn important content in the meeting information that is highly relevant to themselves, thereby improving the accuracy of the meeting minutes.

[0050] In particular, in actual situations, there may be some text sentences with ambiguous semantics and unclear descriptions in the text information generated based on the meeting content. The text sentences may also have the problem of multiple identity tags corresponding to multiple keywords, and the focus of the semantic description is not clear. Therefore, the present invention introduces adjacent text sentences for text sentences that do not meet the recognition standards to form text fragments, expand the analysis scope, and determine the minutes fragments to be extracted based on the association between the subject words and the characteristic identity tags in the text fragments, thereby improving the matching degree between the minutes fragments and the user end, making it convenient for non-attendants or related personnel to quickly learn important content in the meeting information that is highly relevant to themselves, thereby improving the accuracy of the meeting minutes.

[0051] In particular, the present invention makes targeted adjustments to the number of adjacent text sentences introduced based on the mean semantic relevance and identity labels, and further increases the number of adjacent text sentences introduced for text sentences whose mean semantic relevance is less than the semantic relevance standard threshold and for which no identity labels exist, thereby ensuring the completeness of the text fragment analysis and the integrity of the meeting minutes. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A step diagram of a conference project management method based on a digital full-cycle according to an embodiment of the invention;

[0053] Figure 2 A logic block diagram for determining whether a text sentence meets the recognition criteria according to an embodiment of the invention;

[0054] Figure 3 A logical block diagram of a method for analyzing text sentences selected in an embodiment of the invention;

[0055] Figure 4 A logic block diagram of verifying whether an embodiment of the present invention meets associated standards. DETAILED DESCRIPTION

[0056] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0058] See also Figures 1 to 4 As shown, Figure 1 This is a step diagram of a conference project management method based on digitalization of a full-cycle according to an embodiment of the invention. Figure 2 This is a logic block diagram of an embodiment of the invention for determining whether a text sentence meets the recognition criteria. Figure 3 A logic block diagram of a method for analyzing text sentences selected in an embodiment of the invention, Figure 4 The logic block diagram of the embodiment of the invention for verifying whether the associated standard is met is as follows: The conference project management method based on digitalization of the full cycle of the invention includes:

[0059] Step S1, constructing an instruction sample database including instruction sample sentences, wherein the instruction sample sentences contain instruction keywords;

[0060] Step S2, obtaining the identity tag of the participating user terminal, synchronously converting the audio information transmitted by the user terminal during the meeting into text information, determining the mean value of the semantic association between each text sentence and each of the command sample sentences, and determining whether the text sentence meets the recognition standard in combination with the identity tag in the text sentence;

[0061] Step S3, analyzing the text sentence based on the determination result of the text sentence to determine the minutes segment and the association relationship between each of the minutes segment and the identity tag, including:

[0062] Determine the text sentence as a minutes segment, extract the identity tag in the text sentence, and establish an association relationship between the minutes segment and the identity tag;

[0063] Or, based on the text sentence, introducing the context to determine the minutes segment, including,

[0064] Determine the number of adjacent text sentences to be introduced, introduce a corresponding number of adjacent text sentences to form a text segment, determine the subject words based on the probability of occurrence of each keyword in the text segment, and determine the characteristic identity tags based on the probability of occurrence of each identity tag in the text segment, verify whether the characteristic identity tags and the subject words meet the association standard based on the conditional probability in the text information, extract the text sentences as the minutes segment based on the subject words, and construct the association relationship between the minutes segment and the characteristic identity tags;

[0065] Step S4: Generate exclusive meeting minutes for each of the user terminals based on the minutes fragments associated with the identity tags.

[0066] Specifically, the identity tag of the user terminal is pre-entered into the personal information of the user terminal. It can be understood that a single user terminal only corresponds to a single identity tag. The identity tag may include some information representing the identity, such as name, representative unit, group, etc. Those skilled in the art can expand and delete the identity tag, which will not be repeated here.

[0067] Specifically, there is no limitation on the specific method of synchronously converting audio information into text information, which can be implemented by using relevant speech recognition models. Those skilled in the art can make their own choices, which will not be elaborated here.

[0068] Specifically, there is no limitation on the specific method of constructing the instruction sample database;

[0069] The instruction keywords are pre-designated keywords used to characterize the existence of instruction meanings, for example, synonyms such as responsible, need, undertake, should be responsible, require, must, and necessary, which can be obtained by technicians in this field through a dictionary database.

[0070] The instruction sample sentences can be obtained by crawling a number of text sentences including instruction keywords through a crawler program, and the instruction sample database is formed after the number of instruction sample sentences are stored.

[0071] Specifically, in step S2, the process of determining whether the text sentence meets the recognition standard includes:

[0072] Determining the mean value of the semantic relevance between the text sentence and each of the instruction sample sentences;

[0073] comparing the semantic relevance mean with a predetermined semantic relevance standard threshold;

[0074] Determining whether an identity tag exists in the text sentence;

[0075] If the semantic association mean is greater than or equal to the semantic association standard threshold and there is an identity tag in the text sentence, it is determined that the text sentence meets the recognition standard.

[0076] Specifically, there is no limitation on the specific method of determining the semantic similarity between sentences. For example, the Word2Vec model can be used to vectorize text sentences, and the semantic similarity is calculated by calculating the vector similarity between text sentences. Of course, other methods can also be used, which will not be repeated here.

[0077] Specifically, the semantic association standard threshold M0 is obtained in advance by pre-determining the semantic similarity mean ΔM between each instruction sample statement in the instruction sample database, and setting M0=β×ΔM, where β represents the precision deviation coefficient, 0.85<β<0.95.

[0078] Specifically, in step S2, a method for analyzing the text sentence is selected based on the determination result of the text sentence, including:

[0079] If it is determined that the text sentence meets the recognition standard, the text sentence is determined to be a minutes segment, the identity tag in the text sentence is extracted, and an association relationship between the minutes segment and the identity tag is established;

[0080] If it is determined that the text sentence does not meet the recognition standard, a context determination summary segment is introduced based on the text sentence.

[0081] Specifically, the present invention determines whether a text sentence meets the recognition standard by combining the mean value of the semantic correlation between each text sentence and each of the command sample sentences with the identity tag in the text sentence. The purpose is to identify text sentences in the meeting that have a command tendency and have an identity tag. In actual situations, project meetings often involve content with a strong command tendency, such as task allocation, division of labor explanation, etc. The meeting time is long and the content is complicated. The relevant meeting records in the form of transcoded records involve a large amount of text information with unclear key points. Therefore, the present invention determines text sentences that meet the recognition standard. Such text sentences have a strong command tendency and have an identity tag. Subsequently, it is convenient to determine the user-specific meeting minutes based on the identity tag, which is convenient for non-attendants or related personnel to quickly learn important content in the meeting information that is highly relevant to themselves, thereby improving the accuracy of the meeting minutes.

[0082] Specifically, the process of determining the number of adjacent text sentences to be introduced includes:

[0083] If the semantic relevance mean is less than the semantic relevance standard threshold and there is no identity tag in the text sentence, a first number of adjacent text sentences need to be introduced;

[0084] If the semantic relevance mean is less than the semantic relevance standard threshold or the identity tag does not exist in the text sentence, a second number of adjacent text sentences need to be introduced;

[0085] The first number is greater than the second number.

[0086] In this embodiment, in order to make the text segment formed after the context information is introduced to have reference value, the first number is selected within the interval (10, 20], and the second number is selected within the interval [5, 10].

[0087] In actual situations, there may be some text sentences with ambiguous semantics and unclear descriptions in the text information generated based on the meeting content. The text sentences may also have the problem of multiple identity tags corresponding to multiple keywords, and the focus of the semantic description is not clear. Therefore, the present invention introduces adjacent text sentences for text sentences that do not meet the recognition standards to form text fragments, expand the analysis scope, and determine the minutes fragments to be extracted based on the association between the subject words and the characteristic identity tags in the text fragments, thereby improving the matching degree between the minutes fragments and the user end, making it convenient for non-attendants or related personnel to quickly learn important content in the meeting information that is highly relevant to themselves, thereby improving the accuracy of the meeting minutes.

[0088] Specifically, the process of determining the subject words based on the occurrence probability ranking of each keyword in the text segment includes:

[0089] Arrange the keywords in descending order according to their corresponding probability of occurrence to obtain the keyword ranking;

[0090] A predetermined number of keywords are selected in sequence from the keyword sorting head as subject words.

[0091] Specifically, the predetermined number is selected within the interval [2,3].

[0092] Specifically, the keywords can be obtained by word segmentation by word segmentation software, which will not be elaborated here.

[0093] Specifically, the identity tag corresponding to the maximum occurrence probability is determined as the feature identity tag.

[0094] It is understandable that many identity tags may appear in a text segment. In order to identify the identity tag with the highest correlation, the identity tag corresponding to the maximum probability of occurrence is determined as the characteristic identity tag to ensure the pertinence of the meeting minutes.

[0095] Specifically, the process of verifying whether the semantic association between the feature identity tag and the subject word meets the association standard includes:

[0096] Determine the conditional probability of the subject word appearing under the condition that the characteristic identity tag appears in each comparison segment of the text information;

[0097] If the conditional probability is greater than a predetermined conditional probability standard threshold, it is determined that the association standard is met;

[0098] The comparison segments are a predetermined number of adjacent text sentences.

[0099] Specifically, the size of the predetermined conditional probability standard threshold affects the verification accuracy, and those skilled in the art may select it within the interval [0.3, 0.6].

[0100] It is understood that the comparison segment should contain at least two text sentences.

[0101] Specifically, the process of extracting text sentences as minutes fragments based on subject words includes:

[0102] Determine each text sentence where the subject word is located;

[0103] Each of the text sentences is determined as a minutes segment.

[0104] It is understandable that for text sentences where the subject terms have been determined, there is no need to streamline them to avoid missing key information.

[0105] Specifically, the process of generating exclusive meeting minutes for each of the user terminals includes:

[0106] Determine the identity tag corresponding to the user end;

[0107] Extracting the minutes fragment associated with the identity tag;

[0108] The minutes fragments are combined to obtain a dedicated meeting minutes for the user terminal.

[0109] In the embodiment, it is understandable that a single user terminal corresponds to a single identity tag, and a single identity tag may correspond to multiple different minutes segments.

[0110] Specifically, it also provides a system based on a digital full-cycle conference project management method, including:

[0111] one or more processors;

[0112] Memory;

[0113] and one or more programs,

[0114] The one or more programs are configured to be executed by one or more processors, and the memory includes a storage medium storing computer programs;

[0115] When the computer program is executed by a processor, it is used to implement a digital full-cycle conference project management method.

[0116] If the digital full-cycle conference project management method of the present invention is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention, and the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0117] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A digital full-cycle conference project management method, characterized in that: include: Step S1, constructing an instruction sample database including instruction sample sentences, wherein the instruction sample sentences contain instruction keywords; Step S2, obtaining the identity tag of the participating user terminal, synchronously converting the audio information transmitted by the user terminal during the meeting into text information, determining the mean value of the semantic association between each text sentence and each of the command sample sentences, and determining whether the text sentence meets the recognition standard in combination with the identity tag in the text sentence; Step S3, analyzing the text sentence based on the determination result of the text sentence to determine the minutes segment and the association relationship between each of the minutes segment and the identity tag, including: Determine the text sentence as a minutes segment, extract the identity tag in the text sentence, and establish an association relationship between the minutes segment and the identity tag; Or, based on the text sentence, introducing the context to determine the minutes segment, including, Determine the number of adjacent text sentences to be introduced, introduce a corresponding number of adjacent text sentences to form a text segment, determine the subject words based on the probability of occurrence of each keyword in the text segment, and determine the characteristic identity tags based on the probability of occurrence of each identity tag in the text segment, verify whether the characteristic identity tags and the subject words meet the association standard based on the conditional probability in the text information, extract the text sentences as the minutes segment based on the subject words, and construct the association relationship between the minutes segment and the characteristic identity tags; Step S4, generating exclusive meeting minutes for each of the user terminals based on the minutes fragment associated with the identity tag; The process of determining whether the text sentence meets the recognition criteria includes: Determining the mean value of the semantic relevance between the text sentence and each of the instruction sample sentences; comparing the semantic relevance mean with a predetermined semantic relevance standard threshold; Determining whether an identity tag exists in the text sentence; If the semantic association mean is greater than or equal to the semantic association standard threshold and there is an identity tag in the text sentence, then it is determined that the text sentence meets the recognition standard; The process of determining the number of adjacent text sentences to be introduced includes: If the semantic relevance mean is less than the semantic relevance standard threshold and there is no identity tag in the text sentence, a first number of adjacent text sentences need to be introduced; If the semantic relevance mean is less than the semantic relevance standard threshold or the identity tag does not exist in the text sentence, a second number of adjacent text sentences need to be introduced; The first amount is greater than the second amount; The process of verifying whether the semantic association between the feature identity tag and each subject word meets the association standard includes: Determine the conditional probability of the subject word appearing under the condition that the characteristic identity tag appears in each comparison segment of the text information; If the conditional probability is greater than a predetermined conditional probability standard threshold, it is determined that the association standard is met; Wherein, the comparison segments are a predetermined number of adjacent text sentences; The pre-defined subject words that meet the association criteria are determined as subject words in the text segment.

2. The digital full-cycle conference project management method according to claim 1 is characterized in that: In the step S2, a method for analyzing the text sentence is selected based on the determination result of the text sentence, including: If it is determined that the text sentence meets the recognition standard, the text sentence is determined to be a minutes segment, the identity tag in the text sentence is extracted, and an association relationship between the minutes segment and the identity tag is established; If it is determined that the text sentence does not meet the recognition standard, a context determination summary segment is introduced based on the text sentence.

3. The digital full-cycle conference project management method according to claim 1 is characterized in that: The process of determining the subject words based on the occurrence probability ranking of each keyword in the text segment includes: Arrange the keywords in descending order according to their corresponding probability of occurrence to obtain the keyword ranking; A predetermined number of keywords are selected in sequence from the keyword sorting head as subject words.

4. The digital full-cycle conference project management method according to claim 1 is characterized in that: Determine the characteristic identity tags include, The identity label corresponding to the maximum occurrence probability is determined as the feature identity label.

5. The digital full-cycle conference project management method according to claim 1 is characterized in that: The process of extracting text sentences as minutes fragments based on subject words includes: Determine each text sentence where the subject word is located; Each of the text sentences is determined as a minutes segment.

6. The digital full-cycle conference project management method according to claim 1 is characterized in that: The process of generating exclusive meeting minutes for each of the user terminals includes: Determine the identity tag corresponding to the user end; Extracting the minutes fragment associated with the identity tag; The minutes fragments are combined to obtain a dedicated meeting minutes for the user terminal.

7. A system using the digital full-cycle conference project management method according to any one of claims 1 to 6, characterized in that: include: one or more processors; Memory; and one or more programs, The one or more programs are configured to be executed by one or more processors, and the memory includes a storage medium storing computer programs; When the computer program is executed by a processor, it is used to implement a digital full-cycle conference project management method.

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