CN119202201A - Delivery control system and method based on large model and sound cloning technology - Google Patents
Delivery control system and method based on large model and sound cloning technology Download PDFInfo
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Abstract
The invention discloses a delivery control system and a delivery control method based on a large model and a sound cloning technology, which relate to the technical field of text delivery, and the invention classifies texts to be delivered in history, generates templates of each text and constructs a template database; the method comprises the steps of inputting user data into a text template, automatically generating a delivery text, constructing a text generation large model, selecting a receiving channel with highest comprehensive efficiency as a delivery channel, processing the text according to the type of the delivery channel, delivering the text to a delivery target, generating an evidence chain by using the data in the delivery process, judging whether a delivery task is successful or not according to the generated evidence chain, carrying out secondary delivery by using the optimized delivery channel, carrying out secondary judgment on the delivery task, communicating with the delivery target by using personalized voice, explaining text content to the delivery target, and carrying out opinion feedback after the text content is clear to the delivery target.
Description
Technical Field
The invention relates to the technical field of text delivery, in particular to a delivery control system and method based on a large model and a sound cloning technology.
Background
Traditional legal text delivery relies primarily on manual delivery, mailing, or advertisement delivery. Legal staff needs to send text to the party by going to the gate or mailing. This approach is time consuming, costly, and difficult to guarantee delivery to power. With the rapid development of information technology, legal text delivery mode has undergone the transition from traditional manual delivery to modern intelligent and automatic delivery, especially the introduction of information technology, big data, artificial Intelligence (AI) and other technologies in recent years, so that legal text delivery control becomes more efficient, safe and standard. With the popularization of mobile phones, short message delivery gradually becomes a high-efficiency and convenient delivery mode, and is particularly suitable for short legal texts such as summons, reminding notices and the like. When it is difficult for a party to contact or avoid delivery, the system may post legal text on a designated website via a web advertisement, thereby completing the delivery procedure. However, with the development of informatization and the proliferation of delivery channels, how to select a proper delivery channel when delivering texts is difficult to judge in real time, and many delivery targets are not understood for most legal texts, so how to improve the participation of the delivery targets, understand texts more easily, and increase the text delivery efficiency is important.
Disclosure of Invention
The invention aims to provide a delivery control system and a delivery control method based on a large model and a sound cloning technology, so as to solve the problems in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the method for controlling the delivery based on the large model and the sound cloning technology comprises the following steps:
s100, collecting all texts to be sent in the history work, extracting the characteristics of each text, classifying the texts to be sent in the history, analyzing each type of text structure, generating templates of each text and constructing a template database;
Further, the specific steps of generating templates of each text and constructing a template database are as follows:
s101, collecting all texts to be sent in history work, and extracting words in each text in history as ,Representing the 1 st, 2 nd, 3 rd, n-th words in each text in the extracted history, wherein n is a positive integer, and calculating the number of each extracted word as follows,
Representing the number of times of occurrence of the 1 st, 2 nd, 3 rd, n th words in the calculated text, comparing the number of times of occurrence of each word in the text, taking the word with the largest number of occurrence as a text feature, and screening to obtain the feature of each text in the history, wherein the feature is as follows,The method comprises the steps of representing the characteristics of the 1 st, 2 nd, 3 rd, third and n th texts in the collected history, judging all texts in the collected history, classifying the texts with the same characteristics into the same category, and classifying the texts to obtain all the text categories as,The 1 st, 2 nd, 3 rd, m th texts obtained by classification, m being a positive integer;
s102, after classifying texts in the history, scanning each text image by using a scanning technology, and extracting the positions of all words in each text as ,The method comprises the steps of representing the positions of 1,2, 3, and p words in an extracted text, wherein p is a positive integer, analyzing the position of each word in the text, judging that adjacent words are the same part when each word is adjacent transversely, judging that the words are independent parts when the word positions are not adjacent words, and judging all words in the text to obtain that all word parts in the text are parts through judging all words in the text,Representing the 1 st, 2 nd, 3 rd, q th word portions obtained after text analysis, q being a positive integer;
S103, after analyzing all word parts in the text, extracting the word positions of the text of the same kind for h times, judging the extracted word positions, judging the corresponding words as fixed words when the positions of the same words in the h times of extraction are the same, judging the corresponding words as replacement words when the positions of the words in the h times of extraction are different, and judging the fixed words in each word part in the text as fixed words after the judgment ,Representing that the 1 st, 2 nd, 3 rd, i th fixed words in each word part are obtained through judgment, i is a positive integer, and the replacement words of each part in the text are obtained through judgment,The method comprises the steps of judging 1 st, 2 nd, 3 rd, j th replacement words in each word part, constructing a replacement word set of each text by utilizing all types of the replacement words, combining all word parts obtained by analyzing the text with corresponding fixed words to construct templates of each text, and storing all templates to obtain a template database.
S200, acquiring user data and text features to be generated of a target in real time, judging the types of the texts to be generated by using the text features, selecting templates corresponding to the types of the texts in a template database, inputting the user data into the text templates to automatically generate a text to be transmitted, and constructing a text generation large model;
The method comprises the steps of classifying text contents in history, generating templates of each text, training to obtain texts, automatically generating a large model, only extracting replacement words in user data to fill the large model, automatically generating required texts, replacing a large amount of manual writing work, saving time and labor cost, and automatically generating texts to avoid wrongly written words, grammar errors or logic inconsistencies possibly occurring in manual writing and ensure consistency and accuracy of output. In an information-intensive scenario, the automated generation system can quickly summarize data and information, generating meaningful reports.
Further, the specific steps of constructing the text to generate the large model are as follows:
S201, acquiring characteristics of a text to be generated in real time as Ts, searching a text template with the same characteristics in a template database by utilizing the acquired real-time text characteristics, and extracting a replacement word set in the text template after searching the text template with the characteristics of the real-time text;
S202, acquiring user data of a target in real time as ,Extracting the same kind of data from the collected user data by using the kind of words in the extracted replacement word set to obtain the input word of the generated text, extracting the position of each replacement word in the S103, marking the input word by using the word part position of the replacement word in the text, and setting the marked input word as the positive integer,Represents the 1 st, 2 nd, 3 rd, g th input data after marking,B represents the word part of the input data in the text, W represents the specific position of the input data in the word part of the text, and the marked input data is used for processing the text according to the word part of the input dataThe position of the text template is input into the searched text template, and a real-time delivery text is automatically generated;
s203, training the real-time characteristic search of the classification of the S201 and the generation of the text of the S202 by using machine learning, obtaining a text generation large model after training, and automatically generating the text which needs to be generated in real time in work by using the text generation large model.
S300, collecting all information receiving channels used by a delivery target in a history, calculating the comprehensive efficiency of each receiving channel in the history of the delivery target, selecting the receiving channel with the highest comprehensive efficiency as a conveying channel, processing a text according to the type of the conveying channel, and conveying the text to the delivery target;
further, the specific steps of processing the text according to the type of the conveying channel and conveying the text to the conveying target are as follows:
S301, collecting all information receiving channels used by the target in the history as ,The 1 st, 2 nd, 3 rd, r information receiving channels used for sending the target in the collected history, r being a positive integer;
S302, extracting the use times, the successful reception times and the reception time of each information receiving channel from the collected use data of each information receiving channel, and calculating the comprehensive efficiency of each information receiving communication, wherein the formula is as follows:
;
In the formula, ef represents the calculated comprehensive efficiency of each information receiving channel, C represents the number of times each information receiving channel is used, Representing total usage times of all information receiving channels, su represents successful times of reception after each information receiving channel is used, tt represents receiving time of each information receiving channel, and comprehensive efficiency of each information receiving channel is calculated as,The comprehensive efficiency of the r information receiving channels is calculated, and the information receiving channel with the highest comprehensive efficiency is compared and selected as a delivery channel for delivering the text;
s303, after the delivery channel of the delivered text is selected, carrying out data processing on the delivered text according to the transmission type of the delivery channel, and carrying out delivery on the delivered text after converting the delivered text into the information type of delivery.
Predicting the most effective delivery mode and time according to the behavior preference, the historical response time and the position of the delivery target, thereby improving the delivery power and efficiency;
S400, recording all data in the text delivery process in real time, generating an evidence chain by using the data in the delivery process, and judging whether the delivery task is successful or not according to the generated evidence chain;
Further, the specific steps of judging whether the delivery task is successful or not according to the generated evidence chain are as follows:
s401, analyzing the whole delivery process of the delivered text, recording each time the delivered text is processed, analyzing nodes, and constructing a delivery task chain as a text generation-channel selection-text conversion-transmission-delivery node -Receiving a feedback of the presence of the user,Representing each node reached during the text delivery,Representing delivery to a target node;
S402, when a delivery task is executed on a delivery text, recording the time of each step in a delivery task chain, marking the delivery task chain by using a time stamp to generate an evidence chain, collecting the time Tj when an information receiving channel selected in a history successfully receives information, and calculating an information delivery time threshold, wherein the formula is as follows: In the formula, ty represents a calculated time threshold value of the information receiving channel, Representing the time average of the information received by the selected information receiving channel in the collected history when the information was successfully received,Representing a time standard deviation of the information receiving channel selected from the collected history when the information receiving channel successfully receives the information;
S403, when delivering the text, extracting the time stamp of the sending stage in the evidence chain as Tf, collecting the real-time as Tm, judging the delivering task by using the information delivering time threshold, and when When feedback is not received, judging that the delivery task fails, and delivering the target to receive textAnd when feedback is received, judging that the delivery task is successful, and receiving the text by the delivery target.
The system is internally provided with a whole-course tracking function, an evidence chain of a delivery process is automatically generated, the evidence chain comprises time of each voice delivery, feedback of a receiver, recording of voice content and switching of delivery modes, delivery traceability is ensured, and all delivery behaviors and feedback information are recorded safely and tamper-proof.
S500, carrying out failure tracing on the delivery task by utilizing an evidence chain after judging that the delivery task fails, carrying out omnibearing optimization on a text delivery channel according to a tracing result, carrying out secondary delivery by utilizing the optimized delivery channel, and carrying out secondary judgment on the delivery task;
Further, the specific steps of carrying out secondary judgment on the transport task are as follows:
S501, after judging that a delivery task fails, extracting an evidence chain in the delivery process, checking the evidence chain for tracing, extracting delivery nodes in the evidence chain, and taking the last recorded delivery node as a delivery failure source point;
S502, screening according to the order of the comprehensive efficiency from high to low in S302, checking the delivery nodes needing to pass through in each information receiving channel, extracting the information receiving channels without delivery failure source points in the delivery channels, and selecting the delivery channel with the highest comprehensive efficiency in the extracted information receiving channels as the optimized delivery channel;
s503, conveying the delivery text by using the optimized conveying channel, and performing secondary judgment on the optimized delivery task according to the method in S403.
The method has the advantages that the source tracing searching can be clearly carried out on the failure reasons by utilizing the delivery nodes in the evidence chain to obtain failure source points, the delivery channel is optimized by utilizing the failure source points, the delivery of a new round is carried out, the reliability and the efficiency of the delivery are greatly enhanced, and the failure reason checking time is shortened.
And S600, when judging that the delivery task is completed and the text delivery is successful, the delivery target reads the delivery texts of different types in different modes by using the client, the client generates personalized voice according to the text types by using a sound cloning technology, communicates with the delivery target by using the personalized voice, interprets the text content to the delivery target, and feeds back comments after delivering the clear text content to the delivery target.
Further, the specific steps of feeding back after the target clear text content is sent are as follows:
S601, when judging that a delivery task is completed and text delivery is successful, a client acquires the type of the delivered text in real time, reads the delivered text in a corresponding type reading mode, generates personalized voice according to the text type by utilizing a sound cloning technology after reading the text, and utilizes the personalized voice to communicate with a delivery target, and utilizes a transformer algorithm to process problems raised by a user in the communication process, and the personalized voice solves the problems raised by the user;
S602, when the user understands the text content and responds and executes according to the text content, user opinion feedback is carried out through the client side, and the delivery task is completed.
When the system transmits a notice through voice, a user can perform real-time interactive inquiry through voice or text input to know text details or supplementary relevant information. The multi-mode interaction mode can enable the delivery process to be more efficient and flexible, and enhance the participation of the parties. Different types of voices are generated according to the types of the texts by utilizing a voice cloning technology, serious voices are generated when the texts are important and urgent, and relaxed voices are generated when the texts are non-urgent, so that the delivery target can feel the text contents in a self-cutting manner, and the delivery target can quickly understand the text contents.
The delivery control system based on the large model and the sound cloning technology comprises a data collection module, a text generation module, a channel selection module, a task judgment module, an optimization module and a personalized voice module;
The data collection module is used for collecting information receiving channels and use data which are sent to a target in the history;
The text generation module is used for classifying texts by utilizing texts generated in historical work, constructing templates of each text, and automatically generating the texts by utilizing the text templates;
The channel selection module is used for analyzing according to the collected information receiving channels used for delivering the target in the history, calculating the comprehensive efficiency and selecting a delivery channel for delivering the text;
The task judging module is used for recording the time stamp generated in the delivering process to form an evidence chain, calculating an information receiving time threshold according to the historical receiving time, and judging whether the delivering task is successful or not by using the time threshold and the evidence chain;
The optimizing module is used for tracing the failure by utilizing an evidence chain after judging that the delivery task fails to acquire a delivery failure source point, analyzing the delivery failure source point and optimizing and re-delivering the delivery channel;
The personalized voice module is used for generating personalized voice according to the text type by utilizing a voice cloning technology after the text is sent, communicating with a sending target by utilizing the personalized voice, processing the problem posed by the user by utilizing a transformer algorithm in the communication process, and solving the problem posed by the user by utilizing the personalized voice
The text generation module comprises a classification unit, a template construction unit and a text generation large model unit;
The classifying unit is used for extracting the characteristics of all texts generated in the history and classifying the texts according to the characteristics of each text;
The template construction unit is used for scanning the texts, searching word parts, fixed words and replacement words of the texts, and constructing templates of each text;
The text generation large model unit is used for collecting user data sent to a target, extracting replacement words in the user data as input words, marking the positions of the input words, inputting the positions of the input words into the template to automatically generate a text, and generating the text by utilizing machine learning training to generate a large model.
The task judging module comprises a delivering task chain unit, an evidence chain unit, a threshold unit and a judging unit;
The delivery task chain unit is used for analyzing the delivery process and generating a delivery task chain;
The evidence chain unit is used for extracting the time of delivering each node in the task chain, generating a time stamp mark and constructing an evidence chain;
the threshold unit is used for calculating and obtaining an information delivery time threshold according to the time of receiving information by the delivery channel in the history;
the judging unit is used for extracting the time stamp and the real-time of the sending stage in the evidence chain and judging the sending task by utilizing the time threshold.
Compared with the prior art, the invention has the beneficial effects that:
1. the method and the device generate templates of each text after classifying the text content in the history, train the templates to obtain the text, automatically generate a large model, only extract the replacement words in the user data to fill the large model to automatically generate the required text, replace a large amount of manual writing work, save time and labor cost, and automatically generate the text to avoid wrongly written words, grammar errors or logic inconsistencies possibly occurring in manual writing and ensure the consistency and accuracy of output.
2. The invention automatically generates the evidence chain of the delivery process, which comprises the time of each voice delivery, the feedback of a receiver, the recording of voice content and the switching process of delivery modes, ensures the legality and traceability of delivery, and ensures the transparency and fairness of legal delivery when all delivery behaviors and feedback information are recorded safely and non-falsely as evidence in future litigation or dispute.
3. The user can perform real-time interactive query through voice or text input, and know text details or supplement related information. By utilizing the multi-mode interaction mode, the delivering process can be more efficient and flexible, and the participation of the parties is enhanced.
Drawings
FIG. 1 is a block diagram of a delivery control system based on a large model and acoustic cloning technique in accordance with the present invention;
FIG. 2 is a schematic diagram showing steps of a method for controlling delivery based on a large model and a sound cloning technique according to the present invention;
FIG. 3 is a schematic diagram of a text template of a delivery control method based on a large model and a sound cloning technique according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment as shown in fig. 1-3, the present invention provides a technical solution,
The method for controlling the delivery based on the large model and the sound cloning technology comprises the following steps:
s100, collecting all texts to be sent in the history work, extracting the characteristics of each text, classifying the texts to be sent in the history, analyzing each type of text structure, generating templates of each text and constructing a template database;
The specific steps of generating templates of each text and constructing a template database are as follows:
s101, collecting all texts to be sent in history work, and extracting words in each text in history as ,Representing the 1 st, 2 nd, 3 rd, n-th words in each text in the extracted history, wherein n is a positive integer, and calculating the number of each extracted word as follows,
Representing the number of times of occurrence of the 1 st, 2 nd, 3 rd, n th words in the calculated text, comparing the number of times of occurrence of each word in the text, taking the word with the largest number of occurrence as a text feature, and screening to obtain the feature of each text in the history, wherein the feature is as follows,The method comprises the steps of representing the characteristics of the 1 st, 2 nd, 3 rd, third and n th texts in the collected history, judging all texts in the collected history, classifying the texts with the same characteristics into the same category, and classifying the texts to obtain all the text categories as,The 1 st, 2 nd, 3 rd, m th texts obtained by classification, m being a positive integer;
s102, after classifying texts in the history, scanning each text image by using a scanning technology, and extracting the positions of all words in each text as ,The method comprises the steps of representing the positions of 1,2, 3, and p words in an extracted text, wherein p is a positive integer, analyzing the position of each word in the text, judging that adjacent words are the same part when each word is adjacent transversely, judging that the words are independent parts when the word positions are not adjacent words, and judging all words in the text to obtain that all word parts in the text are parts through judging all words in the text,Representing the 1 st, 2 nd, 3 rd, q th word portions obtained after text analysis, q being a positive integer;
S103, after analyzing all word parts in the text, extracting the word positions of the text of the same kind for h times, judging the extracted word positions, judging the corresponding words as fixed words when the positions of the same words in the h times of extraction are the same, judging the corresponding words as replacement words when the positions of the words in the h times of extraction are different, and judging the fixed words in each word part in the text as fixed words after the judgment ,Representing that the 1 st, 2 nd, 3 rd, i th fixed words in each word part are obtained through judgment, i is a positive integer, and the replacement words of each part in the text are obtained through judgment,The method comprises the steps of judging 1 st, 2 nd, 3 rd, j th replacement words in each word part, constructing a replacement word set of each text by utilizing all types of the replacement words, combining all word parts obtained by analyzing the text with corresponding fixed words to construct templates of each text, and storing all templates to obtain a template database.
S200, acquiring user data and text features to be generated of a target in real time, judging the types of the texts to be generated by using the text features, selecting templates corresponding to the types of the texts in a template database, inputting the user data into the text templates to automatically generate a text to be transmitted, and constructing a text generation large model;
The method comprises the steps of classifying text contents in history, generating templates of each text, training to obtain texts, automatically generating a large model, only extracting replacement words in user data to fill the large model, automatically generating required texts, replacing a large amount of manual writing work, saving time and labor cost, and automatically generating texts to avoid wrongly written words, grammar errors or logic inconsistencies possibly occurring in manual writing and ensure consistency and accuracy of output. In an information-intensive scenario, the automated generation system can quickly summarize data and information, generating meaningful reports.
The specific steps of constructing the text generation large model are as follows:
S201, acquiring characteristics of a text to be generated in real time as Ts, searching a text template with the same characteristics in a template database by utilizing the acquired real-time text characteristics, and extracting a replacement word set in the text template after searching the text template with the characteristics of the real-time text;
S202, acquiring user data of a target in real time as ,Extracting the same kind of data from the collected user data by using the kind of words in the extracted replacement word set to obtain the input word of the generated text, extracting the position of each replacement word in the S103, marking the input word by using the word part position of the replacement word in the text, and setting the marked input word as the positive integer,Represents the 1 st, 2 nd, 3 rd, g th input data after marking,B represents the word part of the input data in the text, W represents the specific position of the input data in the word part of the text, and the marked input data is used for processing the text according to the word part of the input dataThe position of the text template is input into the searched text template, and a real-time delivery text is automatically generated;
s203, training the real-time characteristic search of the classification of the S201 and the generation of the text of the S202 by using machine learning, obtaining a text generation large model after training, and automatically generating the text which needs to be generated in real time in work by using the text generation large model.
S300, collecting all information receiving channels used by a delivery target in a history, calculating the comprehensive efficiency of each receiving channel in the history of the delivery target, selecting the receiving channel with the highest comprehensive efficiency as a conveying channel, processing a text according to the type of the conveying channel, and conveying the text to the delivery target;
the specific steps of processing the text according to the type of the conveying channel and conveying the text to the conveying target are as follows:
S301, collecting all information receiving channels used by the target in the history as ,The 1 st, 2 nd, 3 rd, r information receiving channels used for sending the target in the collected history, r being a positive integer;
S302, extracting the use times, the successful reception times and the reception time of each information receiving channel from the collected use data of each information receiving channel, and calculating the comprehensive efficiency of each information receiving communication, wherein the formula is as follows:
;
In the formula, ef represents the calculated comprehensive efficiency of each information receiving channel, C represents the number of times each information receiving channel is used, Representing total usage times of all information receiving channels, su represents successful times of reception after each information receiving channel is used, tt represents receiving time of each information receiving channel, and comprehensive efficiency of each information receiving channel is calculated as,The comprehensive efficiency of the r information receiving channels is calculated, and the information receiving channel with the highest comprehensive efficiency is compared and selected as a delivery channel for delivering the text;
s303, after the delivery channel of the delivered text is selected, carrying out data processing on the delivered text according to the transmission type of the delivery channel, and carrying out delivery on the delivered text after converting the delivered text into the information type of delivery.
Predicting the most effective delivery mode and time according to the behavior preference, the historical response time and the position of the delivery target, thereby improving the delivery power and efficiency;
S400, recording all data in the text delivery process in real time, generating an evidence chain by using the data in the delivery process, and judging whether the delivery task is successful or not according to the generated evidence chain;
the specific steps of judging whether the delivery task is successful or not according to the generated evidence chain are as follows:
s401, analyzing the whole delivery process of the delivered text, recording each time the delivered text is processed, analyzing nodes, and constructing a delivery task chain as a text generation-channel selection-text conversion-transmission-delivery node -Receiving a feedback of the presence of the user,Representing each node reached during the text delivery,Representing delivery to a target node;
S402, when a delivery task is executed on a delivery text, recording the time of each step in a delivery task chain, marking the delivery task chain by using a time stamp to generate an evidence chain, collecting the time Tj when an information receiving channel selected in a history successfully receives information, and calculating an information delivery time threshold, wherein the formula is as follows: In the formula, ty represents a calculated time threshold value of the information receiving channel, Representing the time average of the information received by the selected information receiving channel in the collected history when the information was successfully received,Representing a time standard deviation of the information receiving channel selected from the collected history when the information receiving channel successfully receives the information;
S403, when delivering the text, extracting the time stamp of the sending stage in the evidence chain as Tf, collecting the real-time as Tm, judging the delivering task by using the information delivering time threshold, and when When feedback is not received, judging that the delivery task fails, and delivering the target to receive textAnd when feedback is received, judging that the delivery task is successful, and receiving the text by the delivery target.
The system is internally provided with a whole-course tracking function, an evidence chain of a delivery process is automatically generated, the system comprises time of each voice delivery, feedback of a receiver, recording of voice content and switching of delivery modes, the validity and traceability of delivery are ensured, all delivery behaviors and feedback information are recorded safely and non-falsely and used as evidence in future litigation or dispute, and the transparency and fairness of legal delivery are ensured.
S500, carrying out failure tracing on the delivery task by utilizing an evidence chain after judging that the delivery task fails, carrying out omnibearing optimization on a text delivery channel according to a tracing result, carrying out secondary delivery by utilizing the optimized delivery channel, and carrying out secondary judgment on the delivery task;
The method for carrying out secondary judgment on the transport task comprises the following specific steps:
S501, after judging that a delivery task fails, extracting an evidence chain in the delivery process, checking the evidence chain for tracing, extracting delivery nodes in the evidence chain, and taking the last recorded delivery node as a delivery failure source point;
S502, screening according to the order of the comprehensive efficiency from high to low in S302, checking the delivery nodes needing to pass through in each information receiving channel, extracting the information receiving channels without delivery failure source points in the delivery channels, and selecting the delivery channel with the highest comprehensive efficiency in the extracted information receiving channels as the optimized delivery channel;
s503, conveying the delivery text by using the optimized conveying channel, and performing secondary judgment on the optimized delivery task according to the method in S403.
The method has the advantages that the source tracing searching can be clearly carried out on the failure reasons by utilizing the delivery nodes in the evidence chain to obtain failure source points, the delivery channel is optimized by utilizing the failure source points, the delivery of a new round is carried out, the reliability and the efficiency of the delivery are greatly enhanced, and the failure reason checking time is shortened.
And S600, when judging that the delivery task is completed and the text delivery is successful, the delivery target reads the delivery texts of different types in different modes by using the client, the client generates personalized voice according to the text types by using a sound cloning technology, communicates with the delivery target by using the personalized voice, interprets the text content to the delivery target, and feeds back comments after delivering the clear text content to the delivery target.
The specific steps of feeding back after the target clear text content is sent are as follows:
S601, when judging that a delivery task is completed and text delivery is successful, a client acquires the type of the delivered text in real time, reads the delivered text in a corresponding type reading mode, generates personalized voice according to the text type by utilizing a sound cloning technology after reading the text, and utilizes the personalized voice to communicate with a delivery target, and utilizes a transformer algorithm to process problems raised by a user in the communication process, and the personalized voice solves the problems raised by the user;
S602, when the user understands the text content and responds and executes according to the text content, user opinion feedback is carried out through the client side, and the delivery task is completed.
When the system transmits a notice through voice, a user can perform real-time interactive inquiry through voice or text input to know text details or supplementary relevant information. The multi-mode interaction mode can enable the delivery process to be more efficient and flexible, and enhance the participation of the parties. Different types of voices are generated according to the types of the texts by utilizing a voice cloning technology, serious voices are generated when the texts are important and urgent, and relaxed voices are generated when the texts are non-urgent, so that the delivery target can feel the text contents in a self-cutting manner, and the delivery target can quickly understand the text contents.
The delivery control system based on the large model and the sound cloning technology comprises a data collection module, a text generation module, a channel selection module, a task judgment module, an optimization module and a personalized voice module;
The data collection module is used for collecting information receiving channels and use data which are sent to a target in the history;
The text generation module is used for classifying texts by utilizing texts generated in historical work, constructing templates of each text, and automatically generating the texts by utilizing the text templates;
The channel selection module is used for analyzing according to the collected information receiving channels used for delivering the target in the history, calculating the comprehensive efficiency and selecting a delivery channel for delivering the text;
The task judging module is used for recording the time stamp generated in the delivering process to form an evidence chain, calculating an information receiving time threshold according to the historical receiving time, and judging whether the delivering task is successful or not by using the time threshold and the evidence chain;
The optimizing module is used for tracing the failure by utilizing an evidence chain after judging that the delivery task fails to acquire a delivery failure source point, analyzing the delivery failure source point and optimizing and re-delivering the delivery channel;
The personalized voice module is used for generating personalized voice according to the text type by utilizing a voice cloning technology after the text is sent, communicating with a sending target by utilizing the personalized voice, processing the problem posed by the user by utilizing a transformer algorithm in the communication process, and solving the problem posed by the user by utilizing the personalized voice
The text generation module comprises a classification unit, a template construction unit and a text generation large model unit;
The classifying unit is used for extracting the characteristics of all texts generated in the history and classifying the texts according to the characteristics of each text;
The template construction unit is used for scanning the texts, searching word parts, fixed words and replacement words of the texts, and constructing templates of each text;
The text generation large model unit is used for collecting user data sent to a target, extracting replacement words in the user data as input words, marking the positions of the input words, inputting the positions of the input words into the template to automatically generate a text, and generating the text by utilizing machine learning training to generate a large model.
The task judging module comprises a delivering task chain unit, an evidence chain unit, a threshold unit and a judging unit;
The delivery task chain unit is used for analyzing the delivery process and generating a delivery task chain;
The evidence chain unit is used for extracting the time of delivering each node in the task chain, generating a time stamp mark and constructing an evidence chain;
the threshold unit is used for calculating and obtaining an information delivery time threshold according to the time of receiving information by the delivery channel in the history;
the judging unit is used for extracting the time stamp and the real-time of the sending stage in the evidence chain and judging the sending task by utilizing the time threshold.
Analyzing legal texts generated in histories, classifying the texts into civil complaints, authorized delegated books and the like;
Using an authorized code book as an example, generating a text template by scanning and determining a word part, a fixed word and a replacement word as shown in fig. 3, wherein xxx represents the replacement word;
extracting and inputting replacement words in user data reaching a target, and automatically generating an authorization notice;
The information receiving channel used for the target in the history is a mailbox, an express, a short message and a telephone voice, and the mailbox with the highest comprehensive efficiency is obtained by calculation;
Calculating to obtain a delivery time threshold of 30min, extracting the sending time of 12:00 and the real-time of 13:00 in an evidence chain, judging that the number of times system is not fed back yet, and judging that delivery fails;
And tracing the source of failure by using the evidence chain to obtain the failure source point as the mailbox address error, reselecting the telephone voice as a conveying channel to carry out new conveying, and converting the text into voice data transmission.
After the delivery task is completed, the delivery target receives the delivery text, the text type is acquired by the client, the text type is voice, the client reads the authorization notice by adopting a voice reading mode, the voice cloning technology is utilized to generate personalized voice to explain the content of the authorization notice to the delivery target, the delivery target is helped to understand the text, the delivery target is understood, and user opinion feedback is carried out in the client according to the text when the delivery target is understood, so that delivery is completed.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
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