Information monitoring management system based on intensive platform

Abstract

The invention belongs to the technical field of text information monitoring, and discloses an information monitoring management system based on an intensive platform, which comprises a text information preprocessing module, a text information word segmentation module, a text information anomaly recognition module, a text information error correction module, a text information error correction quality analysis module and a text information error correction quality anomaly recognition module. According to the method, the forward word segmentation operation and the reverse word segmentation operation are respectively carried out when the information word segmentation operation is carried out, the appropriate text information is selected as the target text information word segmentation based on the word segmentation result, the correct combination mode of the vocabularies in the text can be more accurately recognized through the analysis mode, and the overall word segmentation accuracy is high. According to the method, after text information error correction is carried out, analysis and anomaly recognition are further carried out on the text information error correction quality, errors in the error correction process can be found and corrected in time through the analysis mode, and therefore the error correction accuracy is improved. And the accuracy and the reliability of the text information can be ensured.

Description

Technical Field

[0001] The invention belongs to the technical field of text information monitoring, and relates to an information monitoring management system based on an intensive platform. Background Art

[0002] Information monitoring refers to the process of conducting planned and continuous monitoring and measurement of the information of the monitored object through certain technical means, in order to analyze, detect and evaluate the status of the object, and implement intervention measures accordingly. According to the form of information reflection, it is divided into text information, image information and sound information. Among them, text information is an important tool for communication between people, and plays an important role in the fields of enterprise and government decision-making, academic research and knowledge innovation. Therefore, information monitoring research based on text information is of great significance.

[0003] The existing text information monitoring process only involves the identification and correction of text information anomalies, while ignoring further research on the quality of text information correction. This analysis method cannot discover the problems and bottlenecks of current technology. This will limit the further development of technology and make it difficult for text information monitoring technology to make breakthrough progress. Poor correction quality may make text information monitoring technology unable to play its due role in some high-demand application scenarios. Frequent correction errors may reduce users' trust in text information and affect the dissemination effect and influence of information. Summary of the invention

[0004] In view of this, in order to solve the problems raised in the above background technology, an information monitoring and management system based on an intensive platform is proposed.

[0005] The purpose of the present invention can be achieved through the following technical solutions: an information monitoring and management system based on an intensive platform, comprising: a text information preprocessing module, used to extract target text information of a target website, and then preprocess the target text information to obtain preprocessed text information.

[0006] The text information word segmentation module is used to perform text information word segmentation operations on the preprocessed text information to obtain each target text information word segmentation.

[0007] The text information anomaly identification module is used to determine whether there are anomalies in the word segmentation of each target text information.

[0008] The text information error correction module is used to provide error correction prompts when it is determined that there is an abnormality in the word segmentation of a certain target text information.

[0009] The text information error correction quality analysis module is used to count the error correction data of the target text information and analyze the error correction quality of the target text information.

[0010] The text information error correction quality abnormality identification module is used to determine whether there is a text information error correction quality abnormality based on the error correction quality of the target text information, and provide feedback if so.

[0011] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention performs forward segmentation and reverse segmentation respectively when performing the information segmentation operation, and selects the appropriate segmentation as the target text information based on the segmentation result. This analysis method can more accurately identify the correct combination of words in the text, reduce the wrong division caused by a single segmentation direction, and improve the overall segmentation accuracy. At the same time, the system can better adapt to the input of various text styles and improve the processing ability of texts from different sources.

[0012] After performing text information error correction, the present invention further analyzes the error correction quality of the text information and identifies anomalies. This analysis method can timely discover and correct errors in the error correction process, thereby improving the accuracy of error correction. This helps to ensure the accuracy and reliability of text information. Through the analysis of error correction quality and anomaly identification, problems and bottlenecks in current technology can be discovered. This helps scientific researchers and technicians understand the shortcomings of the system, so as to make targeted improvements and optimizations and promote the sustainable development of technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0014] Figure 1 It is a schematic diagram of the connection of each module of the system of the present invention.

[0015] Figure 2 The present invention provides a forward word segmentation operation embodiment corresponding to the implementation steps.

[0016] Figure 3 The present invention provides a reverse word segmentation operation embodiment corresponding to the implementation steps.

[0017] Figure 4 A schematic diagram of an error correction prompt window embodiment provided by the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] See also Figure 1 As shown, the present invention provides an information monitoring and management system based on an intensive platform, including a text information preprocessing module, a text information segmentation module, a text information anomaly recognition module, a text information error correction module, a text information error correction quality analysis module and a text information error correction quality anomaly recognition module, wherein the text information preprocessing module is connected to the text information segmentation module, the text information segmentation module is connected to the text information anomaly recognition module, the text information anomaly recognition module is connected to the text information error correction module, the text information error correction module is connected to the text information error correction quality analysis module, and the text information error correction quality analysis module is connected to the text information error correction quality anomaly recognition module.

[0020] The text information preprocessing module is used to extract target text information of a target website, and then perform a preprocessing operation on the target text information to obtain preprocessed text information.

[0021] In a preferred embodiment of the present invention, the specific steps of performing the preprocessing operation are as follows: extracting target text information of the target website, removing punctuation marks, special symbols, numbers and spaces from the target text information, and then obtaining preprocessed text information.

[0022] It should be noted that the reasons for preprocessing before performing the word segmentation of text information are: 1. Spaces are usually used to separate words or sentences, but in Chinese text, spaces are not used as word boundaries. Therefore, removing spaces can prevent the word segmentation algorithm from mistakenly dividing the characters before and after the space into a word, thereby improving the accuracy of word segmentation. 2. Special symbols and punctuation marks play a specific role in the text, but they do not constitute a word. Removing these symbols can ensure that they will not be combined with surrounding characters into wrong words during word segmentation, thereby improving the accuracy of word segmentation. 3. Numbers may represent dates, times, numbers, etc. in the text, and numbers are not treated as independent words. Removing numbers can prevent numbers from being combined with surrounding characters into unrelated words during word segmentation, especially when numbers and Chinese characters appear mixed. This helps to improve the accuracy of word segmentation.

[0023] Exemplarily, the punctuation marks include but are not limited to “,”, “.”, “?”, the special symbols include but are not limited to “@”, “#”, “￥”, and the numbers include but are not limited to “0”, “1”, “2”.

[0024] The text information word segmentation module is used to perform text information word segmentation on the preprocessed text information to obtain each target text information word segmentation.

[0025] In a preferred embodiment of the present invention, please refer to Figure 2 As shown, the specific process of performing text information word segmentation includes performing forward word segmentation operation, and the specific steps are as follows: A1. Extract the preprocessed text information.

[0026] A2. Use a word segmentation software to perform a left-to-right forward scan on the preprocessed text information based on the set scanning window to obtain the corresponding text information word segmentation.

[0027] A3. Match the text information word segmentation with each vocabulary in the dictionary saved in the word segmentation software.

[0028] A4. If the match is successful, mark the corresponding text information word segmentation as the target text information word segmentation, delete the text information word segmentation, and obtain the remaining text information after forward matching. Adjust the width of the scanning window, and then scan the remaining text information until all the preprocessed text information is scanned to obtain each text information word segmentation.

[0029] A5. If the match fails, adjust the width of the scanning window, and then re-perform a left-to-right forward scan and vocabulary matching on the preprocessed text information until all the preprocessed text information is scanned to obtain each text information word segmentation.

[0030] A6. Extract each text information word segmentation corresponding to the forward analysis operation, and count the number of text information word segmentations corresponding to the forward word segmentation operation.

[0031] Exemplarily, if the target text information is "I plant apple trees", the specific method of the forward word segmentation operation is as follows: 1. Scan the target text information "I plant apple trees". First, match the longest vocabulary "I plant apple trees" with the vocabulary in the dictionary. Since the vocabulary is not entered in the dictionary, the match fails.

[0032] 2. Adjust the width of the scanning window and scan again to get "I plant apples". Match this vocabulary with the vocabulary in the dictionary. Since the vocabulary is not entered in the dictionary, the match fails.

[0033] 3. Adjust the width of the scanning window and scan again to get "I plant apple". Match this vocabulary with the vocabulary in the dictionary. Since the vocabulary is not entered in the dictionary, the match fails.

[0034] 4. Adjust the width of the scanning window and scan again to get "I plant". Match this vocabulary with the vocabulary in the dictionary. Since the vocabulary is not entered in the dictionary, the match fails.

[0035] 5. Adjust the scanning window width and scan again to get "I", match the word with the words in the dictionary. The word has been entered in the dictionary, so the match is successful. Delete "I" to get the remaining text information "plant apple trees".

[0036] 6. Repeat the scanning operation, and finally get the results of the forward word segmentation operation "I", "plant", and "apple tree".

[0037] In a preferred embodiment of the present invention, please refer to Figure 3 As shown, the specific process of performing the word segmentation operation of the text information also includes performing a reverse word segmentation operation, and the specific steps are as follows: B1, extracting pre-processed text information.

[0038] B2. Use the word segmentation software to perform reverse scanning from right to left on the pre-processed text information based on the set scanning window to obtain the corresponding text information word segmentation.

[0039] B3. Match the text information word segments with the words in the dictionary stored in the word segmentation software.

[0040] B4. If the match is successful, the corresponding text information segmentation is recorded as the target text information segmentation, and the text information segmentation is deleted to obtain the reverse matching remaining text information, and the width of the scanning window is adjusted to scan the remaining text information until all the pre-processed text information is scanned to obtain each text information segmentation.

[0041] B5. If the match fails, the width of the scanning window is adjusted, and then the pre-processed text information is scanned and matched again from right to left in reverse order, until all the pre-processed text information is scanned and each word of the text information is segmented.

[0042] B6. Extract each text information segment corresponding to the reverse analysis operation, and obtain the number of text information segment corresponding to the reverse segmentation operation by counting.

[0043] Exemplarily, if the target text information is "I plant apple trees", the specific method of the reverse word segmentation operation is: 1. Scan the target text information "I plant apple trees", first match the longest word "I plant apple trees" with the words in the dictionary, the word is not entered in the dictionary, so the match fails.

[0044] 2. Adjust the scanning window width and scan again to get "plant apple trees". Match the word with the words in the dictionary. The word is not entered in the dictionary, so the match fails.

[0045] 3. Adjust the scanning window width and scan again to get "apple tree". Match the word with the words in the dictionary. The word has been entered in the dictionary, so the match is successful. Delete "apple tree" to get the remaining text information "I planted".

[0046] 4. Repeat the scanning operation, and finally get the results of the forward word segmentation operation "I", "plant", and "apple tree".

[0047] In a preferred embodiment of the present invention, the text information segmentation operation also includes identifying specific target text information segments, and the specific method is as follows: extracting the number of text information segmentations corresponding to the forward segmentation operation and the number of text information segmentations corresponding to the reverse segmentation operation, and then comparing them.

[0048] It should be noted that, in certain specific circumstances, the results obtained by performing the forward word segmentation operation and the reverse word segmentation operation may be different. For example, if the target text information is "They should have come back yesterday", the corresponding result of the forward word segmentation operation is "they", "yesterday", "should", "come back", and the corresponding result of the reverse word segmentation operation is "they", "yesterday", "Japan", "should", "come back". At this time, the results obtained by the forward word segmentation operation and the reverse word segmentation operation are inconsistent.

[0049] If the number of text information segmentations corresponding to the forward segmentation operation is greater than or equal to the number of text information segmentations corresponding to the reverse segmentation operation, the text information segmentations corresponding to the reverse analysis operation are used as target text information segmentations.

[0050] If the number of text information segmentations corresponding to the forward segmentation operation is less than the number of text information segmentations corresponding to the forward segmentation operation, the text information segmentations corresponding to the forward analysis operation are used as the target text information segmentations.

[0051] It should be noted that the present invention performs forward segmentation and reverse segmentation respectively when performing the information segmentation operation, and selects the appropriate segmentation as the target text information based on the segmentation result. This analysis method can more accurately identify the correct combination of words in the text, reduce the wrong division caused by a single segmentation direction, and improve the overall segmentation accuracy. At the same time, the system can better adapt to the input of various text styles and improve the processing ability of texts from different sources.

[0052] The text information anomaly identification module is used to determine whether there is an anomaly in each target text information segmentation.

[0053] In a preferred embodiment of the present invention, the specific steps of determining whether there are abnormalities in each target text information segmentation are as follows: extracting the target text information and each target text information segmentation, locating the sentence of each target text information segmentation in the target text information, obtaining the pinyin of each target text information segmentation, and simultaneously obtaining the pinyin of the target text information, and obtaining each word in the target text information that is consistent with the pinyin of each target text information segmentation.

[0054] If the number of words in the target text information that are consistent with the pinyin corresponding to each target text information segmentation is 0, it is determined that there is no abnormality in the corresponding target text information segmentation.

[0055] If the number of words in the target text information that are consistent with the pinyin corresponding to each target text information segmentation is not 0, it is determined that the corresponding target text information segmentation has an abnormality.

[0056] It should be noted that the reason for marking each word that is inconsistent with the previous homophone as a possible anomaly is that it is very important to maintain the consistency of the text during text editing and proofreading. If a word appears in the text that is homophonic but has a different meaning from the previous text, it may mean that an error occurred during the editing or input process of the text. Marking these words as possible anomalies can help editors quickly locate and correct these errors, thereby ensuring the consistency and accuracy of the text.

[0057] The text information error correction module is used to provide error correction prompts when it is determined that a certain target text information segmentation has an abnormality.

[0058] In a preferred embodiment of the present invention, the specific method of performing error correction prompts is as follows: recording the target text information segmentation that is judged to have an abnormality as an abnormal target text information segmentation.

[0059] Extract the words in the target text information that are consistent with the corresponding pinyin of each abnormal target text information segmentation, then count the number of words corresponding to each abnormal target text information segmentation, arrange them in order from large to small, and select the first word as the correction reference word for the corresponding abnormal target text information segmentation.

[0060] See also Figure 4 As shown, error correction is performed using an error correction prompt window, which includes abnormal target text information segmentation, correction reference vocabulary, a "yes" selection key and a "no" selection key.

[0061] The text information error correction quality analysis module is used to collect statistics on the error correction data of the target text information and analyze the error correction quality of the target text information.

[0062] In a preferred embodiment of the present invention, the error correction data of the target text information specifically includes error correction time, error correction accuracy and error correction amount, and the specific analysis method is as follows: obtain the input time of each abnormal target text information segmentation and the display time of the corresponding error correction prompt window, calculate the difference between the display time and the input time of the error correction prompt window of each abnormal target text information segmentation to obtain the error correction time of each abnormal target text information segmentation, and then perform the average calculation to obtain the error correction time of the target text information.

[0063] The number of “yes” selected by the target personnel when receiving error correction prompts for each abnormal target text information segmentation is counted and recorded as the correct error correction amount of the target text information.

[0064] The number of error correction prompts for the target text information is counted and recorded as the error correction amount of the target text information.

[0065] In a preferred embodiment of the present invention, the error correction quality of the target text information needs to be analyzed to construct an error correction quality index of the target text information, and the specific method is as follows: extract the error correction time, error correction accuracy and error correction amount corresponding to the target text information, and record them as , , .

[0066] It should be explained that the reasons for taking the error correction time and the correct amount of error correction as influencing factors of the error correction quality index of the target text information are: 1. The error correction time reflects the efficiency of the error correction process. In text error correction, it is crucial to quickly and accurately identify and correct errors. A longer error correction time may mean that the error correction process is cumbersome or the performance of the error correction tool needs to be improved. Therefore, the error correction time is an important indicator for measuring the quality of error correction, which is directly related to the practicality and user experience of the error correction system. A shorter error correction time means that users can get the corrected text faster, thereby improving work efficiency. 2. The correct amount of error correction directly reflects the accuracy of error correction. An efficient error correction system not only needs to complete the error correction task quickly, but also needs to ensure the accuracy of the error correction results. If the error correction system frequently makes mistakes, then even if its error correction speed is fast, it cannot be regarded as a high-quality error correction system. Therefore, the correct amount of error correction is one of the key indicators for evaluating the performance of the error correction system. A higher correct amount of error correction means that the error correction system can more accurately identify and correct errors in the text, thereby improving the quality and readability of the text.

[0067] Using the formula Analyze and obtain the error correction quality index of the target text information ,in Indicates the preset reference error correction time. , They represent the weight factors corresponding to the error correction time and the amount of error correction respectively.

[0068] For example, It should be noted that the basis for setting the weight factors corresponding to the error correction time and the amount of error correction is: setting the weight factors corresponding to the error correction time and the amount of error correction is a complex and meticulous process, which requires comprehensive consideration of multiple factors to ensure its scientificity and rationality. By setting reasonable weight factors, the error correction quality index of the target text information can be more accurately evaluated, providing strong support for subsequent improvements and enhancements.

[0069] For example, , .

[0070] The text information error correction quality abnormality identification module is used to determine whether there is a text information error correction quality abnormality based on the error correction quality of the target text information, and provide feedback if so.

[0071] In a preferred embodiment of the present invention, the specific method for determining whether there is an abnormality in the error correction quality of the text information is as follows: extracting the error correction quality index of the target text information, and then comparing it with a preset error correction quality index threshold; if the error correction quality index of the target text information is greater than or equal to the error correction quality index threshold, it is determined that there is no abnormality in the error correction quality of the text information; if the error correction quality index of the target text information is less than the error correction quality index threshold, it is determined that there is an abnormality in the error correction quality of the text information.

[0072] Exemplarily, the error correction quality index threshold is 0.85.

[0073] It should be noted that the present invention further analyzes the quality of text information correction and identifies anomalies after performing text information correction. This analysis method can timely discover and correct errors in the error correction process, thereby improving the accuracy of error correction. It helps to ensure the accuracy and reliability of text information. Through the analysis of error correction quality and anomaly identification, problems and bottlenecks in current technology can be discovered. This helps scientific researchers and technicians understand the shortcomings of the system, so as to make targeted improvements and optimizations and promote the sustainable development of technology.

[0074] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An information monitoring and management system based on an intensive platform, characterized in that: include: A text information preprocessing module is used to extract target text information from a target website, and then perform a preprocessing operation on the target text information to obtain preprocessed text information; A text information word segmentation module is used to perform a text information word segmentation operation on the preprocessed text information to obtain each target text information word segmentation; The text information anomaly identification module is used to determine whether there are anomalies in the segmentation of each target text information; The text information error correction module is used to provide error correction prompts when it is determined that there is an abnormality in the word segmentation of a certain target text information; A text information error correction quality analysis module is used to collect statistics on the error correction data of the target text information and analyze the error correction quality of the target text information; The text information error correction quality abnormality identification module is used to determine whether there is a text information error correction quality abnormality based on the error correction quality of the target text information, and provide feedback if so.

2. The information monitoring and management system based on an intensive platform as claimed in claim 1, characterized in that: The specific steps of the pre-processing operation are as follows: The target text information of the target website is extracted, and punctuation marks, special symbols, numbers and spaces are removed from the target text information to obtain preprocessed text information.

3. The information monitoring and management system based on an intensive platform as claimed in claim 1, characterized in that: The specific process of performing the text information word segmentation operation includes performing a forward word segmentation operation, and the specific steps are as follows: A1. Extract preprocessed text information; A2. Scan the pre-processed text information from left to right based on the set scanning window using the word segmentation software to obtain the corresponding text information word segmentation; A3, matching the text information segmentation with each word in the dictionary stored in the segmentation software; A4. If the match is successful, the corresponding text information segmentation is recorded as the target text information segmentation, the text information segmentation is deleted to obtain the remaining text information for positive matching, the width of the scanning window is adjusted, and the remaining text information is scanned until all the pre-processed text information is scanned to obtain each text information segmentation; A5. If the match fails, the width of the scanning window is adjusted, and the pre-processed text information is re-scanned from left to right and the vocabulary is matched until all the pre-processed text information is scanned and the word segmentation of each text information is obtained; A6. Extract each text information segment corresponding to the forward analysis operation, and obtain the number of text information segment corresponding to the forward segmentation operation by counting.

4. The information monitoring and management system based on an intensive platform as claimed in claim 3, characterized in that: The specific process of performing the word segmentation operation of the text information also includes performing a reverse word segmentation operation, and the specific steps are as follows: B1. Extract preprocessed text information; B2. Using the word segmentation software to perform reverse scanning from right to left on the pre-processed text information based on the set scanning window to obtain the corresponding text information word segmentation; B3, matching the text information segmentation with the words in the dictionary stored in the segmentation software; B4. If the match is successful, the corresponding text information segmentation is recorded as the target text information segmentation, the text information segmentation is deleted to obtain the reverse matching remaining text information, the width of the scanning window is adjusted, and the remaining text information is scanned until all the pre-processed text information is scanned to obtain each text information segmentation; B5. If the match fails, the width of the scanning window is adjusted, and the pre-processed text information is scanned and matched again from right to left in reverse order, until all the pre-processed text information is scanned and each text information is segmented; B6. Extract each text information segment corresponding to the reverse analysis operation, and obtain the number of text information segment corresponding to the reverse segmentation operation by counting.

5. The information monitoring and management system based on an intensive platform as claimed in claim 4, characterized in that: The text information segmentation operation further includes identifying specific target text information segmentations, and the specific method is as follows: Extract the number of text information segmentations corresponding to the forward segmentation operation and the number of text information segmentations corresponding to the reverse segmentation operation, and then compare them; If the number of text information segmentations corresponding to the forward segmentation operation is greater than or equal to the number of text information segmentations corresponding to the reverse segmentation operation, then the text information segmentations corresponding to the reverse analysis operation are used as the target text information segmentations; If the number of text information segmentations corresponding to the forward segmentation operation is less than the number of text information segmentations corresponding to the forward segmentation operation, the text information segmentations corresponding to the forward analysis operation are used as the target text information segmentations.

6. The information monitoring and management system based on an intensive platform as claimed in claim 5, characterized in that: The specific steps of determining whether there is an abnormality in the word segmentation of each target text information are as follows: Extract the target text information and each target text information segmentation, locate the sentence of each target text information segmentation in the target text information, obtain the pinyin of each target text information segmentation, and simultaneously obtain the pinyin of the target text information, and obtain each word in the target text information that is consistent with the pinyin of each target text information segmentation; If the number of words in the target text information that are consistent with the pinyin corresponding to each target text information segmentation is 0, it is determined that there is no abnormality in the corresponding target text information segmentation; If the number of words in the target text information that are consistent with the pinyin corresponding to each target text information segmentation is not 0, it is determined that the corresponding target text information segmentation has an abnormality.

7. The information monitoring and management system based on an intensive platform as claimed in claim 6, characterized in that: The specific method of providing error correction prompt is as follows: Recording the target text information segmentation that is judged to be abnormal as abnormal target text information segmentation; Extract the words in the target text information that are consistent with the pinyin corresponding to each abnormal target text information segmentation, and then count the number of words corresponding to each abnormal target text information segmentation, arrange them in order from large to small, and select the first word as the correction reference word for the corresponding abnormal target text information segmentation; The error correction is performed using an error correction prompt window, which includes abnormal target text information segmentation, correction reference vocabulary, a "yes" selection key and a "no" selection key.

8. The information monitoring and management system based on an intensive platform as claimed in claim 1, characterized in that: The error correction data of the target text information specifically includes the error correction time, the correct amount of error correction and the amount of error correction, and the specific analysis method is as follows: Obtain the input time of each abnormal target text information segmentation and the display time of the corresponding error correction prompt window, perform difference calculation on the display time of the error correction prompt window of each abnormal target text information segmentation and the input time to obtain the error correction time of each abnormal target text information segmentation, and then perform mean calculation to obtain the error correction time of the target text information; The number of "yes" selected by the target personnel when the error correction prompt is given for each abnormal target text information segmentation is counted and recorded as the correct error correction amount of the target text information; The number of error correction prompts for the target text information is counted and recorded as the error correction amount of the target text information.

9. The information monitoring and management system based on an intensive platform as claimed in claim 8, characterized in that: The analysis of the error correction quality of the target text information requires constructing an error correction quality index of the target text information, and the specific method is as follows: The error correction time, error correction accuracy and error correction amount corresponding to the target text information are extracted and recorded as , , ; Using the formula Analyze and obtain the error correction quality index of the target text information ,in Indicates the preset reference error correction time. , They represent the weight factors corresponding to the error correction time and the amount of error correction respectively.

10. The information monitoring and management system based on an intensive platform as claimed in claim 9, characterized in that: The specific method of determining whether there is an abnormality in text information error correction quality is as follows: The error correction quality index of the target text information is extracted, and then compared with the preset error correction quality index threshold. If the error correction quality index of the target text information is greater than or equal to the error correction quality index threshold, it is judged that there is no text information error correction quality abnormality. If the error correction quality index of the target text information is less than the error correction quality index threshold, it is judged that there is a text information error correction quality abnormality.

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