A method and apparatus for information retrieval and guidance based on a big data software system

Abstract

The present invention discloses a method and apparatus for information retrieval and guidance based on a big data software system. The present invention relates to the technical field of data processing, and solves the technical problem that the lack of in-depth analysis of retrieval problems and effective integration and screening of retrieval results often leads to a large amount of irrelevant or redundant information in the retrieval results. By finely splitting the retrieval problem according to the theme content, the present invention can more accurately understand the user's retrieval intention. Compared with the traditional retrieval method based on overall keywords, the targeting and accuracy of the retrieval are improved, and the confusion and inaccuracy of the retrieval results caused by the general use of keywords are avoided. The semantic-based analysis method can more accurately identify information with similar content, overcomes the limitations of traditional vocabulary-based matching only, effectively reduces redundant information, makes the retrieval results more refined and valuable, improves the efficiency of users to obtain useful information, and improves the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and device for information retrieval and guidance based on a big data software system. Background Art

[0002] In the field of information retrieval, with the explosive growth of data volume, traditional information retrieval methods face many challenges. When users are faced with a vast amount of information, it is often difficult to quickly and accurately obtain information that is highly relevant and comprehensive to the retrieval problem.

[0003] A patent application with the publication number CN115510085A discloses a method and device for information retrieval and guidance based on a big data software system, which relates to the technical field of data processing, and includes: obtaining keywords input by a user; processing the keywords using a preset tag attribute model to obtain a retrieval result of the keywords; wherein, the retrieval result includes: function field information with exact match or fuzzy match; the function field information includes: the label of the function field, the name of the function field, the meaning of the function field, and the uniform resource locator URL of the function field; in response to the user's access operation on the URL, jumping to the management page corresponding to the function field to guide the user to use the management function corresponding to the function field in the big data software system based on the management page.

[0004] However, most of the existing retrieval systems are mostly based on simple keyword matching, lacking in-depth analysis of the retrieval problem and effective integration and screening of the retrieval results, resulting in the retrieval results often containing a large amount of irrelevant or redundant information, and users need to spend a lot of time and effort to identify useful content from the complicated results. Moreover, for complex retrieval requirements, it is unable to effectively mine the correlation relationships between different parts, difficult to provide systematic and targeted information, and cannot well meet the growing demand for accurate information acquisition by users in academic research, business decision-making, professional technical exploration, etc. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a method and device for information retrieval and guidance based on a big data software system, which solves the problem that due to the lack of in-depth analysis of the retrieval problem and effective integration and screening of the retrieval results, the retrieval results often contain a large amount of irrelevant or redundant information.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for information retrieval and guidance based on a big data software system, the method includes the following steps:

[0007] Step 1: Obtain the user's retrieval information. At the same time, split the retrieval question according to the retrieval information to obtain the split retrieval content. Match the obtained split retrieval content with the database to get the split matching results, and filter the same matching results according to the split matching results;

[0008] Step 2: Obtain the remaining split matching results. At the same time, conduct an analysis of relevant content to obtain the relevant content, and integrate the relevant content to obtain the result to be analyzed;

[0009] Step 3: Conduct a comprehensive analysis of the obtained same matching results and the results to be analyzed to obtain the comprehensive analysis results. Integrate the comprehensive analysis results corresponding to the same semantics to obtain the secondary classification results. At the same time, analyze the duplicate information corresponding to the secondary classification results to generate the duplicate analysis results;

[0010] Step 4: Analyze the obtained duplicate analysis results, screen by calculating the relevance between the content of the duplicate analysis results and the retrieval question, and sort them in descending order according to the calculated relevance to generate the retrieval information.

[0011] As a further solution of the present invention, the specific method for obtaining the same matching results in Step 1 is as follows:

[0012] Obtain the retrieval information of the retrieval user. Then split the retrieval question according to the theme content to obtain the split retrieval content. Obtain the split retrieval content and label it as n, where n = 1, 2,..., m, and m represents the number of split retrieval content. Then, respectively retrieve and match the split retrieval content n with the corresponding database to obtain the corresponding split matching results. Obtain the split matching results corresponding to different split retrieval content. Then, classify the split matching results to obtain the same matching results.

[0013] As a further solution of the present invention, the specific method for obtaining the result to be analyzed in Step 2 is as follows:

[0014] Obtain all the remaining split matching results, and distinguish and label the remaining split matching results as split matching results a, where a = A, B,..., and a represents the type of split retrieval content corresponding to the remaining split matching results. Then, obtain any type of split matching result as the analysis object, and record the corresponding matching results in the analysis object as i, where i = 1, 2,..., j, and j represents the number of matching results. At the same time, identify the matching content of the matching result i, and then perform a relevance matching of the matching result i with the database to obtain the relevant content. By analogy, analyze all the split matching results to obtain the corresponding relevant content;

[0015] Next, perform similar analysis on the associated content corresponding to different split matching results, and screen out the associated content with similar content to generate the result to be analyzed.

[0016] As a further solution of the present invention, the specific method for obtaining the secondary classification result in step three is as follows:

[0017] Obtain all the same matching results and the results to be analyzed, combine the two to obtain a comprehensive analysis result, and label it as c at the same time, and c = 1, 2,..., r, where r represents the number of comprehensive analysis results. Then, analyze the semantics of the content corresponding to the comprehensive analysis result c, calculate the similarity of the content semantics, integrate the comprehensive analysis results with the same semantics to obtain the secondary classification result, and at the same time obtain the result quantity corresponding to the secondary classification result, calculate the quantity proportion corresponding to the secondary classification result, and then sort them from largest to smallest according to the quantity proportion.

[0018] As a further solution of the present invention, the specific method for obtaining the repeated analysis result in step three is as follows:

[0019] Obtain all the secondary classification results. At the same time, take any group of secondary classification results as the target object, and obtain the corresponding comprehensive analysis result in the target object. Then, extract the repeated information in the comprehensive analysis result, obtain the quantity of the repeated information corresponding to the comprehensive analysis result in the target object, and obtain the corresponding comprehensive analysis result denoted as the same result based on the repeated information. By analogy, analyze all the repeated information and generate the corresponding same result;

[0020] Next, obtain the types of repeated information of the same result. If there is only one type of repeated information corresponding to the same result, then eliminate the same result. If there are multiple types of repeated information corresponding to the same result, then obtain the comprehensive analysis results corresponding to the multiple types of repeated information and retain them. At the same time, eliminate the remaining comprehensive analysis results in the same result to generate the repeated analysis result.

[0021] As a further solution of the present invention, the specific method for generating the retrieval information in step four is as follows:

[0022] Obtain all the repeated analysis results and label them as k, and k = 1, 2,..., h, where h represents the number of repeated analysis results. Then, obtain the retrieval question. At the same time, take the retrieval question as the standard to obtain the same vocabulary existing in the repeated analysis results, and obtain the similarity value between the repeated analysis results and the retrieval question. Then, substitute the obtained number of the same vocabulary and the similarity value into the formula Q = (S + G) × u to calculate the relevance of the repeated analysis result, where u is a preset proportionality coefficient, S is the number of the same vocabulary, and G is the similarity value;

[0023] By analogy, calculate the relevance Qk corresponding to all repeated analysis results, and calculate the mean value of all relevance sum values. At the same time, use the mean value as the standard to screen according to the relevance Qk. Eliminate the repeated analysis results with a relevance Qk less than the mean relevance, and retain the repeated analysis results with a relevance Qk greater than the mean relevance. Then sort them from largest to smallest according to the relevance Qk to generate retrieval information.

[0024] An information retrieval and guidance device based on a big data software system, including an information collection module, an information matching and analysis module, a comprehensive analysis and processing module, and a retrieval information output module;

[0025] The information collection module is used to obtain the retrieval information of the user and transmit the retrieval information to the information matching and analysis module;

[0026] The information matching and analysis module splits the retrieval problem according to the retrieval information to obtain the split retrieval content, matches the obtained split retrieval content with the database to obtain the split matching result, and at the same time screens the same matching result according to the split matching result, analyzes the relevant content to obtain the relevant content, and integrates the relevant content to obtain the result to be analyzed. Then transmit the result to be analyzed to the comprehensive analysis and processing module;

[0027] The comprehensive analysis and processing module comprehensively analyzes the obtained same matching result and the result to be analyzed to obtain the comprehensive analysis result, integrates the comprehensive analysis results corresponding to the same semantics to obtain the secondary classification result, and at the same time analyzes the repeated information corresponding to the secondary classification result to generate the repeated analysis result. Screen by calculating the relevance between the content of the repeated analysis result and the retrieval problem, and sort from largest to smallest according to the calculated relevance to generate the retrieval information, and transmit the retrieval information to the retrieval information output module;

[0028] The retrieval information output module is used to display the obtained retrieval information to the corresponding operator.

[0029] The present invention provides a method and device for information retrieval and guidance based on a big data software system. Compared with the prior art, it has the following beneficial effects:

[0030] By finely splitting the retrieval problem according to the subject content, the present invention can more accurately understand the user's retrieval intention. Compared with the traditional retrieval method based on overall keywords, it improves the pertinence and accuracy of retrieval, avoids the chaos and inaccuracy of retrieval results caused by the general use of keywords. The semantic-based analysis method can more accurately identify information with similar content, overcomes the limitations of traditional vocabulary-based matching, effectively reduces redundant information, makes the retrieval results more refined and valuable. By mining potential related content through relevance content analysis and integrating it into the result to be analyzed, it can re-integrate and utilize relevant information that might otherwise be overlooked, broaden the scope of information obtained by users, and provide users with a more comprehensive and in-depth knowledge system.

[0031] By calculating the relevance between the repeated analysis result content and the retrieval problem, and screening and sorting according to the relevance, it ensures the high relevance of the retrieval information presented to users to the original retrieval problem in a scientific quantitative way, improves the efficiency of users obtaining useful information, and improves the user experience. Traditional technologies often have difficulty in effectively quantifying and ranking the relevance between retrieval results and problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flowchart of the method steps of the present invention;

[0033] Figure 2 It is a block diagram of the system principle of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0035] Embodiment 1, please refer to Figure 1 , the present application provides a method for information retrieval and guidance based on a big data software system, and the method specifically includes the following steps:

[0036] Step 1: Obtain the user's retrieval information, at the same time split the retrieval problem according to the retrieval information to obtain split retrieval content, match the obtained split retrieval content with the database to obtain a split matching result, and at the same time screen the same matching result according to the split matching result.

[0037] First, obtain the retrieval information of the retrieval user, and the retrieval information includes the retrieval problem corresponding to the user. Then split the retrieval problem according to the subject content to obtain split retrieval content.

[0038] For example, the retrieved information input by the user is "Research materials on the current situation and development trends of the application of artificial intelligence in the medical field". According to the internal logic and semantic association of the theme content, it can be finely split into multiple split retrieval contents such as "the current situation of the application of artificial intelligence in the medical field", "the development trends of artificial intelligence in the medical field", and "research materials on the application of artificial intelligence in medicine".

[0039] Obtain the split retrieval contents and label them as n, where n = 1, 2, …, m, and m represents the number of split retrieval contents. Then, separately retrieve and match the split retrieval content n with the corresponding database to obtain the corresponding split matching results. And the split matching results obtained here are the matching results corresponding to different split retrieval contents alone. Obtain the split matching results corresponding to different split retrieval contents, and then classify the split matching results to obtain the same matching results. And the classification process here is specifically to classify the split matching results with similar content. Specifically, the similarity of the content is calculated by calculating the similarity value, and the cosine similarity calculation method is used for calculation.

[0040] Step 2: Obtain the remaining split matching results, simultaneously conduct an analysis of relevant content to obtain relevant content, and integrate the relevant content to obtain the result to be analyzed.

[0041] Obtain all the remaining split matching results. And the remaining split matching results here refer to the split matching results remaining after removing the same matching results, specifically including the different split matching results corresponding to the split retrieval contents. For example, if there are two split retrieval contents, the corresponding different split matching results are the matching results corresponding to the two split retrieval contents. And mark the remaining split matching results as split matching result a, where a = A, B, … Specifically, the meaning of the matching result here is the same as that of the remaining split matching results. Among them, a represents the type of split retrieval content corresponding to the remaining split matching results. Then, obtain any type of split matching result as the analysis object, and mark the corresponding matching result in the analysis object as i, where i = 1, 2, …, j, and j represents the number of matching results. At the same time, identify the matching content of the matching result i, and then conduct a relevant matching of the matching result i with the database. And the relevant matching here means conducting a relevant matching of the corresponding content to obtain relevant content. By analogy, analyze all the split matching results to obtain the corresponding relevant content;

[0042] Then, conduct a similar analysis of the relevant content corresponding to different split matching results, and screen out the relevant content with similar content to generate the result to be analyzed.

[0043] Step 3: Perform a comprehensive analysis on the identical matching results and the results to be analyzed to obtain a comprehensive analysis result, integrate the comprehensive analysis results corresponding to the same semantics to obtain a secondary classification result, and analyze the repeated information corresponding to the secondary classification result to generate a repeated analysis result.

[0044] Obtain all identical matching results and results to be analyzed, and combine the two to obtain a comprehensive analysis result, which is labeled c, and c=1, 2, ..., r, where r represents the number of comprehensive analysis results. Then, the content semantics corresponding to the comprehensive analysis result c is analyzed, and the similarity of the content semantics is calculated. The comprehensive analysis results with the same semantics are integrated to obtain a secondary classification result, and the secondary classification result here is specifically expressed as a comprehensive analysis result with the same semantic expression content. At the same time, the number of results corresponding to the secondary classification result is obtained, and the number proportion corresponding to the secondary classification result is calculated, and then the results are sorted from large to small according to the number proportion;

[0045] All secondary classification results are obtained, and any group of secondary classification results is obtained as a target object, and the corresponding comprehensive analysis results in the target object are obtained, and then the repeated information in the comprehensive analysis results is extracted, and the repeated information here is represented by the same information existing in different comprehensive analysis results, and specifically can be the same information existing in two groups of comprehensive analysis results, or three groups, etc., and the number of repeated information corresponding to the comprehensive analysis results in the target object is obtained, and the number of types of repeated information is obtained here, and the corresponding comprehensive analysis results obtained with the repeated information as the standard are recorded as the same results, and the same results generated here are represented as comprehensive analysis results with the same repeated information, and so on, all repeated information is analyzed, and the corresponding same results are generated;

[0046] Then, the types of repeated information of the same result are obtained, and if there is only one type of repeated information corresponding to the same result, the same result is eliminated. If there are multiple types of repeated information corresponding to the same result, the comprehensive analysis results corresponding to the multiple types of repeated information are obtained and retained, and the remaining comprehensive analysis results of the same result are eliminated to generate repeated analysis results.

[0047] Specifically, for the comprehensive analysis results with multiple types of repeated information in the same result, for example, there are five groups, and each group corresponds to a different type of repeated information. In this case, the comprehensive analysis result with the most types of repeated information is selected as the standard for retention, and the rest are directly discarded.

[0048] Step 4: Analyze the obtained duplicate analysis results, filter them by calculating the relevance between the content of the duplicate analysis results and the search question, and sort them from large to small according to the calculated relevance to generate search information.

[0049] Obtain all repeated analysis results and label them as k, where k = 1, 2, …, h, and h represents the number of repeated analysis results. Then, obtain the retrieval problem, and at the same time, obtain the same vocabulary existing in the repeated analysis results based on the retrieval problem. Here, the same vocabulary is referred to as the vocabulary type, and obtain the similarity value between the repeated analysis result and the retrieval problem. The similarity value is calculated by the Euclidean distance formula. Then, substitute the obtained number of the same vocabulary and the similarity value into the formula Q = (S + G) × u to calculate the relevance of the repeated analysis result, where u is a preset proportionality coefficient, S is the number of the same vocabulary, and G is the similarity value;

[0050] By analogy, calculate the relevance Qk corresponding to all repeated analysis results, and calculate the mean value of the sum of all relevance values. At the same time, based on the mean value, screen according to the relevance Qk, remove the repeated analysis results with the relevance Qk less than the mean value of the relevance, and retain the repeated analysis results with the relevance Qk greater than the mean value of the relevance. Then, sort them in descending order according to the relevance Qk to generate retrieval information, and display the retrieval information to the corresponding operator.

[0051] Embodiment 2. Please refer to Figure 2 , this application provides a device for information retrieval and guidance based on a big data software system. The device specifically includes an information collection module, an information matching and analysis module, a comprehensive analysis and processing module, and a retrieval information output module.

[0052] The information collection module is used to obtain the retrieval information of the user and transmit the retrieval information to the information matching and analysis module;

[0053] The information matching and analysis module splits the retrieval problem according to the retrieval information to obtain the split retrieval content, matches the split retrieval content with the database to obtain the split matching result, at the same time screens the same matching result according to the split matching result, conducts relevance content analysis to obtain the relevant content, and integrates the relevant content to obtain the result to be analyzed. Then, transmit the result to be analyzed to the comprehensive analysis and processing module, and the processing method here is the same as the processing processes of steps two and three in Embodiment 1;

[0054] Comprehensive analysis and processing module, which comprehensively analyzes the obtained identical matching results and results to be analyzed to obtain comprehensive analysis results, integrates the comprehensive analysis results corresponding to the same semantics to obtain secondary classification results, and simultaneously analyzes the duplicate information corresponding to the secondary classification results to generate duplicate analysis results. Screening is carried out by calculating the relevance between the content of the duplicate analysis results and the retrieval problem, and at the same time, sorting is performed from large to small according to the calculated relevance to generate retrieval information, and the retrieval information is transmitted to the retrieval information output module. The processing method here is implemented in the same way as the processing procedures in steps three and four of Embodiment 1;

[0055] Retrieval information output module, which is used to display the obtained retrieval information to the corresponding operator.

[0056] Some of the data in the above formula are numerically calculated by removing their dimensions, and the content not described in detail in this specification belongs to the prior art well-known to those skilled in the art.

[0057] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A method for information retrieval and guidance based on a big data software system, characterized in that, The method includes the following steps: Step 1: Obtain the retrieval information of the user. At the same time, split the retrieval problem according to the retrieval information to obtain the split retrieval content. Match the obtained split retrieval content with the database to obtain the split matching result. At the same time, screen the same matching result according to the split matching result; Step 2: Obtain the remaining split matching results, where the remaining split matching results here refer to the split matching results remaining after removing the same matching results. At the same time, perform relevant content analysis to obtain the relevant content, and integrate the relevant content to obtain the result to be analyzed. The specific processing method is as follows: Obtain all the remaining split matching results, and distinguish and mark the remaining split matching results as split matching results. Denote the type of the split retrieval content corresponding to the remaining split matching results as a. Then obtain any type of split matching result as the analysis object, and denote the corresponding matching result in the analysis object as i, where i = 1, 2,..., j, and j represents the number of matching results. At the same time, identify the matching content of the matching result i, and then perform relevant matching between the matching result i and the database to obtain the relevant content. And so on, analyze all the split matching results to obtain the corresponding relevant content; Then perform similarity analysis on the relevant content corresponding to different split matching results, and screen the relevant content with similar content to generate the result to be analyzed; Step 3: Perform comprehensive analysis on the obtained same matching result and the result to be analyzed to obtain the comprehensive analysis result. Integrate the comprehensive analysis results corresponding to the same semantics to obtain the secondary classification result. At the same time, analyze the duplicate information corresponding to the secondary classification result to generate the duplicate analysis result. The specific processing method is as follows: Obtain all the secondary classification results. At the same time, obtain any group of secondary classification results as the target object, and obtain the corresponding comprehensive analysis result in the target object. Then extract the duplicate information in the comprehensive analysis result, and obtain the number of duplicate information corresponding to the comprehensive analysis result in the target object. And obtain the corresponding comprehensive analysis result denoted as the same result based on the duplicate information. And so on, analyze all the duplicate information and generate the corresponding same result; Then obtain the types of duplicate information of the same result. If there is only one type of duplicate information corresponding to the same result, then eliminate the same result. If there are multiple types of duplicate information corresponding to the same result, then obtain the comprehensive analysis results corresponding to the multiple duplicate information and retain them. At the same time, eliminate the remaining comprehensive analysis results in the same result to generate the duplicate analysis result; Step 4: Analyze the obtained duplicate analysis result, screen it by calculating the relevance between the content of the duplicate analysis result and the retrieval problem, and sort it from large to small according to the calculated relevance to generate the retrieval information.

2. The method for information retrieval and guidance based on a big data software system according to claim 1, wherein The specific way to obtain the same matching result in Step 1 is: Obtain the retrieval information of the retrieval user, then split the retrieval question according to the theme content to obtain the split retrieval content, obtain the split retrieval content and label it as n, and n = 1, 2, …, m, where m represents the number of split retrieval contents. Then, respectively retrieve and match the split retrieval content n with the corresponding database, and obtain the corresponding split matching results. Obtain the split matching results corresponding to different split retrieval contents, and then classify the split matching results to obtain the same matching results.

3. A method for information retrieval and guidance based on a big data software system according to claim 1, characterized in that, The specific method for obtaining the secondary classification results in step three is as follows: Obtain all the same matching results and the results to be analyzed, and combine the two to obtain the comprehensive analysis results, and label them as c at the same time, and c = 1, 2, …, r, where r represents the number of comprehensive analysis results. Then, analyze the content semantics corresponding to the comprehensive analysis result c, calculate the similarity of the content semantics, integrate the comprehensive analysis results with the same semantics to obtain the secondary classification results, and at the same time obtain the result quantity corresponding to the secondary classification results, calculate the quantity proportion corresponding to the secondary classification results, and then sort them from largest to smallest according to the quantity proportion.

4. A method for information retrieval and guidance based on a big data software system according to claim 1, characterized in that, The specific method for generating the retrieval information in step four is as follows: Obtain all the repeated analysis results and label them as k, and k = 1, 2, …, h, where h represents the number of repeated analysis results. Then, obtain the retrieval question, and at the same time obtain the same words existing in the repeated analysis results based on the retrieval question, and obtain the similarity value between the repeated analysis results and the retrieval question. Then, substitute the obtained number of the same words and the similarity value into the formula Q = (S + G) × u to calculate the relevance of the repeated analysis results, where u is a preset proportional coefficient, S is the number of the same words, and G is the similarity value; By analogy, calculate the relevance Qk corresponding to all the repeated analysis results, calculate the average value of all the relevances, and at the same time screen according to the relevance Qk based on the average value. Eliminate the repeated analysis results with the relevance Qk less than the average value of the relevance, retain the repeated analysis results with the relevance Qk greater than the average value of the relevance, and then sort them from largest to smallest according to the relevance Qk to generate the retrieval information.

5. An information retrieval and guidance device based on a big data software system, for implementing the method of information retrieval and guidance based on a big data software system according to any one of claims 1-4, characterized in that, It includes an information collection module, an information matching and analysis module, a comprehensive analysis and processing module, and a retrieval information output module.

6. An apparatus for information retrieval and guidance based on a big data software system according to claim 5, characterized in that, The information collection module is used to obtain the retrieval information of the user and transmit the retrieval information to the information matching and analysis module; The information matching and analysis module splits the retrieval question according to the retrieval information to obtain the split retrieval content, matches the obtained split retrieval content with the database to obtain the split matching results, screens the same matching results according to the split matching results, conducts relevance content analysis to obtain the relevant content, and integrates the relevant content to obtain the results to be analyzed. Then, it transmits the results to be analyzed to the comprehensive analysis and processing module; The comprehensive analysis and processing module comprehensively analyzes the obtained identical matching results and the results to be analyzed to obtain comprehensive analysis results, integrates the comprehensive analysis results corresponding to the same semantics to obtain secondary classification results, and at the same time analyzes the duplicate information corresponding to the secondary classification results to generate duplicate analysis results. It screens by calculating the relevance between the content of the duplicate analysis results and the retrieval question, sorts the relevance calculated from large to small to generate retrieval information, and transmits the retrieval information to the retrieval information output module; The retrieval information output module is used to display the obtained retrieval information to the corresponding operator.

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