Human-computer interaction method, device, system and storage medium

Abstract

The present application discloses a human-computer interaction method, device, system and storage medium, which relate to the field of human-computer interaction. The method comprises an information acquisition module, a human-computer interaction module 1, a human-computer interaction module 2 and a human-computer interaction module 3. By comprehensively analyzing the reading hobbies of target buyers and the historical book purchase information corresponding to the target buyers, the accuracy and effectiveness of intelligent book recommendations for book buyers are improved, the purchase choices and book purchase satisfaction of book buyers are increased, and by intelligently analyzing the purchase needs of book buyers and effectively recommending other book categories corresponding to book lovers of the same category, the book selection categories corresponding to book buyers are increased, the transaction completion volume is increased, and the purchase experience of book buyers is improved.

Description

Technical Field

[0001] The present application relates to the field of human-computer interaction, and in particular to a human-computer interaction method, device, system and storage medium. Background Art

[0002] Through human-computer interaction technologies, such as self-service book purchase systems, intelligent navigation and query systems, readers can complete book purchases and information queries more quickly without having to wait for manual services for a long time, thereby greatly improving service efficiency. Personalized recommendation systems can also provide customized reading suggestions based on readers' reading habits and interest preferences, enhancing users' reading experience. However, existing technologies still have the following shortcomings:

[0003] When making intelligent recommendations based on the purchase needs of book buyers, existing technologies do not take into account all aspects and do not fully combine the target buyer's reading preferences and the target buyer's corresponding historical book purchase information for comprehensive analysis, which reduces the accuracy and effectiveness of intelligent recommendations for book buyers and reduces the purchase choices of book buyers.

[0004] Existing technologies lack effective recommendations for other types of books corresponding to book lovers of the same type when making intelligent recommendations for book buyers' purchasing needs, which reduces the types of books available to book buyers, reduces the amount of completed transactions, and reduces the purchasing experience of book buyers. Summary of the invention

[0005] The object of the present invention is to provide a human-computer interaction method, device, system and storage medium to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a human-computer interaction system, comprising:

[0007] Information acquisition module: used to acquire the target buyer's purchase intention and obtain the target buyer's corresponding purchase expectations;

[0008] Human-computer interaction module 1: used to identify the target buyer and obtain the target buyer's corresponding historical purchase information;

[0009] Human-computer interaction module 2: used to analyze the target buyer's corresponding historical purchase information and the target buyer's corresponding purchase expectations to obtain a book recommendation list corresponding to the target buyer;

[0010] Human-computer interaction module three: used to analyze the book recommendation list corresponding to the target buyer, obtain the book auxiliary recommendation list corresponding to the target buyer, obtain the book purchase selection corresponding to the target buyer, and upload the book purchase selection corresponding to the target buyer to the shared database.

[0011] In a preferred embodiment of this scheme, the specific implementation method of the information acquisition module is as follows:

[0012] A data extraction relationship is established between the information acquisition module and the database to extract the purchase expectations filled in by the target purchaser corresponding to the user interaction center stored in the database, wherein the purchase expectations include the expected book purchase categories and expected book price ranges corresponding to the target purchaser.

[0013] In the preferred embodiment of this scheme, the specific implementation method of the human-computer interaction module 1 is as follows:

[0014] Establishing a data extraction relationship between the database and the human-computer interaction module 1, extracting the unlocking key corresponding to each personal information library stored in the database, wherein the unlocking key refers to the facial feature recognition set of the purchaser corresponding to each personal information library, wherein the facial feature recognition set includes the data values ​​of various facial features;

[0015] Perform image detection on the target purchaser through a preset camera device above the human-computer interaction device to obtain an omnidirectional facial image corresponding to the target purchaser, perform a preset contour scanner on the omnidirectional facial image corresponding to the target purchaser to obtain a facial three-dimensional model corresponding to the target purchaser, and establish a facial contour model corresponding to the target purchaser;

[0016] Perform facial feature recognition data extraction on the facial contour model corresponding to the target purchaser through the facial feature recognition and extraction method preset by the human-computer interaction system to obtain data values ​​of various facial features corresponding to the target purchaser;

[0017] By calculating the formula , get the matching coefficient of the target buyer corresponding to each facial feature recognition set ,in It is represented by the data values ​​of each facial feature corresponding to each facial feature set. Represents the data values ​​of various facial features corresponding to the target buyer, Expressed as the preset matching coefficient influencing factor, , Represents the number of various facial features, Expressed as the number of preset facial features, It represents the number corresponding to each facial feature recognition set;

[0018] The matching coefficients of the target purchaser's corresponding facial feature recognition sets are screened to obtain the facial feature recognition set corresponding to the maximum matching coefficient, and the personal information database corresponding to the facial feature recognition set is recorded as the personal information database corresponding to the target purchaser, wherein the personal information database includes the target purchaser's historical purchase information, the historical purchase information includes the target purchaser's historically purchased books, the number of words in each historically purchased book, and the type of books purchased by the target purchaser, the historical purchase quantity of each type of book, and the word count range corresponding to each historically purchased book are obtained by statistics, and the historical purchase quantity corresponding to the target purchaser's expected book purchase type and the number of words corresponding to each historically purchased book are obtained by screening.

[0019] In the preferred embodiment of this scheme, the specific implementation method of the human-computer interaction module 2 is as follows:

[0020] Establish a data extraction relationship between the human-computer interaction module 2 and the database, and extract the book database in the database, wherein the book database includes the book category, book word count, book price and reading difficulty corresponding to each book;

[0021] Screening is performed by the target buyer's purchase expectation and the target buyer's historical purchase information to obtain preliminary corresponding books of the target buyer in the book database, wherein the preliminary corresponding books refer to the books in the book database that meet the target buyer's expected book purchase category and expected book price range while excluding the target buyer's expected book purchase category and historical purchase books, and the reading difficulty of the preliminary corresponding books is obtained;

[0022] By calculating the formula , calculate the target buyer's reading depth for the desired book purchase category ,in It is represented by the number of historical books purchased by the target buyer corresponding to the expected book purchase category. It is represented by the number of words in each historically purchased book corresponding to the target buyer's expected book purchase category. It is expressed as the conversion factor of the preset number of words in the book corresponding to the reading difficulty. Expressed as the preset reading depth impact factor, It is represented by the number of each historically purchased book corresponding to the desired book purchase category of the target buyer. , Expressed as a natural constant;

[0023] Extract the reading difficulty ranges corresponding to the reading depths of various types of books stored in the database, screen them according to the reading difficulty corresponding to the preliminary corresponding books of the target buyers, obtain the valid recommended books corresponding to the target buyers, arrange the valid recommended books corresponding to the target buyers in order from small to large according to the book prices corresponding to the valid recommended books, and obtain the book recommendation list corresponding to the target buyers.

[0024] In the preferred embodiment of this scheme, the specific implementation method of the human-computer interaction module 3 is as follows:

[0025] Establish a data extraction relationship between the human-computer interaction module three and the database, extract each transaction order corresponding to the user interaction center stored in the database, extract information from each transaction order corresponding to the user interaction center, obtain a purchase book list corresponding to each transaction order, compare the purchase book list corresponding to each transaction order with each valid recommended book in the book recommendation list corresponding to the target purchaser, screen and count the transaction orders with any valid recommended book, record them as each preliminary comparison order corresponding to the target purchaser, and obtain the number of books that overlap between each preliminary comparison order corresponding to the target purchaser and the book recommendation list corresponding to the target purchaser, which is recorded as the number of books that overlap between each preliminary comparison order corresponding to the target purchaser and the book recommendation list corresponding to the target purchaser;

[0026] Compare the number of book overlaps of each preliminary comparison order corresponding to the target purchaser with a preset book overlap threshold, select preliminary comparison orders with a number greater than the book overlap threshold, and record them as valid comparison orders corresponding to the target purchaser;

[0027] The remaining books in each valid comparison order are counted to obtain the number of book repetitions and the book categories corresponding to each remaining book in the valid comparison order set, and the corresponding appearance ratios of various types of books in the remaining books corresponding to the valid comparison order set are counted;

[0028] Compare and analyze the appearance ratio of various types of books in the remaining books corresponding to the effective comparison order set with the preset ratio threshold. If the appearance ratio of a certain book type is greater than the preset ratio threshold, then record the book type as the auxiliary recommended book type corresponding to the target buyer. Arrange the books from large to small according to the number of repetitions of each book type corresponding to the auxiliary recommended book type, and obtain a list of various types of auxiliary recommended books corresponding to the target buyer.

[0029] Display the book recommendation list corresponding to the target buyer and various auxiliary recommended book lists corresponding to the target buyer through the human-computer interaction device, and obtain the book purchase list corresponding to the target buyer;

[0030] Upload the book purchase list corresponding to the target buyer to the user interaction center.

[0031] To achieve the above object, the present invention also provides the following technical solution: a human-computer interaction method, comprising the following steps:

[0032] Obtain the target buyer's purchase intention and obtain the target buyer's corresponding purchase expectations;

[0033] Perform character recognition on the target buyer and obtain the target buyer's corresponding historical purchase information;

[0034] Analyze the target buyer's corresponding historical purchase information and the target buyer's corresponding purchase expectations to obtain a recommended list of books corresponding to the target buyer;

[0035] An analysis is performed based on the book recommendation list corresponding to the target buyer, an auxiliary book recommendation list corresponding to the target buyer is obtained, and the book purchase options corresponding to the target buyer are obtained, and the book purchase options corresponding to the target buyer are uploaded to a shared database.

[0036] To achieve the above object, the present invention also provides the following technical solution: a human-computer interaction device, comprising the following steps:

[0037] Obtain the target buyer's purchase intention and obtain the target buyer's corresponding purchase expectations;

[0038] Perform character recognition on the target buyer and obtain the target buyer's corresponding historical purchase information;

[0039] Analyze the target buyer's corresponding historical purchase information and the target buyer's corresponding purchase expectations to obtain a recommended list of books corresponding to the target buyer;

[0040] An analysis is performed based on the book recommendation list corresponding to the target buyer, an auxiliary book recommendation list corresponding to the target buyer is obtained, and the book purchase options corresponding to the target buyer are obtained, and the book purchase options corresponding to the target buyer are uploaded to a shared database.

[0041] To achieve the above object, the present invention also provides the following technical solution: a human-computer interaction storage medium, comprising the following steps:

[0042] Obtain the target buyer's purchase intention and obtain the target buyer's corresponding purchase expectations;

[0043] Perform character recognition on the target buyer and obtain the target buyer's corresponding historical purchase information;

[0044] Analyze the target buyer's corresponding historical purchase information and the target buyer's corresponding purchase expectations to obtain a recommended list of books corresponding to the target buyer;

[0045] An analysis is performed based on the book recommendation list corresponding to the target buyer, an auxiliary book recommendation list corresponding to the target buyer is obtained, and the book purchase options corresponding to the target buyer are obtained, and the book purchase options corresponding to the target buyer are uploaded to a shared database.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention improves the accuracy and effectiveness of intelligent book recommendations for book buyers by comprehensively analyzing the target buyer's reading preferences and the target buyer's corresponding historical book purchase information, thereby increasing the purchase choices and book purchase satisfaction of the book buyers.

[0048] The present invention intelligently analyzes the purchasing needs of book buyers and effectively recommends other book categories corresponding to book lovers of the same category, thereby increasing the types of book choices corresponding to book buyers, increasing the transaction completion rate, and improving the purchasing experience of book buyers. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0050] Figure 1 This is a schematic diagram of module connection according to an embodiment of the present invention.

[0051] Figure 2 It is a schematic diagram of the connection steps of an embodiment of the present invention. DETAILED DESCRIPTION

[0052] 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.

[0053] See also Figure 1 The present invention provides a human-computer interaction system, which includes an information acquisition module, a human-computer interaction module 1, a human-computer interaction module 2 and a human-computer interaction module 3:

[0054] The information acquisition module is connected to the human-computer interaction module one, the human-computer interaction module one is connected to the human-computer interaction module two, and the human-computer interaction module two is connected to the human-computer interaction module three.

[0055] The information acquisition module is used to acquire the target buyer's purchase intention and obtain the target buyer's corresponding purchase expectations;

[0056] Furthermore, the specific implementation of the information acquisition module is as follows:

[0057] A data extraction relationship is established between the information acquisition module and the database to extract the purchase expectations filled in by the target purchaser corresponding to the user interaction center stored in the database, wherein the purchase expectations include the expected book purchase categories and expected book price ranges corresponding to the target purchaser.

[0058] The human-computer interaction module 1 is used to identify the target buyer and obtain the historical purchase information corresponding to the target buyer;

[0059] Furthermore, the specific implementation method of the human-computer interaction module 1 is as follows:

[0060] Establishing a data extraction relationship between the database and the human-computer interaction module 1, extracting the unlocking key corresponding to each personal information library stored in the database, wherein the unlocking key refers to the facial feature recognition set of the purchaser corresponding to each personal information library, wherein the facial feature recognition set includes the data values ​​of various facial features;

[0061] Perform image detection on the target purchaser through a preset camera device above the human-computer interaction device to obtain an omnidirectional facial image corresponding to the target purchaser, perform a preset contour scanner on the omnidirectional facial image corresponding to the target purchaser to obtain a facial three-dimensional model corresponding to the target purchaser, and establish a facial contour model corresponding to the target purchaser;

[0062] Perform facial feature recognition data extraction on the facial contour model corresponding to the target purchaser through the facial feature recognition and extraction method preset by the human-computer interaction system to obtain data values ​​of various facial features corresponding to the target purchaser;

[0063] By calculating the formula , get the matching coefficient of the target buyer corresponding to each facial feature recognition set ,in It is represented by the data values ​​of each facial feature corresponding to each facial feature set. Represents the data values ​​of various facial features corresponding to the target buyer, Expressed as the preset matching coefficient influencing factor, , Represents the number of various facial features, Expressed as the number of preset facial features, It represents the number corresponding to each facial feature recognition set;

[0064] It should be noted that: the smaller the difference between the data values ​​of each facial feature corresponding to each facial feature of each facial feature recognition set in the formula and the data values ​​of each facial feature corresponding to the target purchaser, the higher the matching degree between the target purchaser and the facial feature recognition set;

[0065] The data values ​​of each facial feature corresponding to each facial feature in each facial feature recognition set in the formula and the data values ​​of each facial feature corresponding to the target purchaser will not affect each other.

[0066] The matching coefficients of the target purchaser's corresponding facial feature recognition sets are screened to obtain the facial feature recognition set corresponding to the maximum matching coefficient, and the personal information database corresponding to the facial feature recognition set is recorded as the personal information database corresponding to the target purchaser, wherein the personal information database includes the target purchaser's historical purchase information, the historical purchase information includes the target purchaser's historically purchased books, the number of words in each historically purchased book, and the type of books purchased by the target purchaser, the historical purchase quantity of each type of book, and the word count range corresponding to each historically purchased book are obtained by statistics, and the historical purchase quantity corresponding to the target purchaser's expected book purchase type and the number of words corresponding to each historically purchased book are obtained by screening.

[0067] The second human-computer interaction module is used to analyze the target buyer's corresponding historical purchase information and the target buyer's corresponding purchase expectations to obtain a book recommendation list corresponding to the target buyer;

[0068] Furthermore, the specific implementation method of the human-computer interaction module 2 is as follows:

[0069] Establish a data extraction relationship between the human-computer interaction module 2 and the database, and extract the book database in the database, wherein the book database includes the book category, book word count, book price and reading difficulty corresponding to each book;

[0070] Screening is performed by the target buyer's purchase expectation and the target buyer's historical purchase information to obtain preliminary corresponding books of the target buyer in the book database, wherein the preliminary corresponding books refer to the books in the book database that meet the target buyer's expected book purchase category and expected book price range while excluding the target buyer's expected book purchase category and historical purchase books, and the reading difficulty of the preliminary corresponding books is obtained;

[0071] By calculating the formula , calculate the target buyer's reading depth for the desired book purchase category ,in It is represented by the number of historical books purchased by the target buyer corresponding to the expected book purchase category. It is represented by the number of words in each historically purchased book corresponding to the target buyer's expected book purchase category. It is expressed as the conversion factor of the preset number of words in the book corresponding to the reading difficulty. Expressed as the preset reading depth impact factor, It is represented by the number of each historically purchased book corresponding to the desired book purchase category of the target buyer. , Expressed as a natural constant;

[0072] Extract the reading difficulty ranges corresponding to the reading depths of various types of books stored in the database, screen them according to the reading difficulty corresponding to the preliminary corresponding books of the target buyers, obtain the valid recommended books corresponding to the target buyers, arrange the valid recommended books corresponding to the target buyers in order from small to large according to the book prices corresponding to the valid recommended books, and obtain the book recommendation list corresponding to the target buyers.

[0073] The human-computer interaction module three is used to analyze the book recommendation list corresponding to the target buyer, obtain the book auxiliary recommendation list corresponding to the target buyer, obtain the book purchase selection corresponding to the target buyer, and upload the book purchase selection corresponding to the target buyer to the shared database.

[0074] Furthermore, the specific implementation method of the human-computer interaction module 3 is as follows:

[0075] Establish a data extraction relationship between the human-computer interaction module three and the database, extract each transaction order corresponding to the user interaction center stored in the database, extract information from each transaction order corresponding to the user interaction center, obtain a purchase book list corresponding to each transaction order, compare the purchase book list corresponding to each transaction order with each valid recommended book in the book recommendation list corresponding to the target purchaser, screen and count the transaction orders with any valid recommended book, record them as each preliminary comparison order corresponding to the target purchaser, and obtain the number of books that overlap between each preliminary comparison order corresponding to the target purchaser and the book recommendation list corresponding to the target purchaser, which is recorded as the number of books that overlap between each preliminary comparison order corresponding to the target purchaser and the book recommendation list corresponding to the target purchaser;

[0076] Compare the number of book overlaps of each preliminary comparison order corresponding to the target purchaser with a preset book overlap threshold, select preliminary comparison orders with a number greater than the book overlap threshold, and record them as valid comparison orders corresponding to the target purchaser;

[0077] The remaining books in each valid comparison order are counted to obtain the number of book repetitions and the book categories corresponding to each remaining book in the valid comparison order set, and the corresponding appearance ratios of various types of books in the remaining books corresponding to the valid comparison order set are counted;

[0078] It should be noted that the remaining books refer to the remaining books in the valid comparison order excluding the valid recommended books in the book recommendation list corresponding to the target purchaser.

[0079] It should be noted that: a valid comparison order set is established according to each valid comparison order corresponding to the target purchaser.

[0080] Compare and analyze the appearance ratio of various types of books in the remaining books corresponding to the effective comparison order set with the preset ratio threshold. If the appearance ratio of a certain book type is greater than the preset ratio threshold, then record the book type as the auxiliary recommended book type corresponding to the target buyer. Arrange the books from large to small according to the number of repetitions of each book type corresponding to the auxiliary recommended book type, and obtain a list of various types of auxiliary recommended books corresponding to the target buyer.

[0081] Display the book recommendation list corresponding to the target buyer and various auxiliary recommended book lists corresponding to the target buyer through the human-computer interaction device, and obtain the book purchase list corresponding to the target buyer;

[0082] Upload the book purchase list corresponding to the target buyer to the user interaction center.

[0083] See also Figure 2 To achieve the above purpose, the present invention also provides the following technical solution: a human-computer interaction method, comprising the following steps:

[0084] Obtain the target buyer's purchase intention and obtain the target buyer's corresponding purchase expectations;

[0085] Perform character recognition on the target buyer and obtain the target buyer's corresponding historical purchase information;

[0086] Analyze the target buyer's corresponding historical purchase information and the target buyer's corresponding purchase expectations to obtain a recommended list of books corresponding to the target buyer;

[0087] An analysis is performed based on the book recommendation list corresponding to the target buyer, an auxiliary book recommendation list corresponding to the target buyer is obtained, and the book purchase options corresponding to the target buyer are obtained, and the book purchase options corresponding to the target buyer are uploaded to a shared database.

[0088] To achieve the above object, the present invention also provides the following technical solution: a human-computer interaction device, comprising the following steps:

[0089] Obtain the target buyer's purchase intention and obtain the target buyer's corresponding purchase expectations;

[0090] Perform character recognition on the target buyer and obtain the target buyer's corresponding historical purchase information;

[0091] Analyze the target buyer's corresponding historical purchase information and the target buyer's corresponding purchase expectations to obtain a recommended list of books corresponding to the target buyer;

[0092] An analysis is performed based on the book recommendation list corresponding to the target buyer, an auxiliary book recommendation list corresponding to the target buyer is obtained, and the book purchase options corresponding to the target buyer are obtained, and the book purchase options corresponding to the target buyer are uploaded to a shared database.

[0093] To achieve the above object, the present invention also provides the following technical solution: a human-computer interaction storage medium, comprising the following steps:

[0094] Obtain the target buyer's purchase intention and obtain the target buyer's corresponding purchase expectations;

[0095] Perform character recognition on the target buyer and obtain the target buyer's corresponding historical purchase information;

[0096] Analyze the target buyer's corresponding historical purchase information and the target buyer's corresponding purchase expectations to obtain a recommended list of books corresponding to the target buyer;

[0097] An analysis is performed based on the book recommendation list corresponding to the target buyer, an auxiliary book recommendation list corresponding to the target buyer is obtained, and the book purchase options corresponding to the target buyer are obtained, and the book purchase options corresponding to the target buyer are uploaded to a shared database.

[0098] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A human-computer interaction system, characterized in that: include: Information acquisition module: used to acquire the target buyer's purchase intention and obtain the target buyer's corresponding purchase expectations; The specific implementation of the information acquisition module is as follows: Establishing a data extraction relationship between the information acquisition module and the database, extracting the purchase expectations filled in by the target purchaser corresponding to the user interaction center stored in the database, wherein the purchase expectations include the expected book purchase categories and expected book price ranges corresponding to the target purchaser; Human-computer interaction module 1: used to identify the target buyer and obtain the target buyer's corresponding historical purchase information; Human-computer interaction module 2: used to analyze the target buyer's corresponding historical purchase information and the target buyer's corresponding purchase expectations to obtain a book recommendation list corresponding to the target buyer; The specific implementation method of human-computer interaction module 2 is as follows: Establish a data extraction relationship between the human-computer interaction module 2 and the database, and extract the book database in the database, wherein the book database includes the book category, book word count, book price and reading difficulty corresponding to each book; Screening is performed by the target buyer's purchase expectation and the target buyer's historical purchase information to obtain preliminary corresponding books of the target buyer in the book database, wherein the preliminary corresponding books refer to the books in the book database that meet the target buyer's expected book purchase category and expected book price range while excluding the target buyer's expected book purchase category and historical purchase books, and the reading difficulty of the preliminary corresponding books is obtained; By calculating the formula , calculate the target buyer's reading depth for the desired book purchase category ,in It is represented by the number of historical books purchased by the target buyer corresponding to the expected book purchase category. It is represented by the number of words in each historically purchased book corresponding to the target buyer's expected book purchase category. It is expressed as the conversion factor of the preset number of words in the book corresponding to the reading difficulty. Expressed as the preset reading depth impact factor, It is represented by the number of each historically purchased book corresponding to the desired book purchase category of the target buyer. , Expressed as a natural constant; Extract the reading difficulty ranges corresponding to the reading depths of various types of books stored in the database, screen them according to the reading difficulty corresponding to the preliminary corresponding books of the target buyers, obtain the effective recommended books corresponding to the target buyers, arrange the effective recommended books corresponding to the target buyers in order from small to large according to the book prices corresponding to the effective recommended books, and obtain the book recommendation list corresponding to the target buyers; Human-computer interaction module three: used to analyze the book recommendation list corresponding to the target buyer, obtain the book auxiliary recommendation list corresponding to the target buyer, obtain the book purchase selection corresponding to the target buyer, and upload the book purchase selection corresponding to the target buyer to the shared database; The specific implementation method of human-computer interaction module 3 is as follows: Establish a data extraction relationship between the human-computer interaction module three and the database, extract each transaction order corresponding to the user interaction center stored in the database, extract information from each transaction order corresponding to the user interaction center, obtain a purchase book list corresponding to each transaction order, compare the purchase book list corresponding to each transaction order with each valid recommended book in the book recommendation list corresponding to the target purchaser, screen and count the transaction orders with any valid recommended book, record them as each preliminary comparison order corresponding to the target purchaser, and obtain the number of books that overlap between each preliminary comparison order corresponding to the target purchaser and the book recommendation list corresponding to the target purchaser, which is recorded as the number of books that overlap between each preliminary comparison order corresponding to the target purchaser and the book recommendation list corresponding to the target purchaser; Compare the number of book overlaps of each preliminary comparison order corresponding to the target purchaser with a preset book overlap threshold, select preliminary comparison orders with a number greater than the book overlap threshold, and record them as valid comparison orders corresponding to the target purchaser; The remaining books in each valid comparison order are counted to obtain the number of book repetitions and the book categories corresponding to each remaining book in the valid comparison order set, and the corresponding appearance ratios of various types of books in the remaining books corresponding to the valid comparison order set are counted; Compare and analyze the appearance ratio of various types of books in the remaining books corresponding to the effective comparison order set with the preset ratio threshold. If the appearance ratio of a certain book type is greater than the preset ratio threshold, then record the book type as the auxiliary recommended book type corresponding to the target buyer. Arrange the books from large to small according to the number of repetitions of each book type corresponding to the auxiliary recommended book type, and obtain a list of various types of auxiliary recommended books corresponding to the target buyer. Display the book recommendation list corresponding to the target buyer and various auxiliary recommended book lists corresponding to the target buyer through the human-computer interaction device, and obtain the book purchase list corresponding to the target buyer; Upload the book purchase list corresponding to the target buyer to the user interaction center.

2. A human-computer interaction system according to claim 1, characterized in that: The specific implementation method of the human-computer interaction module 1 is as follows: Establishing a data extraction relationship between the database and the human-computer interaction module 1, extracting the unlocking key corresponding to each personal information library stored in the database, wherein the unlocking key refers to the facial feature recognition set of the purchaser corresponding to each personal information library, wherein the facial feature recognition set includes the data values ​​of various facial features; Perform image detection on the target purchaser through a preset camera device above the human-computer interaction device to obtain an omnidirectional facial image corresponding to the target purchaser, perform a preset contour scanner on the omnidirectional facial image corresponding to the target purchaser to obtain a facial three-dimensional model corresponding to the target purchaser, and establish a facial contour model corresponding to the target purchaser; Perform facial feature recognition data extraction on the facial contour model corresponding to the target purchaser through the facial feature recognition and extraction method preset by the human-computer interaction system to obtain data values ​​of various facial features corresponding to the target purchaser; By calculating the formula , get the matching coefficient of the target buyer corresponding to each facial feature recognition set ,in It is represented by the data values ​​of each facial feature corresponding to each facial feature set. Represents the data values ​​of various facial features corresponding to the target buyer, Expressed as the preset matching coefficient influencing factor, , Represents the number of various facial features, Expressed as the number of preset facial features, It represents the number corresponding to each facial feature recognition set; The matching coefficients of the target purchaser's corresponding facial feature recognition sets are screened to obtain the facial feature recognition set corresponding to the maximum matching coefficient, and the personal information database corresponding to the facial feature recognition set is recorded as the personal information database corresponding to the target purchaser, wherein the personal information database includes the target purchaser's historical purchase information, the historical purchase information includes the target purchaser's historically purchased books, the number of words in each historically purchased book, and the type of books purchased by the target purchaser, the historical purchase quantity of each type of book, and the word count range corresponding to each historically purchased book are obtained by statistics, and the historical purchase quantity corresponding to the target purchaser's expected book purchase type and the number of words corresponding to each historically purchased book are obtained by screening.

3. A human-computer interaction method, applied to a human-computer interaction system as claimed in any one of claims 1 to 2, characterized in that: include: Obtain the target buyer's purchase intention and obtain the target buyer's corresponding purchase expectations; Perform character recognition on the target buyer and obtain the target buyer's corresponding historical purchase information; Analyze the target buyer's corresponding historical purchase information and the target buyer's corresponding purchase expectations to obtain a recommended list of books corresponding to the target buyer; An analysis is performed based on the book recommendation list corresponding to the target buyer, an auxiliary book recommendation list corresponding to the target buyer is obtained, and the book purchase options corresponding to the target buyer are obtained, and the book purchase options corresponding to the target buyer are uploaded to a shared database.

4. A human-computer interaction device, applied to a human-computer interaction system as claimed in any one of claims 1 to 2, characterized in that: include: Obtain the target buyer's purchase intention and obtain the target buyer's corresponding purchase expectations; Perform character recognition on the target buyer and obtain the target buyer's corresponding historical purchase information; Analyze the target buyer's corresponding historical purchase information and the target buyer's corresponding purchase expectations to obtain a recommended list of books corresponding to the target buyer; An analysis is performed based on the book recommendation list corresponding to the target buyer, an auxiliary book recommendation list corresponding to the target buyer is obtained, and the book purchase options corresponding to the target buyer are obtained, and the book purchase options corresponding to the target buyer are uploaded to a shared database.

5. A human-computer interaction storage medium, applied to a human-computer interaction system as claimed in any one of claims 1 to 2, characterized in that: include: Obtain the target buyer's purchase intention and obtain the target buyer's corresponding purchase expectations; Perform character recognition on the target buyer and obtain the target buyer's corresponding historical purchase information; Analyze the target buyer's corresponding historical purchase information and the target buyer's corresponding purchase expectations to obtain a recommended list of books corresponding to the target buyer; An analysis is performed based on the book recommendation list corresponding to the target buyer, an auxiliary book recommendation list corresponding to the target buyer is obtained, and the book purchase options corresponding to the target buyer are obtained, and the book purchase options corresponding to the target buyer are uploaded to a shared database.

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