Data processing method and device, electronic equipment and computer readable medium

Abstract

The embodiment of the invention discloses a data processing method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the steps of performing semantic recognition on question information to obtain question semantic feature information; displaying the supplementary question information in the question and answer window, and displaying a three-dimensional simulation model of the target product in an interface where the question and answer window is located; performing feature fusion on the supplementary reply information and the question semantic feature information to obtain fused question feature information; according to the fused question feature information, performing question retrieval on a preset product question database to obtain product question feedback information, and displaying the product question feedback information in a question and answer window; and in response to the detected confirmation operation for the product accessory replacement information in the product problem feedback information, product accessory delivery is performed according to the product accessory replacement information. According to the embodiment, waste of logistics resources can be avoided.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of human-computer interaction technology, and in particular to a data processing method, device, electronic device, and computer-readable medium. Background Art

[0002] When using a product, users often need to access various information about the product. At present, when performing data processing interaction, the common method is to use a large language model to answer the abnormal product questions raised by users. Or directly retrieve the product problem handling method stored in the database and push it to the user so that the user can perform product maintenance according to the product problem handling method.

[0003] However, it is found in practice that when the above method is used to process data related to product information interaction on a product information interaction platform, the following technical problems often occur: The training of large language models is relatively complex, and the questions answered to users are mostly based on public data on the Internet. As a result, it is difficult to accurately locate the target product and it is difficult to accurately respond to users' product anomaly questions about the target product. In addition, since the questions in the preset database are often not comprehensive, it is difficult to retrieve the appropriate product problem handling method, but fuzzy matching returns the handling method of similar questions to the user. Therefore, it is also difficult to accurately respond to users' product anomaly questions about the target product, resulting in mismatches in product accessories when users select product accessories for delivery based on feedback data from platform interactions. This leads to a waste of logistics resources.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0005] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0006] Some embodiments of the present disclosure propose data processing methods, devices, electronic devices, and computer-readable media to solve one or more of the technical problems mentioned in the above background technology section.

[0007] In a first aspect, some embodiments of the present disclosure provide a data processing method, the method comprising: in response to detecting question information issued by a user in a question-and-answer window on the product information interaction platform regarding a target product, performing semantic recognition on the question information to obtain question semantic feature information; in response to determining that the question semantic feature information does not satisfy a preset question determination condition, displaying supplementary question information in the question-and-answer window based on the question semantic feature information, and displaying a three-dimensional simulation model of the target product in an interface where the question-and-answer window is located, wherein the displayed three-dimensional simulation model matches the question semantic feature information, and the supplementary question information is generated by the following steps: determining the question semantic feature information; missing problem information of feature information, and generating supplementary problem information based on the above missing problem information; in response to detecting supplementary reply information for the above supplementary question, performing feature fusion on the above supplementary reply information and the above question semantic feature information to obtain fused problem feature information; based on the above fused problem feature information, performing question retrieval on a preset product problem database to obtain product problem feedback information, and displaying the above product problem feedback information in the above question and answer window, wherein the above product problem feedback information includes product accessory replacement information; in response to detecting a confirmation operation for the product accessory replacement information in the above product problem feedback information, shipping product accessories according to the above product accessory replacement information.

[0008] In a second aspect, some embodiments of the present disclosure provide a data processing device, which includes: a semantic recognition unit, configured to, in response to detecting question information issued by a user regarding a target product in a question and answer window on the above-mentioned product information interaction platform, perform semantic recognition on the above-mentioned question information to obtain question semantic feature information; a display unit, configured to, in response to determining that the above-mentioned question semantic feature information does not meet preset question judgment conditions, display supplementary question information in the above-mentioned question and answer window according to the above-mentioned question semantic feature information, and display a three-dimensional simulation model of the above-mentioned target product in an interface where the above-mentioned question and answer window is located, wherein the displayed three-dimensional simulation model matches the above-mentioned question semantic feature information, and the above-mentioned supplementary question information is generated by the following steps: determining the missing of the above-mentioned question semantic feature information according to the above-mentioned question semantic feature information question information, and generating supplementary question information based on the missing question information; a feature fusion unit, configured to, in response to detecting the supplementary reply information for the supplementary question, fuse the features of the supplementary reply information and the question semantic feature information to obtain the fused question feature information; a question retrieval unit, configured to, based on the fused question feature information, perform question retrieval on a preset product question database to obtain product question feedback information, and display the product question feedback information in the question-and-answer window, wherein the product question feedback information includes product accessory replacement information; a confirmation and delivery unit, configured to, in response to detecting the confirmation operation for the product accessory replacement information in the product question feedback information, deliver the product accessories according to the product accessory replacement information.

[0009] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0010] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0011] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the data processing method of some embodiments of the present disclosure, the accuracy of replying to the abnormal product problems raised by the user for the target product can be improved, thereby avoiding the wrong delivery of product accessories and avoiding the waste of logistics resources. Specifically, the reason for the waste of logistics resources is that the training of the large language model is relatively complex, and the questions answered to the user are mostly based on the data disclosed on the Internet. Therefore, it is difficult to accurately locate the target product and it is difficult to accurately reply to the abnormal product problems raised by the user for the target product. In addition, since the questions in the preset database are often not comprehensive, it is difficult to retrieve the appropriate product problem processing method, but fuzzy matching returns the processing method of the approximate problem to the user, so it is also difficult to accurately reply to the abnormal product problems raised by the user for the target product, resulting in the situation that the product accessories do not match when the user selects the product accessories for delivery according to the feedback data of the platform interaction. Based on this, the data processing method of some embodiments of the present disclosure, first, through the question and answer window of the above-mentioned product information interaction platform, obtains the question information input by the user for the target product. Here, in order to avoid directly using the large model for problem parsing and feedback, semantic recognition is used to determine the semantic feature information of the problem. Then, considering that the user's question has an inaccurate description, the problem judgment condition is introduced to determine whether the question is accurate. Here, for inaccurate questions, the method of displaying supplementary question information in the above-mentioned question and answer window is adopted to facilitate users to further explain the product problem. At the same time, by displaying the three-dimensional simulation model of the target product for users to refer to and describe, the accuracy of the user's description of the required questions can be further improved. In this way, the user's demand questions for the product can be located end-to-end. Then, through feature fusion, the features of all the information replied by the user can be comprehensively processed to obtain the fused problem feature information. After that, the question is retrieved through the product problem database to obtain product problem feedback information. Here, the accuracy of the obtained user needs can also be ensured. Therefore, the corresponding product problem feedback information can be accurately retrieved from the product problem database. Therefore, it is convenient for users to confirm the accessories that need to be replaced according to the product problem feedback information to ship the product accessories. Therefore, the waste of logistics resources is avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0013] Figure 1 are flow charts of some embodiments of the data processing method according to the present disclosure; Figure 2is a schematic diagram of an interface of a question-and-answer window according to some embodiments of the data processing method disclosed herein; Figure 3 is a schematic diagram of the structure of some embodiments of the data processing device according to the present disclosure; Figure 4 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0014] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0015] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0016] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0017] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0018] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0019] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0020] Figure 1 The process 100 of some embodiments of the data processing method according to the present disclosure is shown. The data processing method, applied to the product information interaction platform, may include the following steps: Step 101, in response to detecting question information sent by a user regarding a target product in a question-and-answer window on a product information interaction platform, semantic recognition is performed on the question information to obtain question semantic feature information.

[0021] In some embodiments, the execution subject of the data processing method may perform semantic recognition on the question information in response to detecting the question information sent by the user in the question-and-answer window on the product information interaction platform regarding the target product, and obtain the question semantic feature information. The question-and-answer window may be a page pop-up window.

[0022] Here, the product information interaction platform may be a pre-set platform for information interaction of one or more products. A user may query various information of a desired product in a question and answer window of the platform.

[0023] As an example, the above question and answer window can be Figure 2 Specifically, taking the excavator as an example, the product information interaction platform allows users to Figure 2 Query information in the question and answer window shown.

[0024] In practice, in the process of adopting technical solutions to solve the problems mentioned in the background technology, the following technical problem 2 is often accompanied: Considering direct semantic analysis, there is a situation where the subject is unclear. Then, there are often situations where users only ask questions but do not determine the requirements of the questions, making it difficult to judge the user's needs. Or there are situations where users find it difficult to express the questions clearly. Therefore, the meaning of the extracted sentences has large errors. It is difficult to provide users with accurate responses to questions. In response to the above technical problem 2, the inventor decided to adopt the following solution.

[0025] In some optional implementations of some embodiments, the execution subject performs semantic recognition on the question information to obtain question semantic feature information, which may include the following steps: The first step is to extract the product feature words in the above question information. The product feature words in the above question information can be extracted by a preset feature word extraction algorithm. For example, the product feature word can be "excavator crawler".

[0026] As an example, the feature word extraction algorithm may include but is not limited to at least one of the following: a Bag of Words model, a TF-IDF (Term Frequency-Inverse Document Frequency) algorithm.

[0027] The second step is to extract the semantics of the question information based on the product feature words to generate the semantic feature information of the question. The product feature words can be used as keywords to extract the semantics of the question information through a preset semantic extraction algorithm to generate the semantic feature information of the question.

[0028] As an example, the semantic extraction algorithm may include but is not limited to at least one of the following: Named Entity Recognition (NER) algorithm, Support Vector Machine (SVM), Word Embedding model, etc. For example, the obtained semantic feature information of the question may be "there is vibration when the excavator is started".

[0029] In practice, considering that direct semantic analysis may result in unclear topics, we can first extract product feature words and then use the product feature words as keywords for semantic extraction to improve the accuracy of the generated question semantic feature information.

[0030] Step 102, in response to determining that the question semantic feature information does not meet the preset question determination conditions, display supplementary question information in the question and answer window according to the question semantic feature information, and display a three-dimensional simulation model of the target product in the interface where the question and answer window is located.

[0031] In some embodiments, the above-mentioned execution entity can, in response to determining that the above-mentioned question semantic feature information does not meet the preset question judgment conditions, display supplementary question information in the above-mentioned question and answer window according to the above-mentioned question semantic feature information, and display the three-dimensional simulation model of the above-mentioned target product in the interface where the above-mentioned question and answer window is located.

[0032] The displayed three-dimensional simulation model matches the above-mentioned question semantic feature information, and the above-mentioned supplementary question information is generated by the following steps: Determine the missing question information of the question semantic feature information, and generate supplementary question information based on the missing question information.

[0033] In some optional implementations of some embodiments, in response to determining that the question semantic feature information does not meet the preset question determination condition, the execution subject displays the supplementary question information in the question-and-answer window according to the question semantic feature information, which may include the following steps: The first step is to retrieve product problem description information that matches the product feature word from the product problem database. The product problem database includes component identifiers corresponding to the various components of the target product and problem identifiers for various problems. There is also problem association description information between the component identifier and the problem identifier. Therefore, the component identifier that is the same as the product feature word can be retrieved from the product problem database, and the corresponding problem association description information can be determined as the product problem description information. Here, the product problem description information can correspond to multiple problem identifiers.

[0034] As an example, different questions may be preset with different identification codes.

[0035] In the second step, in response to the fact that the product problem description information does not match the problem semantic feature information, it is determined that the problem semantic feature information does not meet the preset problem determination conditions. Among them, since a data identifier stored in the product problem database may correspond to multiple problem-related description information. Therefore, the retrieved product problem description information does not match the problem semantic feature information. Thus, it can be determined that the problem semantic feature information does not meet the preset problem determination conditions. Here, the problem determination condition can be a condition for determining whether the problem semantic feature information can retrieve matching product problem description information. In addition, it can be determined that the product problem description information and multiple problem identifiers in the problem semantic feature information match the product feature words. Specifically, it can be found whether there is associated description information between the product feature words and multiple problem identifiers.

[0036] The third step is to display supplementary question information in the question-and-answer window based on the above question semantic feature information.

[0037] Optionally, the execution entity displays the three-dimensional simulation model of the target product in the interface where the question-and-answer window is located, which may include the following steps: The first step is to call a product simulation model corresponding to the target product from a database. The product simulation model is constructed according to the product structure of the target product. The product simulation model may include sub-models of each product component. The sub-models of the product components are combined into the product simulation model according to the product structure.

[0038] The second step is to determine the product component node that matches the product feature word in the product simulation model. Each sub-model in the product simulation model includes a component identifier of the corresponding component. The component identifier that represents the same component as the product feature word can be determined as the product component node.

[0039] The third step is to render the model of the product component nodes and adjust the display perspective of the product simulation model to display the rendered product component nodes. The interface where the question and answer window is located can also display the product digital passport of the target product. The product digital passport can include but is not limited to at least one of the following: product digital certificate, product carbon consumption, product specification parameters, product supply chain, product maintenance information, etc.

[0040] In some optional implementations of some embodiments, the execution subject, determining the missing question information of the question semantic feature information, and generating the supplementary question information according to the missing question information, may include the following steps: The first step is to extract user demand feature words from the above question semantic feature information, and in response to not extracting them, instruct the user to send the user demand feature words through the above question-answer window. The user demand feature words can be extracted from the above question semantic feature information by the above feature word extraction algorithm. Here, the user demand feature words can be keywords that represent the information the user needs. For example, "display accessory information", "provide maintenance steps", etc.

[0041] In practice, users often only ask questions without determining the requirements. Therefore, it is necessary to extract the demand feature words. For example, a user asks "excavator crawler abnormality" but does not provide specific information.

[0042] In the second step, in response to determining that the above-mentioned user demand feature word belongs to the product failure category, a product failure problem chain set corresponding to the above-mentioned product component node is extracted from the above-mentioned product problem database. Among them, each product failure problem chain in the above-mentioned product failure problem chain set may include at least one component failure node, and the component failure nodes are connected by fault pointer information. Here, the component failure node can be a node corresponding to a component of the target product. The product failure category, that is, the information characterizing the user demand interaction, belongs to the problem of product failure. Specifically, for each failure problem, a product failure problem chain can be established in the above-mentioned product problem database based on the pre-stored component identification and problem identification to characterize the components involved in solving the failure problem. Then the fault pointer information between the component failure nodes is the problem association description information.

[0043] As an example, taking an excavator as an example, the component failure node can be a track node. The corresponding product failure problem chain can also include: track drive wheel node, track support wheel node, etc. Then, the fault pointer information can be: track support wheel node (severe wear) - track drive wheel node (loose track meshing) - track node (causing abnormal track transmission).

[0044] In the third step, using the above user demand feature words and the above problem semantic feature information, a matching product failure problem chain is selected from the above product failure problem chain set as the target product failure problem chain. Among them, the word vector set of the above user demand feature words and the above problem semantic feature information can be determined. Then, the correlation between each product failure problem chain and the word vector set can be determined respectively. Finally, the product failure problem chain with the greatest correlation can be determined as the target product failure problem chain.

[0045] The fourth step is to use the above-mentioned product feature words as core words and generate supplementary question information according to a preset problem description sentence set. Among them, the above-mentioned supplementary question information may include a supplementary question prompt text group corresponding to the above-mentioned target product failure problem chain. Each supplementary question prompt text in the above-mentioned supplementary question prompt text group may correspond to a question. Here, the problem description sentence pattern may be a pre-established sentence with missing vocabulary. Thus, the above-mentioned product feature words can be used as core words to fill in the supplementary question prompt text in each problem description sentence pattern. In addition, the supplementary question prompt text group can be constructed in advance based on the two adjacent component failure nodes in the target product failure problem chain and the fault pointer information between them.

[0046] In practice, it is often difficult for users to express their questions clearly. Therefore, it is necessary to ask users questions through multiple guided inquiries in order to guide them to supplement the product problems that need to be solved.

[0047] In the fifth step, the supplementary question prompt text group in the above supplementary question information is sent to the above question-answering window for display, so that the user can answer. Among them, the sent supplementary question prompt text can be used for the user to select questions. It can also be used for the user to freely organize language to answer.

[0048] Step 103, in response to detecting the supplementary reply information for the supplementary question, feature fusion is performed on the supplementary reply information and the question semantic feature information to obtain fused question feature information.

[0049] In some embodiments, the execution subject may, in response to detecting the supplementary reply information for the supplementary question, perform feature fusion on the supplementary reply information and the question semantic feature information to obtain fused question feature information.

[0050] In some optional implementations of some embodiments, the execution subject performs feature fusion on the supplementary reply information and the question semantic feature information to obtain fused question feature information, which may include the following steps: The first step is to determine the user response results corresponding to each supplementary question prompt text in the supplementary response information. The user response results corresponding to each supplementary question prompt text in the supplementary response information can be determined by the above-mentioned feature word extraction algorithm. Here, the user response results can include response keywords.

[0051] The second step is to determine the question fields corresponding to each user's reply results in the product question database to obtain a question field group. The corresponding question fields in the product question database can be selected by the reply keywords in the user's reply results.

[0052] The third step is to add the problem field group to the problem semantic feature information to obtain the fused problem feature information. The problem field group can be added to the problem semantic feature information in the order of the component failure nodes in the target product failure problem chain to obtain the fused problem feature information.

[0053] The above steps 101-103 and their related contents, as an inventive point of an embodiment of the present disclosure, solve the second technical problem mentioned in the background technology, "Considering that the semantic analysis is directly performed, there is a situation where the subject is unclear. Then, there is often a situation where the user only asks a question but does not determine the problem requirements, and it is difficult to judge the user's needs. Or there is a situation where the user has difficulty expressing the problem clearly. Therefore, the extracted sentence meaning error is large. It is difficult to provide users with accurate question replies." The factors that make it difficult to provide users with accurate question replies are often as follows: Considering that the semantic analysis is directly performed, there is a situation where the subject is unclear. Then, there is often a situation where the user only asks a question but does not determine the problem requirements, and it is difficult to judge the user's needs. Or there is a situation where the user has difficulty expressing the problem clearly. Therefore, the extracted sentence meaning error is large. In order to achieve this effect, first, by extracting product feature words first, and then performing semantic extraction with product feature words as keywords, it can be used to improve the accuracy of the generated question semantic feature information. Then, by preliminarily searching the product question database, it is determined whether the user question is accurate. At the same time, considering that there are users who only ask questions but do not determine the problem requirements. Therefore, it is necessary to extract demand feature words in order to locate the specific needs of users. Then, considering the situation that users find it difficult to express their questions clearly, it is necessary to ask questions to users through multiple guided inquiries. In order to guide users to supplement the product problems that need to be solved. At the same time, by displaying the product component nodes rendered by the three-dimensional simulation model and the product digital passport of the above target product, it can further facilitate user reference to improve the clarity of user answers. In this way, it is convenient to locate user needs more accurately and provide users with accurate answers to questions. In addition, the required accessories can be accurately sent to users, avoiding the waste of logistics resources.

[0054] Step 104, based on the fused question feature information, perform question retrieval on a preset product question database to obtain product question feedback information, and display the product question feedback information in a question and answer window.

[0055] In some embodiments, the execution subject may search for problems in a preset product problem database based on the fused problem feature information, obtain product problem feedback information, and display the product problem feedback information in the question-and-answer window. The product problem feedback information includes product accessory replacement information.

[0056] In the process of adopting technical solutions to solve the problems mentioned in the background technology, the following technical problem three often occurs: although the user's problem needs can be accurately located, it is still difficult to accurately respond to the user's product abnormality issues raised about the target product because the questions in the preset database are often not comprehensive. In response to the above technical problem three, the inventor decided to adopt the following solution.

[0057] In some optional implementations of some embodiments, the execution subject searches a preset product problem database according to the fused problem feature information to obtain product problem feedback information, which may include the following steps: In the first step, in response to determining that the user demand feature word belongs to the product failure category, the fault discrimination information matching the fused problem feature information is retrieved from the product problem database as product problem feedback information. The fault discrimination information matching the fused problem feature information can be retrieved from the product problem database again as product problem feedback information.

[0058] As an example, the product parts replacement information in the product problem feedback information may include "the crawler support wheel of the excavator needs to be replaced."

[0059] In the second step, in response to determining that the user demand feature word belongs to the product maintenance category, the usage data of the target product is called. The usage data can be obtained from the user terminal of the target product. The usage data of the target product uploaded by the user can also be obtained.

[0060] As an example, the usage data of the target product may include, but is not limited to, at least one of the following: product usage time, product component replacement time, product component maintenance time, etc.

[0061] The third step is to perform feature matching on the pre-stored product maintenance process structure tree corresponding to the above-mentioned target product and the usage data of the above-mentioned target product to obtain a group of product nodes to be maintained. Among them, nodes that match the usage data of the above-mentioned target product can be selected from the product maintenance process structure tree as product nodes to be maintained. Here, the nodes in the product maintenance process structure tree can be arranged in timestamp order. Each node can correspond to a component or product production node. In addition, each node can also include node maintenance guide information arranged in chronological order. Therefore, it can be determined that the usage data of the target product corresponds to the component node in the above-mentioned product maintenance process structure tree.

[0062] In practice, the main node of the product maintenance process structure tree is the completion node of the above product, and the above completion node includes the time point when the product production is completed.

[0063] As an example, the main node can be defined first. For example, the main node corresponds to the product process node of the excavator's factory date. Then the production process nodes corresponding to the various components of the excavator under the main node can be defined as sub-nodes under the main node. For each sub-node, the production process node of the raw materials required to generate the component of the sub-node can be defined as a sub-sub-node. Thus, a production process structure tree is established according to the product's production supply chain and product production log. Here, since each production process node corresponds to a timestamp, each node in the production process structure tree can also be established according to the time difference, with the time sequence and production sequence characteristics of the product supply chain. In addition, the node maintenance guide information can include component maintenance three-dimensional animation and component maintenance step instruction information.

[0064] The fourth step is to send the component maintenance name corresponding to each product maintenance node in the above product maintenance node group to the above question and answer window, so that the user can select the component maintenance name to be maintained and obtain the target component maintenance name.

[0065] The fifth step is to call the maintenance step information matching the target component maintenance name. The maintenance step information may include component maintenance 3D animation and component maintenance step instruction information. Here, the component maintenance 3D animation and component maintenance step instruction information may be extracted from the node maintenance guide information in the component node corresponding to the target component maintenance name as the matching maintenance step information.

[0066] The sixth step is to render the above maintenance step information including the component maintenance three-dimensional animation and the component maintenance step instruction information onto the three-dimensional simulation model displayed in the above interface to demonstrate the maintenance steps of the target product.

[0067] Step 105 , in response to detecting a confirmation operation on the product accessory replacement information in the product problem feedback information, the product accessories are shipped according to the product accessory replacement information.

[0068] In some embodiments, the execution entity may, in response to detecting a confirmation operation on the product accessory replacement information in the product problem feedback information, ship the product accessories according to the product accessory replacement information.

[0069] Optionally, the above execution entity may further perform the following steps: The first step is to decompose the above fused problem feature information to obtain the decomposed problem data chain; The second step is to update the above decomposed problem data chain to the above product problem database.

[0070] The above steps 104-105 and related contents, as an inventive point of the embodiment of the present disclosure, solve the technical problem 3 mentioned in the background technology: "Although the problem requirements required by the user can be accurately located, it is still difficult to accurately reply to the product abnormality problems raised by the user for the target product because the problems in the preset database are often not comprehensive." The factors that make it difficult to accurately reply to the product abnormality problems raised by the user for the target product are often as follows: Although the problem requirements required by the user can be accurately located, the problems in the preset database are often not comprehensive. In order to achieve this effect, first of all, considering the situation that the problems stored in the database are not comprehensive, therefore, for each product, a component identifier and a problem identifier are added to the database. At the same time, the component fault nodes are associated with the fault pointer information as a product fault problem chain. Thus, various problems involved in the product can be ensured. Thus, it is convenient to quickly retrieve the product fault problem chain according to user needs to generate product problem feedback information. In addition, it is also considered that the questions asked by the user have two situations: product fault category and product maintenance category. Therefore, by introducing the product maintenance process structure tree, the product process can be structured. At the same time, because the product process node, product component node and maintenance step information are recorded. Therefore, when the user demand is for product maintenance, the database can be assisted to accurately push maintenance step information to the user. Thus, the user interaction time can be reduced, the amount of interaction information can be reduced, and the transmission resource usage can be reduced.

[0071] The above-mentioned embodiments of the present disclosure have the following beneficial effects: through the data processing method of some embodiments of the present disclosure, the accuracy of replying to the abnormal product problems raised by the user for the target product can be improved, thereby avoiding the wrong delivery of product accessories and avoiding the waste of logistics resources. Specifically, the reason for the waste of logistics resources is that the training of the large language model is relatively complex, and the questions answered to the user are mostly based on the data disclosed on the Internet. Therefore, it is difficult to accurately locate the target product and it is difficult to accurately reply to the abnormal product problems raised by the user for the target product. In addition, since the questions in the preset database are often not comprehensive, it is difficult to retrieve the appropriate product problem processing method, but fuzzy matching returns the processing method of the approximate problem to the user, so it is also difficult to accurately reply to the abnormal product problems raised by the user for the target product, resulting in the situation that the product accessories do not match when the user selects the product accessories for delivery according to the feedback data of the platform interaction. Based on this, the data processing method of some embodiments of the present disclosure, first, through the question and answer window of the above-mentioned product information interaction platform, obtains the question information input by the user for the target product. Here, in order to avoid directly using the large model for problem parsing and feedback, semantic recognition is used to determine the semantic feature information of the problem. Then, considering that the user's question has an inaccurate description, the problem judgment condition is introduced to determine whether the question is accurate. Here, for inaccurate questions, the method of displaying supplementary question information in the above-mentioned question and answer window is adopted to facilitate users to further explain the product problem. At the same time, by displaying the three-dimensional simulation model of the target product for users to refer to and describe, the accuracy of the user's description of the required questions can be further improved. In this way, the user's demand questions for the product can be located end-to-end. Then, through feature fusion, the features of all the information replied by the user can be comprehensively processed to obtain the fused problem feature information. After that, the question is retrieved through the product problem database to obtain product problem feedback information. Here, the accuracy of the obtained user needs can also be ensured. Therefore, the corresponding product problem feedback information can be accurately retrieved from the product problem database. Therefore, it is convenient for users to confirm the accessories that need to be replaced according to the product problem feedback information to ship the product accessories. Therefore, the waste of logistics resources is avoided.

[0072] Further references Figure 3 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a data processing device. These device embodiments are Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0073] like Figure 3As shown, the data processing device 300 of some embodiments includes: a semantic recognition unit 301, a display unit 302, a feature fusion unit 303, a question retrieval unit 304, and a confirmation and delivery unit 305. The semantic recognition unit 301 is configured to, in response to detecting question information issued by a user for a target product in a question-and-answer window on the product information interaction platform, perform semantic recognition on the question information to obtain question semantic feature information; the display unit 302 is configured to, in response to determining that the question semantic feature information does not meet a preset question determination condition, display supplementary question information in the question-and-answer window according to the question semantic feature information, and display a three-dimensional simulation model of the target product in the interface where the question-and-answer window is located, wherein the displayed three-dimensional simulation model matches the question semantic feature information, and the supplementary question information is generated by the following steps: determining missing question information of the question semantic feature information, and obtaining a three-dimensional simulation model of the target product in the interface where the question-and-answer window is located according to the missing question information. information, generating supplementary question information; a feature fusion unit 303, configured to, in response to detecting the supplementary reply information for the above-mentioned supplementary question, fuse the features of the above-mentioned supplementary reply information and the above-mentioned question semantic feature information, so as to obtain the fused question feature information; a question retrieval unit 304, configured to, based on the above-mentioned fused question feature information, perform question retrieval on a preset product question database, obtain product question feedback information, and display the above-mentioned product question feedback information in the above-mentioned question-and-answer window, wherein the above-mentioned product question feedback information includes product accessory replacement information; a confirmation and delivery unit 305, configured to, in response to detecting the confirmation operation for the product accessory replacement information in the above-mentioned product question feedback information, deliver the product accessories according to the above-mentioned product accessory replacement information.

[0074] It is understood that the units described in the device 300 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 300 and the units contained therein, and will not be described in detail here.

[0075] Reference below Figure 4 , which shows a structural schematic diagram of an electronic device (eg, a computing device) 400 suitable for implementing some embodiments of the present disclosure. Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0076] like Figure 4As shown, the electronic device 400 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory 402 or a program loaded from a storage device 408 to a random access memory 403. Various programs and data required for the operation of the electronic device 400 are also stored in the random access memory 403. The processing device 401, the read-only memory 402, and the random access memory 403 are connected to each other via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0077] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 4 The electronic device 400 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 4 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0078] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 409, or installed from the storage device 408, or installed from the read-only memory 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0079] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0080] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0081] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: in response to detecting question information issued by a user regarding a target product in the question and answer window on the above-mentioned product information interaction platform, performs semantic recognition on the above-mentioned question information to obtain question semantic feature information; in response to determining that the above-mentioned question semantic feature information does not meet the preset question judgment conditions, displays supplementary question information in the above-mentioned question and answer window according to the above-mentioned question semantic feature information, and displays a three-dimensional simulation model of the above-mentioned target product in the interface where the above-mentioned question and answer window is located, wherein the displayed three-dimensional simulation model matches the above-mentioned question semantic feature information, and the above-mentioned supplementary question information is generated by the following steps: : Determine the missing question information of the above question semantic feature information, and generate supplementary question information based on the above missing question information; in response to detecting the supplementary reply information for the above supplementary question, perform feature fusion on the above supplementary reply information and the above question semantic feature information to obtain fused question feature information; based on the above fused question feature information, perform question retrieval on a preset product question database to obtain product question feedback information, and display the above product question feedback information in the above question and answer window, wherein the above product question feedback information includes product accessory replacement information; in response to detecting the confirmation operation for the product accessory replacement information in the above product question feedback information, ship the product accessories according to the above product accessory replacement information.

[0082] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0083] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0084] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be provided in a processor, for example, may be described as: a processor including a semantic recognition unit, a display unit, a feature fusion unit, a question retrieval unit, a confirmation and a delivery unit. Among them, the names of these units do not constitute a limitation on the units themselves in certain cases, for example, the semantic recognition unit may also be described as a "unit for semantically recognizing question information".

[0085] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0086] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A data processing method, applied to a product information interaction platform, comprising: In response to detecting question information sent by a user in a question-and-answer window on the product information interaction platform with respect to a target product, performing semantic recognition on the question information to obtain question semantic feature information; In response to determining that the question semantic feature information does not meet the preset question determination condition, supplementary question information is displayed in the question and answer window according to the question semantic feature information, and a three-dimensional simulation model of the target product is displayed in the interface where the question and answer window is located, wherein the displayed three-dimensional simulation model matches the question semantic feature information, and the supplementary question information is generated by the following steps: Determining missing question information of the question semantic feature information, and generating supplementary question information based on the missing question information; In response to detecting the supplementary answer information for the supplementary question, performing feature fusion on the supplementary answer information and the question semantic feature information to obtain fused question feature information; According to the fused problem feature information, searching a preset product problem database to obtain product problem feedback information, and displaying the product problem feedback information in the question-and-answer window, wherein the product problem feedback information includes product accessory replacement information; In response to detecting a confirmation operation on the product accessory replacement information in the product problem feedback information, the product accessories are shipped according to the product accessory replacement information.

2. The method according to claim 1, wherein: The method further comprises: Decomposing the fused problem feature information to obtain a decomposed problem data chain; The decomposed problem data is linked to update the product problem database.

3. The method according to claim 1, wherein: The performing semantic recognition on the question information to obtain question semantic feature information includes: Extracting product feature words from the question information; According to the product feature words, question semantics are extracted from the question information to generate question semantic feature information.

4. The method according to claim 2, wherein: In response to determining that the question semantic feature information does not meet a preset question determination condition, displaying supplementary question information in the question-answering window according to the question semantic feature information includes: Retrieving product problem description information matching the product feature word from the product problem database; In response to the product problem description information not matching the problem semantic feature information, determining that the problem semantic feature information does not meet a preset problem determination condition; Supplementary question information is displayed in the question-answering window based on the question semantic feature information.

5. The method according to claim 4, wherein: The displaying of the three-dimensional simulation model of the target product in the interface where the question-answer window is located includes: Retrieving a product simulation model corresponding to the target product from a database, wherein the product simulation model is constructed according to the product structure of the target product; Determining a product component node in the product simulation model that matches the product feature word; The product component nodes are modeled and the display perspective of the product simulation model is adjusted to display the rendered product component nodes, wherein the product digital passport of the target product is also displayed in the interface where the question and answer window is located.

6. The method according to claim 4, wherein: The determining of missing question information of the question semantic feature information, and generating supplementary question information according to the missing question information, comprises: Extracting user demand feature words from the question semantic feature information, and in response to not extracting the user demand feature words, instructing the user to send the user demand feature words through the question-answer window; In response to determining that the user demand feature word belongs to the product failure category, extracting a product failure problem chain set corresponding to the product component node from the product problem database, wherein each product failure problem chain in the product failure problem chain set includes at least one component failure node, and the component failure nodes are connected by failure pointer information; Using the user demand feature words and the question semantic feature information, selecting a matching product failure problem chain from the product failure problem chain set as a target product failure problem chain; Taking the product feature word as a core word and according to a preset problem description sentence set, generating supplementary question information, wherein the supplementary question information includes a supplementary question prompt text group corresponding to the target product failure problem chain, and each supplementary question prompt text in the supplementary question prompt text group corresponds to a question; The supplementary question prompt text groups in the supplementary question information are sent to the question-and-answer window in sequence for display so that the user can answer.

7. The method according to claim 6, wherein: The step of fusing the supplementary reply information and the question semantic feature information to obtain fused question feature information includes: Determine the user response result corresponding to each supplementary question prompt text in the supplementary response information; Determine the question fields corresponding to the answer results of each user in the product question database to obtain a question field group; The question field group is added to the question semantic feature information to obtain fused question feature information.

8. A data processing device, comprising: a semantic recognition unit configured to, in response to detecting question information sent by a user in a question-and-answer window on the product information interaction platform with respect to a target product, perform semantic recognition on the question information to obtain question semantic feature information; A display unit is configured to, in response to determining that the question semantic feature information does not meet a preset question determination condition, display supplementary question information in the question-and-answer window according to the question semantic feature information, and display a three-dimensional simulation model of the target product in an interface where the question-and-answer window is located, wherein the displayed three-dimensional simulation model matches the question semantic feature information, and the supplementary question information is generated by the following steps: Determining missing question information of the question semantic feature information, and generating supplementary question information based on the missing question information; a feature fusion unit configured to, in response to detecting the supplementary answer information for the supplementary question, perform feature fusion on the supplementary answer information and the question semantic feature information to obtain fused question feature information; a question retrieval unit, configured to perform a question search on a preset product question database according to the fused question feature information, obtain product question feedback information, and display the product question feedback information in the question-answering window, wherein the product question feedback information includes product accessory replacement information; The confirmation and delivery unit is configured to deliver product accessories according to the product accessory replacement information in response to detecting a confirmation operation for the product accessory replacement information in the product problem feedback information.

9. An electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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