Commodity page display method and device, computer equipment and storage medium

Abstract

According to the commodity page display scheme provided by the invention, when the target commodity is updated, firstly, the latest detection report is quickly retrieved to ensure the timeliness of information acquisition, and then the difference between the latest detection report and the current detection report is judged; and further determining a difference item under the condition that the difference exists, updating the commodity display page and marking the difference item. The steps are coordinated, so that the accuracy and timeliness of the commodity display page information are ensured, the commodity information acquisition efficiency of the user is improved, the user can intuitively understand the change of the commodity detection report, and the user experience is optimized.

Description

Technical Field

[0001] The present application relates to the technical field of product page display, and in particular to a product page display method, device, computer equipment and storage medium. Background Art

[0002] Currently, product pages typically display basic product information and test reports to help consumers understand product quality and compliance. However, when product updates result in changes to test reports, report updates can become out of sync, hindering the consumer experience and product information transparency. Summary of the Invention

[0003] The purpose of this application is to solve at least one of the above-mentioned technical defects, and in particular to provide a product page display method that can solve the problem of asynchronous update of product inspection reports.

[0004] In a first aspect, the present application provides a method for displaying a product page, comprising:

[0005] When it is detected that the target product has been updated, the latest test report is retrieved from the test report database according to the unique identifier of the target product;

[0006] Determine whether there are any discrepancies between the latest test report and the current test report;

[0007] If so, determine the differences between the latest test report and the current test report;

[0008] Update the target product's display page based on the latest test report and mark the differences in the latest test report.

[0009] In one embodiment, determining whether there is a difference between the latest test report and the current test report includes:

[0010] Generate a first field table according to the current test report; the first field table includes all first fields in the current test report;

[0011] Generate a second field table according to the latest test report; the second field table includes all second fields in the latest test report;

[0012] Match each first field with each second field according to the field name;

[0013] If there is no unmatched first field or second field, then if the change in the field value of each matching field pair is less than the first threshold, it is determined that there is no difference; if the change in the field value of each matching field pair is greater than the first threshold, it is determined that there is a difference;

[0014] If there is a first field and / or a second field that cannot be matched, it is determined that there is a difference.

[0015] In one embodiment, the difference item includes a numerical change item, an increase item, and a decrease item, and determining the difference item between the latest test report and the current test report includes:

[0016] Determine the field value change in each matching field pair that is greater than a first threshold as a value change item;

[0017] Determine the first field that cannot be matched as a reduction item;

[0018] The second field that cannot be matched is determined to be an added item.

[0019] In one embodiment, the product page display method further includes:

[0020] In response to a target user's request for explanation of a difference item, determining the difference item to be explained as a target difference item; the target user is a user who is browsing the target product page;

[0021] A description request prompt word is formed according to the type label of the target difference item, the field name of the target difference item, and the target product name;

[0022] Input the explanation request prompt words into the explanation model to obtain the explanation result;

[0023] Present the explanation results to the target users.

[0024] In one embodiment, responding to a target user's request for explanation of a difference item includes:

[0025] If it is detected that the target user performs a hover operation on any difference item, an explanation request for the difference item is generated.

[0026] In one embodiment, the difference items are marked in the latest test report, including:

[0027] Obtain the user profile of the target user; the target user is the user who is browsing the target product page;

[0028] Predict the target user's interest in each difference item based on the user portrait;

[0029] Determine the visual emphasis level of the difference item based on the interest level; the higher the interest level, the higher the corresponding visual intensity level;

[0030] Select the corresponding annotation method according to the visual emphasis level to mark the difference items.

[0031] In one embodiment, the user profile includes one or more attribute tags, each of which is configured with a corresponding set of interest fields. Predicting the target user's interest in each difference item based on the user profile includes:

[0032] Semantically match the difference items with the set of fields of interest corresponding to each attribute label;

[0033] The interest level is determined based on the highest semantic similarity among the semantic matching results.

[0034] In one embodiment, the product page display method further includes:

[0035] Batch number of the target product to be monitored;

[0036] When the product batch number is updated, it is determined that the target product is updated.

[0037] In a second aspect, the present application provides a product page display device, comprising:

[0038] A retrieval module is used to retrieve the latest test report from the test report database based on the unique identifier of the target product when it is detected that the target product has been updated;

[0039] A judgment module is used to judge whether there is a difference between the latest test report and the current test report;

[0040] A difference determination module is used to determine the difference items between the latest test report and the current test report when there is a difference between the latest test report and the current test report;

[0041] The update module is used to update the display page of the target product according to the latest test report and mark the differences in the latest test report.

[0042] In a third aspect, the present application provides a computer device comprising one or more processors and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the steps of the product page display method in any of the above embodiments are executed.

[0043] In a fourth aspect, the present application provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the product page display method in any of the above embodiments.

[0044] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0045] Based on the product page display method in this solution, when a target product is updated, the latest test report is quickly retrieved to ensure timely information acquisition. The differences between the latest and current test reports are then determined. If any discrepancies exist, the differences are further identified, the product display page is updated, and the discrepancies are marked. These steps work together to ensure the accuracy and timeliness of information on the product display page, improving user access to product information and enabling users to intuitively understand changes in product test reports, thus optimizing the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0047] Figure 1 A flowchart of a product page display method provided in one embodiment of the present application;

[0048] Figure 2 This is a flowchart of determining whether there is a difference between the current test report and the latest test report in one embodiment of the present application;

[0049] Figure 3 This is a flow chart of the difference item categories in one embodiment of the present application;

[0050] Figure 4 This is a flowchart illustrating the differences in one embodiment of the present application;

[0051] Figure 5 This is a schematic diagram of a process for marking difference items in one embodiment of the present application;

[0052] Figure 6 A diagram of the internal structure of a computer device provided for one embodiment of the present application. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. The embodiments described in the specification are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0054] This application provides a method for displaying product pages. Figure 1 , including steps S102 to S108.

[0055] S102: When it is detected that the target product is updated, the latest test report is retrieved from the test report database according to the unique identifier of the target product.

[0056] It can be understood that the target product refers to the specific product for which the report update synchronization needs to be ensured on the e-commerce platform. It has a unique product code to distinguish it from other products. The unique identifier refers to a unique string or number combination assigned to the target product. It is usually generated by the platform according to a specific algorithm. It is unique and can ensure that the product is accurately identified in different systems and databases. The test report database is a database that stores product test reports. It contains various reports such as product quality inspection, safety certification, performance testing, etc. These reports are stored according to preset data formats and indexing rules for quick retrieval. The latest test report refers to the latest version of the test report generated after the target product is re-tested. Under the trigger condition that the target product is updated, the association between the target product and the test report database is established through the unique identifier to obtain the latest test report. When the information of the target product, such as specifications, ingredients, production process, etc., is updated, the system needs to obtain the test report reflecting the status of these updated products in a timely manner so that the product display page can be accurately updated in the subsequent steps.

[0057] When an update is detected for a target product, the system extracts the product's unique identifier and uses Structured Query Language (SQL) to query the inspection report database, identifying the latest inspection report with a matching unique identifier and the most recent entry date. For example, upon receiving a signal that a product has been updated, the system retrieves the product's unique identifier, "PROD123," from the product information table and then executes the SQL statement "SELECT * FROM inspection_reports WHERE product_id = 'PROD123' ORDER BY report_date DESCLIMIT1" in the inspection report database to retrieve the latest inspection report.

[0058] S104: Determine whether there is a difference between the latest test report and the current test report.

[0059] The current test report refers to the test report currently displayed on the product page before the target product was updated. It records the test indicators and results before the product was updated. A discrepancy refers to the differences in content between the latest test report and the current test report, including changes in test indicator values, additions or deletions to test items, and alterations to test conclusions. Judgment is the process by which the system compares and analyzes the latest and current test reports using specific algorithms and logic to determine whether there are any differences between the two.

[0060] The main function of step S104 is to take the latest test report obtained in step S102 and compare it with the test report currently in use to determine whether there are any differences. This is the key link in determining whether the subsequent steps will be executed. If there are no differences, there is no need to perform subsequent difference item determination and page update operations, saving system resources; if there are differences, step S106 is triggered to determine the difference items. This step provides a decision-making basis for the entire method by systematically comparing the two reports, ensuring that subsequent processing is only carried out when the test report has indeed changed, making the update of the product display page more targeted and effective, forming an organic connection with the previous and subsequent steps, and ensuring the efficiency and accuracy of the method.

[0061] S106: If yes, determine the difference between the latest test report and the current test report.

[0062] It can be understood that the difference items refer to the specific content units that are different between the latest test report and the current test report, such as the numerical value of a single test indicator, the description of the test item, the statement of the test conclusion, etc. Determining the difference items refers to the process of accurately locating and extracting the specific content of the differences from the two test reports through specific technical means and methods. Step S106 is executed after step S104 determines that there are differences. Its main purpose is to clarify the specific differences between the two reports and provide specific content for the page update and difference item annotation of step S108. This step conducts an in-depth analysis of the two reports with differences, refines and extracts the difference content, and facilitates subsequent annotation on the latest test report.

[0063] S108: Update the display page of the target product according to the latest test report, and mark the difference items in the latest test report.

[0064] It can be understood that a product display page refers to a webpage or application interface on an e-commerce platform that displays target product information to users, including product images, prices, specifications, test reports, and other content. Updating a target product display page refers to modifying, supplementing, or replacing the test report-related portion of the product display page based on the contents of the latest test report. Marking differences refers to highlighting differences in the latest test report through specific visual markers, such as color highlighting, underlining, or borders, to enable users to intuitively identify content that differs from the current test report.

[0065] Step S108 is the final step in the entire product page display method. Its function is to reflect the differences determined in step S106 on the product display page, allowing users to promptly understand the latest status and changes in the product test report. This step follows the previous steps, replacing the original test report with the latest one and highlighting the differences between the two reports. This allows users to more accurately monitor the dynamic changes of the target product during the update process.

[0066] Based on the product page display method in this solution, when a target product is updated, the latest test report is quickly retrieved to ensure timely information acquisition. The differences between the latest and current test reports are then determined. If any discrepancies exist, the differences are further identified, the product display page is updated, and the discrepancies are marked. These steps work together to ensure the accuracy and timeliness of information on the product display page, improving user access to product information and enabling users to intuitively understand changes in product test reports, thus optimizing the user experience.

[0067] In one embodiment, to determine whether there is a difference between the latest test report and the current test report, refer to Figure 2 , including steps S202 to S210.

[0068] S202: Generate a first field table according to the current detection report. The first field table includes all first fields in the current detection report.

[0069] It is understandable that the first field table is a structured data set generated based on the current test report, wherein the first field refers to an information unit with a specific meaning in the current test report, including the name of each test item, test index, etc., and each first field corresponds to a unique field name and a corresponding field value. Generating the first field table refers to the process of extracting the information in the current test report and organizing it according to a preset data structure through data parsing technology to form a structured table that can be processed by a computer. Its core function is to convert the unstructured current test report into a structured first field table, providing a standardized data basis for field matching and difference judgment in subsequent steps. By decomposing the current test report into multiple first fields, each field contains a clear field name and field value, so that the subsequent comparison with the second field table can be carried out under a unified data structure, avoiding the comparison error caused by differences in report formats.

[0070] Specifically, regular expressions can be used to parse the current test report to generate the first field table. The text format of the current test report can be analyzed to determine the typical expressions of field names and field values, and then corresponding regular expressions can be written to extract field names and corresponding field values ​​from the report text through regular matching, and all extracted field names and field values ​​are stored in the first field table in the form of key-value pairs. The first field table uses the data table structure in a relational database, which contains two columns "field name" and "field value", and each row corresponds to a first field. A natural language processing model based on deep learning can also be used for field extraction and first field table generation. The system uses a large number of test report samples marked with field names and field values ​​to train a large language model, which can automatically identify field boundaries and semantic information in the current test report. When generating the first field table, the current test report is input into the trained model, and the model outputs the field name and field value of each field by analyzing the text sequence.

[0071] S204: Generate a second field table according to the latest test report. The second field table includes all second fields in the latest test report.

[0072] It is understood that the second field table is a structured data set generated based on the latest test report, wherein the second field refers to an information unit with a specific meaning in the latest test report, corresponding to the definition of the first field, and each second field also contains a unique field name and a corresponding field value. Generating the second field table refers to converting the information in the latest test report into a form that conforms to a preset data structure through data extraction and structured processing technology, so as to perform subsequent comparison operations. Its function is to convert the latest test report into a structured second field table, forming a comparison data pair with the first field table, and providing data support for the other party for the field matching in step S206. By adopting the same data structure and extraction rules as those used to generate the first field table, the consistency of the two field tables in format is ensured, so that field matching can be carried out based on the same standards, reducing matching errors caused by structural differences. The second field table generated in this step and the first field table together constitute the basic data for difference judgment. The structured processing of the two enables subsequent field matching, field value comparison and other operations to be carried out efficiently. The extraction of the second field table can also be carried out in a manner similar to the first field table.

[0073] S206: Match each first field with each second field according to the field name.

[0074] As can be understood, a field name refers to a string or symbol used to identify the meaning of a field. In the first and second field tables, field names serve as identifiers for different fields, such as "Appearance" and "Aroma." Matching refers to the process of associating the first field in the first field table with the second field in the second field table based on the field name. This matching process forms matching field pairs—pairs of data consisting of a first field and a second field with the same field name. Specifically, all fields in the first and second field tables are traversed, and the corresponding relationships between the fields are determined by comparing the field names. Fields with the same field name are then identified and associated. This process primarily determines the corresponding relationships between the fields in the two field tables through field name comparison, providing a basis for subsequently determining whether there are any field mismatches and calculating the magnitude of field value changes. Accurate field matching can clearly identify which fields are common to both reports and which fields are unique to a particular report, which is the prerequisite for determining differences in performance in steps S208 and S210. This step establishes a correspondence between the fields and associates the fields in the two field tables, allowing subsequent difference judgments to be performed on specific field pairs or isolated fields. This ensures the accuracy of difference judgments and works in conjunction with the previous and subsequent steps to form a complete difference judgment logic chain, avoiding difference judgment errors caused by field correspondence errors.

[0075] Specifically, a hash table lookup algorithm can be used for field matching. The system first stores all the field names in the first field table into a hash table. The key of the hash table is the field name, and the value is the index position of the corresponding first field in the table. Then, each second field in the second field table is traversed, its field name is extracted, and the search is performed in the hash table. If the corresponding key is found, a matching relationship is established between the second field and the first field corresponding to the hash table value to form a matching field pair; if not found, it is marked as an unmatched second field. At the same time, the unmatched fields in the first field table are traversed and marked as unmatched first fields. For example, the first field table contains the field names "hardness" and "density", and the second field table contains the field names "hardness" and "elastic modulus". After matching through the hash table, the "hardness" field forms a matching field pair, "density" is marked as an unmatched first field, and "elastic modulus" is marked as an unmatched second field.

[0076] S208: If there is no unmatched first field or second field, it is determined that there is no difference if the change range of the field value of each matching field pair is less than the first threshold; and it is determined that there is a difference if the change range of the field value of each matching field pair is greater than the first threshold.

[0077] It is understood that the first field that cannot be matched refers to a first field that exists in the first field table but cannot find the same or semantically similar field name in the second field table. The second field that cannot be matched refers to a second field that exists in the second field table but cannot find the same or semantically similar field name in the first field table. The matching field pair refers to the paired data consisting of the first field and the second field that are successfully matched in step S206, which contain the same field name and respective field values. The field value variation range refers to the degree of difference between the field value of the second field and the field value of the first field in the matching field pair, which is usually obtained by a specific calculation formula, such as the ratio of the absolute difference to the original field value. The first threshold value refers to a preset critical value for judging whether the field value change is significant. The value is set according to the type of field and actual business needs, and different first threshold values ​​can be set for different fields. Determining that there is no difference refers to the conclusion that the two test reports are not significantly different in content after analyzing the field value variation range of the matching field pair. Determining that there is a difference refers to the conclusion that the two test reports are significantly different in content after analysis.

[0078] This step identifies differences between the two reports' common fields, starting with the degree of change in their values. This overcomes the shortcomings of judging solely by the presence of a field and can identify differences where the field name is the same but the field value has changed significantly. Based on the determination that all fields match in step S206, further analysis is performed at the field value level, complementing step S210 and forming a complete difference judgment logic. This ensures that both missing fields and significantly changed field values ​​can be accurately identified, improving the comprehensiveness and accuracy of difference judgment.

[0079] S210: If there is a first field and / or a second field that cannot be matched, it is determined that there is a difference.

[0080] It is understandable that the first field that cannot be matched refers to the first field that exists in the first field table but does not have the corresponding matching field in the second field table, indicating that some field information in the current test report is missing in the latest test report. The second field that cannot be matched refers to the second field that exists in the second field table but does not have the corresponding matching field in the first field table, indicating that the latest test report has newly added field information that is not in the current test report. The main effect of step S210 is to directly determine that there are differences between the two test reports when step S206 finds that there are fields that cannot be matched. This step is directed to the difference in field composition between the two reports, that is, the situation that one report contains fields that the other report does not have. This situation usually means that the test items of the commodity have increased or decreased, which is a substantial content change, and therefore needs to be determined as having differences.

[0081] In one embodiment, the difference term includes a value change term, an increase term, and a decrease term. Figure 3 , determining the differences between the latest test report and the current test report, including steps S302 to S306.

[0082] S302: Determine, in each matching field pair, a field value whose change range is greater than a first threshold as a value change item.

[0083] It can be understood that a matching field pair refers to a paired data unit consisting of a first field and a second field that are successfully matched by field name, wherein the first field comes from the first field table generated by the current test report, and the second field comes from the second field table generated by the latest test report, and both have the same or semantically equivalent field names. The first threshold is a pre-set critical value for judging whether the change in field value reaches a significant level. The threshold can be adjusted according to the type, importance and business needs of the field, and different fields can be configured with different first thresholds. The numerical change item refers to the difference item in which the change in the field value in the matching field pair exceeds the first threshold, indicating that the content of the field has changed significantly in the two test reports. Through this step, it is possible to accurately locate the parts of the two test reports that have common fields but have significantly changed content.

[0084] S304: Determine the first field that cannot be matched as a reduction item.

[0085] It can be understood that an unmatched first field refers to a first field that exists in the first field table but has no corresponding matching field in the second field table. In other words, the current test report contains a field that the latest test report does not. A reduced item refers to a difference item caused by the existence of an unmatched first field, indicating that the latest test report has reduced the test content corresponding to the field compared to the current test report.

[0086] S306: Determine the second field that cannot be matched as an additional item.

[0087] It can be understood that the second field that cannot be matched refers to the second field that exists in the second field table but the corresponding matching field is not found in the first field table, that is, the latest test report contains a field but the current test report does not contain the field. An additional item refers to a difference item formed due to the existence of the second field that cannot be matched, indicating that the latest test report has added the test content corresponding to the field relative to the current test report. Determining an additional item refers to the process of clearly classifying the second field that cannot be matched as an additional item. Through this process, the increase in the number of fields in the two test reports can be clearly identified.

[0088] In one embodiment, see Figure 4 The product page display method also includes steps S402 to S408.

[0089] S402 : In response to a target user's request for explanation of a difference item, determining the difference item to be explained as a target difference item.

[0090] It can be understood that the request for explanation of the difference item refers to the request issued by the target user through a specific interactive operation, hoping to obtain a detailed explanation of the difference item. The request can be triggered by clicking the explanation button on the page, entering question text, etc. The difference item to be explained refers to the specific difference item that the target user requests to be explained, which is one or more of the numerical change item, increase item or decrease item. The target difference item refers to the difference item determined from the difference items to be explained, for which the system needs to generate an explanation result, which usually corresponds directly to the user's specific request. Response refers to the process in which the system performs the corresponding operation according to the preset processing logic after receiving the user's explanation request, to ensure that the user request is processed in a timely manner.

[0091] S404: compose a description request prompt word according to the type label of the target difference item, the field name of the target difference item, and the target product name.

[0092] It can be understood that the type label of a target difference item refers to a label used to identify the type of the target difference item, such as "value change item," "increase item," or "decrease item." This label is generated when determining a difference item and is used to distinguish different types of difference items. The field name of a target difference item refers to the field name in the test report corresponding to the target difference item, such as "tensile strength" or "corrosion resistance grade." It is a key identifier for identifying the specific content of the difference item. The target product name is the name of the product to which the target difference item belongs. It is unique and can be used to clarify the scope of the description. The description request prompt is the text message used to issue a description request to the description model. It contains key information required to generate the description result and guides the model to generate the required description content. Composing the description request prompt is the process of combining the target difference item's type label, field name, target product name, and other elements into a prompt according to a preset format and logic. By incorporating elements such as the type label, field name, and target product name into the prompt, the description model can be provided with sufficient contextual information to understand the specific object and difference type to be described, thereby generating a highly targeted and accurate description result. Specifically, a fixed template can be used to fill in the description request prompt. The system pre-sets a prompt word template, which contains placeholders for type labels, field names, and target product names. For example, "Please explain the reasons for the differences in the [field name] ([type label]) of product [target product name] and the resulting impact."

[0093] S406: Input the explanation request prompt words into the explanation model to obtain the explanation result.

[0094] It can be understood that the explanation model refers to a trained artificial intelligence model that can generate explanation content for the difference items based on the input prompt words. The model is trained based on a large amount of product testing, industry standards, technical documents and other data, and has the ability to understand the difference item information and generate professional explanations. The explanation result refers to the explanation content about the target difference item output by the explanation model based on the explanation request prompt words, including information such as the cause, impact, and basis of the difference, presented in the form of natural language text. Input into the explanation model refers to the process of converting the explanation request prompt words into the format required by the large model and passing them to the model for processing. The model generates the corresponding explanation results by parsing and understanding the prompt words. This step converts the structured prompt information into natural language explanation content through the processing of the large model, ensuring that users can quickly obtain the explanation information and enhancing the user's understanding of the product differences.

[0095] S408: Display the explanation result to the target user.

[0096] As can be understood, display refers to the process of presenting explanation results to target users in a user-perceivable manner. This is typically achieved through pop-up windows or embedded text areas within the product display page, ensuring that users can intuitively and conveniently access the explanation content. Displaying explanation results to target users involves the system presenting the generated explanation results to the target user via a preset display method, allowing the user to review and understand the relevant explanations for the differences. This step allows users to gain a deeper understanding of the reasons and impacts behind the differences, enhancing their trust and understanding of the products, thereby promoting their purchasing decisions or increasing their satisfaction with the platform.

[0097] In one embodiment, responding to the target user's request for explanation of the difference item includes: if it is detected that the target user performs a hovering operation on any difference item, generating a request for explanation of the difference item.

[0098] In one embodiment, the difference items are marked in the latest test report, see Figure 5 , including steps S502 to S508.

[0099] S502: Obtain a user profile of the target user.

[0100] It can be understood that a user profile refers to a user feature model constructed through data mining and analysis techniques based on multi-dimensional data such as the target user's basic information, behavioral data, and preference settings. Obtaining a user profile of a target user refers to the process by which the system extracts various types of data associated with the target user's identifier by calling the user database interface, and integrates and analyzes them according to a preset profile model to form structured user profile data. Information such as the user's history of attention to product testing indicators and consumption habits contained in the user profile is a key basis for predicting interest. Through this step, the system can locate the current user's unique characteristics from a large number of users, laying the foundation for differentiated labeling methods and ensuring that subsequent labeling operations are more targeted and effective.

[0101] Specifically, user profiles are obtained through analysis of user behavior logs. The system regularly extracts data from the user behavior log database, including the target user's browsing history, click behavior, product categories purchased, and frequency of test report viewing. For example, this data is counted, including the number of times a user viewed test indicators such as "tensile strength" and "corrosion resistance" in the past 30 days, and the level of attention paid to safety certification items in the test report when purchasing a product. This data is quantified according to preset weights, such as multiplying the number of views by the corresponding weight to convert it into an attention index. This is then integrated to form a user profile containing label information such as the types of test indicators the user is interested in and the intensity of their attention. A neural network classification model can also be used to label and classify the target user's behavior logs. This involves using real-time user behavior data to label the user, generating training data that is then used to train the classification model. For example, if a user quickly browses a product page but spends a longer time on the "heavy metal content" test item, they can be labeled as being concerned about their health.

[0102] S504: predicting the target user's interest in each difference item based on the user portrait.

[0103] As can be understood, interest refers to the target user's level of attention to each difference item. It is a quantitative indicator calculated based on the user profile and difference item characteristics. A higher value indicates greater user interest in that difference item. This step primarily connects the user profile with the difference item annotations. By analyzing the user preferences reflected in the user profile and combining them with the detection indicators and other characteristics of each difference item, the user's interest in each difference item is predicted, providing a basis for the subsequent determination of the visual emphasis level. This step follows the user profile obtained in step S502, converting the user's preferences into a quantitative value of their attention to specific difference items, allowing the annotation of difference items to focus on the user's interests.

[0104] S506: Determine the visual emphasis level of the difference item based on the interest level. The higher the interest level, the higher the corresponding visual intensity level.

[0105] As can be understood, attention level, i.e., the target user's interest in each difference item, is a quantitative indicator predicted in step S504, reflecting the user's level of attention to the different difference items. Visual emphasis level refers to a grading system used to distinguish the visual prominence of a difference item during display. It is typically categorized into multiple levels, such as high, medium, and low. The higher the level, the more visually striking the difference item appears on the page. Determining the visual emphasis level of a difference item based on attention level involves comparing the predicted attention quantization value with a preset grading standard and assigning a corresponding visual emphasis level to each difference item. This process allows the system to rank difference items based on the user's attention level, directing visual resources toward the difference items of greatest interest to the user, improving information transmission efficiency and preventing users from missing important content amidst a large number of difference items. A threshold interval method can be used to determine the visual emphasis level. The system pre-sets multiple attention threshold intervals, each corresponding to a visual emphasis level. For example, an attention level between 0.8 and 1.0 corresponds to high, 0.4 to 0.8 corresponds to medium, and 0 to 0.4 corresponds to low. For example, a difference item with an attention score of 0.85, falling within the 0.8-1.0 range, is assigned a high visual emphasis level. Another difference item with an attention score of 0.5, falling within the 0.4-0.8 range, is assigned a medium visual emphasis level. A dynamic adaptive grading method can also be used to determine visual emphasis levels. The system dynamically adjusts the threshold range based on the number of difference items in the current target product and the distribution of attention scores for each difference item. For example, when the number of difference items is small (e.g., fewer than five), the grading is divided into two levels, with the top 50% of attention scores being high and the bottom 50% being low. When the number of difference items is large (e.g., more than 10), the grading is divided into four levels, with the thresholds for each level determined based on the normal distribution of attention scores. The system also adjusts the granularity of the grading based on the user's information processing ability as reflected in the user profile. For users with strong information processing abilities, more levels are assigned to provide more detailed visual distinctions; for users with weaker information processing abilities, the number of levels is reduced to avoid visual clutter. For example, if the user profile shows that the user is accustomed to browsing quickly, the system will simplify the hierarchy into two levels: high and low to make the focus more prominent.

[0106] S508: Select a corresponding marking method according to the visual emphasis level to mark the difference items.

[0107] It can be understood that the visual emphasis level refers to the level used to measure the visual prominence of the difference item when it is displayed, which is usually divided into multiple levels. The higher the level, the more eye-catching the difference item needs to be presented. The marking method refers to the specific form of marking the difference item in the latest test report, including various visual expression methods such as color highlighting, bold font, adding borders, dynamic flashing, etc. This step is to select the corresponding method from the preset marking method library based on the visual emphasis level determined for each difference item, and visually highlight the difference item to attract user attention. The main function is to convert the visual emphasis level into a specific visual expression form. Through differentiated marking methods, the target user can quickly identify the difference item of interest and improve the efficiency of user information acquisition. Through this step, the system can realize personalized marking of the difference item, which not only highlights the focus of the user's attention, but also avoids the interference of irrelevant information, so that the display of the latest test report is more in line with the user's reading habits and information needs, thereby improving the user's understanding and trust in product information. The preset level-method mapping table can be used to select the marking method. The system pre-establishes a correspondence table between visual emphasis levels and annotation methods. For example, high-level corresponds to red highlighting and bold fonts, medium-level corresponds to yellow highlighting, and low-level corresponds to gray borders. A dynamic combination annotation method is used and combined with user interaction feedback to optimize the annotation effect. It is also possible to configure a combination of multiple annotation methods for each visual emphasis level. For example, for high-level, you can choose combinations such as "red highlight + dynamic underline + slight animation" and "orange background + bold + icon". Appropriate combinations are selected based on the user's sensitivity to different visual elements in the user portrait. For example, users who are sensitive to color will give priority to combinations with large color differences, and users who are sensitive to dynamic elements will add animation effects.

[0108] In one embodiment, a user profile includes one or more attribute tags, each of which is assigned a corresponding set of fields of interest. Predicting the target user's interest in each difference item based on the user profile includes semantically matching the difference item with the fields of interest corresponding to each attribute tag. The interest level is determined based on the highest semantic similarity among the semantic matching results.

[0109] It can be understood that attribute labels are identifiers used to describe user characteristics, such as "maternal and infant product followers" and "strong environmental awareness", and each attribute label reflects a certain aspect of the user's characteristics or preferences. The field set of interest refers to the set of test report fields that the user is interested in, corresponding to each attribute label. For example, the field set of interest corresponding to "maternal and infant product followers" includes fields such as "formaldehyde content", "heavy metal content", and "material safety". Semantic matching refers to the process of calculating the degree of semantic similarity between the difference item and the fields in the field set of interest through natural language processing technology, which is used to determine the relevance of the two in terms of meaning. Semantic similarity is the quantitative result of semantic matching, usually expressed as a value between 0 and 1. The higher the value, the closer the semantics of the two are. The highest semantic similarity refers to the maximum similarity value obtained after semantic matching between the difference item and all fields in the field set of interest corresponding to a certain attribute label.

[0110] The core function of this step is to accurately predict user interests by semantically matching the difference items with the set of fields of interest corresponding to each attribute label in the user profile, finding the highest semantic similarity and using this to determine interest. This step follows the construction of the user profile, using the attribute labels and their corresponding fields of interest as a reference to perform a semantic comparison between the difference items and these fields. This method overcomes the limitations of superficial field name differences and more accurately captures the user's potential interests. By selecting the highest semantic similarity as the basis for determining interest, the highest correlation between the difference item and a specific user attribute is fully considered, avoiding misjudgments of interest due to insufficient matching of a single attribute label. This step closely coordinates with the subsequent step of determining the visual emphasis level. Accurate interest provides a reliable basis for visual emphasis level classification, enabling the annotation of difference items to truly align with the user's interests and preferences. This addresses the inefficient user information acquisition caused by the lack of specificity in traditional annotation methods. Through the synergistic effect of these steps, personalized and precise annotation of difference items is achieved.

[0111] The word vector model is used to calculate semantic similarity. The system pre-trains word vectors for all detection report fields, and uses the Word2Vec model to convert each field into a vector representation of a fixed dimension to form a field vector library. For the set of fields of interest corresponding to each attribute label in the user portrait, each field is also converted into a word vector. When processing difference items, the field name of the difference item is extracted and converted into a word vector. Then, the cosine similarity is calculated with the word vectors of all fields in the field of interest set corresponding to each attribute label. Multiple similarity values ​​are obtained, and the maximum value is selected as the semantic similarity under the attribute label. Then, the highest value is selected from the semantic similarities corresponding to all attribute labels as the highest semantic similarity, and the highest value is directly used as the interest level. For example, the difference item field is named "Benzene Content," and the user profile has the attribute tag "Decoration Materials Buyer." Their field set of interest includes fields such as "Formaldehyde Content" and "VOC Content." Word vector calculations show that the cosine similarity between "Benzene Content" and "VOC Content" is 0.8, the highest value in the field set. The highest semantic similarity corresponding to other attribute tags is 0.6. Therefore, 0.8 is determined as the highest semantic similarity, indicating that the interest level of this difference item is 0.8. The system determines the interest level of all difference items by repeatedly calculating the semantic similarity between each difference item and each field set of interest.

[0112] In one embodiment, the product page display method further includes monitoring the target product's batch number. When the batch number is updated, determining that the target product has been updated. It is understood that a product batch number is a unique code used to identify the production batch of a product. It is typically composed of numbers, letters, or a combination thereof, and contains information such as production time, production line, and team. It can be used to trace the product's production source and quality status. Monitoring the target product's batch number refers to the process by which the system, through pre-set technical means, obtains the target product's current batch number information in real time or periodically and compares it with stored historical batch numbers. A product batch number update occurs when the current batch number of the target product is inconsistent with the historical batch numbers stored in the system, indicating that the product has undergone a change in the production batch. The core function of this embodiment is to determine whether the target product has been updated by monitoring the update of the product batch number, providing trigger conditions for subsequent steps such as test report retrieval and discrepancy determination. The product batch number is directly linked to the product's production batch. An update to the batch number typically indicates that the product may have undergone raw material replacement, process adjustments, or changes in quality inspection standards during the production process, which may in turn cause changes in the product's test report content. This embodiment uses the product batch number as the basis for determining product updates, which can capture changes in the production batch of the product in a timely manner and ensure that the system starts the subsequent processing flow when the product may undergo substantial changes. The product batch number is monitored through a timed polling mechanism. The system sets a fixed time interval (such as every hour), obtains the current batch number of the target product from the product information database through the application interface, and compares it with the historical batch number of the product stored locally in the system. If the two are inconsistent, it is determined that the product batch number has been updated, and then it is determined that the target product has been updated. For example, the historical batch number of the target product stored in the system is "20231005A01", and the current batch number obtained at regular intervals is "20231006A02". After comparison, inconsistencies are found, and the product batch number is determined to be updated, and the target product has been updated. At the same time, the system updates the current batch number to the historical batch number for the next monitoring and comparison.

[0113] The present application provides a product page display device, comprising: a retrieval module for detecting that a target product has been updated and retrieving the latest test report from a test report database based on the unique identifier of the target product. A judgment module for judging whether there is a difference between the latest test report and the current test report. A difference determination module for determining the difference items between the latest test report and the current test report when there is a difference between the latest test report and the current test report. An update module for updating the display page of the target product according to the latest test report and marking the difference items in the latest test report.

[0114] For the specific definition of the product page display device, please refer to the definition of the product page display method above, which will not be repeated here. The various modules in the above-mentioned product page display device can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0115] The present application provides a computer device comprising one or more processors and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the one or more processors, the steps of the product page display method in any of the above embodiments are executed.

[0116] Schematically, as Figure 6 As shown, Figure 6 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. Figure 6 Computer device 600 includes a processing component 602, which further includes one or more processors, and memory resources represented by memory 601 for storing instructions executable by processing component 602, such as applications. The applications stored in memory 601 may include one or more modules, each corresponding to a set of instructions. Furthermore, processing component 602 is configured to execute the instructions to perform the steps of the product page display method of any of the above-described embodiments.

[0117] The present application provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the product page display method in any of the above embodiments.

[0118] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0119] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0120] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A product page display method, characterized in that: include: When it is detected that the target product has been updated, the latest test report is retrieved from the test report database according to the unique identifier of the target product; Determine whether there is a difference between the latest test report and the current test report; If yes, determining the difference between the latest test report and the current test report; The display page of the target product is updated according to the latest test report, and the difference items are marked in the latest test report.

2. The product page display method according to claim 1, characterized in that: The determining whether there is a difference between the latest test report and the current test report includes: Generate a first field table according to the current detection report; the first field table includes all first fields in the current detection report; Generate a second field table according to the latest test report; the second field table includes all second fields in the latest test report; Matching each of the first fields with each of the second fields according to the field name; If there is no unmatched first field or second field, then if the change in the field value of each matching field pair is less than a first threshold, it is determined that there is no difference; if the change in the field value of each matching field pair is greater than the first threshold, it is determined that there is a difference; If there is the first field and / or the second field that cannot be matched, it is determined that there is a difference.

3. The product page display method according to claim 2, characterized in that: The difference items include numerical change items, increase items, and decrease items. The determining of the difference items between the latest test report and the current test report includes: Determining the field value in each of the matching field pairs whose change amplitude is greater than the first threshold as the numerical change item; determining the first field that cannot be matched as the reduced item; The second field that cannot be matched is determined as the added item.

4. The product page display method according to claim 3, characterized in that: Also includes: In response to a target user's request for explanation of the difference item, determining the difference item to be explained as a target difference item; The target user is a user who is browsing the target product page; composing a description request prompt word according to the type label of the target difference item, the field name of the target difference item, and the target product name; Inputting the explanation request prompt words into the explanation model to obtain the explanation result; The explanation result is presented to the target user.

5. The product page display method according to claim 4, characterized in that: The responding to the target user's request for explanation of the difference item includes: If it is detected that the target user performs a hovering operation on any of the difference items, the explanation request for the difference item is generated.

6. The product page display method according to claim 1, characterized in that: The difference items marked in the latest test report include: Obtaining a user profile of a target user; the target user is a user who is browsing the target product page; Predicting the target user's interest in each of the difference items based on the user portrait; Determining a visual emphasis level of the difference item according to the interest level; the higher the interest level, the higher the corresponding visual intensity level; A corresponding marking method is selected according to the visual emphasis level to mark the difference item.

7. The product page display method according to claim 6, characterized in that: The user profile includes one or more attribute tags, each of which is correspondingly configured with an interest field set. The predicting of the target user's interest in each of the difference items based on the user profile includes: Performing semantic matching on the difference items and the set of fields of interest corresponding to each of the attribute tags; The interest level is determined according to the highest semantic similarity in the semantic matching results.

8. The product page display method according to claim 1, characterized in that: Also includes: Monitor the commodity batch number of the target commodity; When the commodity batch number is updated, it is determined that the target commodity is updated.

9. A product page display device, characterized in that: include: A retrieval module is configured to retrieve the latest test report from a test report database based on the unique identifier of the target product when an update of the target product is detected; A judgment module, configured to judge whether there is a difference between the latest test report and the current test report; a difference determination module, configured to determine the difference item between the latest test report and the current test report when there is a difference between the latest test report and the current test report; An updating module is used to update the display page of the target product according to the latest test report and mark the difference items in the latest test report.

10. A computer device, characterized in that: The method comprises one or more processors and a memory, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the one or more processors, the steps of the product page display method according to any one of claims 1 to 8 are executed.

11. A storage medium, characterized in that: The storage medium stores computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to execute the steps of the product page display method according to any one of claims 1 to 8.

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