Commodity display system and method based on virtual technology

Abstract

The invention discloses a commodity display system and method based on a virtual technology, and belongs to the technical field of virtual commodity display, and the method comprises the steps: obtaining a user popularity score calculated based on user interaction data, and a content sensitivity score calculated based on content attribute analysis; based on the user popularity score and the content sensitivity score, a coupling effect score of the virtual commodity display area is calculated through a preset coupling effect calculation model, and by introducing the coupling effect score, accurate recognition of composite risks is achieved, and the method is far better than single-dimensional evaluation in the prior art; on the basis of comparison between the coupling effect score and a plurality of intervention strategy thresholds, differentiated and refined intervention strategies are realized, and the user experience is remarkably optimized; by recording user feedback data and periodically updating model parameters, an adaptive optimization closed loop is constructed, and long-term effectiveness and adaptability of the system are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of virtual commodity display, and particularly to a commodity display system based on virtual technology and a method thereof. BACKGROUND

[0002] With the rapid development of e-commerce, consumers increasingly tend to explore and purchase commodities in an online virtual environment; in order to improve user experience and promote sales, virtual commodity display systems are widely used in 3D model display, virtual reality or augmented reality shopping, etc. These systems strive to attract users to participate through highly immersive visual presentation and rich user interaction.

[0003] However, in a highly interactive virtual display environment, the balance between content safety and user experience becomes particularly important and challenging. For example, when a specific area of a commodity that is highly focused on by users (i.e., with high heat) happens to contain potentially sensitive or illegal information, existing content review or screening mechanisms may adopt a rather rough screening strategy. Although this strategy may meet the requirements of content safety, it directly blocks the user's interaction behavior in the focus area, resulting in a poor user experience. More seriously, if the user's participation heat is high, the experience hindrance caused by "screening" will be amplified by the user's emotions, resulting in a kind of "negative coupling effect". That is, the higher the user's participation and focus, the more intense the user experience loss caused by the conflict between the content. The existing technology faces the dilemma of being difficult to improve user participation while ensuring content safety and not damaging the overall user experience in response to the key failure mode of display interruption and user experience loss caused by the conflict between the user's high focus area and sensitive content.

[0004] The present application is dedicated to providing a technical solution that can accurately identify and dynamically manage the negative coupling effect between content safety and user experience in virtual commodity display, in order to minimize the damage to user experience while ensuring content compliance, and avoid the user experience from being unexpectedly reduced significantly due to the coincidence of focus points and conflict points.

[0005] The above information disclosed in the above BACKGROUND section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The present application aims to provide a commodity display system based on virtual technology and a method thereof to solve the problems raised in the above BACKGROUND.

[0007] A commodity display method based on virtual technology: comprising obtaining a user heat score calculated based on user interaction data, and a content sensitivity score calculated based on content attribute analysis; Based on the user heat score and the content sensitivity score, a coupling effect score of a virtual commodity display area is calculated through a preset coupling effect calculation model; The coupling effect score is compared with a plurality of preset intervention strategy thresholds to determine a specific intervention strategy for the display area; According to the specific intervention strategy, the display area in the user interface is dynamically adjusted in terms of content presentation.

[0008] Preferably, the calculation of the user heat score comprises: Based on a preset event weight rule, the screen stay time, zoom operation amplitude, and view angle rotation trajectory contained in the user interaction data are weighted and quantized to obtain a plurality of weighted quantization values; The plurality of weighted quantization values are summed to obtain an aggregate value; The aggregate value is processed by a preset standardization function to generate the user heat score.

[0009] Preferably, the calculation of the content sensitivity score comprises: Based on a preset content risk rule library, the quantifiable content features in the texture map, geometric details, and associated text description contained in the content attribute are calculated to obtain a plurality of risk scores, respectively; The plurality of risk scores are integrated by a preset aggregation function to generate the content sensitivity score.

[0010] Preferably, the calculation of the coupling effect score specifically comprises: The user heat score is multiplied by a first preset weight coefficient to obtain a heat independent influence value; The content sensitivity score is multiplied by a second preset weight coefficient to obtain a sensitivity independent influence value; The product of the user heat score and the content sensitivity score is multiplied by a third preset weight coefficient to obtain a first-order interaction influence value; A second-order cross-influence value is determined. If the user heat score and the content sensitivity score exceed preset user heat critical value and content sensitivity critical value, respectively, the part of the user heat score exceeding the critical value is multiplied by the part of the content sensitivity score exceeding the critical value, and then the result is multiplied by a fourth preset weight coefficient to obtain the second-order cross-influence value, otherwise, the second-order cross-influence value is zero; adding the heat independent influence value, the sensitivity independent influence value, the first-order interaction influence value, and the second-order cross-influence value to generate the coupling effect score.

[0011] Preferably, the first, second, third, and fourth preset weight coefficients, the user heat critical value, and the content sensitivity critical value are obtained based on a retrospective analysis of historical user interaction data and content security events and are calibrated through a machine learning optimization algorithm.

[0012] Preferably, the determination of the specific intervention strategy further comprises: If the coupling effect score is lower than a first preset intervention strategy threshold, the specific intervention strategy is determined to be a no-intervention strategy; If the coupling effect score is greater than or equal to the first preset intervention strategy threshold and less than a second preset intervention strategy threshold, the specific intervention strategy is determined to be a warning prompt strategy, wherein the warning prompt strategy is to superimpose a semi-transparent warning icon on the edge of the display area; If the coupling effect score is greater than or equal to the second preset intervention strategy threshold and less than a third preset intervention strategy threshold, the specific intervention strategy is determined to be a slight blur strategy, wherein the slight blur strategy is to apply a low-intensity blur filter to the display area; If the coupling effect score is greater than or equal to the third preset intervention strategy threshold, the specific intervention strategy is determined to be a complete shielding strategy, wherein the complete shielding strategy is to replace the display area with a preset general placeholder.

[0013] Preferably, the method further comprises: recording user feedback data generated after the specific intervention strategy is executed; periodically updating the weight coefficients and critical values used by the coupling effect calculation model and the plurality of preset intervention strategy thresholds based on the user feedback data to form an adaptive optimization closed loop.

[0014] Preferably, the user interaction data includes the user's stay time on the screen, the number and amplitude of zoom operations, the rotation and translation trajectory of the viewing angle, and the click event signal for a specific area.

[0015] Preferably, the content attributes include the model's own texture map, surface geometric details, and associated product title, detailed description, and label.

[0016] Preferably, a product display system based on virtual technology comprises: a score acquisition module for acquiring a user heat score calculated based on user interaction data and a content sensitivity score calculated based on content attribute analysis. a coupling effect calculation module configured to calculate a coupling effect score of a virtual commodity display area based on the user heat score and the content sensitivity score by using a preset coupling effect calculation model; an intervention strategy determination module configured to compare the coupling effect score with a plurality of preset intervention strategy thresholds to determine a specific intervention strategy for the display area; a content presentation adjustment module configured to dynamically adjust a content presentation mode of the display area in a user interface according to the specific intervention strategy.

[0017] The present application provides a commodity display system and method based on virtual technology, which has the following improvements and advantages compared with the prior art: 1. The coupling effect score is introduced to realize accurate identification of complex risks, which is far superior to the single-dimensional evaluation of the prior art; 2. The comparison of the coupling effect score with a plurality of intervention strategy thresholds realizes differentiated and refined intervention strategies, which significantly optimizes the user experience; 3. By recording user feedback data and periodically updating model parameters, an adaptive optimization closed loop is constructed to ensure the long-term effectiveness and adaptability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0018] The present application will be further explained in conjunction with the accompanying drawings and embodiments: Figure 1 is a flowchart of a commodity display system and method based on virtual technology of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below in conjunction with specific embodiments.

[0020] Embodiment 1: Please refer to Figure 1 The present application provides a commodity display method based on virtual technology, which comprises: obtaining a user heat score calculated based on user interaction data, and a content sensitivity score calculated based on content attribute analysis; Based on the user heat score and the content sensitivity score, a coupling effect score of a virtual commodity display area is calculated by using a preset coupling effect calculation model; The coupling effect score is compared with a plurality of preset intervention strategy thresholds to determine a specific intervention strategy for the display area; According to the specific intervention strategy, the content presentation mode of the display area in the user interface is dynamically adjusted; The embodiment provides a commodity display method based on virtual technology, which is characterized in that a dynamic and quantitative decision framework is established to realize accurate balance between content security compliance and user immersive experience in a virtual shopping scene, such as 3D model interaction of an online automobile exhibition hall or a virtual clothes fitting application; the method discards simple binary shielding logic in traditional content review, introduces a comprehensive coupling effect score, can identify and quantify the compound risk generated when the area that the user pays high attention to overlaps with potential sensitive content, and can execute differentiated and refined intervention strategies from no intervention, warning prompt to local blurring and even complete shielding according to the risk level; the ultimate purpose is to maximize the smoothness and integrity of user exploration while adhering to the bottom line of content security, avoid unnecessary excessive intervention and reduce user experience, thereby improving user satisfaction and business competitiveness of the platform; The prior art usually adopts an isolated and binary evaluation method when dealing with content security problems in virtual commodity display; for example, a content review system only judges whether the content is illegal, and a user behavior analysis system only evaluates the user interest, and the two are independent of each other; the fundamental defect of this method is that it cannot identify and quantify the compound risk generated when “high user interest” and “high content sensitivity” intersect in the same display area, which is much greater than the simple addition of the two; The application innovatively solves this problem by constructing a coupling effect calculation model; the model not only independently considers the linear influence of user heat score and content sensitivity score, but also introduces a first-order product interaction term and a second-order cross-influence term to accurately calculate the coupling effect score; the actual significance of the score is that it is a comprehensive index that can quantify the risk of “negative experience cliff drop caused by the conflict between the user's high attention area and sensitive content”; for example, when the user heat and the content sensitivity both exceed the respective preset threshold, the second-order cross-influence term will be activated, and the coupling effect score will sharply increase; this design can accurately identify the key conflict points that are most likely to cause strong negative emotions of the user.

[0021] Embodiment 2 The calculation of the user heat score includes: Based on the preset event weight rule, the screen stay time, the zoom operation amplitude and the view angle rotation trajectory contained in the user interaction data are weighted and quantified to obtain a plurality of weighted quantification values; The plurality of weighted quantification values are summed to obtain an aggregated value; The aggregated value is processed by a preset standardization function to generate a user heat score; User interaction data, including user's dwell time on the screen, the number and magnitude of zoom operations, the rotation and translation trajectory of the viewing angle, and the click event signal for specific areas; In this embodiment, the calculation of user heat score aims to convert the degree of user's interest implied in the virtual goods into a measurable numerical value; for example, in the interaction of a user with a virtual watch model, the system not only records the user's behavior of clicking the crown, but also quantifies the zoom depth of repeatedly zooming in the dial to view the tourbillon details, and the behavior of long-time staying the viewing angle on the engraved back of the watch; these original user interaction data are generated into a standardized heat score through a fine calculation process; the calculation process is defined by the following formula: This formula is derived from mature practices in the field of user behavior analysis, and its technical motivation is to provide a key trigger premise for the "negative coupling effect" of the core of the invention; only when the user's attention to a certain area is high enough, any intervention to the content of the area can cause significant negative experience, therefore, the degree of user's interest must be accurately and reliably quantified.

[0022] In the formula, represents the final user heat score of the area , whose value is mapped to a standardized interval, and the higher the value, the deeper the user's attention or participation in the area; represents the set of all user interaction events occurring in the area within a certain time window; represents a specific user interaction event in the set , such as a click, a zoom operation or a stay; represents the weight function assigned to a single interaction event ; the design of this function aims to reflect the difference in user interest implied by different behaviors; for example, this function can be designed as a piecewise function, in which the weight of behavior with a stay time shorter than a preset minimum threshold is zero, and for longer stay, a logarithmic function is used to calculate the weight to reflect the diminishing marginal utility of attention; represents a standardized data preprocessing function, which maps the original aggregated value after weighted summation to a unified and comparable scale; for example, the min-max normalization method can be used, which is calculated as (original aggregated value-historical minimum aggregated value) / (historical maximum aggregated value-historical minimum aggregated value), so as to eliminate the influence of the difference in interaction level between different users or different goods; The calculated user heat score is one of the core inputs for the subsequent coupling effect calculation; the technical effect lies in that it provides the system with an objective and dynamically updated quantitative basis to determine which areas on the virtual goods are high user attention areas, so that the system can accurately identify those key risk points that are most likely to cause a sharp decline in user experience due to content conflicts; the specific form of the function and the weight coefficient, and the statistical parameters used by the function, are all key adjustable parameters of the system; the initial values of these parameters can be set based on the experience of domain experts, and then continuously optimized through online A / B testing, by deploying test groups with different parameter configurations and monitoring the performance of each group in key performance indicators such as user retention rate and task completion rate, to iteratively optimize in a data-driven manner.

[0023] Embodiment 3 The calculation of the content sensitivity score includes: Based on the pre-set content risk rule library, the quantifiable content features in the texture map, geometric details and associated text description in the content attributes are calculated to obtain multiple risk scores; The multiple risk scores are comprehensively processed by a pre-set aggregation function to generate a content sensitivity score; The content attributes include the texture map, surface geometric details of the model itself, and the product title, detailed description and label associated therewith; In this embodiment, the calculation of the content sensitivity score aims to quantify the inherent and potential compliance risks of the content of a specific area of the virtual goods; this process is independent of user behavior and is a static or quasi-static analysis of the content attributes of the goods; for example, for a virtual T-shirt, the system will scan whether the chest print (texture map) contains inappropriate patterns, and analyze whether the product description text (associated text) contains prohibited words; the calculation process of this score can be represented by the following formula: ; The construction of this formula draws on the multi-dimensional risk quantification and aggregation techniques commonly used in the field of content security risk control; the technical motivation lies in providing another key dimension of quantitative input for the subsequent coupling effect analysis, i.e., the inherent risk level of the content; this enables the system to distinguish different levels of risk and lays the foundation for differentiated intervention; The comprehensive content sensitivity score of the region is also standardized in value, with a higher value representing a higher compliance risk or sensitivity of the content in that region; refers to a specific region on the virtual goods being analyzed; a specific quantifiable content feature detected within a region , such as the number of occurrences of a specific sensitive word, the confidence score of a rule violation element identified by an image recognition model, or the intensity of negative sentiment in a piece of text analyzed by a natural language processing model; a score representing the risk assessment of a single content feature , based on the output of a pre-defined rule library or content recognition model; is a data aggregation function responsible for aggregating the risk scores of all detected content features within a region to form a single sensitivity score representing the overall risk of the region; for example, the function can be implemented as a max function to focus on the most severe risk item, or as a weighted average function to reflect the importance difference of different types of risks; the calculated content sensitivity score is sent to the coupling effect management module together with the user heat score ; the technical effect lies in providing an objective and standardized measure of the inherent risk of content, which is a prerequisite for precise intervention; The scoring criteria and aggregation strategies in the function are the core strategy configurations of the system; these parameters are set and optimized by content strategy experts according to industry regulations and platform policies to set baseline rules; through backtracking analysis of a large number of historical cases, or by using machine learning techniques, the scoring thresholds and aggregation weights are automatically adjusted, and the optimization goal is to maximize the detection rate of real risk events while minimizing the false positive rate of normal content.

[0024] Embodiment 4 The calculation of the coupling effect score specifically includes: multiplying the user heat score by a first pre-set weight coefficient to obtain a heat independent influence value; multiplying the content sensitivity score by a second pre-set weight coefficient to obtain a sensitivity independent influence value; multiplying the product of the user heat score and the content sensitivity score by a third pre-set weight coefficient to obtain a first-order interaction influence value; determining a second-order cross-influence value, if the user heat score and the content sensitivity score exceed pre-set user heat and content sensitivity threshold values respectively, then multiplying the part of the user heat score exceeding its threshold value by the part of the content sensitivity score exceeding its threshold value, and then multiplying the result by a fourth pre-set weight coefficient to obtain the second-order cross-influence value, otherwise the second-order cross-influence value is zero; adding the heat independent influence value, the sensitivity independent influence value, the first-order interaction influence value, and the second-order cross-influence value to generate the coupling effect score; The first, second, third, and fourth preset weight coefficients, the user heat critical value, and the content sensitivity critical value are obtained based on retrospective analysis of historical user interaction data and content security events and calibration through a machine learning optimization algorithm. In the present embodiment, the calculation of the coupling effect score is the technical core of the present application. It non-linearly fuses data in two dimensions of user heat and content sensitivity through a multi-component mathematical model to reveal the amplified risk generated when the two are coupled. The model is precisely defined by the following formula: The construction of this coupling effect score model is a direct solution to the key technical problem of "significant and sudden degradation of user experience caused by conflict between high user attention areas and sensitive content". The technical motivation is to create a mathematical tool that can accurately quantify this compound risk. It must go beyond simple linear superposition to capture the synergistic negative effect of disproportionate risk growth when both user heat and content sensitivity reach high levels, thereby providing a decision basis for the system to take appropriate intervention measures that match the risk level. The formula consists of four parts, representing risk contributions at different levels: In the formula, represents the final coupling effect score of the area , which is the direct basis for the system to make intervention decisions. and are the user heat score and content sensitivity score calculated as described above, respectively. represents the independent linear influence of user heat. represents the independent linear influence of content sensitivity. represents the first-order product interaction effect between the two, reflecting that when both exist, they will have a stronger associated influence than when they exist alone. is the key part of the model, representing the second-order cross effect. This item is only activated when and both exceed their respective preset critical values and , thereby highlighting the most dangerous compound risk scenario of "high heat + high sensitivity". are four weight coefficients used to adjust the relative importance of each risk component. and is the critical value of user heat and content sensitivity, and is the threshold value of triggering the second-order cross effect.

[0025] the calculated coupling effect score is a comprehensive risk indicator, which is directly compared with a series of preset intervention strategy thresholds to map to specific intervention actions; the technical effect is to realize accurate identification and classification of risks, which can clearly distinguish various scenarios such as "high heat but no content risk", "content sensitive but no one pays attention", and "high heat and content sensitive", so that the system can use the strongest intervention means only in the most needed place, thereby minimizing the damage to user experience on the premise of ensuring safety; the weight coefficient and the critical value are the core adjustable parameters of the model; as described above, the calibration process is data-driven, for example, by defining an objective function to minimize the difference between the model-predicted risk and the true risk label obtained from user complaints or manual review, and then searching for the optimal solution in the parameter space using algorithms such as Bayesian optimization; The prior art often adopts a one-size-fits-all rough shielding strategy after identifying potential risks, such as directly replacing the relevant area with a gray placeholder; although this approach ensures content safety, it severely damages user experience, especially when users are highly interested in the area, and such abrupt shielding can cause great frustration and interruption of information acquisition; The present application realizes fine classification of intervention measures by comparing the calculated coupling effect score with a plurality of preset intervention strategy thresholds; the system can determine and execute specific intervention strategies including no intervention strategy, warning prompt strategy, slight blur strategy, and complete shielding strategy according to different numerical intervals of the coupling effect score; for example, for an area with a medium coupling effect score, the system may only execute "slight blur", which reminds the risk, preserves the context information of the content, and avoids unnecessary experience interruption; this progressive and strictly matched intervention method with risk level minimizes the interference with normal exploration behavior on the premise of ensuring content compliance, thereby significantly improving overall user satisfaction and platform appeal.

[0026] Embodiment 5 The determination of the specific intervention strategy further comprises: If the coupling effect score is lower than the first preset intervention strategy threshold, the specific intervention strategy is determined as no intervention strategy; If the coupling effect score is greater than or equal to the first preset intervention strategy threshold and less than the second preset intervention strategy threshold, the specific intervention strategy is determined as a warning prompt strategy, wherein the warning prompt strategy is a semi-transparent warning icon superimposed on the edge of the display area; If the coupling effect score is greater than or equal to the second preset intervention strategy threshold and less than the third preset intervention strategy threshold, the specific intervention strategy is determined to be a slight blur strategy, wherein the slight blur strategy is to apply a low-intensity blur filter to the display area; If the coupling effect score is greater than or equal to the third preset intervention strategy threshold, the specific intervention strategy is determined to be a complete shielding strategy, wherein the complete shielding strategy is to replace the display area with a preset general placeholder; The method further comprises: Recording user feedback data generated after the specific intervention strategy is executed; Based on the user feedback data, periodically updating the weight coefficients and critical values used by the coupling effect calculation model, and the plurality of preset intervention strategy thresholds, to form an adaptive optimization closed loop; In the embodiment, the determination of the specific intervention strategy and the adaptive optimization closed loop constitute a complete process of the present application from decision-making to execution and then to learning; based on the calculated coupling effect score , the system makes a precise response through a clear, hierarchical threshold system, and can learn from the results of the response and continuously evolve; Specifically, when the coupling effect score is calculated, the system compares it with the preset intervention strategy threshold ; for example, in a virtual art exhibition, the local area of a painting is calculated to be 0.6; assuming that the preset threshold is , since , the system accurately maps this case to the "slight blur" strategy; the content display module immediately receives the instruction to apply a low-intensity Gaussian blur filter to the specific area of the painting in real time, so that the details are not easily recognizable, but the overall outline is still visible; this processing method not only reduces the potential risk of visual impact, but also avoids the jarring feeling and information loss caused by directly replacing the painting with a gray square.

[0027] Furthermore, as described above, the present application constructs an adaptive optimization closed loop; the system will continuously record user feedback data after the intervention strategy is executed, which includes direct feedback and indirect feedback; this structured data is used to periodically calibrate the entire decision-making system; if the data shows that a large number of users choose to leave immediately after slight blur processing, it may indicate that the current threshold is set too low; the strategy configuration module will use this information to automatically fine-tune The value of the feedback data is recorded, and the weight coefficient and the critical value used in the coupling effect calculation model and the multiple preset intervention strategy threshold values are periodically updated based on the feedback data; if the data shows that a certain intervention strategy leads to a high user exit rate, the system can automatically learn and adjust the corresponding threshold value or intervention parameter; the establishment of the adaptive optimization closed loop enables the method of the present application to continuously approach the optimal balance point between content safety and user experience, ensuring that the technical solution remains efficient and accurate when facing a constantly changing external environment, which is a dynamic adaptability that static rule systems do not have; The content review rules and intervention strategies of the prior art are usually static and need to be adjusted manually on a regular basis, making it difficult to adapt to rapidly changing market environments, user preferences and new risks; The present application builds an intelligent system that can evolve itself by introducing a feedback mechanism; the system continuously records user feedback data generated after the execution of specific intervention strategies, periodically updates the weight coefficients and critical values used in the coupling effect calculation model, and multiple preset intervention strategy threshold values based on the user feedback data; if the data shows that a certain intervention strategy leads to a high user exit rate, the system can automatically learn and adjust the corresponding threshold value or intervention parameter; the establishment of this adaptive optimization closed loop enables the method of the present application to continuously approach the optimal balance point between content safety and user experience, ensuring that the technical solution remains efficient and accurate when facing a constantly changing external environment, which is a dynamic adaptability that static rule systems do not have; In summary, the present application fundamentally changes the management of content safety and user experience in virtual product display by building a complete technical solution that includes coupling effect score calculation, hierarchical intervention strategy determination and adaptive optimization closed loop; it replaces extensive screening with precise quantification and intelligent decision-making, thereby ensuring platform safety compliance while providing users with a smoother, more coherent and satisfactory exploration experience, and has practical value and commercial prospects.

[0028] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting; although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application.

Claims

1. A commodity display method based on virtual technology, characterized by: include: Obtain user popularity scores calculated based on user interaction data, and content sensitivity scores calculated based on content attribute analysis; Based on the user popularity score and the content sensitivity score, a coupling effect score of the virtual product display area is calculated using a preset coupling effect calculation model; comparing the coupling effect score with a plurality of preset intervention strategy thresholds to determine a specific intervention strategy for the display area; According to the specific intervention strategy, the content presentation mode of the display area in the user interface is dynamically adjusted.

2. The commodity display method based on virtual technology according to claim 1, characterized in that: The calculation of the user heat score includes: Based on a preset event weight rule, weighted quantization is performed on the screen dwell time, zoom operation amplitude, and view rotation trajectory included in the user interaction data to obtain a plurality of weighted quantization values; Summing the multiple weighted quantization values ​​to obtain an aggregate value; The aggregate value is processed by a preset normalization function to generate the user popularity score.

3. The commodity display method based on virtual technology according to claim 1, characterized in that: The calculation of the content sensitivity score includes: Based on a preset content risk rule library, multiple risk scores are calculated for quantifiable content features in the texture maps, geometric details, and associated text descriptions contained in the content attributes; The multiple risk scores are integrated and processed through a preset aggregation function to generate the content sensitivity score.

4. The commodity display method based on virtual technology according to claim 1, characterized in that: The calculation of the coupling effect score specifically includes: Multiplying the user popularity score by a first preset weight coefficient to obtain a popularity independent influence value; Multiplying the content sensitivity score by a second preset weight coefficient to obtain a sensitivity independent impact value; Multiplying the product of the user popularity score and the content sensitivity score by a third preset weight coefficient to obtain a first-order interaction influence value; Determining a second-order cross-influence value: if the user popularity score and the content sensitivity score exceed a preset user popularity threshold and content sensitivity threshold, respectively, multiplying the portion of the user popularity score that exceeds the threshold by the portion of the content sensitivity score that exceeds the threshold, and then multiplying the result by a fourth preset weight coefficient to obtain the second-order cross-influence value; otherwise, the second-order cross-influence value is zero; The heat independent influence value, the sensitivity independent influence value, the first-order interaction influence value, and the second-order cross-influence value are added together to generate the coupling effect score.

5. The commodity display method based on virtual technology according to claim 4, characterized in that: The first, second, third and fourth preset weight coefficients, the user heat threshold and the content sensitivity threshold are based on retrospective analysis of historical user interaction data and content security events, and are calibrated through a machine learning optimization algorithm.

6. The commodity display method based on virtual technology according to claim 1, characterized in that: The determination of the specific intervention strategy further includes: If the coupling effect score is lower than a first preset intervention strategy threshold, determining the specific intervention strategy as a no-intervention strategy; If the coupling effect score is greater than or equal to the first preset intervention strategy threshold and less than the second preset intervention strategy threshold, determining that the specific intervention strategy is a warning prompt strategy, wherein the warning prompt strategy is to superimpose a semi-transparent warning icon on the edge of the display area; If the coupling effect score is greater than or equal to the second preset intervention strategy threshold and less than a third preset intervention strategy threshold, determining that the specific intervention strategy is a slight blur strategy, wherein the slight blur strategy is applying a low-intensity blur filter to the display area; If the coupling effect score is greater than or equal to the third preset intervention strategy threshold, the specific intervention strategy is determined to be a complete shielding strategy, wherein the complete shielding strategy is to replace the display area with a preset universal placeholder.

7. The commodity display method based on virtual technology according to claim 1, characterized in that: The method further comprises: Recording user feedback data generated after implementing the specific intervention strategy; Based on the user feedback data, the weight coefficients and critical values ​​used in the coupling effect calculation model, as well as the multiple preset intervention strategy thresholds, are periodically updated to form an adaptive optimization closed loop.

8. The commodity display method based on virtual technology according to claim 2, characterized in that: The user interaction data includes the length of time the user stays on the screen, the number and amplitude of zoom operations, the rotation and translation trajectory of the viewing angle, and click event signals for specific areas.

9. The commodity display method based on virtual technology according to claim 3, characterized in that: The content attributes include the model's own texture map, surface geometric details, and the product title, detailed description, and tags associated with it.

10. A commodity display system based on virtual technology, applying the commodity display method based on virtual technology according to any one of claims 1 to 9, characterized in that: include: The rating acquisition module is used to obtain the user popularity rating calculated based on user interaction data and the content sensitivity rating calculated based on content attribute analysis; A coupling effect calculation module, configured to calculate a coupling effect score of a virtual product display area based on the user popularity score and the content sensitivity score using a preset coupling effect calculation model; an intervention strategy determination module, configured to compare the coupling effect score with a plurality of preset intervention strategy thresholds to determine a specific intervention strategy for the display area; The content presentation adjustment module is used to dynamically adjust the content presentation mode of the display area in the user interface according to the specific intervention strategy.

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