Dynamic pricing method and system for rotational molding toys

Abstract

The invention relates to the technical field of rotational moulding toys, and discloses a rotational moulding toy dynamic pricing method and system, and the method comprises the steps: obtaining environmental protection characteristic data and safety characteristic data of a rotational moulding toy, carrying out the analytic hierarchy process of the environmental protection characteristic data, obtaining environmental protection price characteristics, and carrying out the risk assessment of the safety characteristic data. According to the method, environmental protection characteristic data and safety characteristic data of the rotational molding toy are obtained, safety price characteristics are obtained, then the environmental protection characteristic data and the safety characteristic data are input into a pre-trained added value model, and a basic additional price is obtained. According to the method, the environment-friendly price characteristics and the safe price characteristics are extracted, so that the environment-friendly property and the safety of the rotational molding toy can be accurately quantified, and the characteristics are input into the pre-trained added value model to obtain the basic added price, so that the adjustment of the rotational molding toy price is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of rotational molding toys. More specifically, the present invention relates to a method and system for dynamic pricing of rotational molding toys. Background Art

[0002] Rotational molding toys, as toys produced by rotational molding technology, have high durability, complex shapes and diverse designs, and are widely used in the fields of children's play and education. The rotational molding technology has unique advantages, such as uniform wall thickness, strong impact resistance and flexible appearance design, which enable rotational molding toys to occupy a place in the market. With the increasing demand of children for the safety and interactivity of toys, rotational molding toys also pay more and more attention to innovation in materials and functions. In addition, the application of environmentally friendly materials and non-toxic coatings has gradually become an aspect that cannot be ignored in the production of rotational molding toys.

[0003] However, the existing pricing of rotational molding toys generally has large fluctuations, mainly affected by factors such as production costs, material selection and market demand. Traditional price setting usually depends on the competitive situation in the market, sales data and the functionality of toys, etc., but these factors often cannot fully reflect the unique characteristics of rotational molding toys themselves, such as environmental protection and safety. Especially in the context of the increasing awareness of environmental protection today, the environmental protection performance of rotational molding toys has become one of the important factors that consumers pay attention to. How to quantify the environmental protection and safety of rotational molding toys and adjust the price of rotational molding toys accordingly has not been fully considered in the existing technology.

[0004] In view of this, the present invention proposes a method and system for dynamic pricing of rotational molding toys to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for dynamic pricing of rotational molding toys.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] In a first aspect, a method for dynamic pricing of rotational molding toys is provided, including:

[0008] Obtain the environmental protection characteristic data and safety characteristic data of the rotational molding toy, perform hierarchical analysis on the environmental protection characteristic data to obtain the environmental protection price feature, perform risk assessment on the safety characteristic data to obtain the safety price feature, the environmental protection characteristic data includes material type information and environmental protection certification information, and the safety characteristic data includes structural safety data and material safety data;

[0009] Input the environmental protection price feature and the safety price feature into the pre-trained additional value model to obtain the basic additional price, and the basic additional price is the basic additional price determined according to the environmental protection characteristics and safety characteristics of the rotational molding toy.

[0010] In some embodiments, the method for obtaining the environmental protection price feature by performing hierarchical analysis on environmental protection characteristic data includes:

[0011] Performing keyword extraction processing on the environmental protection characteristic data through a preset bag-of-words model to obtain Q keywords, assigning values to the Q keywords through the weighted analytic hierarchy process to obtain corresponding first weights, arranging the Q keywords in descending order according to the first weights, and performing cascade fusion on the sorted Q keywords to obtain the environmental protection price feature.

[0012] In some embodiments, the method for obtaining corresponding first weights by assigning values to Q keywords through the weighted analytic hierarchy process includes:

[0013] Assigning values to the Q keywords through the weighted analytic hierarchy process to generate a weight matrix, calculating the average value corresponding to each keyword in the weight matrix, and taking the average value as the first weight of the keyword.

[0014] In some embodiments, the method for obtaining structural safety data includes:

[0015] Obtaining H first surface images of the rotational molding toy, performing edge detection on each first surface image to obtain the number of sharp edges, and taking the number of sharp edges as the structural safety data, wherein the shooting angles of each first surface image are different.

[0016] In some embodiments, the method for obtaining material safety data includes:

[0017] Continuously heating the rotational molding toy at a preset standard temperature, detecting whether abnormal information appears on the surface of the rotational molding toy. When abnormal information appears on the surface of the rotational molding toy, stop heating, and take the total heating duration as the material safety data, where the abnormal information includes deformation information and color change information.

[0018] In some embodiments, the method for detecting whether abnormal information appears on the surface of the rotational molding toy includes:

[0019] During the continuous heating process, obtaining a second surface image of the rotational molding toy, calculating the optical flow vector of the second surface image to obtain the target displacement amount. When the target displacement amount is greater than the preset displacement amount, take the target displacement amount as the deformation information, performing saturation detection on the second surface image to obtain the real-time saturation, calculating the saturation difference between the real-time saturation and the standard saturation. When the saturation difference is greater than the preset difference threshold, take the saturation difference as the color change information.

[0020] In some embodiments, the method for performing risk assessment on safety characteristic data to obtain the safety price feature includes:

[0021] Obtain the toy type of the rotational molding toy. When the toy type is outdoor, assign the second weight to the material safety data and the third weight to the structural safety data. When the toy type is non-outdoor, assign the second weight to the structural safety data and the third weight to the material safety data. Take the reciprocal of the structural safety data and perform weighted cascading fusion with the material safety data to obtain the safety price feature. Among them, the toy type includes outdoor type and non-outdoor type, and the second weight is greater than the third weight.

[0022] In some embodiments, the training method of the added value model includes:

[0023] Use a preset fully connected neural network as the basic model. The input layer in the fully connected neural network receives the historical environmental protection price feature and the historical safety price feature, and the output layer in the fully connected neural network outputs the historical basic added price;

[0024] When training the fully connected neural network, select the cross-entropy loss function as the loss function, and minimize the loss function by the gradient descent method;

[0025] Update the weight parameters of the fully connected neural network, and obtain the added value model through iterative training.

[0026] In some embodiments, it further includes: obtaining the safety design data of the rotational molding toy, performing safety analysis according to the safety design data to obtain the safety design degree value, and adjusting the price of the basic added price according to the safety design degree value to obtain the accurate added price. The safety design data refers to the functional data related to the safety of the rotational molding toy.

[0027] In a second aspect, a rotational molding toy dynamic pricing system is provided, which is used to implement the above-mentioned rotational molding toy dynamic pricing method, and includes:

[0028] Data processing module: used to obtain the environmental protection characteristic data and safety characteristic data of the rotational molding toy, perform hierarchical analysis on the environmental protection characteristic data to obtain the environmental protection price feature, and perform risk assessment on the safety characteristic data to obtain the safety price feature. The environmental protection characteristic data includes material type information and environmental protection certification information, and the safety characteristic data includes structural safety data and material safety data;

[0029] Adjustment module: used to input the environmental protection price feature and the safety price feature into the pre-trained added value model to obtain the basic added price. The basic added price is the basic added price determined according to the environmental protection characteristics and safety characteristics of the rotational molding toy.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] The present invention first obtains the environmental protection characteristic data and safety characteristic data of the rotational molding toy, conducts hierarchical analysis on the environmental protection characteristic data to obtain the environmental protection price characteristics, conducts risk assessment on the safety characteristic data to obtain the safety price characteristics, and then inputs the environmental protection price characteristics and the safety price characteristics into the pre-trained added value model to obtain the basic added price. Then, by obtaining the environmental protection characteristic data and safety characteristic data of the rotational molding toy, conducting hierarchical analysis and risk assessment on them, and extracting the environmental protection price characteristics and safety price characteristics, the present invention can accurately quantify the environmental protection and safety of the rotational molding toy, input these characteristics into the pre-trained added value model to obtain the basic added price, thereby realizing the adjustment of the price of the rotational molding toy. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic flow chart of a dynamic pricing method for a rotational molding toy in the present invention;

[0033] Figure 2 It is a schematic structural diagram of a dynamic pricing system for a rotational molding toy in the present invention;

[0034] Figure 3 It is a schematic flow chart of a training method for the added value model in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] To make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the following further describes the present invention in detail with reference to specific embodiments and the accompanying drawings. In the following detailed description, many specific details are set forth to provide a thorough understanding of the described exemplary embodiments. However, it will be apparent to those skilled in the art that some or all of these specific details may be practiced without these specific details. In other exemplary embodiments, well-known structures are not described in detail to avoid unnecessarily obscuring the concepts of the present disclosure. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. At the same time, the various aspects described in the embodiments can be combined arbitrarily without conflict.

[0036] Embodiment 1

[0037] Please refer to Figure 1 As shown, this embodiment discloses a dynamic pricing method for a rotational molding toy, including:

[0038] S10: Obtain the environmental protection characteristic data and safety characteristic data of the rotational molding toy, conduct hierarchical analysis on the environmental protection characteristic data to obtain the environmental protection price characteristics, conduct risk assessment on the safety characteristic data to obtain the safety price characteristics. The environmental protection characteristic data includes material type information and environmental protection certification information, and the safety characteristic data includes structural safety data and material safety data;

[0039] In this embodiment, the environmental protection characteristic data includes material type information and environmental protection certification information. The material type information characterizes the environmental protection of different types of materials used in the rotational molding toys. The environmental protection certification information characterizes whether the rotational molding toys and the materials used in their production process have passed the formal environmental protection standards and certifications. The environmental protection certification information includes, but is not limited to, ISO certification information and non-toxic certification information.

[0040] The method for obtaining the environmental protection price characteristics by performing hierarchical analysis on the environmental protection characteristic data includes:

[0041] Performing keyword extraction processing on the environmental protection characteristic data through a preset bag-of-words model to obtain Q keywords, assigning values to the Q keywords through the weighted analytic hierarchy process to obtain the corresponding first weights, arranging the Q keywords in descending order according to the first weights, and performing cascading fusion on the sorted Q keywords to obtain the environmental protection price characteristics.

[0042] The method for assigning values to the Q keywords through the weighted analytic hierarchy process to obtain the corresponding first weights includes:

[0043] Assigning values to the Q keywords through the weighted analytic hierarchy process to generate a weight matrix, calculating the average value corresponding to each keyword in the weight matrix, and taking the average value as the first weight of the keyword.

[0044] It should be noted that the bag-of-words model is a common text representation method widely used in natural language processing (NLP) tasks. The basic idea of this model is to convert text into a fixed-length vector and represent the characteristics of the text by counting the occurrence frequencies of each word in the text. The weighted analytic hierarchy process is an extended method based on the classical analytic hierarchy process. In the classical analytic hierarchy process, the decision-making problem is decomposed into multiple hierarchical structures, and the pairwise comparison method is used to evaluate the importance of each factor. The specific example is as follows:

[0045] Suppose there is environmental protection characteristic data of a rotational molding toy, including the following keywords: recyclable, non-toxic, biodegradable, ISO certification, and non-toxic certification. According to the evaluation of the importance of environmental protection characteristics, pairwise comparisons are made, and the 1-9 scale method is used to evaluate the relative importance of each factor. For example:

[0046] Recyclable and non-toxic: "Non-toxic" is more important than "recyclable", and a score of 3 is given, indicating that "non-toxic" is 3 times more important than "recyclable".

[0047] Non-toxic and biodegradable: "Non-toxic" is slightly more important than "biodegradable", and a value of 2 can be assigned.

[0048] Non-toxic and ISO certification: "Non-toxic certification" and "ISO certification" are about equally important, and a value of 1 can be assigned;

[0049] Table 1. Weight Matrix Table

[0050] Recyclable Non-toxic Biodegradable ISO Certification Non-toxic Certification Recyclable 1 1 / 3 1 / 5 1 / 7 1 / 6 Non-toxic 3 1 2 1 1 Biodegradable 5 1 / 2 1 3 2 ISO Certification 7 1 1 / 3 1 1 / 2 Non-toxic Certification 6 1 1 / 2 2 1

[0051] Then, the first weights of each criterion and keyword are calculated by the Analytic Hierarchy Process (AHP). First, each column is normalized to obtain the normalized table as shown in Table 2 below:

[0052] Table 2. Normalized Table

[0053] Recyclable Non-toxic Biodegradable ISO Certification Non-toxic Certification Recyclable 0.0455 0.0875 0.0501 0.0196 0.0357 Non-toxic 0.1364 0.2604 0.5024 0.1400 0.2143 Biodegradable 0.2273 0.1302 0.2512 0.4200 0.1071 ISO Certification 0.3182 0.2604 0.0837 0.1400 0.4286 Non-toxic Certification 0.2727 0.2604 0.1256 0.2800 0.2143

[0054] Next, for each row, calculate its average value to obtain the first weight of the criterion. For example:

[0055] The first weight of recyclable:

[0056] ;

[0057] Similarly, the first weights of other keywords can also be obtained by calculating the average value of each row.

[0058] In this embodiment, the environmental protection characteristics are carefully analyzed by the weighted analytic hierarchy process, which can provide a basis for the environmental impact of product pricing. The risk assessments of structural safety and material safety ensure the safety of the product during use. If the order is reversed, it may lead to neglecting the safety risks when pursuing environmental protection characteristics. This comprehensive evaluation order ensures equal emphasis on environmental protection and safety, making the market positioning of the final product meet both environmental protection requirements and the safety needs of users.

[0059] In this embodiment, by first using the bag-of-words model to extract keywords, the keywords of environmental protection characteristics can be systematically sorted out, and they are assigned values by the weighted analytic hierarchy process to further clarify the relative importance of each environmental protection characteristic. This step subdivides and quantifies the environmental protection characteristics, which helps to form price characteristics based on changes in environmental characteristics. After this characteristic is generated, it provides a favorable pricing framework for the subsequent evaluation of safety characteristic data, so that comprehensive consideration can be better carried out in the subsequent risk assessment.

[0060] In this embodiment, the safety characteristic data includes structural safety data and material safety data. The structural safety data represents whether the design and structure of the rotational molding toy meet the safety requirements, and the material safety data represents whether the materials used in the rotational molding toy meet the use environment.

[0061] The methods for obtaining structural safety data include:

[0062] Obtain H first surface images of the rotational molding toy, perform edge detection on each first surface image, obtain the number of sharp edges, and use the number of sharp edges as structural safety data. Among them, the shooting angles of each first surface image are different.

[0063] In this embodiment, edge detection is a common image processing method that can extract the significantly changing parts in the image, that is, edges. Common edge detection algorithms such as the Canny algorithm can accurately detect the boundaries in the image, especially sharp edges. These edges represent the changes in the surface contour of the object and are usually potential dangerous areas. By using an edge detection algorithm to identify all the edges in the image, especially sharp edges, since the surface of the rotational molding toy is complex and may have sharp parts, edge detection helps to accurately locate these parts and assist in evaluating the structural safety of the toy. This step is a preliminary identification of potential safety hazards on the toy surface. Counting the number of sharp edges can objectively and quantitatively reflect the structural safety of the toy surface and provide quantitative data for subsequent safety detections. This step provides an intuitive safety evaluation criterion for judging whether the toy meets the safety requirements.

[0064] The methods for obtaining material safety data include:

[0065] Continuously heat the rotational molding toy at a preset standard temperature, detect whether there is abnormal information on the surface of the rotational molding toy. When abnormal information appears on the surface of the rotational molding toy, stop heating and use the total heating duration as material safety data. The abnormal information includes deformation information and color change information.

[0066] The methods for detecting whether there is abnormal information on the surface of the rotational molding toy include:

[0067] During the continuous heating process, obtain the second surface image of the rotational molding toy, calculate the optical flow vector of the second surface image to obtain the target displacement. When the target displacement is greater than the preset displacement, use the target displacement as the deformation information. Perform saturation detection on the second surface image to obtain the real-time saturation, calculate the saturation difference between the real-time saturation and the standard saturation. When the saturation difference is greater than the preset difference threshold, use the saturation difference as the color change information.

[0068] It is understandable that through the heating test, the high-temperature environment that the rotational molding toy may encounter during use is simulated. For example, the standard temperature is set to 50 degrees because rotational molding toys usually experience long-term exposure to sunlight, and the temperature may reach 50 degrees or higher. In this case, the physical properties of the toy material (such as flexibility, thermal expansion, color, etc.) will change, which may cause deformation (such as softening, bending) or color change (such as fading, darkening) on the toy surface. Setting the heating test with a standard temperature of 50 degrees can effectively simulate this real environment, verify the performance of the toy under specific high temperatures, and ensure that there are no safety hazards when the material is used in the outdoor environment in summer.

[0069] In this embodiment, the optical flow vector calculation is to evaluate the deformation of the object surface between different time points by analyzing the displacement of each pixel in the image sequence. Using the optical flow algorithm (such as the Lucas-Kanade method), the horizontal and vertical displacements of each pixel can be calculated, so as to obtain the target displacement amount. The target displacement amount calculated by the optical flow method can accurately identify whether there is deformation (such as expansion, compression, etc.) on the surface of the rotational molding toy. Especially during the heating process, the surface may undergo obvious deformation due to thermal expansion, and the optical flow calculation helps to detect these tiny changes.

[0070] The above saturation detection is to judge whether there is a color change on the toy surface by analyzing the color information of the image. Specifically, the image can be converted from the RGB color space to the HSV color space, and then the color saturation in the image is calculated. This value can reflect the purity of the color. By the change of the saturation value, it can be judged whether there is a color change on the toy surface. By comparing the real-time saturation with the standard saturation value and calculating the difference between them, when the difference between the real-time saturation and the standard saturation exceeds a certain set threshold, it can be determined that the color has changed. Usually, the standard saturation value represents the normal state of the toy, while the real-time saturation reflects the change after heating.

[0071] The method of obtaining the safety price feature by performing a risk assessment on the safety feature data includes:

[0072] Obtain the toy type of the rotational molding toy. When the toy type is an outdoor type, the second weight is assigned to the material safety data, and the third weight is assigned to the structural safety data. When the toy type is a non-outdoor type, the second weight is assigned to the structural safety data, and the third weight is assigned to the material safety data. The reciprocal of the structural safety data and the material safety data are weighted and cascaded and fused to obtain the safety price feature. Among them, the toy type includes outdoor type and non-outdoor type, and the second weight is greater than the third weight.

[0073] It should be noted that when the toy type is outdoor, the material safety data is given the second weight, and the structural safety data is given the third weight. Outdoor toys are usually exposed to higher temperature, humidity, sunlight and other environmental conditions. Therefore, the durability and anti-aging performance of materials are more critical. For these toys, the safety of materials directly affects the service life and safety of toys in extreme environments. To ensure the safety of outdoor toys, giving higher weight to material safety data helps to more accurately evaluate the performance of materials in outdoor environments. For non-outdoor toys, the structural safety data is given the second weight, while the material safety data is given the third weight. Non-outdoor toys are usually exposed to indoor environments and have closer contact with children. Therefore, more consideration is given to structural safety. The purpose of weighted cascade fusion is to reasonably adjust the influence of material safety and structural safety according to different toy types. In outdoor toys, material safety will play a more important role; while in non-outdoor toys, more attention is given to structural safety.

[0074] The above steps ensure that the price of the toy matches its safety. By linking the safety assessment with the price, the market price can truly reflect the safety guarantee level of the toy. This not only helps consumers make more informed choices when purchasing, but also helps manufacturers price reasonably according to market demand and safety requirements, thus improving the market competitiveness of products.

[0075] This embodiment, combined with the ordered sequence of the above steps, can produce the following effects:

[0076] Gradual identification and quantification of structural safety data and material safety data: First, by obtaining the first surface image and performing edge detection, potential safety hazards (such as sharp edges) on the toy surface can be accurately identified. This preliminary structural safety assessment provides a basis for subsequent acquisition of material safety data. When obtaining material safety data, by simulating high-temperature environments through heating tests, the reactions of toy materials in different environments can be accurately detected to ensure the heat resistance and anti-deformation properties of the materials. Through this sequence, it can be ensured that structural safety and material safety issues are fully evaluated at different levels.

[0077] Establishment of Systematic Risk Assessment and Pricing Mechanism: After obtaining structural safety data and material safety data, in the risk assessment step, different weights are assigned to these two types of data according to the toy type (outdoor and non-outdoor), forming a comprehensive safety price feature. This assessment sequence can reasonably allocate weights according to the actual usage environment of the toy, enabling different types of toys to have different focuses in safety assessment. For example, outdoor toys pay more attention to material safety, while non-outdoor toys focus more on structural safety. This dynamic adjustment mechanism can accurately reflect the actual safety needs of different toys and further link safety with pricing to ensure that the price truly reflects the safety guarantee of the toy.

[0078] Effective Docking of Comprehensive Assessment and Market Pricing: By first independently assessing the structural safety and material safety of the toy, and then weighted integrating the safety characteristics of both through the risk assessment step, a final safety price feature can be obtained. This process ensures that when pricing the toy in the market, not only the impact of environmental factors on material safety is considered, but also structural safety is incorporated, ensuring the comprehensiveness of the comprehensive assessment. This makes the pricing of the toy both reasonable and in line with the true safety needs of consumers.

[0079] Higher Precision in Market Adaptability and Competitiveness: Through the above steps, it is possible to effectively reflect the safety guarantee level according to the different toy types, making the market price more competitive. Outdoor toys can obtain a higher price due to the high requirements for material safety, while non-outdoor toys can be reasonably priced based on their structural safety. This assessment and pricing mechanism can create products in the market that are more in line with safety requirements and consumer needs, enhancing the market adaptability and competitiveness of the products.

[0080] S20: Input the environmental protection price feature and the safety price feature into the pre-trained added value model to obtain the basic added price, which is the basic added price determined according to the environmental protection characteristics and safety characteristics of the rotational molding toy;

[0081] In this embodiment, as Figure 3 shown, the training method of the added value model includes:

[0082] Taking a preset fully connected neural network as the basic model, the input layer in the fully connected neural network receives the historical environmental protection price feature and the historical safety price feature, and the output layer in the fully connected neural network outputs the historical basic added price;

[0083] When training the fully connected neural network, select the cross-entropy loss function as the loss function and minimize the loss function through the gradient descent method;

[0084] Update the weight parameters of the fully connected neural network, and through iterative training, obtain the added value model.

[0085] It should be added that the environmental protection price feature reflects the environmental protection performance of rotational molding toys, including whether non-toxic materials are used and whether they meet environmental protection certifications, etc. Generally, toys that meet high environmental protection standards incur higher costs during production, so their additional prices will also be correspondingly higher. The safety price feature reflects the safety of rotational molding toys, such as whether there are safety hazards like sharp edges. Higher safety requirements usually increase production costs, thus affecting the additional price. By training a fully connected neural network, the additional value model can predict the basic additional price of the toy based on the input environmental protection features and safety features. This process learns based on a large amount of historical data, which includes the environmental protection features, safety features, and their corresponding additional prices of various rotational molding toys. Through the additional value model, it is possible to accurately predict the additional price according to the specific characteristics of rotational molding toys, making the pricing of rotational molding toys more reasonable and accurate, meeting market demands and safety standards.

[0086] In this embodiment, first, obtain the environmental protection characteristic data and safety characteristic data of the rotational molding toy, perform hierarchical analysis on the environmental protection characteristic data to obtain the environmental protection price feature, perform risk assessment on the safety characteristic data to obtain the safety price feature, and then input the environmental protection price feature and the safety price feature into the pre-trained additional value model to obtain the basic additional price. Then, in this embodiment, by obtaining the environmental protection characteristic data and safety characteristic data of the rotational molding toy, and performing hierarchical analysis and risk assessment on them, extracting the environmental protection price feature and the safety price feature, it is possible to accurately quantify the environmental protection and safety of the rotational molding toy. Inputting these features into the pre-trained additional value model to obtain the basic additional price, thus realizing the adjustment of the price of the rotational molding toy.

[0087] Embodiment 2

[0088] Based on Embodiment 1, it further includes: obtaining the safety design data of the rotational molding toy, performing safety analysis according to the safety design data to obtain a safety design degree value, and adjusting the basic additional price according to the safety design degree value to obtain an accurate additional price. The safety design data refers to the functional data related to the safety of the rotational molding toy;

[0089] In this embodiment, the safety design data includes design chart data and design text data. The design text data refers to the written descriptions and explanations related to the safety design of the rotational molding toy, and the design chart data refers to the design that shows the structure, appearance, and functions of the rotational molding toy in the form of drawings, diagrams, or 3D models, etc.

[0090] The method for performing safety analysis according to the safety design data to obtain a safety design degree value includes:

[0091] Extract keywords from the design text data to obtain the inverse document frequency. Perform three-dimensional grid analysis on the design chart data to obtain the connection strength value and local stress corresponding to each grid. Take the ratio of the connection strength value to the local stress as the connection stability value, and cascade and fuse the inverse document frequency with the connection stability value to obtain the safety design degree value.

[0092] In this embodiment, the inverse document frequency is an important concept related to the design text data. It is used in the process of keyword extraction to help quantify and evaluate the importance of certain feature words (keywords) in the text. Specifically, the inverse document frequency is an index that measures the frequency of a certain word in all documents, reflecting the uniqueness of the word in the document set. A high inverse document frequency means that the word rarely appears in most documents, with strong discrimination and uniqueness. Therefore, it is more likely to represent key and informative features in the text. In the design text data, the inverse document frequency is used to measure the importance of certain keywords related to safety design in the design documents. For example, the design documents may mention safety feature words such as "non-toxic material", "anti-sharp edge design", and "compliance with CE certification":

[0093] "Non-toxic material" appears frequently in the design documents, indicating that it is a common feature in the design and makes a great contribution to safety, but the inverse document frequency is low and not particularly discriminatory. "Anti-sharp edge design" is mentioned in fewer documents, and this word has a high inverse document frequency, meaning it is more important in certain specific documents.

[0094] It should be added that the three-dimensional grid analysis is based on computer-aided design (CAD) models and finite element analysis (FEA) techniques. It transforms the three-dimensional design of the toy into a structure composed of many small grid units (such as small triangles or tetrahedrons). These grid units form an overall structure through connecting nodes, allowing mechanical analysis of each grid unit to calculate the connection strength and local stress. The connection strength refers to the resistance of the connection point or connecting component to external forces at a specific position, which reflects whether the structure can maintain stability and avoid loosening or breaking. The local stress refers to the stress distribution caused by external forces (such as compression, tension, or bending, etc.) in a specific area.

[0095] In this embodiment, first, the entire three-dimensional model is divided into small grid cells, each grid cell having an independent computing node and physical properties. According to the actual application scenario, appropriate boundary conditions and loads (such as gravity, impact force, friction, etc.) are set. These loads and boundary conditions help simulate the force conditions of the toy during actual use. By solving through finite element analysis, the stress and deformation at each connection point (node) are calculated, thereby obtaining the connection strength value. Then, through finite element analysis, the stress distribution of each grid cell under the action of external forces is solved, and the local stress is calculated, which represents the pressure or tension borne by the material in this area.

[0096] The method of adjusting the basic additional price according to the safety design degree value to obtain the accurate additional price includes:

[0097] Input the safety design degree value and the basic additional price into the price adjustment model to obtain the accurate additional price.

[0098] It can be understood that the training steps of the price adjustment model are the same as those of the above-mentioned additional value model, except that the input data is changed to historical safety design degree values and historical basic additional prices, and the output data is changed to historical accurate additional prices. Therefore, the training steps of the price adjustment model are not elaborated too much in this embodiment. In this embodiment, if the safety design degree value is relatively high (for example, the toy has better materials, structural designs, and safety features), it indicates that the toy is safer during use, and the basic additional price can be appropriately increased. On the contrary, if the safety design degree value is relatively low (that is, there are design defects), the basic additional price needs to be reduced.

[0099] In Embodiment 1, the safety price feature mainly focuses on passive safety, that is, the inherent safety in the toy design, such as whether the material is non-toxic and whether the appearance meets the safety standards. These are all passive safety features, which help ensure that users will not be harmed due to design defects in the toy during use. In Embodiment 2, by introducing the safety design degree value and combining the inverse document frequency and connection stability value, the safety assessment is not limited to static passive safety features, but can also dynamically reflect the safety of the toy in different use scenarios.

[0100] Active safety refers to the dynamic analysis of safety design data to identify potential safety hazards in advance and improve safety through corresponding design optimizations. For example, in the design chart data, three-dimensional grid analysis can help identify the parts of the toy structure that are prone to stress concentration, and these parts may cause material fatigue or structural damage during long-term use. Through the calculation of the safety design degree value, these problems can be foreseen in advance, so as to optimize in the product design stage and reduce safety risks.

[0101] Through the safety design data analysis and price adjustment mechanism introduced in Embodiment 2, the overall technical solution not only emphasizes passive safety (i.e., basic design safety), but also enhances the active safety of the product. The introduction of the safety design degree value enables design defects of the product to be identified and improved during the design stage, thereby reducing the risk of safety accidents that may occur during use. All of this is achieved through an accurate price adjustment model, finding a more reasonable balance between the safety of the toy and its market pricing.

[0102] Embodiment 3

[0103] Please refer to Figure 2 As shown, based on the same inventive concept, this embodiment discloses and provides a rotational molding toy dynamic pricing system. For the details not described in this embodiment, please refer to the relevant part of Embodiment 1. The system includes:

[0104] Data processing module: used to obtain the environmental protection characteristic data and safety characteristic data of the rotational molding toy, perform hierarchical analysis on the environmental protection characteristic data to obtain environmental protection price characteristics, perform risk assessment on the safety characteristic data to obtain safety price characteristics. The environmental protection characteristic data includes material type information and environmental protection certification information, and the safety characteristic data includes structural safety data and material safety data;

[0105] In this embodiment, the environmental protection characteristic data includes material type information and environmental protection certification information. The material type information characterizes the environmental protection of different types of materials used in the rotational molding toy, and the environmental protection certification information characterizes whether the rotational molding toy and the materials used in its production process have passed formal environmental protection standards and certifications. The environmental protection certification information includes, but is not limited to, ISO certification information and non-toxic certification information.

[0106] The method for performing hierarchical analysis on the environmental protection characteristic data to obtain environmental protection price characteristics includes:

[0107] Perform keyword extraction processing on the environmental protection characteristic data through a preset word bag model to obtain Q keywords, assign values to the Q keywords through the weighted hierarchical analysis method to obtain corresponding first weights, sort the Q keywords in descending order according to the first weights, and cascade and fuse the sorted Q keywords to obtain environmental protection price characteristics.

[0108] The method for assigning values to the Q keywords through the weighted hierarchical analysis method to obtain corresponding first weights includes:

[0109] Assign values to the Q keywords through the weighted hierarchical analysis method to generate a weight matrix, calculate the average value corresponding to each keyword in the weight matrix, and use the average value as the first weight of the keyword.

[0110] The method for obtaining structural safety data includes:

[0111] Obtain H first surface images of the rotational molding toy, perform edge detection on each first surface image to obtain the number of sharp edges, and use the number of sharp edges as structural safety data, where the shooting angles of each first surface image are different.

[0112] The method for obtaining material safety data includes:

[0113] Continuously heat the rotational molding toy at a preset standard temperature, detect whether abnormal information appears on the surface of the rotational molding toy. When abnormal information appears on the surface of the rotational molding toy, stop heating and use the total heating duration as material safety data. The abnormal information includes deformation information and color change information.

[0114] The method for detecting whether abnormal information appears on the surface of the rotational molding toy includes:

[0115] During the continuous heating process, obtain the second surface image of the rotational molding toy, calculate the optical flow vector of the second surface image to obtain the target displacement. When the target displacement is greater than the preset displacement, use the target displacement as the deformation information. Perform saturation detection on the second surface image to obtain the real-time saturation, calculate the saturation difference between the real-time saturation and the standard saturation. When the saturation difference is greater than the preset difference threshold, use the saturation difference as the color change information.

[0116] Adjustment module: used to input the environmental protection price feature and the safety price feature into the pre-trained added value model to obtain the basic added price, and the basic added price is the basic added price determined according to the environmental protection characteristics and safety characteristics of the rotational molding toy.

[0117] The detailed description set forth above in connection with the accompanying drawings describes examples and does not represent all examples that can be implemented or fall within the scope of the claims. The terms "example" and "exemplary" when used in this specification mean "serving as an example, instance, or illustration" and do not mean "superior to or better than other examples".

[0118] The reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with that embodiment is included in at least one embodiment of the present invention. Thus, the use of these phrases may refer to more than just one embodiment. Additionally, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0119] It should also be noted that these embodiments may be described as processes depicted as flowcharts, structure diagrams, or block diagrams. Although a flowchart may describe the operations as a sequential process, many of these operations can be performed in parallel or concurrently. Additionally, the order of these operations may be rearranged.

Claims

1. A dynamic pricing method for roto-molded toys, characterized in that: include: Obtaining environmental characteristic data and safety characteristic data of rotomolded toys, performing hierarchical analysis on the environmental characteristic data to obtain environmental price characteristics, performing risk assessment on the safety characteristic data to obtain safety price characteristics, wherein the environmental characteristic data includes material type information and environmental certification information, and the safety characteristic data includes structural safety data and material safety data; The environmental protection price feature and the safety price feature are input into the pre-trained value-added model to obtain a basic added price, which is a basic added price determined according to the environmental protection characteristics and safety characteristics of the rotomolded toys.

2. A method for dynamic pricing of roto-molded toys according to claim 1, characterized in that: The method of performing hierarchical analysis on environmental protection characteristic data to obtain environmental protection price characteristics includes: The environmental protection characteristic data is processed by keyword extraction through the preset bag-of-words model to obtain Q keywords, and the Q keywords are assigned values ​​through the weighted hierarchical analysis method to obtain the corresponding first weights. The Q keywords are arranged in descending order according to the first weights, and the sorted Q keywords are cascaded and integrated to obtain the environmental protection price characteristics.

3. A method for dynamic pricing of roto-molded toys according to claim 2, characterized in that: The method of assigning values ​​to the Q keywords by weighted analytic hierarchy process to obtain corresponding first weights includes: The Q keywords are assigned values ​​through weighted analytic hierarchy process to generate a weight matrix, and the average value corresponding to each keyword in the weight matrix is ​​calculated, and the average value is used as the first weight of the keyword.

4. A method for dynamic pricing of roto-molded toys according to claim 1, characterized in that: The method for obtaining structural safety data comprises: H first surface images of the rotomolded toy are obtained, edge detection is performed on each first surface image, the number of sharp edges is obtained, and the number of sharp edges is used as structural safety data, wherein each first surface image is taken at a different angle.

5. A method for dynamic pricing of roto-molded toys according to claim 1, characterized in that: The method for obtaining material safety data comprises: The rotomolded toys are continuously heated at a preset standard temperature to detect whether there is any abnormal information on the surface of the rotomolded toys. If abnormal information appears on the surface of the rotomolded toys, the heating is stopped and the total heating time is used as the material safety data. The abnormal information includes deformation information and color change information.

6. A method for dynamic pricing of roto-molded toys according to claim 5, characterized in that: The method for detecting whether abnormal information appears on the surface of a roto-molded toy comprises: During the continuous heating process, the second surface image of the rotationally molded toy is obtained, and the optical flow vector is calculated for the second surface image to obtain the target displacement. When the target displacement is greater than the preset displacement, the target displacement is used as deformation information, and the saturation of the second surface image is detected to obtain the real-time saturation. The saturation difference between the real-time saturation and the standard saturation is calculated. When the saturation difference is greater than the preset difference threshold, the saturation difference is used as color change information.

7. A method for dynamic pricing of roto-molded toys according to claim 1, characterized in that: The method of performing risk assessment on the security characteristic data to obtain the security price characteristics includes: The toy type of the rotomolded toy is obtained. When the toy type is an outdoor type, the second weight is assigned to the material safety data, and the third weight is assigned to the structural safety data. When the toy type is a non-outdoor type, the second weight is assigned to the structural safety data, and the third weight is assigned to the material safety data. The inverse of the structural safety data and the material safety data are weightedly cascaded and fused to obtain a safety price feature, wherein the toy type includes outdoor type and non-outdoor type, and the second weight is greater than the third weight.

8. A method for dynamic pricing of roto-molded toys according to claim 1, characterized in that: The training method of the value-added model includes: A preset fully connected neural network is used as the basic model, the input layer in the fully connected neural network receives historical environmental protection price features and historical safety price features, and the output layer in the fully connected neural network outputs the historical basic additional price; When training a fully connected neural network, the cross entropy loss function is selected as the loss function, and the loss function is minimized by the gradient descent method; Update the weight parameters of the fully connected neural network and obtain the value-added model through iterative training.

9. A method for dynamic pricing of roto-molded toys according to claim 1, characterized in that: Also includes: Obtain the safety design data of rotomolded toys, conduct safety analysis based on the safety design data, obtain the safety design degree value, adjust the basic additional price based on the safety design degree value, and obtain the accurate additional price. The safety design data refers to the functional data related to the safety of rotomolded toys.

10. A rotomolded toy dynamic pricing system, used to implement a rotomolded toy dynamic pricing method according to any one of claims 1 to 8, characterized in that: include: Data processing module: used to obtain environmental characteristic data and safety characteristic data of roto-molded toys, perform hierarchical analysis on the environmental characteristic data to obtain environmental price characteristics, perform risk assessment on the safety characteristic data to obtain safety price characteristics, the environmental characteristic data includes material type information and environmental certification information, and the safety characteristic data includes structural safety data and material safety data; Adjustment module: used to input environmental price features and safety price features into the pre-trained value-added model to obtain a basic added price, which is a basic added price determined based on the environmental characteristics and safety characteristics of the rotomolded toys.

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