A home improvement project pricing method, apparatus and program product

Abstract

The application relates to a home decoration project pricing method, device and program product. The home decoration project pricing method comprises the following steps: obtaining dimension information of a target home decoration project, wherein the dimension information comprises home decoration cost information; analyzing additional costs of various home decoration projects caused by risk events in historical risk event data through a neural network model to obtain a risk cost coefficient; then, obtaining a target cost of the target home decoration project according to the home decoration cost information and the risk cost coefficient; obtaining a target selling price of the target home decoration project according to a preset gross profit rate and the home decoration cost information; and finally, obtaining a final selling price of the target home decoration project based on the target cost. The method provided in the application realizes dynamic and flexible home decoration pricing.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method, device and program product for pricing home decoration projects. Background Technology

[0002] Currently, home decoration project pricing systems mainly adopt two implementation methods: one is a cost-plus calculation module based on a fixed formula. This module can only process structured data on direct material and labor costs and cannot access and integrate heterogeneous data sources generated during project execution (such as real-time quotations from supplier systems, time data collected by IoT devices, and unstructured risk records from historical projects); the other is a data query module based on the average market price. This module only returns a static benchmark price and cannot dynamically adjust parameters or perform real-time calculations based on project-specific information. Summary of the Invention

[0003] To address the aforementioned technical issues, this disclosure provides a method, equipment, and program for pricing home decoration projects.

[0004] In a first aspect, embodiments of this disclosure provide a method for pricing home decoration projects, including: Obtain dimensional information about the target home renovation project, including home renovation cost information; By analyzing the additional costs of each home renovation project caused by risk events in historical risk event data using a neural network model, a risk cost coefficient is obtained. Based on home decoration cost information and risk cost coefficients, the target cost of the target home decoration project is obtained; Based on the target cost, obtain the final selling price of the target home renovation project.

[0005] Secondly, embodiments of this disclosure provide a pricing device for home decoration projects, comprising: The acquisition unit is used to acquire dimensional information of the target home decoration project, including home decoration cost information. The analysis unit is used to analyze the additional costs of each home decoration project caused by risk events in historical risk event data through a neural network model, and obtain the risk cost coefficient. The cost calculation unit is used to obtain the target cost of the target home decoration project based on home decoration cost information and risk cost coefficient; The pricing unit is used to obtain the final selling price of the target home improvement project based on the target cost.

[0006] Thirdly, embodiments of this disclosure provide an electronic device, including: Memory; Processor; and Computer programs; The computer program is stored in memory and configured to be executed by a processor to implement the first aspect of the method described above.

[0007] Fourthly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0008] Fifthly, embodiments of this disclosure provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in the first aspect above.

[0009] The home decoration project pricing method disclosed herein includes: obtaining dimensional information of the target home decoration project, wherein the dimensional information includes home decoration cost information; analyzing the additional costs of each home decoration project caused by risk events in historical risk event data through a neural network model to obtain a risk cost coefficient; subsequently, obtaining the target cost of the target home decoration project based on the home decoration cost information and the risk cost coefficient; obtaining the target selling price of the target home decoration project based on the preset gross profit margin and home decoration cost information; and finally, obtaining the final selling price of the target home decoration project based on the target cost. The method provided in this application obtains multi-dimensional information of the home decoration project, avoiding the limitations of related methods that only focus on a single or a few cost and selling price factors; by analyzing historical risk event data of the home decoration project through a neural network model, it quantifies the additional costs that the project may incur due to risk events such as construction delays and design changes, thereby generating a risk cost coefficient to adjust the total cost, transforming uncertainty into a quantifiable cost budget, achieving dynamic and flexible home decoration pricing, and further enhancing the profitability and financial stability of the home decoration platform. Attached Figure Description

[0010] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0011] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a pricing method for home decoration projects provided in this embodiment of the disclosure; Figure 2 for Figure 1 A detailed flowchart of S103 in a home decoration project pricing method is shown; Figure 3A flowchart illustrating another home decoration project pricing method provided in this embodiment of the disclosure; Figure 4 A schematic diagram of a pricing device for home decoration projects provided in this embodiment of the present disclosure; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0013] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0014] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0015] Specifically, in the current increasingly competitive home improvement industry, home improvement platforms face many complex and interconnected challenges, which have a key impact on project gross profit margins and the sustainable development of enterprises.

[0016] (i) Complex and variable cost structure Basic package costs: Basic home renovation packages of different styles and levels vary significantly in the items included and the materials used, making it difficult to accurately predict and control costs. For example, a minimalist style basic package might focus on simple plumbing and electrical work and whitewashing the walls, while a European style package might involve more complex decorative lines and high-end basic materials, resulting in a large cost difference. Furthermore, the prices of raw materials fluctuate frequently; for example, the prices of basic building materials such as timber and steel are affected by international markets, seasons, and policies, further increasing the uncertainty of basic package costs.

[0017] Material Costs: Customers have a wide variety of personalized material choices during the renovation process, ranging from ordinary brands to high-end imported brands, and from conventional materials to new environmentally friendly materials, resulting in significant price differences. Furthermore, factors such as supplier pricing strategies, procurement batches, and transportation distances can all cause fluctuations in material costs. For example, the purchase price of a certain imported tile may increase sharply in a short period due to exchange rate fluctuations or tariff adjustments.

[0018] (ii) Market competition and diversified customer needs Intense Market Competition: The home improvement market is crowded with participants, leading to increasingly fierce competition. To attract customers, various platforms offer a range of promotions and differentiated services, making price competition a crucial tool. In this environment, simply relying on higher selling prices to maintain profit margins becomes increasingly difficult; platforms need to find a precise balance between cost control and pricing strategies.

[0019] Personalized Customer Needs: Modern consumers have an increasing demand for personalized home decoration, with unique requirements not only for decoration style and material quality, but also significant differences in service experience and price sensitivity. Traditional uniform pricing models cannot meet these diverse needs, potentially leading to customer loss due to unreasonable pricing, thus impacting the platform's market share and profitability.

[0020] (III) Limitations of relevant pricing methods Simple cost-plus pricing: This method determines the selling price by adding a fixed percentage of profit to the direct costs (basic package costs and material costs). This approach ignores important factors such as market demand elasticity, customer personalization needs, indirect costs, and risk costs.

[0021] Pricing based on market averages: Some home improvement companies set prices by referencing the average price of similar packages in the market. While this can ensure market adaptability to a certain extent, it fails to fully consider their own cost structure and customer needs. Companies may blindly follow market prices, ignoring their own unique cost advantages or disadvantages, resulting in pricing that does not reflect actual costs, thus affecting gross profit margins.

[0022] In conclusion, existing home decoration pricing methods are insufficient to effectively address complex and ever-changing cost structures, fierce market competition, and diverse customer needs.

[0023] To address the aforementioned technical issues, this disclosure provides a pricing method for home decoration projects. This method comprehensively considers multiple factors to achieve dynamic and accurate pricing, thereby ensuring the gross profit margin of home decoration projects and enhancing the market competitiveness and profitability of home decoration platforms. Detailed explanations are provided through one or more of the following embodiments.

[0024] The home decoration project pricing method provided in this disclosure is applicable to home decoration project pricing scenarios. This method can be executed by a home decoration project pricing device, which can be implemented in software and / or hardware and can be integrated into an electronic device. The electronic device can include, but is not limited to, mobile terminals such as smartphones, laptops, digital radio receivers, personal digital assistants (PDAs), tablet PCs, portable multimedia players (PMPs), in-vehicle terminals (e.g., in-vehicle navigation terminals), wearable devices, etc., as well as fixed terminals such as digital televisions, desktop computers, smart home devices, etc.

[0025] Figure 1 This is a flowchart illustrating a home decoration project pricing method provided in an embodiment of the present disclosure, specifically including as follows: Figure 1 The following steps are shown: S101. Obtain dimensional information of the target home decoration project.

[0026] Among them, the dimensional information includes home decoration cost information.

[0027] Understandably, the target home renovation project is determined. This project can be set by the platform itself, for example, it may include design fees, construction fees, and material costs. Next, multi-dimensional information about the project is obtained. This multi-dimensional information includes at least the cost information for each item within the project, such as material costs and labor costs. Additionally, market information about the target project can be obtained as another dimension. This market information primarily refers to external environmental factors influencing the project, such as competitor pricing, regional consumption levels, and brand positioning. Subsequently, the overall price of the home renovation project is determined by comprehensively considering this multi-dimensional information. This approach moves beyond focusing solely on a single or few cost and price factors, achieving a fusion of internal and external information. This makes pricing no longer static, but a dynamically adjusted intelligent output that adapts to market conditions, competitive landscape, and the platform's own strategies.

[0028] S102. Analyze the additional costs of each home decoration project caused by risk events in historical risk event data through a neural network model to obtain the risk cost coefficient.

[0029] Understandably, based on S101 above, the neural network model can be a pre-trained model used for risk cost prediction. The specific network model is not limited. This model learns and analyzes a large amount of historical risk event data (such as past cases of construction delays, material damage, and design changes) to uncover the complex nonlinear relationship between risk events and cost overruns. Risk cost refers to the additional cost caused by changes in other factors. For example, increased material transportation costs due to natural disasters, or price increases for a certain material due to market fluctuations; the increased cost in this case is the additional cost / risk cost. By statistically analyzing historical risk event data through the neural network model, the additional costs resulting from risk events during the home renovation process are determined, yielding a risk cost coefficient. The risk cost coefficient is essentially a quantified proportional parameter. For example, a risk event might be material damage or construction delays. Another example is a 3-day delay due to weather, which increases the cost by 5% compared to the original plan. Subsequently, based on the risk cost coefficient, the hidden costs of home decoration projects were accurately quantified and forward-lookingly predicted, thereby estimating unforeseen hidden risk costs and greatly enhancing the accuracy of project quotations and risk resistance.

[0030] In one embodiment, the predicted risk cost coefficient is: k=0.01.

[0031] S103. Based on the home decoration cost information and risk cost coefficient, obtain the target cost of the target home decoration project.

[0032] Understandably, based on the above S102, home renovation cost information includes basic package costs. Basic package costs are fixed prices predefined by the platform for a standardized combination of construction and services to meet the most common and basic housing needs. For example, basic package costs include fixed costs such as the brand and model of basic renovation materials and labor, determined by the home renovation platform based on cost accounting for different levels and styles of basic renovation packages. If the customer does not add any additional materials and only uses the basic package, the total cost of the home renovation project (i.e., the target cost) is obtained based on the home renovation cost information and the risk cost coefficient. The target cost can also be understood as the direct cost, and the risk cost coefficient is used to adjust the basic package cost.

[0033] In one embodiment, the target cost is calculated to be 100 yuan if the customer does not add any additional material selections.

[0034] S104. Based on the target cost, obtain the final selling price of the target home decoration project.

[0035] Understandably, based on the aforementioned S103, after accurately calculating the target cost of the target home renovation project (which comprehensively covers the basic package cost, additional material costs, potential expenditures calculated using risk cost coefficients, and platform indirect costs, etc.), the target cost is further converted into a selling price by combining it with the preset gross profit margin, thus obtaining the final selling price of the target home renovation project. This conversion process is not a simple cost addition, but rather a dynamic pricing model that integrates factors such as platform operational goals, market competition, and personalized customer needs into the selling price calculation, ultimately outputting a final selling price for the home renovation project that not only guarantees the platform's preset gross profit margin but also possesses market competitiveness and customer acceptance.

[0036] Optionally, based on the target cost, obtain the final selling price of the target home improvement project, including: Based on the preset gross profit margin and home decoration cost information, the target selling price of the target home decoration project is obtained; based on the preset gross profit margin, target cost, and target selling price, the final selling price of the target home decoration project is determined.

[0037] Understandably, if customers don't add any extra materials and only use the basic package, the target selling price for the home renovation project is calculated based on the preset gross profit margin and the cost of the basic package. The target selling price can also be understood as the basic package price. One calculation method uses a simple cost-plus approach, adding a certain percentage of profit to the basic package cost to determine the selling price. For example, calculating the basic package cost multiplied by (1 + preset gross profit margin) yields the final selling price. Another calculation method involves the platform comprehensively considering factors such as cost, market positioning, and competitor pricing to determine the target selling price.

[0038] In one embodiment, the dynamically set preset gross profit margin is: If the customer does not add any additional materials, the target selling price is calculated to be 110 yuan.

[0039] Optionally, before obtaining the target selling price of the target home decoration project based on the preset gross profit margin and home decoration cost information, the method further includes: Obtain the platform operation requirements of the target platform; input the home decoration market information and platform operation requirements into the gross profit margin optimization model, and obtain the gross profit margin adjustment coefficient output by the gross profit margin optimization model; wherein, the gross profit margin optimization model analyzes the comprehensive impact of different gross profit margin levels on the platform's profitability and market competitiveness through a multi-objective optimization algorithm, and outputs a gross profit margin adjustment coefficient that conforms to the current home decoration market environment and platform operation requirements; optimize the preset gross profit margin based on the gross profit margin adjustment coefficient.

[0040] Understandably, a pre-trained gross profit margin optimization model generates a preset gross profit margin based on home decoration market information and platform operational needs. In other words, the platform sets a preset gross profit margin according to its own operational goals and market conditions, achieving dynamic determination of the preset gross profit margin. It can also generate a gross profit margin adjustment coefficient, which allows for dynamic adjustment of the preset gross profit margin.

[0041] Understandably, the base selling price is calculated based on the target cost (i.e., the total cost of the home renovation project) and the preset gross profit margin. If the customer does not add any additional materials, the base selling price and the target selling price are the same. Then, it is determined whether the target selling price is greater than or equal to the base selling price. If the target selling price is greater than or equal to the base selling price, the target selling price is determined as the final selling price of the home renovation project. If the target selling price is less than the base selling price, the final selling price is recalculated.

[0042] This disclosure provides a pricing method for home decoration projects, aiming to address the problems of inaccurate pricing and large fluctuations in gross profit margins caused by neglecting factors such as risk costs and market demand. By systematically integrating internal and external multi-dimensional information, it achieves dynamic and intelligent pricing for home decoration projects. Specifically, it acquires multi-dimensional information affecting home decoration project pricing, forming a comprehensive data foundation for pricing decisions; analyzes historical risk event data to quantify and predict the additional costs that may result from risk events such as construction delays and design changes for specific projects, thereby generating a risk cost coefficient and transforming uncertainty into a quantifiable cost budget; and dynamically adjusts the final selling price through algorithms to ensure that it accurately achieves the expected gross profit margin target after covering all direct and risk costs, fundamentally enhancing the profitability certainty and financial stability of the home decoration platform.

[0043] Based on the above embodiments, Figure 2 for Figure 1 The diagram illustrates a detailed process flow of step S103 in a home renovation project pricing method. Optionally, based on home renovation cost information and risk cost coefficients, the target cost of the target home renovation project is obtained, specifically including, for example... Figure 2 The following steps are shown: S201. Determine the target percentage of customers who choose additional materials in home decoration projects based on historical order data.

[0044] Understandably, by analyzing a large amount of historical order data from the statistical platform, the frequency of customers choosing additional material options in home decoration projects is analyzed, and the proportion of customers choosing additional material options is determined based on this frequency and a set range, which is recorded as the target proportion. In other words, the proportion of orders with additional material options in a large number of historical orders is analyzed. The value of the target proportion is between 0 and 1.

[0045] In one embodiment, the proportion of additional materials selected by customers as determined by statistical analysis is r=0.1.

[0046] S202. Obtain additional material selection information for the target home decoration project.

[0047] Understandably, based on the above S201, if a customer adds items to the basic package, the information on the added items will be obtained. For example, the customer may replace material A in the basic package with material B, or replace brand A with brand B. Such additions and replacements will generally cause price fluctuations.

[0048] S203. Calculate the cost of additional material selection based on the additional material selection information and the obtained material procurement information.

[0049] Understandably, based on the above S202, the cost of additional material selection is calculated according to the additional material selection information and the platform's material procurement information. This cost depends on the type, brand, quantity, etc. of the materials selected by the customer. It can be calculated from the procurement price negotiated between the platform and the supplier and the customer's specific material selection list. The specific calculation method will not be elaborated here.

[0050] In one embodiment, the additional material selection cost for Customer 1 is: =100 yuan.

[0051] S204. Based on home decoration cost information, risk cost coefficient, target ratio, and additional material selection costs, obtain the target cost of the target home decoration project.

[0052] Understandably, based on the above S203, direct costs are calculated according to formula (1).

[0053] Formula (1) In the formula, C is the target cost, and k is the risk cost coefficient. Basic package cost, For the target ratio, This is to increase the cost of material selection.

[0054] In one embodiment, the basic package cost for customer 1 is: .

[0055] Optionally, based on home renovation cost information, risk cost coefficient, target ratio, and additional material selection costs, the target cost of the target home renovation project can be obtained. This can be achieved through the following steps: Analyze the correlation between various operational activities and cost consumption in the target platform's operational expenditure data to determine the operational costs of the target platform in executing the target home decoration project; determine the indirect costs of the target home decoration project based on the operational costs according to the set allocation rules, where the set allocation rules represent the mapping relationship between the attribute characteristics of the home decoration project and the indirect costs; input the indirect costs of home decoration, home decoration cost information, risk cost coefficient, target ratio, and additional material selection costs into the cost prediction model, and analyze the correlation between different factors and the comprehensive impact of different factors on the total cost of the home decoration project through the cost prediction model to obtain the target cost of the target home decoration project.

[0056] Indirect costs in current home renovation projects, such as transportation fees, warehousing fees, and management fees, while individual amounts may be small, have a significant impact on the total cost in large-scale operations. Furthermore, numerous risks exist during the renovation process, such as material damage during construction and delays due to force majeure events like weather, all of which incur additional risk costs. However, pricing methods often underestimate or fail to scientifically quantify these indirect and risk costs.

[0057] Understandably, to address the aforementioned indirect cost issue, the platform's operating costs are systematically collected and calculated. For example, the correlation between various operational activities and cost consumption in the platform's operating expenditure data is analyzed to determine the operating costs incurred by the platform in executing target home decoration projects. Platform operating costs refer to the sum of all indirect expenses required to maintain the daily operation and services of the home decoration platform. These costs include technical service fees (such as cloud server rental, software development and maintenance), platform management and marketing expenses (such as personnel salaries, market promotion, and brand advertising), customer service system construction fees, and general office and administrative expenses. These costs form the basis for the platform to support all projects and are not directly attributed to any single project. Subsequently, the collected platform operating costs are allocated to each specific home decoration project according to the established allocation rules, thereby calculating the indirect costs of the target home decoration project. This calculation represents the mapping relationship between the attributes of the home decoration project and its indirect costs. The allocation rules are based on one or more quantifiable cost drivers, such as the total sales amount of the project. Specific allocation methods are not elaborated upon here. The indirect costs of home decoration, home decoration cost information, risk cost coefficient, target ratio, and additional material selection costs are input into a pre-trained cost prediction model. The cost prediction model is used to analyze the relationship between different elements and the comprehensive impact of different elements on the total cost of the home decoration project, and finally the target cost of the target home decoration project is obtained. Alternatively, the target cost of the target home decoration project can be calculated using formula (2). This method accurately quantifies and traces the macro-level and general platform operating expenses to each micro-level home decoration project, so that the total cost accounting of the project is upgraded from the original partial cost that only included direct materials and labor to a global cost that comprehensively covers direct costs, risk costs (determined by risk cost coefficient) and platform indirect costs, fundamentally avoiding the problem of inflated gross profit margin caused by incomplete cost accounting.

[0058] Formula (2) In the formula, Indirect costs associated with home renovation.

[0059] In one embodiment, the indirect costs of home renovation for Customer 1 are: .

[0060] Based on the above embodiments, the total computational cost C for Customer 1 is:

[0061]

[0062]

[0063]

[0064]

[0065] 121.1 The home decoration project pricing method provided in this disclosure calculates the platform's operating costs and accurately allocates them to specific projects according to scientific rules, thereby calculating comprehensive indirect costs for home decoration. By integrating and analyzing direct costs, quantified risk cost coefficients, indirect costs, and multi-dimensional market dynamics, the accuracy and completeness of cost accounting are greatly improved. The resulting target cost more accurately reflects the project's true resource consumption and market demand, providing a reliable guarantee for achieving core gross profit margin targets and significantly enhancing the market competitiveness of the pricing strategy and the company's financial stability.

[0066] Based on the above embodiments, Figure 3 This is a flowchart illustrating another home decoration project pricing method provided in this embodiment of the disclosure. Optionally, the target selling price of the target home decoration project is obtained based on a preset gross profit margin and home decoration cost information, specifically including, as follows: Figure 3 The following steps are shown: S301. Based on the preset gross profit margin and the basic package cost, the basic package price is obtained.

[0067] Understandably, the price of the basic package is calculated based on the preset gross profit margin and the cost of the basic package. The specific price can be determined by the platform by comprehensively considering factors such as cost, market positioning, and competitor prices. This provides a certain profit guarantee mechanism for the basic package, so that the profit level of each order is locked in before the transaction occurs, thereby greatly enhancing the accuracy of corporate financial planning and operational stability.

[0068] In one embodiment, the basic package price for customer 1 is: .

[0069] S302. Through the customer value assessment system, assess the customer value coefficient of the target customer based on the customer information of the target customer who initiated the target home decoration project.

[0070] Understandably, based on the aforementioned S301, a pre-built customer value assessment system determines a customer value coefficient based on the customer's evaluation of the platform. The coefficient typically ranges from 0 to 1. Assessment factors include the customer's consumption history and the number of new customers referred. Consumption history includes the user's housing transaction records, thus determining whether the customer was referred after purchasing a property or came through organic traffic. This customer value assessment enables personalized pricing, satisfying diverse customer needs while optimizing platform revenue and enhancing market competitiveness.

[0071] In one embodiment, the value coefficient of customer 1 is v=0.9.

[0072] S303. Conduct data analysis on market research information and home decoration market information to determine the coefficient for additional material selection.

[0073] Understandably, based on the above S302, data analysis of market research information and home decoration market information is used to determine the coefficient for additional material selection. This transforms customers' personalized preferences and market trends into a quantifiable premium adjustment parameter, enabling the platform to not only guarantee the basic gross profit margin when setting the price of the entire home decoration project (after the basic package and additional material selection), but also to reserve scientific pricing space and marketing strategies for potential high-profit additional services, thereby accurately capturing customer value and increasing the overall average order value.

[0074] Optionally, data analysis of market research and home decoration market information can be conducted to determine the coefficient for additional material selection. This can be achieved through the following steps: Price adjustment parameters are determined based on home decoration market information and platform pricing strategies. Market research information and historical sales data of home decoration projects are used to analyze price change trends. A price elasticity coefficient is obtained based on the correlation between price change trends and preset price change relationships. This coefficient characterizes the sensitivity of home decoration demand to price changes. Target material combinations from the additional material selection information are input into a discount optimization model, and the package discount coefficient of the target material combination output by the model is obtained. The discount optimization model analyzes the correlation between different material items within different material combinations based on the transaction probability data and profit contribution data of different material combinations in historical order data. The additional material selection coefficient includes price adjustment parameters, price elasticity coefficient, and package discount coefficient.

[0075] Understandably, the optional extras selection coefficient includes price adjustment parameters, price elasticity coefficients, and package discount coefficients. The price adjustment parameter is determined based on market competition and the platform's pricing strategy, typically ranging from 0 to 1. The price elasticity coefficient is determined by analyzing historical sales data and price changes through market research on home improvement projects. This coefficient measures the sensitivity of customer demand to price changes. The customer's selected optional extras are input into a pre-trained discount optimization model. This model analyzes the relationships between different optional items within different combinations to determine the package discount coefficient, which also ranges from 0 to 1. Different combinations correspond to different discounts; for example, using products from the same brand after adding optional extras will result in a discount. The discount optimization model is trained based on historical order data showing the probability of successful transactions and profit contribution data for different optional combinations; the specific training method is not limited. This pricing strategy, which considers market elasticity and customer segmentation, makes the platform's prices more competitive, better attracting and retaining customers and increasing market share.

[0076] In one embodiment, the additional material selection coefficient for customer 1 is: price adjustment parameter α = 0.1, and price elasticity coefficient is... =-0.1, the package discount coefficient is d=0.1.

[0077] S304. Based on the preset gross profit margin, the additional material selection coefficient, and the additional material selection cost, obtain the additional material selection price of the target home decoration project.

[0078] Understandably, based on the aforementioned S303, and while ensuring a basic profit margin, dynamic pricing is strategically implemented to cater to personalized needs. Specifically, the cost of additional material selections is used as a base, and a preset gross profit margin is applied to ensure that the service itself meets the platform's overall profit target, forming the base price. Subsequently, quantitative parameters derived from market data analysis regarding the willingness to add additional materials and the willingness to pay a premium are introduced—that is, the additional material selection coefficient—to adjust the base price, resulting in the additional material selection price for the target home improvement project, which is the extra price charged to the customer after adding additional materials.

[0079] In one embodiment, the price for customer 1's additional material selection is: .

[0080] Optionally, the selling price of additional materials for the target home decoration project can be obtained based on the preset gross profit margin, the additional material selection coefficient, and the additional material selection cost. This can be achieved through the following steps: Based on the preset gross profit margin, basic package price, target ratio, indirect costs of home decoration, and basic package cost, the basic material selection price for the target home decoration project is obtained. The price elasticity coefficient and price adjustment parameters are input into the market elasticity analysis model. The market elasticity analysis model is used to analyze the changes in the sensitivity of price adjustments under different market conditions and obtain the market elasticity coefficient of the home decoration project. The basic material selection price is adjusted based on the market elasticity coefficient. Based on the adjusted basic material selection price, package discount coefficient, target ratio, and preset gross profit margin, the additional material selection price for the target home decoration project is obtained.

[0081] Understandably, considering the adjustment of the difference between cost and expected selling price by market elasticity factors, the price elasticity coefficient and price adjustment parameters are input into the market elasticity analysis model. The market elasticity coefficient of the home decoration project is obtained by analyzing the sensitivity changes of price adjustment under different market conditions through the market elasticity analysis model. Subsequently, the basic material price is adjusted through the market elasticity coefficient. That is, after determining the basic price of the material selection considering the gross profit margin of the material selection, it is dynamically adjusted in combination with market factors. Finally, the actual selling price of the customer after adding material selection (i.e., the price of adding material selection) is obtained by combining the preset gross profit margin, the proportion of additional material selection (i.e., the target proportion) and the package discount coefficient. Specifically, it can be calculated by formula (3). Other calculation methods are not limited.

[0082] Formula (3) In the formula, The price is for additional material selection. Adjust parameters for price. This is the price elasticity coefficient, where 1 represents a set threshold. Basic package price, This is the discount factor for the package. This represents the market elasticity coefficient.

[0083] Optionally, based on the adjusted base material price, package discount coefficient, target percentage, and preset gross profit margin, the additional material price for the target home renovation project can be obtained. This can be achieved through the following steps: Initial adjustment parameters are generated based on preset gross profit margin and target ratio; compliance judgment and adaptive correction of the initial adjustment parameters are performed through preset business verification rules. The preset business verification rules are used to trigger the taking of absolute values ​​and weighted optimization based on historical order data when the initial adjustment parameters are lower than the preset lower limit; the corrected initial adjustment parameters, the adjusted basic material selection price, and the package discount coefficient are integrated and calculated to obtain the additional material selection price of the target home decoration project.

[0084] Understandably, initial adjustment parameters are generated based on the proportion of customer add-on materials in historical orders and a preset gross profit margin. Subsequently, the initial adjustment parameters are subject to compliance checks and adaptive corrections using preset business validation rules. When the initial adjustment parameters fall below a preset lower limit, for example, if the initial adjustment parameters are negative, the negative value (possibly due to parameter anomalies) is converted to a positive value to ensure the selling price is not negative. Simultaneously, transaction data from similar projects (e.g., similar apartment types, same material combinations) in the historical database are retrieved. Through weighted averaging or similarity matching, the corrected adjustment parameters are smoothed to better reflect actual, feasible home renovation project scenarios, thereby enhancing the parameters' rationality and market adaptability through historical experience data. Finally, through preset algorithms (e.g., weighted summation or multiplicative adjustment), a personalized selling price for the customer's selected add-on combination is output.

[0085] In one embodiment, the price for customer 1's additional material selection is:

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] S305. Based on the target ratio, customer value coefficient, additional material selection price, and basic package price, obtain the target price of the target home decoration project after the target customer selects additional materials.

[0092] Understandably, based on the above S304, by using the basic package price, the price of additional materials, the proportion of additional materials selected by the customer (i.e., the target proportion), and the customer value coefficient, the final price of the home decoration project after the additional materials are selected (i.e., the target price) is obtained, thus realizing dynamic pricing. This allows the platform to more scientifically explore potential consumption capacity by predicting and incentivizing high-value additional items while ensuring the profit of the basic package, thereby systematically improving the average order value and overall profitability. For example, the formula for calculating the target price is shown in formula (4).

[0093] Formula (4) In the formula, Target selling price, Price of the basic package.

[0094] In one embodiment, the final selling price for customer 1 is:

[0095]

[0096]

[0097] This disclosure provides a home decoration project pricing method that achieves the dual goals of platform resource optimization and precise pricing by constructing a refined cost-benefit accounting system and a customer value assessment system. First, based on a clear calculation of the overall cost of the home decoration project (including direct costs, risk costs, and platform indirect costs), dynamic adjustments and iterative optimization of basic packages are performed, significantly improving supply chain efficiency and the rationality of resource allocation. Second, by introducing a customer value coefficient and analyzing customers' historical selection preferences, a unified pricing strategy can be transformed into personalized quotes for different customer groups, achieving customer segmentation pricing. This not only effectively improves the satisfaction and loyalty of high-value customers but also taps into the maximum customer willingness to pay through differentiated pricing strategies, thereby optimizing the platform's revenue structure overall and achieving synergistic growth in operational efficiency and profitability.

[0098] Figure 4 This is a schematic diagram of a home decoration project pricing device provided in an embodiment of this disclosure. The home decoration project pricing device provided in this embodiment can execute the processing flow provided in the home decoration project pricing method embodiment, such as… Figure 4 As shown, the home renovation project pricing device 400 includes: The acquisition unit 401 is used to acquire the dimensional information of the target home decoration project, wherein the dimensional information includes home decoration cost information; Analysis unit 402 is used to analyze the additional costs of each home decoration project caused by risk events in historical risk event data through a neural network model, and obtain the risk cost coefficient. Cost calculation unit 403 is used to obtain the target cost of the target home decoration project based on home decoration cost information and risk cost coefficient; Price determination unit 404 is used to obtain the final price of the target home improvement project based on the target cost.

[0099] Optionally, the price determination unit 404 is used for: Based on the preset gross profit margin and home decoration cost information, the target selling price of the target home decoration project is obtained; The final selling price of the target home improvement project is determined based on the preset gross profit margin, target cost, and target selling price.

[0100] Optionally, cost calculation unit 403 is used for: Determine the target percentage of customers adding materials to home decoration projects by using historical order data; Obtain information on additional material selection for the target home renovation project; Calculate the cost of additional material selection based on the information on additional material selection and the obtained material procurement information; Based on home decoration cost information, risk cost coefficient, target ratio, and additional material selection costs, the target cost of the target home decoration project is obtained.

[0101] Optionally, cost calculation unit 403 is used for: Analyze the correlation between various operational activities and cost consumption in the target platform's operational expenditure data to determine the operational costs incurred by the target platform in executing the target home decoration project; The indirect costs of the target home decoration project are determined based on the operating costs according to the set allocation rules. The set allocation rules represent the mapping relationship between the attribute characteristics of the home decoration project and the indirect costs. By inputting indirect costs of home decoration, home decoration cost information, risk cost coefficient, target ratio, and additional material selection costs into the cost prediction model, the model analyzes the relationship between different factors and the comprehensive impact of different factors on the total cost of the home decoration project, thus obtaining the target cost of the target home decoration project.

[0102] Optionally, the price determination unit 404 is used for: The basic package price is obtained based on the preset gross profit margin and the basic package cost; Through a customer value assessment system, the customer value coefficient of the target customers who initiate the target home decoration project is assessed based on their customer information. Analyze market research and home decoration market information to determine the coefficient for additional material selection items; Based on the preset gross profit margin, the additional material selection coefficient, and the additional material selection cost, the additional material selection price of the target home decoration project is obtained; Based on the target ratio, customer value coefficient, additional material selection price, and basic package price, the target price of the target home decoration project after the target customer selects additional materials is obtained.

[0103] Optionally, the price determination unit 404 is used for: The price adjustment parameters are determined based on home decoration market information and platform pricing strategies. By analyzing market research information and historical sales data of home decoration projects, the price change trend of home decoration projects is analyzed, and the price elasticity coefficient is obtained based on the correspondence between the price change trend and the preset price change relationship. The price elasticity coefficient is used to characterize the sensitivity of home decoration demand to price changes. Input the target selection combination in the additional selection information into the discount optimization model, and obtain the package discount coefficient of the target selection combination output by the discount optimization model. The discount optimization model is based on the transaction probability data and profit contribution data of different selection combinations in historical order data, and analyzes the correlation between each selection item in different selection combinations to output the package discount coefficient. The additional material selection coefficients include price adjustment parameters, price elasticity coefficients, and package discount coefficients.

[0104] Optionally, the price determination unit 404 is used for: Based on the preset gross profit margin, basic package price, target ratio, indirect costs of home decoration, and basic package cost, the basic material selection price for adding materials to the target home decoration project is obtained. The price elasticity coefficient and price adjustment parameters are input into the market elasticity analysis model. The market elasticity analysis model is used to analyze the changes in the sensitivity of price adjustments under different market conditions and obtain the market elasticity coefficient of the home decoration project. Adjust the price of basic materials by using the market elasticity coefficient; Based on the adjusted base material prices, package discount coefficients, target ratios, and preset gross profit margins, the additional material prices for the target home decoration project are obtained.

[0105] Optionally, the price determination unit 404 is used for: Initial adjustment parameters are generated based on the preset gross profit margin and target ratio; The initial adjustment parameters are judged for compliance and adaptively corrected by preset business verification rules. The preset business verification rules are used to trigger the taking of absolute values ​​and weighted optimization based on historical order data when the initial adjustment parameters are lower than the preset lower limit. The revised initial adjustment parameters, the adjusted basic material prices, and the package discount coefficient are combined and calculated to obtain the additional material prices for the target home decoration project.

[0106] Optionally, the home improvement project pricing device 400 is also used for: Obtain the platform operation requirements of the target platform; The home decoration market information and platform operation requirements are input into the gross profit margin optimization model, and the gross profit margin adjustment coefficient output by the gross profit margin optimization model is obtained. The gross profit margin optimization model analyzes the comprehensive impact of different gross profit margin levels on the platform's profitability and market competitiveness through a multi-objective optimization algorithm, and outputs a gross profit margin adjustment coefficient that conforms to the current home decoration market environment and platform operation requirements. Optimize the preset gross profit margin based on the gross profit margin adjustment factor.

[0107] Figure 4 The home decoration project pricing device shown in the embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0108] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. See below for details. Figure 5 The diagram illustrates a structural schematic suitable for implementing the electronic device 500 in the embodiments of this disclosure. The electronic device 500 in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), wearable electronic devices, etc., as well as fixed terminals such as digital TVs, desktop computers, smart home devices, etc. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0109] like Figure 5 As shown, the electronic device 500 may include a processing unit 501 (e.g., a central processing unit, a graphics processor, etc.) that can perform various appropriate actions and processes to implement the home improvement project pricing method as described in the embodiments of this disclosure, based on a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0110] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0111] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts, thereby implementing the above-described home decoration project pricing method. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a storage device 508, or installed from a ROM 502. When the computer program is executed by the processing device 501, it performs the functions defined in the methods of embodiments of this disclosure.

[0112] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0113] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0114] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0115] Optionally, when one or more of the above-described procedures are executed by the electronic device, the electronic device may also execute other steps of the above embodiments.

[0116] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0117] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0118] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0119] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0120] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0121] It should be noted that in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or gateway that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or gateway. Without further limitations, an element defined by the phrase "comprising a home improvement project pricing" does not exclude the presence of other identical elements in the process, method, article, or gateway that includes the element.

[0122] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for pricing home decoration projects, characterized in that, include: Obtain dimensional information of the target home decoration project, wherein the dimensional information includes home decoration cost information; By analyzing the additional costs of each home renovation project caused by risk events in historical risk event data using a neural network model, a risk cost coefficient is obtained. Based on the home decoration cost information and the risk cost coefficient, the target cost of the target home decoration project is obtained; Based on the target cost, the final selling price of the target home decoration project is obtained.

2. The method according to claim 1, characterized in that, The process of obtaining the final selling price of the target home renovation project based on the target cost includes: Based on the preset gross profit margin and the home decoration cost information, the target selling price of the target home decoration project is obtained; The final selling price of the target home decoration project is determined based on the preset gross profit margin, the target cost, and the target selling price.

3. The method according to claim 2, characterized in that, The step of obtaining the target cost of the target home renovation project based on the home renovation cost information and the risk cost coefficient includes: Determine the target percentage of customers adding materials to home decoration projects by using historical order data; Obtain the additional material selection information for the target home decoration project; Calculate the cost of the additional material selection based on the aforementioned additional material selection information and the obtained material procurement information; Based on the home decoration cost information, the risk cost coefficient, the target ratio, and the cost of additional material selection, the target cost of the target home decoration project is obtained.

4. The method according to claim 3, characterized in that, The step of obtaining the target cost of the target home renovation project based on the home renovation cost information, the risk cost coefficient, the target ratio, and the cost of additional material selections includes: Analyze the correlation between various operational activities and cost consumption in the operational expenditure data of the target platform to determine the operational costs incurred by the target platform in executing the target home decoration project; The indirect costs of the target home decoration project are determined according to the operating costs based on the set allocation rules, wherein the set allocation rules characterize the mapping relationship between the attribute characteristics of the home decoration project and the indirect costs; The indirect costs of home decoration, the home decoration cost information, the risk cost coefficient, the target ratio, and the cost of additional materials are input into the cost prediction model. The cost prediction model is used to analyze the correlation between different factors and the comprehensive impact of different factors on the total cost of the home decoration project, so as to obtain the target cost of the target home decoration project.

5. The method according to claim 3, characterized in that, The home renovation cost information includes the basic package cost, and the dimension information also includes home renovation market information. The step of obtaining the target selling price of the target home renovation project based on the preset gross profit margin and the home renovation cost information includes: The basic package price is obtained based on the preset gross profit margin and the basic package cost; The customer value coefficient of the target customer is evaluated based on the customer information of the target customer who initiated the target home decoration project through the customer value assessment system. Data analysis was conducted on market research information and the aforementioned home decoration market information to determine the coefficient for additional material selection items; The selling price of additional materials for the target home decoration project is obtained based on the preset gross profit margin, the additional material selection coefficient, and the additional material selection cost. Based on the target ratio, the customer value coefficient, the price of the additional material selections, and the price of the basic package, the target price of the target home decoration project after the target customer selects additional materials is obtained.

6. The method according to claim 5, characterized in that, The process of analyzing market research information and home decoration market information to determine the coefficient for additional material selection includes: The price adjustment parameters are determined based on the aforementioned home decoration market information and the platform's pricing strategy. By analyzing the market research information and historical order data of the home decoration projects, the price change trend of the home decoration projects is analyzed, and the price elasticity coefficient is obtained according to the correspondence between the price change trend and the preset price change relationship. The price elasticity coefficient is used to characterize the sensitivity of home decoration demand to price changes. Input the target selection combination in the additional selection information into the discount optimization model, and obtain the package discount coefficient of the target selection combination output by the discount optimization model. The discount optimization model is based on the transaction probability data and profit contribution data of different selection combinations in the historical order data, and analyzes the correlation between each selection item in different selection combinations to output the package discount coefficient. The additional material selection coefficient includes the price adjustment parameter, the price elasticity coefficient, and the package discount coefficient.

7. The method according to claim 6, characterized in that, The step of obtaining the selling price of additional materials for the target home decoration project based on the preset gross profit margin, the additional material selection coefficient, and the additional material selection cost includes: Based on the preset gross profit margin, the basic package price, the target ratio, the indirect cost of home decoration for the target home decoration project, and the basic package cost, the basic material selection price for the target home decoration project with additional material selection is obtained; The price elasticity coefficient and the price adjustment parameter are input into the market elasticity analysis model. The market elasticity analysis model is used to analyze the changes in the sensitivity of price adjustments under different market conditions, and the market elasticity coefficient of the home decoration project is obtained from its output. The price of the basic selected materials is adjusted using the market elasticity coefficient. The additional material prices for the target home decoration project are obtained based on the adjusted basic material prices, the package discount coefficient, the target ratio, and the preset gross profit margin.

8. The method according to claim 7, characterized in that, The step of obtaining the additional material pricing for the target home decoration project based on the adjusted basic material pricing, the package discount coefficient, the target ratio, and the preset gross profit margin includes: Initial adjustment parameters are generated based on the preset gross profit margin and the target ratio; The initial adjustment parameters are subjected to compliance judgment and adaptive correction by a preset business verification rule. The preset business verification rule is used to trigger the taking of absolute value and weighted optimization based on historical order data when the initial adjustment parameters are lower than a preset lower limit. The revised initial adjustment parameters, the adjusted basic material price, and the package discount coefficient are combined and calculated to obtain the additional material price for the target home decoration project.

9. The method according to claim 2, characterized in that, Before obtaining the target selling price of the target home decoration project based on the preset gross profit margin and the home decoration cost information, the method further includes: Obtain the platform operation requirements of the target platform; The home decoration market information and the platform's operational requirements are input into the gross profit margin optimization model, and the gross profit margin adjustment coefficient output by the gross profit margin optimization model is obtained. The gross profit margin optimization model analyzes the comprehensive impact of different gross profit margin levels on the platform's profitability and market competitiveness through a multi-objective optimization algorithm, and outputs a gross profit margin adjustment coefficient that conforms to the current home decoration market environment and the platform's operational requirements. The preset gross profit margin is optimized based on the gross profit margin adjustment coefficient.

10. An electronic device, characterized in that, include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the home decoration project pricing method as described in any one of claims 1 to 9.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the home decoration project pricing method as described in any one of claims 1 to 9.

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