A digital intelligent management method and system for furniture production

Abstract

The present application discloses a digital intelligent management method and system for furniture production, which relates to the technical field of furniture production. The method includes: S100: Obtain the specific requirements of the user and generate a three-dimensional design model of the furniture. After being modified and confirmed by the user, place an order; S200: Obtain the furniture design drawings corresponding to the three-dimensional design model of the furniture, extract the furniture product parameters through the furniture design drawings, group them according to the grouping rules according to the furniture product parameters, and generate corresponding group numbers; S300: Identify according to the group numbers, select the group numbers with the same first two digits, and perform unified planning and cutting according to the specific order numbers; S400: Verify according to the cutting plan and optimize the grouping rules; adopt a matching algorithm to identify and match the shapes and sizes of multiple orders, increasing the utilization rate of veneers and greatly reducing the generation of waste materials, thereby achieving the effects of improving the utilization rate of boards and reducing waste materials.

Description

Technical Field

[0001] The present invention relates to the technical field of furniture production, and particularly to a digital intelligent management method and system for furniture production. Background Art

[0002] In the production of customized furniture, especially in the production of some cabinets, there may be a large difference between the initially designed effect and the effect after installation at home, which may lead to customer dissatisfaction with the customized furniture. At the same time, during the production process of some cabinets, due to problems such as poor cutting optimization and unreasonable production scheduling, there may be serious waste of plates, and at the same time, the production efficiency is low.

[0003] For example, in Chinese Patent Publication No. CN116050815A, a furniture automated production method and device based on intelligent manufacturing, wherein the method includes: receiving demand information of furniture design from a customer and customer's living room house type data, and uploading the demand information and customer's living room house type data to a remote server; generating a three-dimensional furniture design model based on the demand information; rendering the three-dimensional furniture design model based on the customer's living room house type data, and loading the rendered three-dimensional furniture design model to a client for interactive display; when the customer confirms the three-dimensional furniture design model based on the interactive display, sending the furniture design drawings corresponding to the three-dimensional furniture design model to a production management system; performing cutting optimization and production data scheduling processing based on the furniture design drawings; scheduling corresponding equipment on an automated production line based on the cutting optimization result and production data scheduling result to perform furniture automated production operations.

[0004] In the prior art, when performing cutting optimization and production data scheduling processing based on furniture design drawings, due to the extremely limited usage amount of a single order, a large amount of leftover materials will be generated when cutting for an order. Even if cutting optimization and production data scheduling processing are performed, a large amount of leftover materials will inevitably be generated, resulting in waste of plates. Summary of the Invention

[0005] The present application provides a digital intelligent management method and system for furniture production, which solves the problem of a large amount of leftover materials generated during cutting of a single order in the prior art, resulting in waste of plates, and achieves the technical effect of comprehensive optimization of multiple orders to improve the utilization rate of plates.

[0006] The present application provides a digital intelligent management method for furniture production, and the method includes:

[0007] S100: Obtain the specific requirements of the user and generate a three-dimensional furniture design model. After being modified and confirmed by the user, place an order, and at the same time generate retention information and the order confirmation time. Form a unique order number based on the retention information and the order confirmation time, and establish an order database for storage;

[0008] S200: Obtain the furniture design drawings corresponding to the 3D furniture design model, extract furniture product parameters from the furniture design drawings, group them according to the grouping rules, and generate corresponding group numbers; the furniture product parameters include board type, demand quantity, shape, size, color, and delivery date.

[0009] S300: Identify according to the group numbers, select the group numbers with the same first two digits, perform unified planning and cutting according to the specific order numbers, and further optimize according to the generated preliminary design plan to generate the final cutting plan.

[0010] S400: Verify according to the cutting plan and optimize the grouping rules; obtain the maximum size and shipment volume of a single board, compare and verify the maximum size and shipment volume with all the demand quantities and sizes in each group. The total size and demand quantity required by multiple orders should be less than the maximum size and shipment volume of a single board.

[0011] Further, in step S200, the grouping rules are first preliminarily divided according to the board type and delivery date, and those with the same type and similar delivery dates are classified; then the orders of the same category are grouped according to the matching algorithm to generate corresponding group numbers; the matching algorithm obtains the order numbers and corresponding furniture design drawings of all orders in the same category, automatically identifies the shapes and sizes in the furniture design drawings, compares the shapes and sizes of multiple orders with the single board used to generate the corresponding matching degree, and groups according to the matching degree.

[0012] Further, the matching algorithm converts the images of the furniture design drawings and the single board used into a unified digital image format, performs preprocessing operations on the digital images, obtains the maximum size of the single board used, and highlights the shape contours of the furniture design drawings; uses edge detection algorithms to identify the shape edges in the images, based on the detected edges, extracts the shape contours, smooths the extracted contours to reduce noise and refinement errors; calculates the sizes of the shapes according to the pixel coordinates of the contours and converts them into actual physical sizes; verifies the conformity of the identified shapes and calculated sizes with the marked data in the furniture design drawings. After the conformity verification passes within the error range, an identification record form is generated; the identification record form includes order numbers, shapes, and sizes.

[0013] Further, arrange according to the sizes in the recognition record table, screen out the recognition record tables with large sizes, place the recognized shapes and calculated sizes on the digital image of the board to generate a pre-arrangement image; select the recognition record tables with small sizes, place the recognized shapes and calculated sizes on the pre-arrangement image for filling until no more filling can be done on the board to generate a final arrangement image; when selecting the recognition record tables with small sizes, fill them in sequence from largest to smallest according to the arrangement order; the large size refers to more than one-third of the largest size of a single board.

[0014] Further, the ratio of the size of the filled area in the final arrangement image to the largest size of the single board used is the matching degree; the matching algorithm is divided into 3 major groups according to the level of the matching degree; each major group is divided into multiple subgroups according to the number of orders; the group number adopts the combination form of major group, subgroup and order number.

[0015] Further, identify the unfilled area according to the final arrangement image to generate a surplus material information table, and establish a surplus material database; the surplus material information table includes the shape, size and board type of the surplus material, and generates a unique identification number according to the shape, size and board type of the surplus material; obtain the furniture product parameters of the new order, and preferentially query the available surplus materials in the surplus material database through the identification number. When querying, compare the size and shape of the surplus material with the size and shape of the new order. If the size and shape match, it is defined as available surplus material; if there are multiple available surplus materials, preferentially select the surplus material with the least remaining part after use; if no available surplus material is found, continue to execute step S200.

[0016] Further, obtain the retained information, query in the order database according to the user name and the last four digits of the mobile phone number to obtain all the order records of the user, analyze the personalized preference requirements of the user according to the furniture product parameters in the user's order, and design and generate surplus material value-added products according to the personalized preference requirements and the available surplus materials to recommend to the user to increase the aesthetic and practicality of the original furniture design drawing; establish a user satisfaction feedback mechanism, obtain the user's satisfaction with the surplus material value-added products, and generate a user feedback analysis report, summarize the surplus material value-added products with high satisfaction, and establish a surplus material creative furniture table to record the names, board types, shapes and sizes of the surplus material value-added products with high satisfaction.

[0017] Further, obtain the pre-arrangement image and establish a coordination mechanism for board optimization and surplus material value-added products; the coordination mechanism for board optimization and surplus material value-added products is to monitor and compare the fit degree between the shape and size of the unfilled area on the pre-arrangement image and the shape and size of the surplus material value-added products in the surplus material creative furniture table in real time. If the fit degree is higher than 90%, the unfilled area is defined as a reserved optimization area.

[0018] Furthermore, the collaborative mechanism for sheet material optimization and surplus material value-added products also includes filling the reserved optimization area with small-sized identification record forms, calculating the fitting degree of the filling. If the fitting degree in all identification record forms is lower than 90%, the reserved optimization area is defined as the surplus material optimization area; if there is an identification record form with a fitting degree higher than 90%, the shape and size in the identification record form are used for filling, and the reserved optimization area is cancelled.

[0019] A digital intelligent management system for furniture production, which is equipped with the above-mentioned digital intelligent management method for furniture production. The system includes:

[0020] An interaction module, which is used to receive the specific requirements and relevant data of furniture design from users, generate a 3D furniture design model based on the specific requirements and relevant data, transmit the generated 3D furniture design model to the user side, receive the modification suggestions from users at the same time, and make modifications in a timely manner; after confirmation, the user side places an order, and generates retention information and the order time;

[0021] An information extraction module, which is used to receive furniture design drawings and extract furniture product parameters;

[0022] A matching processing module, which is used to receive furniture product parameters, perform matching grouping according to grouping rules and matching algorithms, and generate a final cutting plan;

[0023] A manufacturing module, which is used to receive the cutting plan and perform automated production based on the cutting plan;

[0024] A storage module, which is used to store all data, including all data information in the order database and the surplus material database;

[0025] A verification and optimization module, which is used to verify the cutting plan and iteratively optimize the grouping rules;

[0026] A collaborative module for sheet material optimization and surplus material value-added products, which is used to obtain the surplus material creative furniture list and select and determine the surplus material optimization area according to the surplus material creative furniture list.

[0027] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0028] By adopting a matching algorithm, the shapes and sizes of multiple orders are identified and matched, and cutting is performed on the same sheet material, which increases the utilization rate of single boards, greatly reduces the generation of surplus materials, considers multiple orders for cutting at the same time, improves the progress of orders, and thus realizes the effects of identification, matching, unified cutting, improving the utilization rate of sheet materials, and reducing surplus materials. Description of the Drawings

[0029] Figure 1Flowchart of a digital intelligent management method for furniture production in an embodiment of the present invention;

[0030] Figure 2 Schematic diagram of the grouping rule process in a digital intelligent management method for furniture production in an embodiment of the present invention;

[0031] Figure 3 System architecture diagram of a digital intelligent management system for furniture production in an embodiment of the present invention. Detailed implementation manners

[0032] To facilitate the understanding of the present invention, the present application will be described more comprehensively with reference to the relevant accompanying drawings; the preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs; the terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0034] Embodiment 1: As Figure 1 shown, a digital intelligent management method for furniture production in the present application, the method includes:

[0035] S100: Obtain the specific requirements of the user and generate a 3D design model of the furniture. After being modified and confirmed by the user, place an order, and at the same time generate retention information and the confirmed order time. Form a unique order number based on the retention information and the confirmed order time, and establish an order database for storage.

[0036] Based on the interactive terminal, obtain the specific requirements of the user and generate a 3D design model of the furniture. The user puts forward modification suggestions for the 3D design model of the furniture. After the modification is completed, it is confirmed by the user. After the confirmation is completed, place an order, and form a unique order number according to the retention information and the confirmed order time. There are multiple interactions with the user to obtain the user's modification suggestions, fully consider the user's suggestions and ideas, and then confirm the order by the user to perform subsequent operations. It can fully consider the user's suggestions, meet the user's needs, and in subsequent production, it can more easily produce furniture products that the user is very satisfied with, improving user satisfaction.

[0037] The user retention information includes the user's name, contact information (mobile phone number), and contact address; extract the name, the last four digits of the mobile phone number, and the order confirmation time to form an order number. For example, User A-xxxx-xxxx. Store the order number in the order database and set an index so that it can be queried based on the name, the last four digits of the mobile phone number, and the order confirmation time.

[0038] S200: Obtain the furniture design drawings corresponding to the 3D furniture design model, extract furniture product parameters from the furniture design drawings, and group them according to the grouping rules to generate corresponding group numbers; the furniture product parameters include board type, demand quantity, shape, size, color, and delivery date.

[0039] In step S200, the grouping rules first make a preliminary division according to the board type and delivery date, and classify those with the same type and similar delivery dates; then group the orders of the same category according to the matching algorithm to generate corresponding group numbers; the matching algorithm obtains the order numbers and corresponding furniture design drawings of all orders in the same category, automatically identifies the shape and size in the furniture design drawings, compares the shapes and sizes of multiple orders with the single board used to generate the corresponding matching degree, and groups according to the matching degree.

[0040] The matching algorithm converts the images of the furniture design drawings and the single board used into a unified digital image format, performs preprocessing operations on the digital images, obtains the maximum size of the single board used, and highlights the shape contour of the furniture design drawings; uses an edge detection algorithm to identify the shape edges in the images, based on the detected edges, extracts the shape contour, smooths the extracted contour to reduce noise and refinement errors; calculates the size of the shape according to the pixel coordinates of the contour and converts it into the actual physical size; verifies the conformity of the identified shape and the calculated size with the marked data in the furniture design drawings. After the conformity verification passes within the error range, a recognition record form is generated; the recognition record form includes the order number, shape, and size.

[0041] The data image format can be PNG or other formats, as long as the formats are unified. The preprocessing includes grayscale conversion, noise reduction, and binarization operations. Functions in the image processing library OpenCV are used to convert a color image into a grayscale image. OpenCV (Open Source Computer Vision Library) is an open-source computer vision library. Grayscale conversion is to convert the three RGB components of a color image into a single grayscale value, simplifying the subsequent image processing process; Gaussian filtering is used to reduce noise in the image. Gaussian filtering smooths the image by convolving a normal distribution, thus reducing the impact of noise on subsequent edge detection; by setting a threshold, the grayscale image is converted into a binary image. In a binary image, the pixel values are only 0 (black) and 255 (white), which can more clearly highlight the shape contour. The Otsu (Otsu's thresholding) method is used to automatically calculate an optimal threshold for image binarization. The Otsu thresholding method is an image segmentation method, also known as the maximum inter-class variance method. It divides the image into a background and a foreground part according to the grayscale characteristics of the image, and determines a global threshold by calculating the inter-class variance, so that the inter-class variance of the foreground and background images after segmentation is the largest, thus achieving the best segmentation effect. The Canny (Canny Edge Detection Algorithm) edge detection algorithm is used to identify the shape edges in the image; the Sobel (Sobel operator) is a discrete difference operator, mainly used to obtain the first-order gradient of a digital image, calculate the gradient intensity and direction of each pixel point, and check whether the gradient intensity of each pixel point is the maximum in its gradient direction neighborhood to eliminate the spurious response brought by edge detection; two thresholds, a high threshold and a low threshold, are set. Points with a gradient intensity greater than the high threshold are considered edge points, points less than the low threshold are discarded, and points between the two are only regarded as edge points when they are connected to the already determined edges. Preferably, the thresholds of the edge detection algorithm are adjusted to obtain the best edge recognition effect; the findContours function is a function in the OpenCV library, used to detect the contours in the image. It will return a list of contours, each contour consisting of a series of points, representing the boundary of a continuous region in the image, and the extracted contours are smoothed to reduce noise and refinement errors.

[0042] The compliance is to extract the marked shape and dimension data from the furniture design drawings, which have been determined and marked by the designer during the design process. Compare the identified shape with the shape marked on the drawing to confirm whether they are the same. For the dimension data, calculate the difference between the identified dimension and the marked dimension. The shape comparison is done by calculating the degree of overlap of the contours. Set a reasonable error range according to industry standards and actual requirements. Determine whether the calculated error is within the preset error range. If the error is within the allowable range, the compliance verification passes; if the error exceeds the allowable range, the compliance verification fails and the recognition algorithm needs to be rechecked. After passing the compliance verification, record the order number, the identified shape and dimension data in the recognition record form.

[0043] For example, there is a furniture design drawing that marks the dimensions of a rectangular furniture as 100 cm in length and 50 cm in width. The dimensions identified by the recognition algorithm are 99.5 cm in length and 49.8 cm in width. Assume that the allowable error range is ±0.5 cm. Compare the data, marked dimensions: length 100 cm, width 50 cm; identified dimensions: length 99.5 cm, width 49.8 cm. Calculate the error, and the errors in both length and width are within the allowable range. The compliance verification passes. Generate the recognition record form: order number: XXXX-XXXX; shape: rectangle; dimensions: length 99.5 cm, width 49.8 cm.

[0044] As Figure 2 shown, arrange according to the sizes of the dimensions in the recognition record form, select the recognition record forms with large sizes, place the identified shapes and calculated dimensions on the digital image of the board to generate a pre-arrangement image; select the recognition record forms with small sizes, place the identified shapes and calculated dimensions on the pre-arrangement image for filling until no more filling can be done on the board to generate a final arrangement image; when selecting the recognition record forms with small sizes, fill them in order from largest to smallest; the large size refers to more than one-third of the largest size of a single board.

[0045] Specifically, continue to refer to Figure 2 , identify the orders with large sizes, fill each pre-arrangement image formed by the large-size orders respectively to form a final arrangement image, and set the group number.

[0046] When the matching algorithm is matching, select small sizes to fill behind each large size. The more filling is done, the less waste material there is on a single board, and the higher the matching degree; on the contrary, the lower the matching degree, the more waste material there is on a single board.

[0047] When filling, prioritize filling with the shape that best matches the remaining space to reduce space waste. The best filling plan should maximize the space utilization rate of the board, that is, leave as little unused space as possible; according to the size of the side lengths in the shape, select the most fitting side for filling. For example, if the remaining space is long and narrow, choosing a rectangular or strip-shaped shape for filling would be more appropriate than choosing a square. Verify the space utilization rate of the filling plan through calculations. For instance, calculate the area of the remaining space after filling, or calculate the ratio of the used space to the total space. Select the best filling plan based on the space utilization rate to form the final arrangement image.

[0048] The ratio of the size of the filled area in the final arrangement image to the maximum size of the single board used is the matching degree; the matching algorithm is divided into 3 major groups according to the level of the matching degree; each major group is divided into multiple subgroups according to the number of orders; the group number adopts the combination form of the major group, subgroup, and order number.

[0049] The 3 major groups are respectively: the matching degree is above 90%, the matching degree is between 80% - 90%, and the matching degree is below 80%; dividing the major groups by the matching degree can clarify the range of leftover materials generated. When the matching degree is above 90%, almost no usable leftover materials will be generated. In the collection of leftover materials, this group does not need to be considered, reducing the time for leftover material collection.

[0050] For example, the matching algorithm groups the orders in the solid wood category and obtains 3 major groups, labeled as A (the matching degree is above 90%), B (the matching degree is between 80% - 90%), and C (the matching degree is below 80%); each of the 3 major groups is divided into multiple subgroups according to the number of orders. In group A, the sizes and demands of 3 orders are smaller than the maximum size and shipment volume of a single board. Set the group numbers for these 3 orders as "A - 1 - order number 1", "A - 1 - order number 2", and "A - 1 - order number 3" respectively, and set the group numbers for the remaining orders as "A - 2 - order number", etc.

[0051] S300: Identify according to the group number, select the group numbers with the same first two digits, make a unified planning for cutting according to the specific order number, generate a preliminary design plan, and further optimize according to the generated preliminary design plan to generate the final cutting plan.

[0052] The further optimization is to conduct a manual review of the generated preliminary design plan to determine whether it meets the requirements of actual cutting; because there will be cutting errors in actual cutting, the preliminary design plan must meet the cutting errors to be used as the final cutting plan.

[0053] S400: Verify according to the cutting plan and optimize the grouping rules based on the verification results, obtain the maximum size and shipment volume of a single sheet, and compare and verify the maximum size and shipment volume with the total demand and size in each group. The total size and demand of multiple orders should be less than the maximum size and shipment volume of a single sheet.

[0054] The technical solutions in the embodiments of the present application at least have the following technical effects or advantages:

[0055] The present application uses a matching algorithm to identify and match the shapes and sizes of multiple orders, and performs cutting on the same sheet, which increases the utilization rate of single sheets, greatly reduces the generation of waste materials, considers multiple orders for cutting simultaneously, improves the progress of orders, realizes identification, matching, and unified cutting, improves the utilization rate of sheets, and reduces the effect of waste materials.

[0056] Embodiment 2: In Embodiment 1, a matching algorithm is used to identify and match the shapes and sizes of multiple orders, and cutting is performed on the same sheet, which increases the utilization rate of single sheets. However, for sheets with low matching degrees, some waste materials will still be generated.

[0057] Identify the unfilled area based on the final arranged image, generate a waste material information table, and establish a waste material database; the waste material information table includes the shape, size, and sheet type of the waste material, and generates a unique identification number according to the shape, size, and sheet type of the waste material;

[0058] The identification number represents the shape, size, and sheet type of the corresponding waste material, and the parameter information of the waste material can be identified through the identification number, which improves the query and retrieval speed.

[0059] Obtain the furniture product parameters of a new order, and first query the available waste materials in the waste material database through the identification number. When querying, compare the size and shape of the waste material with the size and shape of the new order. If the size and shape match, it is defined as available waste material; if there are multiple available waste materials, preferentially select the waste material with the least remaining part after use; if no available waste material is found, continue to execute step S200.

[0060] Establish a real-time update mechanism. When the waste material is used, update the quantity of the waste material in the waste material database in a timely manner.

[0061] For example, a new order requires a board with dimensions of 80 cm x 60 cm. First, search the leftover material database for leftover materials that match or are close to this size. After the search, two leftover materials are found to meet the requirements: one with dimensions of 85 cm x 65 cm (leftover material A), and the other with dimensions of 90 cm x 70 cm (leftover material B). According to the strategy of maximizing utilization and reducing waste, leftover material A is selected because it is closer to the required size of the new order and there is less remaining material after use. This leftover material is taken out of the inventory, cut and processed, and the records in the leftover material database are updated in a timely manner.

[0062] A leftover material database is established, and an information table of leftover materials is generated by counting the leftover materials produced each time. When a new order is received, the furniture product parameters of the new order are obtained, and whether there are available leftover materials is preferentially queried in the leftover material database. If there are available leftover materials, they are preferentially used for production. By accurately tracking and managing leftover materials, existing resources can be utilized more effectively, and unnecessary waste can be reduced. This helps to reduce raw material costs and improve overall production efficiency.

[0063] The technical solutions in the above embodiments of the present application at least have the following technical effects or advantages:

[0064] The present application establishes a leftover material database, preferentially queries whether there are available leftover materials in the leftover material database according to the furniture product parameters of the new order, preferentially uses the available leftover materials for production, accurately tracks and manages the leftover materials, and reduces unnecessary waste; achieving the effects of efficient utilization, reducing inventory costs, and reducing leftover material waste.

[0065] Embodiment 3: In the above embodiment, a leftover material database is established to effectively manage the leftover materials, reduce leftover material waste, and improve the utilization rate. However, there are still some leftover materials that cannot match the parameters of the order.

[0066] Obtain the retention information, query in the order database according to the user name and the last four digits of the mobile phone number to obtain all the user's order records, analyze the user's personalized preference requirements based on the furniture product parameters in the user's order, and design and generate value-added leftover material products according to the personalized preference requirements and the available leftover materials to recommend to the user for increasing the aesthetic and practicality of the original furniture design drawings; and establish a user satisfaction feedback mechanism, obtain the user's satisfaction with the value-added leftover material products, generate a user feedback analysis report, summarize the value-added leftover material products with high satisfaction, and establish a leftover material creative furniture table to record the names, board types, shapes, and sizes of the value-added leftover material products with high satisfaction.

[0067] Query the remaining materials database according to personalized preference requirements to find the matching remaining materials, which are the available remaining materials; update the inventory information of the remaining materials database in real time according to the usage and production situation of the remaining materials, and regularly clean up the remaining materials that cannot be reused for a long time; the user satisfaction feedback mechanism actively collects opinions and feedback on the value-added products of the remaining materials from users through online questionnaires, telephone interviews or email surveys; according to the user feedback analysis report, judge the recommendation acceptance rate and opinions of the value-added products of the remaining materials, generate the satisfaction degree, and add the value-added products of the remaining materials with a satisfaction degree higher than 80% to the remaining materials creative furniture list for recording.

[0068] The preference requirements are predicted based on the board type, shape, and color in the furniture product parameters. If the same board type, shape, and color are used in multiple orders, it indicates that the user likes this style of furniture, and it also shows the user's demand direction. Query the remaining materials database according to the preference requirements to find the matching remaining materials. Generate value-added products of the remaining materials using the remaining materials according to the user's furniture design drawings. The value-added products of the remaining materials are designed based on the user's preference requirements combined with the current remaining materials, which can enhance the ornamental or practical effect of the original furniture design drawing; recommend the value-added products of the remaining materials to the user, fully considering the user's actual preferences and needs, further improving the utilization rate of the remaining materials and reducing the waste of the remaining materials.

[0069] For example, user A ordered a set of solid wood bookcases. After the order is completed, the order information and product parameters, including the size, color, and material of the bookcase, are automatically recorded. By analyzing user A's order history and furniture design drawings, the system determines that he has a preference for a simple and practical design style.

[0070] Using the available remaining materials in the remaining materials database, design a solid wood magazine rack that matches the style of the bookcase as a value-added product of the remaining materials. This solid wood magazine rack is not only beautiful but also can increase the practicality and overall look of the bookcase.

[0071] After completion, the magazine rack is delivered to user A's residence together with the bookcase for recommendation. To collect user feedback, a satisfaction survey is sent to user A through an online questionnaire. User A highly evaluates the design and practicality of the magazine rack and puts forward some improvement suggestions.

[0072] Based on user A's feedback and the opinions of other users, the magazine rack can be iteratively optimized, added to the remaining materials creative furniture list, record the board type and size information of the magazine rack, and update the inventory information of the remaining materials database.

[0073] In this embodiment, by collecting and analyzing user orders and preferences, value-added furniture products that are both beautiful and practical are designed using leftover materials, thereby enhancing user satisfaction, reducing waste of leftover materials, and improving the market competitiveness of the brand. Preferably, a comprehensive process can be established, from data collection, design of value-added products from leftover materials, production and distribution, to the establishment of a user satisfaction feedback system and the production of analysis reports, and then to the iterative optimization of products and inventory updates, forming a closed-loop system to ensure the maximization of the utilization of leftover materials.

[0074] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0075] In this application, by collecting and analyzing user orders and preferences, value-added products from leftover materials that are both beautiful and practical are designed using leftover materials, enhancing user satisfaction, reducing waste of leftover materials, and ensuring the maximization of the utilization of leftover materials; the effects of improving user satisfaction, reducing waste of leftover materials, and ensuring the maximization of the utilization of leftover materials are achieved.

[0076] Embodiment 4: In the above embodiment, by analyzing user orders, the preference requirements of users are obtained, and value-added products from leftover materials that meet the users are designed based on the preference requirements of users in combination with the current remaining leftover materials, further improving the utilization rate of leftover materials and reducing waste.

[0077] Obtain the pre-arranged image and establish a collaborative mechanism for sheet optimization and value-added products from leftover materials; the collaborative mechanism for sheet optimization and value-added products from leftover materials is to monitor and compare in real time the fit between the shape and size of the unfilled area on the pre-arranged image and the shape and size of the value-added products from leftover materials in the leftover material creative furniture table. If the fit is higher than 90%, the unfilled area is defined as a reserved optimization area;

[0078] The fit is obtained by calculating the matching degree between the shape and size of the unfilled area and the required sheet materials for the value-added products from leftover materials, calculating the coincidence degree of the shape and the similarity degree of the size to obtain the final fit; during the filling process, type classification has been carried out, so only the shape and size need to be matched during the filling process.

[0079] Continue to select small-sized identification record sheets to fill the reserved optimization area, calculate the fit of the filling. If the fit in all identification record sheets is lower than 90%, the reserved optimization area is defined as a leftover material optimization area; if there is an identification record sheet with a fit higher than 90%, use the shape and size in the identification record sheet for filling and cancel the reserved optimization area.

[0080] For example, when filling, if it is recognized that the size and shape of an unfilled area A match the size and shape of the recorded value-added product of the leftover material, with a matching degree reaching 95%, the unfilled area A is defined as a reserved optimization area; when normally selecting a small-size recognition record form to fill this area for the reserved optimization area, after traversing, it is found that the highest matching degree is 80%, which is lower than 95%. A low matching degree means a low utilization rate. Then the remaining leftover material will completely become waste and can only be discarded, resulting in waste; the reserved optimization area is used as a leftover material optimization area for producing value-added products from leftover materials, and the utilization rate can reach 95%.

[0081] In this embodiment, the recognition of the shape and size of the unfilled area and the comparison with the shape and size of the value-added products of the leftover materials in the leftover material creative furniture form both adopt the matching algorithm in Embodiment 1; by comprehensively calculating the coincidence degree and similarity of the shape and size to obtain the matching degree to judge the more suitable production plan for the unfilled area. The matching degree represents the utilization rate of the unfilled area. The higher the matching degree, the higher the utilization rate, indicating less leftover material generated, and reducing the generation of leftover material in the process of planned production.

[0082] According to the value-added products of the leftover materials, those with high satisfaction are selected and added to the leftover material creative furniture form, establishing a collaborative mechanism for sheet material optimization and value-added products of the leftover materials. By real-time monitoring the shape and size of the unfilled area and comparing it with the value-added products of the leftover materials in the leftover material creative furniture form, it can ensure the maximum utilization of the leftover materials, achieve precise matching, not only reduce material waste but also improve production efficiency; continuously use the leftover materials to generate value-added products of the leftover materials and continuously iterate and optimize to form a leftover material creative furniture form with higher satisfaction. According to the value-added products of the leftover materials and the leftover material creative furniture form, it can continuously adapt to the changing needs of users and the market dynamically. Continuous iteration and optimization enable the production process to keep up with the market trend and meet the diverse needs of users.

[0083] By directly dividing the leftover material optimization area and generating value-added products of the leftover materials, it avoids partial leftover material waste caused when using new order filling areas, further improves the utilization rate of the sheet material in the process of material selection, reduces the generation of leftover materials, lowers the production cost, and also reflects the production concept of environmental protection and sustainability. By selecting value-added products of the leftover materials with high satisfaction and adding them to the leftover material creative furniture form, it further improves user satisfaction.

[0084] The technical solutions in the above embodiments of the present application at least have the following technical effects or advantages:

[0085] Based on the value-added products of leftover materials, those with high satisfaction are selected and added to the leftover material creative furniture list, and a collaborative mechanism for sheet material optimization and leftover material value-added products is established, which can ensure the maximum utilization of leftover materials, achieve precise matching, and not only reduce material waste; according to the leftover material value-added products and the leftover material creative furniture list, it can continuously and dynamically adapt to the changing needs of users and the market, and meet the diverse needs of users; it achieves the effects of reducing the generation of leftover materials and improving user satisfaction.

[0086] Embodiment 5: As Figure 3 shown, this embodiment provides a digital intelligent management system for furniture production, and the system includes:

[0087] An interaction module, which is used to receive the specific requirements and relevant data of users for furniture design, generate a 3D furniture design model based on the specific requirements and relevant data, transmit the generated 3D furniture design model to the user terminal, receive the modification suggestions of users at the same time, and make modifications in a timely manner; after confirmation, the user terminal places an order, and generates retention information and the order time;

[0088] An information extraction module, which is used to receive furniture design drawings and extract furniture product parameters;

[0089] A matching processing module, which is used to receive furniture product parameters and perform matching grouping according to grouping rules and matching algorithms to generate a final cutting plan;

[0090] A manufacturing module, which is used to receive the cutting plan and perform automated production based on the cutting plan;

[0091] A storage module, which is used to store all data, including all data information of the order database and the leftover material database;

[0092] A verification and optimization module, which is used to verify the cutting plan and perform iterative optimization on the grouping rules;

[0093] A collaborative module for sheet material optimization and leftover material value-added products, which is used to obtain the leftover material creative furniture list and select and determine the leftover material optimization area according to the leftover material creative furniture list.

[0094] The above is only the preferred embodiment of the present invention and is not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A digital intelligent management method for furniture production, characterized in that: The method comprises: S100: Obtaining the specific needs of the user and generating a three-dimensional furniture design model. After the user modifies and confirms, the order is placed, and the retention information and the confirmed order time are generated. A unique order number is formed based on the retention information and the confirmed order time, and an order database is established for storage; S200: Obtaining furniture design drawings corresponding to the furniture 3D design model, extracting furniture product parameters through the furniture design drawings, grouping the furniture product parameters according to the grouping rules, and generating corresponding group numbers; the furniture product parameters include board type, demand, shape, size, color and delivery date; S300: Identify according to the group number, select the group number with the same first two digits, perform unified planning and cutting according to the specific order number, generate a preliminary design plan, further optimize according to the generated preliminary design plan, and generate a final cutting plan; The further optimization is to manually review the generated preliminary design plan to determine whether it meets the actual cutting requirements; S400: verifying and optimizing the grouping rules according to the cutting plan; obtaining the maximum size and shipment quantity of a single plate, and comparing and verifying the maximum size and shipment quantity with all the demand quantities and sizes in each group, so that the total size and demand quantities required by multiple orders are smaller than the maximum size and shipment quantity of a single plate; Among them, in step S200, the grouping rule first performs preliminary division according to the board type and delivery date, and classifies the orders of the same type and with similar delivery dates; then the orders of the same category are grouped according to the matching algorithm to generate corresponding group numbers; the matching algorithm obtains the order numbers of all orders of the same category and the corresponding furniture design drawings, automatically identifies the shapes and sizes in the furniture design drawings, compares the shapes and sizes of multiple orders with the single board used to generate corresponding matching degrees, and groups them according to the matching degrees; The matching algorithm converts the furniture design drawings and the images of the individual plates used into a unified digital image format, performs preprocessing operations on the digital images, obtains the maximum size of the individual plates used, and highlights the shape outline of the furniture design drawings; uses an edge detection algorithm to identify the shape edges in the image, extracts the shape outline based on the detected edges, and performs smoothing on the extracted outline to reduce noise and refinement errors; calculates the size of the shape based on the pixel coordinates of the outline and converts it into the actual physical size; verifies the conformity of the identified shape and the calculated size with the annotated data in the furniture design drawings, and generates a recognition record table after the conformity verification is passed within the error range; the recognition record table includes the order number, shape and size; Arrange the sizes in the recognition record table, select the recognition record table with large size, put the recognized shape and calculated size on the digital image of the plate, and generate a pre-arranged image; select the recognition record table with small size, put the recognized shape and calculated size on the pre-arranged image to fill it, until the plate can no longer be filled, generate a final arrangement image, and set the group number; the ratio of the filled area size in the final arrangement image to the maximum size of the single plate used is the matching degree; the matching algorithm is divided into 3 large groups according to the matching degree; each large group is divided into multiple small groups according to the number of orders; the group number adopts the combination of large group, small group and order number; When selecting the small-sized identification record table, fill them in order from large to small in the order of arrangement; the large size refers to more than one-third of the maximum size of a single board; obtain the retained information, query the order database according to the user's name and mobile phone number, obtain all the user's order records, and analyze the user's personalized preferences based on the furniture product parameters in the user's order, and design and generate surplus value-added products based on the personalized preferences and available surplus materials to increase the aesthetics and practicality of the original furniture design drawings; establish a user satisfaction feedback mechanism to obtain the user's satisfaction with the surplus value-added products, and generate a user feedback analysis report to summarize the surplus value-added products with high satisfaction, establish a surplus creative furniture table, and record the name, board type, shape and size of the surplus value-added products with high satisfaction; The pre-arranged image is obtained, and a coordination mechanism between board optimization and surplus material value-added products is established; the coordination mechanism between board optimization and surplus material value-added products is to monitor and compare in real time the shape and size of the unfilled area on the pre-arranged image with the shape and size of the surplus material value-added products in the surplus material creative furniture table, and if the degree of conformity is higher than 90%, the unfilled area is defined as a reserved optimization area; The coordination mechanism of plate optimization and surplus material value-added products also includes continuing to select small-sized identification record tables to fill the reserved optimization area, calculating the degree of fit of the filling, and if the degrees of fit in all identification record tables are lower than 90%, the reserved optimization area is defined as the surplus material optimization area; if there is an identification record table with a degree of fit higher than 90%, the shape and size in the identification record table are used to fill, and the reserved optimization area is cancelled.

2. A digital intelligent management method for furniture production as claimed in claim 1, characterized in that: According to the final arrangement image, the unfilled area is identified, a residual material information table is generated, and a residual material database is established; the residual material information table includes the shape, size and plate type of the residual material, and a unique identification number is generated according to the shape, size and plate type of the residual material; the furniture product parameters of the new order are obtained, and the available residual material is preferentially searched in the residual material database by the identification number. When searching, the size and shape of the residual material are compared with the size and shape of the new order. If the size and shape match, it is determined to be an available residual material; If there are multiple available remaining materials, the remaining material with the least remaining part after use is preferentially selected; if no available remaining materials are found, step S200 is continued.

3. A digital intelligent management system for furniture production, supporting a digital intelligent management method for furniture production as described in any one of claims 1 to 2, characterized in that: The system comprises: The interactive module is used to receive the user's specific requirements and related data for furniture design, generate a three-dimensional furniture design model based on the specific requirements and related data, transmit the generated three-dimensional furniture design model to the user terminal, receive the user's modification suggestions, and make modifications in a timely manner; after confirmation, the user terminal places an order and generates retention information and order time; An information extraction module, used to receive furniture design drawings and extract furniture product parameters; A matching processing module is used to receive furniture product parameters, perform matching and grouping according to grouping rules and matching algorithms, and generate a final cutting plan; A manufacturing module, used for receiving a cutting plan and performing automated production based on the cutting plan; Storage module, used to store all data, including all data information of order database and forecast database; Verification and optimization module, used to verify the cutting plan and iteratively optimize the grouping rules; The panel optimization and waste material value-added product collaborative module is used to obtain the waste material creative furniture table and select and determine the waste material optimization area based on the waste material creative furniture table.

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