Mold optimization design method for integrally formed septic tank

Abstract

The application provides a mold optimization design method for integrally formed septic tanks, and relates to the technical field of data processing, comprising the following steps: obtaining target production requirements, determining a predetermined mold production scheme; performing mold region division to obtain a plurality of predetermined regions; obtaining an initial mold, performing injection molding of plastic raw materials to obtain an initial integrally formed septic tank and a plurality of pressure data sequences and a plurality of temperature data sequences; performing pressure distribution characteristic analysis to generate a pressure distribution characteristic analysis result; performing temperature distribution characteristic analysis to generate a temperature distribution characteristic analysis result; constructing a three-dimensional model to perform surface quality detection analysis; based on pressure uneven regions, temperature uneven regions and surface defect regions, optimizing the predetermined mold production scheme to obtain an optimized mold production scheme; and performing mold production. The application solves the technical problem that the traditional mold optimization method often relies on experience and trial and error, and cannot effectively improve the overall quality of mold design and production.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to a method for optimizing the design of molds for integrally molded septic tanks. Background Technology

[0002] One-piece molded septic tanks are essential equipment in sewage treatment systems, widely used in residential buildings, commercial buildings, and industrial facilities. Their main function is to perform preliminary treatment and decomposition of domestic sewage, reducing pollutants and protecting the environment and public health. Traditional mold design methods for one-piece molded septic tanks often rely on the designer's experience and manual calculations, lacking systematic analysis and scientific basis. This approach easily leads to unreasonable designs that fail to meet the specific needs of different application scenarios. Summary of the Invention

[0003] This application provides a mold optimization design method for one-piece molded septic tanks, aiming to solve the technical problem that traditional mold optimization methods often rely on experience and trial and error, which cannot effectively improve the overall quality of mold design and production.

[0004] This application discloses a mold optimization design method for an integrated molded septic tank. The method includes: interacting with a user terminal to obtain the target production requirements of the integrated molded septic tank, and determining a predetermined mold production plan based on the target production requirements. The predetermined mold production plan includes a mold material selection plan, a mold structure design plan, and a mold surface treatment plan. According to the mold structure in the structure design plan, the mold area is divided to obtain multiple predetermined areas. Mold production is carried out based on the predetermined mold production plan to obtain an initial mold. The initial mold is used for injection molding of plastic raw materials. During the injection molding process, pressure data and temperature data of the multiple predetermined areas are collected at a preset collection frequency using pressure sensor arrays and temperature sensor arrays deployed in the multiple predetermined areas to obtain the initial integrated molded septic tank and multiple pressure data sequences. The process involves: analyzing multiple temperature data sequences; performing pressure distribution feature analysis based on the multiple pressure data sequences to generate pressure distribution feature analysis results, wherein the pressure distribution feature analysis results include several pressure unevenness regions; performing temperature distribution feature analysis based on the multiple temperature data sequences to generate temperature distribution feature analysis results, wherein the temperature distribution feature analysis results include several temperature unevenness regions; constructing a three-dimensional model of the initial integrated molded septic tank; performing surface quality detection and analysis of the multiple predetermined areas based on the three-dimensional model to obtain several surface defect areas; optimizing the predetermined mold production scheme based on the several pressure unevenness regions, the several temperature unevenness regions, and the several surface defect areas to obtain an optimized mold production scheme; and producing the mold for the integrated molded septic tank according to the optimized mold production scheme.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] The system acquires and analyzes the target production requirements of the integrated molded septic tank through an interactive user interface, including performance characteristics, specification characteristics, and appearance requirements. A pre-set solution library is used for system matching to ensure that the selected pre-defined mold production solution fully meets the specific needs, improving the accuracy and rationality of mold design. During the mold injection molding process, pressure and temperature data are collected in real time by deploying multiple pre-defined area pressure and temperature sensor arrays to ensure real-time monitoring of key parameters during production. Based on the collected pressure and temperature data, pressure and temperature distribution characteristic analysis is performed, generating analysis results including areas of uneven pressure and temperature. An initial 3D model of the integrated molded septic tank is constructed, and surface quality detection and analysis are performed based on this model to accurately identify surface defect areas. Combining information on uneven pressure, uneven temperature, and surface defect areas, the pre-defined mold production solution is systematically optimized to obtain an optimized mold production solution, improving the overall quality of mold production. Mold production is carried out according to the optimized mold production solution, ensuring optimal control of various parameters and processes during production. In summary, the mold optimization design method for the integrated molded septic tank solves the problems of inaccurate mold design, insufficient control of defects in the production process, lagging quality inspection, and lack of systematic optimization methods in the existing technology through systematic demand analysis, real-time data monitoring and feature analysis, three-dimensional model construction and defect identification, and system optimization. This method significantly improves the mold design and production quality of the integrated molded septic tank, enhances production efficiency, and achieves high quality and high stability of the product.

[0007] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0008] Figure 1 A schematic flowchart of the mold optimization design method for an integrally molded septic tank is provided for the embodiments of this application;

[0009] Figure 2 This application provides a schematic diagram of the process for determining a predetermined mold production scheme in the mold optimization design method for an integrally molded septic tank. Detailed Implementation

[0010] This application provides a mold optimization design method for integrally molded septic tanks, which solves the technical problem that traditional mold optimization methods often rely on experience and trial and error, and cannot effectively improve the overall quality of mold design and production.

[0011] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0012] like Figure 1 As shown in the embodiment of this application, a mold optimization design method for an integrally molded septic tank is provided, the method comprising:

[0013] The interactive user terminal obtains the target production requirements of the integrated molded septic tank and determines a predetermined mold production plan based on the target production requirements. The predetermined mold production plan includes a mold material selection plan, a mold structure design plan, and a mold surface treatment plan.

[0014] Through interactive user interfaces, such as user interfaces and input devices, the target production requirements for integrated molded septic tanks are obtained, including target product performance characteristics, target product specifications, and target product appearance requirements. A pre-built mold production solution library containing multiple solution libraries is constructed in advance, including a material selection solution library, a structural design solution library, and a surface treatment solution library. Based on the extracted requirement characteristics, suitable mold production solutions are matched from the pre-built solution library. Based on the matching process, mold material selection solutions, mold structural design solutions, and mold surface treatment solutions are obtained, forming a complete predetermined mold production solution.

[0015] According to the mold structure in the structural design scheme, the mold area is divided to obtain multiple predetermined areas.

[0016] The structural design scheme includes the overall shape, size, internal structure, and functional area division of the mold. Based on the structural design scheme, the key parts and functional areas of the mold are identified, such as the inlet, runner, gate, cooling system, and venting system. The functional requirements of the mold in different areas are determined, such as pressure distribution, temperature control, and cooling efficiency. Based on the functional requirements and the geometry of the mold, the mold is divided into areas to ensure that each area can meet its functional requirements. Multiple predetermined areas are obtained through division, which serve as the basis for subsequent mold optimization.

[0017] Mold production is carried out based on the predetermined mold production plan to obtain an initial mold. The initial mold is used to perform injection molding of plastic raw materials. During the injection molding process, pressure data and temperature data of the multiple predetermined areas are collected at a preset acquisition frequency by pressure sensor arrays and temperature sensor arrays arranged in the multiple predetermined areas to obtain an initial integral molded septic tank, as well as multiple pressure data sequences and multiple temperature data sequences.

[0018] According to the material selection plan in the predetermined mold production scheme, prepare the corresponding mold materials and produce the mold according to the predetermined mold production scheme to obtain the initial mold. Select suitable plastic raw materials, such as polyethylene (PE) and polypropylene (PP), according to the requirements of the one-piece molded septic tank product. Pre-treat the plastic raw materials, such as drying and mixing, to ensure the uniformity and stability of the raw materials. Start the injection molding machine to perform injection molding operation, inject the plastic raw materials into the mold, and obtain the initial one-piece molded septic tank through cooling and solidification.

[0019] Pressure sensor arrays and temperature sensor arrays are deployed in multiple predetermined areas to monitor the pressure and temperature during the molding process in real time. A reasonable data acquisition frequency is set according to the cycle and process requirements of injection molding to ensure the timeliness and accuracy of the data. A data acquisition system is configured to connect all sensors to ensure that the data can be transmitted in real time. During the injection molding process, pressure data of each predetermined area is collected in real time to form multiple pressure data sequences, and temperature data of each predetermined area is collected simultaneously to form multiple temperature data sequences.

[0020] After injection molding is completed, the initial one-piece molded septic tank is removed from the mold, and the collected pressure data sequence and temperature data sequence are stored as the basis for subsequent analysis.

[0021] Pressure distribution feature analysis is performed based on the multiple pressure data sequences to generate pressure distribution feature analysis results, wherein the pressure distribution feature analysis results include several pressure unevenness regions.

[0022] Based on multiple pressure data sequences after standardization, a confidence pressure data sequence is calculated. A predetermined pressure data sequence from the predetermined mold production plan is loaded as a comparison benchmark. The deviation between the confidence pressure data sequence and the predetermined pressure data sequence is calculated to form a pressure deviation sequence. Based on the preset pressure deviation threshold, significant pressure deviation areas are identified, and several pressure unevenness areas are delineated. These areas are the key parts where the pressure distribution is uneven.

[0023] Temperature distribution feature analysis is performed based on the multiple temperature data sequences to generate temperature distribution feature analysis results, wherein the temperature distribution feature analysis results include several temperature unevenness regions.

[0024] Based on multiple standardized temperature data sequences, a confidence temperature data sequence is calculated. A predetermined temperature data sequence from the predetermined mold production plan is loaded as a comparison benchmark. The deviation between the confidence temperature data sequence and the predetermined temperature data sequence is calculated to form a temperature deviation sequence. Based on the preset temperature deviation threshold, significant temperature deviation areas are identified, and several temperature unevenness areas are delineated. These areas are the key parts where the temperature distribution is uneven.

[0025] A three-dimensional model of the initial integrally molded septic tank is constructed, and surface quality detection and analysis of the multiple predetermined areas are performed based on the three-dimensional model to obtain several surface defect areas.

[0026] Using a pre-deployed array of image acquisition devices, omnidirectional image acquisition is performed on the initially integrated septic tank, obtaining multi-angle images of the septic tank product and forming a product image set. Image processing technology is used to analyze the point cloud data of the septic tank product image set, obtaining a point cloud data set of the septic tank product. Based on the principle of random sampling consistency, a registration and fusion model is constructed. This model is then used to register and fuse the septic tank product point cloud data set, generating complete 3D point cloud data. Based on the point cloud data, surface reconstruction is performed to generate a 3D model of the septic tank product.

[0027] Surface parameters, including surface roughness, flatness, and curvature, are extracted from each predetermined region of the 3D model. Statistical analysis is performed on the extracted surface parameters to determine the surface quality index of each region. A surface defect recognition algorithm is applied to detect surface defect regions in the 3D model, identify different types of surface defects, such as depressions, protrusions, cracks, and bubbles, determine the specific location and extent of the defect regions, and mark them on the 3D model. Based on the detection results, the boundary of each surface defect region is delineated to obtain several surface defect regions.

[0028] Based on the aforementioned uneven pressure regions, uneven temperature regions, and surface defect regions, the predetermined mold production plan is optimized to obtain an optimized mold production plan; the mold for the integrated septic tank is then produced according to the optimized mold production plan.

[0029] Through correlation analysis, the interrelationships and influencing factors between pressure, temperature, surface defects, and production plans are determined. Based on the analysis results of pressure unevenness areas, the mold runner design, gate location, and number are adjusted to optimize pressure distribution. For example, a pressure compensation mechanism is introduced to balance the overall pressure distribution by locally increasing or decreasing pressure. Based on the analysis results of temperature unevenness areas, the cooling system design is adjusted, including cooling pipe layout, coolant flow rate, and cooling time. For example, a hot runner system is introduced or optimized in areas where temperature uniformity is required to ensure uniform temperature distribution. Based on the analysis results of surface defect areas, the mold surface treatment process is improved, such as adding polishing or plating, repairing existing surface defects, and adding protective measures for these areas in the mold design. All optimization measures are integrated to form the final optimized mold production plan.

[0030] The mold is manufactured and processed according to the optimized mold production plan, and surface treatment processes such as polishing, coating, and heat treatment are carried out to improve the surface quality and service life of the mold, so as to achieve efficient and high-quality production of integrated molded septic tanks.

[0031] Furthermore, the method for determining the predetermined mold production plan based on the target production demand includes:

[0032] The target production requirements are subjected to requirement feature extraction to obtain requirement feature extraction results, wherein the requirement feature extraction results include target product performance characteristics, target product specification characteristics, and target product appearance requirement characteristics; a pre-constructed mold production solution library is obtained, including a preset material selection solution library, a preset structural design solution library, and a preset surface treatment solution library; based on the target product performance characteristics, the preset material selection solution library is matched to obtain the mold material selection solution with the highest matching degree; based on the target product specification characteristics, the preset structural design solution library is matched to obtain the mold structural design solution with the highest matching degree; based on the target product appearance requirement characteristics, the preset surface treatment solution library is matched to obtain the mold surface treatment solution with the highest matching degree; based on the mold material selection solution, the mold structural design solution, and the mold surface treatment solution, the predetermined mold production solution is obtained.

[0033] The target production requirements are categorized and organized, including functional requirements, performance requirements, and appearance requirements. Each type of requirement is then refined to form specific requirement indicators, such as wear resistance, corrosion resistance, size, shape, color, and surface finish.

[0034] Based on the target production requirements, extract the performance characteristics of the target product, including wear resistance, corrosion resistance, strength and hardness, and thermal conductivity; extract the specification characteristics of the target product, including size, shape, and capacity; and extract the appearance requirements characteristics of the target product, including surface finish. Summarize all extracted requirements characteristics to form a complete requirement feature extraction result.

[0035] A pre-built mold production solution library containing multiple production options is established, including a material selection solution library, a structural design solution library, and a surface treatment solution library. The material selection solution library includes various materials suitable for mold production, such as steel, aluminum, and alloys, with information on the physical, mechanical, and chemical properties of each material, such as hardness, strength, wear resistance, corrosion resistance, and thermal conductivity. The structural design solution library includes various mold structure designs, such as one-piece molding, split molding, and modular molds, with the characteristics, advantages, and applicable conditions of each structural design, such as structural strength, processing difficulty, and ease of assembly. The surface treatment solution library includes various surface treatment processes, such as polishing, electroplating, heat treatment, and spraying, with the effects and performance of each surface treatment process, such as surface finish, hardness, and decorative properties.

[0036] Key indicators, such as wear resistance, corrosion resistance, strength, hardness, and thermal conductivity, are extracted from the performance characteristics of the target product. These extracted performance characteristics are then converted into feature vectors to facilitate matching with materials in the material selection scheme library. The performance feature vectors of all materials in the preset material selection scheme library are retrieved, and similarity calculation formulas, such as cosine similarity and Euclidean distance, are used to calculate the similarity between the target product's performance feature vector and the feature vector of each material. All materials are then ranked based on their similarity, and the material with the highest similarity is selected as the mold material selection scheme with the highest matching degree.

[0037] Using the exact same method, mold structure design schemes and mold surface treatment schemes are obtained; for the sake of brevity, these will not be elaborated upon here. The resulting mold material selection scheme, mold structure design scheme, and mold surface treatment scheme are integrated to form a predetermined mold production plan, providing detailed guidance for actual production.

[0038] Furthermore, the method for obtaining the mold material selection scheme with the highest matching degree by matching in the preset material selection scheme library based on the performance characteristics of the target product includes:

[0039] Extract a first material selection scheme from the preset material selection scheme library, and extract the first product performance feature of the first material selection scheme; perform a similarity analysis on the target product performance feature and the first product performance feature to obtain a first performance similarity index, and use the first performance similarity index as the first matching degree; traverse the preset material selection scheme library to obtain a matching degree list sorted from high to low, and obtain the mold material selection scheme with the highest matching degree according to the matching degree list.

[0040] Search all material selection schemes in the preset material selection scheme library, randomly extract one material selection scheme and record it as the first material selection scheme, extract the corresponding product performance characteristics from the first material selection scheme and use them as the first product performance characteristics.

[0041] The performance characteristics of the target product are transformed into feature vectors, and the performance characteristics of the first product are also transformed into feature vectors. A similarity calculation formula, such as cosine similarity, is used to calculate the similarity index between the performance feature vector of the target product and the performance feature vector of the first product. The calculated first performance similarity index is used as the first matching degree.

[0042] Each material selection scheme in the preset material selection scheme library is extracted item by item. For each extracted material selection scheme, its corresponding product performance characteristics are extracted to form a corresponding performance feature vector. The similarity index between its performance feature vector and the target product performance feature vector is calculated to obtain the matching degree of each material selection scheme. The schemes are sorted from high to low matching degree to form a matching degree list. From the sorted matching degree list, the material selection scheme with the highest matching degree is selected as the final mold material selection scheme.

[0043] Furthermore, the method for performing a similarity analysis between the performance characteristics of the target product and the performance characteristics of the first product to obtain a first performance similarity index includes:

[0044] Obtain product performance label information for the integrated molded septic tank, including but not limited to wear resistance, corrosion resistance, strength, hardness, and thermal conductivity; based on the product performance label information, and combined with a preset label order and preset grading standards, construct a preset label scheme; label the target product performance characteristics and the first product performance characteristics based on the preset label scheme to obtain a target product performance label vector and a first product performance label vector; use a similarity index calculation formula to calculate a similarity index on the target product performance label vector and the first product performance label vector to obtain a first performance similarity index; wherein, the similarity index calculation formula is as follows:

[0045]

[0046] Where C(A,B) is the first performance similarity index between the target product performance label vector and the first product performance label vector, and A i B is the i-th element of the target product performance label vector. i Let be the i-th element of the first product performance tag vector, and n be the total number of performance tag elements.

[0047] The performance of integrated molded septic tanks is categorized into several key indicators, including but not limited to wear resistance, corrosion resistance, strength, hardness, and thermal conductivity. Based on the importance and relevance of these performance labels, the order of each label is determined, resulting in a pre-defined label order. According to the specific requirements of each performance label, a grading standard is defined for each label, for example, from level 1 to 5, with higher levels indicating better performance. All labels, along with the pre-defined label order and grading standard, are integrated into a complete labeling scheme for subsequent performance characteristic marking.

[0048] According to the preset labeling scheme, each performance characteristic of the target product is labeled to form a performance label vector of the target product; the first product performance characteristics of the first material selection scheme are labeled to form a performance label vector of the first material selection scheme.

[0049] The similarity index is used to quantify the similarity between the target product performance label vector and the first product performance label vector. The calculated similarity index is used as the first performance similarity index. The calculation formula is as follows:

[0050]

[0051] This formula is the cosine similarity formula, which is used to compare the similarity between the target product performance label vector and the first material performance label vector. The larger the calculated first performance similarity index, the higher the similarity between the two sets of performance characteristics. It is used to measure the degree of matching between the two in terms of performance. The closer the value is to 1, the closer the performance of the two is, thus helping to select the most suitable material solution.

[0052] Furthermore, the method for performing pressure distribution feature analysis based on the multiple pressure data sequences to generate pressure distribution feature analysis results, wherein the pressure distribution feature analysis results include several pressure unevenness regions, includes:

[0053] Confidence features are calculated based on the multiple pressure data sequences to generate a confidence pressure data sequence, wherein the confidence feature calculation includes standardization and mean calculation; a predetermined pressure data sequence of the predetermined mold production plan is loaded; based on the predetermined pressure data sequence, deviation calculation is performed on the confidence pressure data sequence to obtain a pressure deviation sequence; several pressure unevenness regions in the pressure deviation sequence that are greater than or equal to a preset pressure deviation are obtained; the confidence pressure data sequence and the several pressure unevenness regions are added to the pressure distribution feature analysis result.

[0054] The multiple pressure data sequences are standardized to eliminate dimensional differences between different data sources and improve data comparability. For example, the Z-score standardization method can be used, as shown in the following formula: Where Z represents the standardized data, X represents the original pressure data, μ represents the mean of the pressure data, and σ represents the standard deviation of the pressure data. The mean of the standardized pressure data is calculated as the basis for the confidence pressure data series. The mean of each standardized data series is then calculated to obtain the confidence pressure data series.

[0055] A predetermined pressure data sequence is extracted from the predetermined mold production plan. The predetermined pressure data sequence represents a standard pressure data sequence and is used as a basis for comparison.

[0056] Based on the predetermined pressure data sequence, the deviation of the confidence pressure data sequence is calculated, that is, the deviation between the pressure data in the actual production process and the predetermined pressure data is calculated, the deviation of each data point is calculated, and a pressure deviation sequence is formed.

[0057] Based on the production process and quality requirements, a reasonable pressure deviation threshold is set to identify areas of uneven pressure. The pressure deviation sequence is traversed, and all data points with deviations greater than or equal to the preset threshold are selected. The areas where these data points are located are marked as areas of uneven pressure.

[0058] By integrating the confidence pressure data sequence with the identified pressure unevenness areas, a complete pressure distribution characteristic analysis result is formed. This can effectively identify pressure distribution problems in mold production and provide a basis for further mold optimization.

[0059] Furthermore, the method for constructing the three-dimensional model of the initial integrally molded septic tank includes:

[0060] The initial integrated septic tank is subjected to omnidirectional image acquisition using a pre-deployed image acquisition device array to obtain a set of septic tank product images. The point cloud data of the septic tank product image set is then analyzed sequentially to obtain a set of septic tank product point cloud data. A registration and fusion model is constructed based on the principle of random sampling consistency. This model is used to analyze the septic tank product point cloud data set to obtain the three-dimensional model of the septic tank product. Specifically, this includes: K1: Extracting a first set of septic tank product point cloud data from the septic tank product point cloud data set using the registration and fusion model; K2: Analyzing the first set of septic tank product point cloud data to obtain the first septic tank product surface parameters; K3: Traversing the septic tank product point cloud data set to obtain a set of septic tank product surface parameters, and generating the three-dimensional model based on the set of septic tank product surface parameters.

[0061] Multiple high-resolution cameras and laser scanning devices are deployed around the septic tank to form a comprehensive image acquisition array. All image acquisition devices are calibrated to ensure that the shooting angle, focal length, and lighting conditions of each device are consistent, thus guaranteeing the quality of the acquired images. The initial one-piece molded septic tank is photographed from different angles to ensure that every angle is covered, thereby acquiring comprehensive image data and forming an image set of the septic tank product.

[0062] The images are preprocessed, including denoising, correction, and enhancement. Image processing algorithms such as SIFT and SURF are used to extract feature points from the images to form preliminary point cloud data. Feature point data collected from different angles are integrated, and preliminary three-dimensional point cloud data is generated through stereo vision technology and multi-view geometric methods. The point cloud data from multiple views are fused to remove redundancy and noise, resulting in a complete set of point cloud data for septic tank products.

[0063] Random Sample Consensus (RANSAC) is an iterative method for estimating mathematical model parameters. It can effectively handle outliers and noise in data. Based on the geometric characteristics of septic tanks, a RANSAC model suitable for point cloud data registration is constructed. The RANSAC model is used to perform preliminary registration of the point cloud data to determine approximate registration parameters. Based on the initial registration results, the iterative nearest point algorithm is used for fine registration to further improve the registration accuracy. The registered point cloud data is then fused to eliminate overlapping areas and redundant data, ensuring the continuity and integrity of the point cloud data. Based on the fused point cloud data, a surface reconstruction algorithm is used to generate a three-dimensional surface model of the septic tank product.

[0064] A registration fusion model constructed using RANSAC and ICP algorithms is used for preliminary and fine registration of point cloud data, ensuring accurate alignment of point cloud data from different perspectives. Representative data points are selected from the point cloud dataset as the initial data group. These data points are then processed by the registration fusion model to extract the point cloud data group for the first septic tank product.

[0065] For the point cloud data set of the first septic tank product, the normal vector of each data point is calculated to obtain the surface normal information. The local curvature of the point cloud data is analyzed to identify surface features such as protrusions and depressions. Through neighborhood statistical analysis, the surface smoothness parameters are calculated. The extracted surface parameters are summarized to form the surface parameter set of the first septic tank product. These parameters include, but are not limited to, normal vector, curvature, and smoothness.

[0066] The entire point cloud dataset is traversed, and each data point is analyzed to extract corresponding surface parameters, forming a global surface parameter set. The surface parameters extracted during the traversal are then summarized to form a complete surface parameter set for the septic tank product. Based on this complete surface parameter set, surface reconstruction algorithms, such as Poisson reconstruction and Delaunay triangulation, are used to generate a 3D model of the septic tank product, providing an accurate data foundation for subsequent quality inspection and analysis.

[0067] Furthermore, the method for optimizing the predetermined mold production plan based on the plurality of pressure unevenness regions, the plurality of temperature unevenness regions, and the plurality of surface defect regions to obtain an optimized mold production plan includes:

[0068] A historical defect record set is collected, including a historical pressure unevenness record set, a historical temperature unevenness record set, and a historical surface defect record set. A first historical defect record is randomly extracted from the historical defect record set. An expert group analyzes the first historical defect record to determine a first defect factor and matches it with a first mold production scheme. Based on a first mapping relationship between the first defect and the first mold production scheme, a defect-production scheme list is constructed, which provides a basis for optimizing the predetermined mold production scheme.

[0069] Historical defect records are collected from production databases, quality inspection reports, and customer feedback. The pressure unevenness record set includes all historical defect records related to pressure unevenness; the temperature unevenness record set includes all historical defect records related to temperature unevenness; and the surface defect record set includes all historical defect records related to surface quality.

[0070] A defect record is extracted from the historical defect record set using a random sampling method, and is denoted as the first historical defect record.

[0071] An analysis team composed of mold design experts, production engineers, and quality control experts was invited to conduct a detailed analysis of the first historical defect record. This team identified the main factors leading to the first defect, which were designated as the first defect factors. These factors included design flaws, material issues, and deviations in process parameters. Based on the first defect factors and their nature and impact, a relevant production plan was matched from a pre-set mold production plan library to form the first mold production plan.

[0072] Based on the matching results between the first defect factor and the first mold production plan, a mapping relationship between defects and production plans is established so that each defect factor can find a corresponding production plan. All defect factors and their corresponding production plans are recorded in a list to form a defect-production plan list. Each record in the list includes information such as defect factor, defect description, matching production plan, and plan description.

[0073] This defect-production plan list serves as the basis for optimizing the predetermined mold production plan. That is, based on the defect-production plan list, potential problems in the predetermined mold production plan can be analyzed and identified, and optimization suggestions can be proposed.

[0074] Furthermore, the method also includes:

[0075] A historical similarity analysis is performed on the target production demand to extract similar mold production schemes; a first optimization direction is generated based on the common features of the similar mold production schemes; and the predetermined mold production scheme is optimized based on the first optimization direction.

[0076] Collect historical production records, including production parameters, mold design schemes, production results, and quality inspection data. Extract key requirement features from the target production requirements, such as performance requirements, specifications, and appearance features. Use similarity algorithms, such as cosine similarity and Euclidean distance, to perform similarity analysis between the target production requirements and historical production records. Based on the similarity analysis results, extract the historical production record with the highest similarity as the similar mold production scheme.

[0077] Common features are extracted from similar mold production schemes, including successful design schemes, production parameter settings, and quality control methods. All common features from similar mold production schemes are summarized to form a set of common features. Key factors in this set of common features are analyzed to identify the features with the greatest impact on production quality and efficiency. Based on the analysis results of these common features, a primary optimization direction is determined, serving as a guide for optimizing the planned mold production scheme.

[0078] Based on the primary optimization direction, the optimization objectives are clearly defined, such as improving mold life, improving product quality, and reducing production costs. Specific optimization measures are then formulated, including improving mold design, adjusting production parameters, and optimizing process flow. These optimization measures are implemented step by step, and the predetermined mold production plan is adjusted and improved. Through trial production and quality inspection, the effectiveness of the optimized mold production plan is verified. Based on the verification results, the mold production plan is further optimized and improved, forming a closed loop of continuous improvement.

[0079] In summary, the mold optimization design method for the integrated septic tank provided in this application has the following technical effects:

[0080] The system acquires and analyzes the target production requirements of the integrated molded septic tank through an interactive user interface, including performance characteristics, specification characteristics, and appearance requirements. A pre-set solution library is used for system matching to ensure that the selected pre-defined mold production solution fully meets the specific needs, improving the accuracy and rationality of mold design. During the mold injection molding process, pressure and temperature data are collected in real time by deploying multiple pre-defined area pressure and temperature sensor arrays to ensure real-time monitoring of key parameters during production. Based on the collected pressure and temperature data, pressure and temperature distribution characteristic analysis is performed, generating analysis results including areas of uneven pressure and temperature. An initial 3D model of the integrated molded septic tank is constructed, and surface quality detection and analysis are performed based on this model to accurately identify surface defect areas. Combining information on uneven pressure, uneven temperature, and surface defect areas, the pre-defined mold production solution is systematically optimized to obtain an optimized mold production solution, improving the overall quality of mold production. Mold production is carried out according to the optimized mold production solution, ensuring optimal control of various parameters and processes during production. In summary, the mold optimization design method for the integrated molded septic tank solves the problems of inaccurate mold design, insufficient control of defects in the production process, lagging quality inspection, and lack of systematic optimization methods in the existing technology through systematic demand analysis, real-time data monitoring and feature analysis, three-dimensional model construction and defect identification, and system optimization. This method significantly improves the mold design and production quality of the integrated molded septic tank, enhances production efficiency, and achieves high quality and high stability of the product.

[0081] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. 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 application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the mold design of an integrated septic tank, characterized in that, The method includes: The interactive user terminal obtains the target production requirements of the integrated molded septic tank and determines the predetermined mold production plan based on the target production requirements. The predetermined mold production plan includes a mold material selection plan, a mold structure design plan, and a mold surface treatment plan. According to the mold structure in the structural design scheme, the mold area is divided to obtain multiple predetermined areas; Mold production is carried out based on the predetermined mold production plan to obtain an initial mold. The initial mold is used to perform injection molding of plastic raw materials. During the injection molding process, pressure data and temperature data of the multiple predetermined areas are collected at a preset acquisition frequency by pressure sensor array and temperature sensor array arranged in the multiple predetermined areas to obtain an initial integrated septic tank, as well as multiple pressure data sequences and multiple temperature data sequences. Pressure distribution feature analysis is performed based on the multiple pressure data sequences to generate pressure distribution feature analysis results, wherein the pressure distribution feature analysis results include several pressure unevenness regions; Temperature distribution feature analysis is performed based on the multiple temperature data sequences to generate temperature distribution feature analysis results, wherein the temperature distribution feature analysis results include several temperature unevenness regions. A three-dimensional model of the initial integrated septic tank is constructed, and surface quality detection and analysis of the multiple predetermined areas are performed based on the three-dimensional model to obtain several surface defect areas. Based on the aforementioned pressure unevenness regions, temperature unevenness regions, and surface defect regions, the predetermined mold production plan is optimized to obtain an optimized mold production plan. The mold for the one-piece molded septic tank is produced according to the optimized mold production scheme.

2. The mold optimization design method for the integrated septic tank as described in claim 1, characterized in that, The method for determining a predetermined mold production plan based on the target production demand includes: Demand features are extracted from the target production demand to obtain the demand feature extraction results, wherein the demand feature extraction results include target product performance features, target product specification features, and target product appearance requirements features; Obtain a pre-built mold production solution library, including a preset material selection solution library, a preset structural design solution library, and a preset surface treatment solution library; Based on the performance characteristics of the target product, a matching scheme is performed in the preset material selection scheme library to obtain the mold material selection scheme with the highest matching degree. Based on the target product specifications, a matching scheme is performed in the preset structural design scheme library to obtain the mold structure design scheme with the highest matching degree. Based on the appearance requirements of the target product, a matching is performed in the preset surface treatment solution library to obtain the mold surface treatment solution with the highest matching degree. Based on the mold material selection scheme, the mold structure design scheme, and the mold surface treatment scheme, the predetermined mold production scheme is obtained.

3. The mold optimization design method for the integrated septic tank as described in claim 2, characterized in that, The method for obtaining the mold material selection scheme with the highest matching degree by matching in the preset material selection scheme library based on the performance characteristics of the target product includes: Extract a first material selection scheme from the preset material selection scheme library, and extract the first product performance characteristics of the first material selection scheme; A similarity analysis is performed between the performance characteristics of the target product and the performance characteristics of the first product to obtain a first performance similarity index, and the first performance similarity index is used as the first matching degree. Traverse the preset material selection scheme library to obtain a matching degree list sorted from high to low, and obtain the mold material selection scheme with the highest matching degree according to the matching degree list.

4. The mold optimization design method for the integrated septic tank as described in claim 3, characterized in that, The method for performing a similarity analysis between the performance characteristics of the target product and the performance characteristics of the first product to obtain a first performance similarity index includes: Obtain the product performance label information of the integrated molded septic tank, including but not limited to wear resistance, corrosion resistance, strength, hardness, and thermal conductivity; Based on the product performance label information, and combined with the preset label order and preset grading standards, a preset label scheme is constructed; Based on the preset labeling scheme, the target product performance characteristics and the first product performance characteristics are labeled to obtain the target product performance label vector and the first product performance label vector. Using the similarity index calculation formula, the similarity index is calculated for the target product performance label vector and the first product performance label vector to obtain the first performance similarity index. The similarity index is calculated using the following formula: Where C(A,B) is the first performance similarity index between the target product performance label vector and the first product performance label vector, and A i B is the i-th element of the target product performance label vector. i Let be the i-th element of the first product performance tag vector, and n be the total number of performance tag elements.

5. The mold optimization design method for the integrally molded septic tank as described in claim 1, characterized in that, The method involves performing pressure distribution feature analysis based on the multiple pressure data sequences to generate pressure distribution feature analysis results, wherein the pressure distribution feature analysis results include several pressure unevenness regions. Confidence features are calculated based on the multiple pressure data sequences to generate a confidence pressure data sequence, wherein the confidence feature calculation includes standardization and mean calculation; Load the predetermined pressure data sequence of the predetermined mold production plan; Based on the predetermined pressure data sequence, the deviation of the confidence pressure data sequence is calculated to obtain a pressure deviation sequence; Obtain several pressure unevenness regions in the pressure deviation sequence that are greater than or equal to a preset pressure deviation. The confidence pressure data sequence and the several pressure unevenness regions are added to the pressure distribution feature analysis results.

6. The mold optimization design method for the integrated septic tank as described in claim 1, characterized in that, The method for constructing the three-dimensional model of the initial integrally molded septic tank includes: The initial integrated septic tank is subjected to all-round image acquisition by a pre-deployed image acquisition device array to obtain a set of septic tank product images. The point cloud data of the septic tank product image set is analyzed sequentially to obtain the septic tank product point cloud data set; A registration and fusion model is constructed based on the principle of random sampling consistency. This model is then used to analyze the point cloud data set of the septic tank product to obtain the 3D model of the septic tank product. Specifically, this includes: K1: The first septic tank product point cloud data group extracted from the septic tank product point cloud data set through the registration and fusion model; K2: Analyze the point cloud data set of the first septic tank product to obtain the surface parameters of the first septic tank product; K3: Traverse the point cloud data set of the septic tank product to obtain the surface parameter set of the septic tank product, and generate the three-dimensional model based on the surface parameter set of the septic tank product.

7. The mold optimization design method for the integrally molded septic tank as described in claim 1, characterized in that, The method for optimizing the predetermined mold production plan based on the plurality of pressure unevenness regions, the plurality of temperature unevenness regions, and the plurality of surface defect regions to obtain an optimized mold production plan includes: Collect a set of historical defect records, wherein the set of historical defect records includes a set of historical pressure unevenness records, a set of historical temperature unevenness records, and a set of historical surface defect records; The first historical defect record is randomly extracted based on the aforementioned historical defect record set; The expert group analyzed the first historical defect record to determine the first defect factor of the first defect, and matched the first mold production plan with the first defect factor. Based on the first mapping relationship between the first defect and the first mold production plan, a defect-production plan list is constructed, which is used to provide a basis for optimizing the predetermined mold production plan.

8. The mold optimization design method for the integrated septic tank as described in claim 1, characterized in that, The method also includes: Perform historical execution similarity analysis on the target production demand to extract similar mold production schemes; The first optimization direction is generated based on the common characteristics of the similar mold production schemes; The predetermined mold production plan is optimized through the first optimization direction.

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