Optimization design method for mold of integrally-formed septic tank

Abstract

The invention provides a mold optimization design method for an integrally-formed septic tank, and relates to the technical field of data processing, and the method comprises the following steps: obtaining a target production demand, and determining a predetermined mold production scheme; performing mold region division to obtain a plurality of predetermined regions; an initial mold is obtained, injection molding of a plastic raw material is carried out, and an initial integrally-formed septic tank, a plurality of pressure data sequences and a plurality of temperature data sequences are obtained; 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; a three-dimensional model is constructed for surface quality detection and analysis; based on the non-uniform pressure area, the non-uniform temperature area and the surface defect area, a preset mold production scheme is optimized, and an optimized mold production scheme is obtained; and carrying out mold production. The technical problem that a traditional mold optimization method often depends on experience and trial and error, and the overall quality of mold design and production cannot be effectively improved is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a mold optimization design method for an integrally formed septic tank. Background Art

[0002] The one-piece septic tank is an important equipment for sewage treatment systems, widely used in residential buildings, commercial buildings, industrial facilities and other fields. Its main function is to preliminarily treat and decompose domestic sewage, reduce pollutants in sewage, and protect the environment and public health. The traditional one-piece septic tank mold design method usually relies on the designer's experience and manual calculations, lacks systematic analysis and scientific basis, and this method easily leads to unreasonable design and is difficult to meet the specific needs of different application scenarios. Summary of the invention

[0003] This application provides a mold optimization design method for an integrated septic tank, aiming to solve 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.

[0004] The present application discloses a mold optimization design method for an integrated septic tank, the method comprising: an interactive user terminal, obtaining a target production requirement of the integrated septic tank, and determining a predetermined mold production plan based on the target production requirement, wherein 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 a plurality of predetermined areas; based on the predetermined mold production plan, mold production is performed to obtain an initial mold, the initial mold is used to perform injection molding of plastic raw materials, and during the injection molding process, pressure data and temperature data of the plurality of predetermined areas are collected at a preset collection frequency by means of a pressure sensor array and a temperature sensor array arranged in the plurality of predetermined areas, to obtain an initial integrated septic tank, and a plurality of pressure data sequences , multiple temperature data sequences; performing pressure distribution characteristic analysis based on the multiple pressure data sequences to generate pressure distribution characteristic analysis results, wherein the pressure distribution characteristic analysis results include multiple pressure uneven areas; performing temperature distribution characteristic analysis based on the multiple temperature data sequences to generate temperature distribution characteristic analysis results, wherein the temperature distribution characteristic analysis results include multiple temperature uneven areas; constructing a three-dimensional model of the initial one-piece septic tank, performing surface quality detection and analysis of the multiple predetermined areas based on the three-dimensional model, and obtaining multiple surface defect areas; optimizing the predetermined mold production plan based on the multiple pressure uneven areas, the multiple temperature uneven areas, and the multiple surface defect areas to obtain an optimized mold production plan; performing mold production of the one-piece septic tank according to the optimized mold production plan.

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

[0006] The target production requirements of the one-piece septic tank, including performance characteristics, specification characteristics and appearance requirements, are obtained and analyzed through the interactive user terminal, and the preset solution library is used for system matching, so that the selected predetermined mold production solution can fully meet the specific needs and improve the accuracy and rationality of the mold design; in the mold injection molding process, by arranging pressure sensor arrays and temperature sensor arrays in multiple predetermined areas, pressure data and temperature data are collected in real time to ensure that the key parameters in the production process are monitored in real time, and based on the collected pressure data and temperature data, pressure distribution characteristic analysis and temperature distribution characteristic analysis are performed to generate analysis results, including uneven pressure areas and uneven temperature areas; by constructing an initial three-dimensional model of the one-piece septic tank, and performing surface quality detection and analysis based on the model, surface defect areas are accurately identified; combined with the information of uneven pressure areas, uneven temperature areas and surface defect areas, the predetermined mold production solution is systematically optimized to obtain an optimized mold production solution and improve the overall quality of mold production; mold production is carried out according to the optimized mold production solution to ensure that various parameters and processes in the production process are optimally controlled. In summary, the mold optimization design method of the one-piece septic tank solves the problems of inaccurate mold design, insufficient defect control in the production process, lagging quality inspection and lack of systematic optimization methods in the prior art 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 one-piece septic tank, improves 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 the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of a mold optimization design method for an integrated septic tank is provided for the embodiment of the present application;

[0009] Figure 2 A schematic flow chart of a process for determining a predetermined mold production plan in a mold optimization design method for an integrated septic tank is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiment of the present application 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 by providing a mold optimization design method for an integrally formed septic tank.

[0011] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0012] like Figure 1 As shown, the embodiment of the present application provides a mold optimization design method for an integrally formed septic tank, the method comprising:

[0013] The interactive user end obtains the target production demand of the one-piece septic tank, and determines a predetermined mold production plan based on the target production demand, wherein the predetermined mold production plan includes a mold material selection plan, a mold structure design plan, and a mold surface treatment plan.

[0014] Through the interactive user end, for example, through the user interface, input device, etc., the target production requirements of the one-piece septic tank are obtained, including the target product performance characteristics, target product specification characteristics, and target product appearance requirements characteristics. A preset 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 demand characteristics, a suitable mold production solution is matched from the pre-built solution library. Based on the matching process, a mold material selection solution, a mold structure design solution, and a mold surface treatment solution are obtained to form a complete predetermined mold production solution.

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

[0016] The structural design plan includes the overall shape, size, internal structure, functional area division, etc. of the mold. According to the structural design plan, the key parts and functional areas of the mold are identified, such as the feed port, runner, gate, cooling system, exhaust system, etc., and the functional requirements of the mold in different areas are determined, such as pressure distribution, temperature control, cooling efficiency, etc. According to the functional requirements and combined with the geometric shape of the mold, the mold is divided into areas to ensure that each area can meet its functional requirements. After division, multiple predetermined areas are obtained as the basis for subsequent mold optimization.

[0017] Mold production is performed based on the predetermined mold production plan to obtain an initial mold, and 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 collection frequency through a pressure sensor array and a temperature sensor array arranged in the multiple predetermined areas to obtain an initial one-piece 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 plan, prepare the corresponding mold materials, and carry out mold production according to the predetermined mold production plan to obtain the initial mold. According to the requirements of the one-piece septic tank product, select suitable plastic raw materials, such as polyethylene (PE), polypropylene (PP), etc., 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 for injection molding operation, inject the plastic raw materials into the mold, and obtain the initial one-piece septic tank through cooling and curing.

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

[0020] After the injection molding is completed, the initial one-piece molded septic tank is taken out of the mold, and the collected multiple pressure data sequences and multiple temperature data sequences are stored as basic data for subsequent analysis.

[0021] A pressure distribution characteristic analysis is performed based on the multiple pressure data sequences to generate a pressure distribution characteristic analysis result, wherein the pressure distribution characteristic analysis result includes a plurality of pressure uneven areas.

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

[0023] A temperature distribution characteristic analysis is performed based on the multiple temperature data sequences to generate a temperature distribution characteristic analysis result, wherein the temperature distribution characteristic analysis result includes a plurality of temperature uneven areas.

[0024] Based on multiple temperature data sequences after standardization, a confident temperature data sequence is calculated, and the predetermined temperature data sequence in the predetermined mold production plan is loaded as a comparison benchmark. The deviation between the confident temperature data sequence and the predetermined temperature data sequence is calculated to form a temperature deviation sequence. According to the preset temperature deviation threshold, significant temperature deviation areas are identified, and several temperature uneven areas are delineated. These areas are key parts of uneven temperature distribution.

[0025] A three-dimensional model of the initial one-piece 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 a number of surface defect areas.

[0026] Using the pre-deployed array of image acquisition equipment, the initial one-piece septic tank is imaged in all directions to obtain multi-angle images of the septic tank product and form a product image set. Through image processing technology, the point cloud data of the septic tank product image set is analyzed to obtain the point cloud data set of the septic tank product. Based on the principle of random sampling consistency, a registration and fusion model is constructed. Through the registration and fusion model, the point cloud data set of the septic tank product is registered and fused to generate complete three-dimensional point cloud data. Based on the point cloud data, surface reconstruction is performed to generate a three-dimensional model of the septic tank product.

[0027] Surface parameters of each predetermined area are extracted from the three-dimensional model, including surface roughness, flatness, curvature, etc. The extracted surface parameters are statistically analyzed to determine the surface quality index of each area. The surface defect recognition algorithm is applied to detect the surface defect area in the three-dimensional model, identify different types of surface defects such as dents, protrusions, cracks, bubbles, etc., determine the specific location and range of the defect area, and mark it on the three-dimensional model. According to the detection results, the boundary of each surface defect area is delineated to obtain several surface defect areas.

[0028] Based on the several pressure uneven areas, the several temperature uneven areas, and the several surface defect areas, the predetermined mold production plan is optimized to obtain an optimized mold production plan; the mold production of the one-piece septic tank is carried out according to the optimized mold production plan.

[0029] Through correlation analysis, the relationship and influencing factors between pressure, temperature, surface defects and production plans are determined. Based on the analysis results of the uneven pressure area, the runner design, gate location and number of the mold are adjusted to optimize the pressure distribution, such as introducing a pressure compensation mechanism to balance the overall pressure distribution by increasing or decreasing the pressure locally. Based on the analysis results of the uneven temperature area, the design of the cooling system is adjusted, including the layout of the cooling pipeline, the coolant flow rate and the cooling time. For example, the hot runner system is introduced or optimized in the required area to ensure the uniformity of the temperature distribution. According to the analysis results of the surface defect area, the surface treatment process of the mold is improved, such as adding polishing, plating, etc., to repair the existing surface defects, and add protection measures for these areas in the mold design. Integrate various optimization measures to form the final optimized mold production plan.

[0030] The mold is produced and processed according to the optimized mold production plan, and surface treatment processes such as polishing, plating, heat treatment, etc. are carried out to improve the surface quality and service life of the mold and realize the efficient and high-quality production of one-piece septic tanks.

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

[0032] Perform demand feature extraction on the target production demand to obtain demand feature extraction results, wherein the demand feature extraction results include target product performance features, target product specification features, and target product appearance requirement features; obtain a pre-constructed mold production solution library, which includes a preset material selection solution library, a preset structure design solution library, and a preset surface treatment solution library; based on the target product performance features, match the preset material selection solution library to obtain the mold material selection solution with the highest matching degree; based on the target product specification features, match the preset structure design solution library to obtain the mold structure design solution with the highest matching degree; based on the target product appearance requirement features, match the preset surface treatment solution library to obtain the mold surface treatment solution with the highest matching degree; based on the mold material selection solution, the mold structure design solution, and the mold surface treatment solution, obtain the predetermined mold production solution.

[0033] Classify and organize the target production requirements, including functional requirements, performance requirements, appearance requirements, etc., and refine each type of requirement to form specific demand indicators, such as wear resistance, corrosion resistance, size, shape, color, surface finish, etc.

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

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

[0036] Extract key indicators from the performance characteristics of the target product, such as wear resistance, corrosion resistance, strength, hardness, thermal conductivity, etc., convert the extracted performance characteristics into feature vectors to facilitate matching with materials in the material selection solution library. In the preset material selection solution library, retrieve the performance feature vectors of all materials, use similarity calculation formulas such as cosine similarity, Euclidean distance, etc., calculate the similarity between the target product performance feature vector and the feature vector of each material, sort the similarities of all materials, and select the material with the highest similarity as the mold material selection solution with the highest matching degree.

[0037] The mold structure design scheme and mold surface treatment scheme are obtained in exactly the same way. For the sake of brevity, they will not be repeated here. The 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 of matching the preset material selection solution library based on the performance characteristics of the target product to obtain the mold material selection solution with the highest matching degree includes:

[0039] A first material selection scheme is extracted from the preset material selection scheme library, and a first product performance feature of the first material selection scheme is extracted; a similarity analysis is performed on the target product performance feature and the first product performance feature to obtain a first performance similarity index, and the first performance similarity index is used as a first matching degree; the preset material selection scheme library is traversed to obtain a matching degree list sorted from high to low, and the mold material selection scheme with the highest matching degree is obtained according to the matching degree list.

[0040] All material selection schemes are retrieved from a preset material selection scheme library, and a material selection scheme is randomly extracted therefrom and recorded as a first material selection scheme. The product performance feature corresponding to the first material selection scheme is extracted from the first material selection scheme and recorded as a first product performance feature.

[0041] The target product performance characteristics are converted into a feature vector, the first product performance characteristics are converted into a feature vector, and a similarity calculation formula, such as cosine similarity, is used to calculate a similarity index between the target product performance feature vector and the first product performance feature vector, and the calculated first performance similarity index is used as a 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 characteristic vector, and the similarity index between its performance characteristic vector and the target product performance characteristic vector is calculated to obtain the matching degree of each material selection scheme, and the matching degrees are sorted from high to low 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 of performing similarity analysis on the target product performance characteristics and the first product performance characteristics to obtain a first performance similarity index includes:

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

[0045]

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

[0047] The product performance of the one-piece septic tank is divided into multiple key indicators, including but not limited to wear resistance, corrosion resistance, strength, hardness, and thermal conductivity; according to the importance and relevance of the performance labels, the order of each performance label is determined to obtain the preset label order; according to the specific requirements of each performance label, the grading standards of each label are defined, for example, divided into 1 to 5 levels, the higher the level, the better the performance, and the preset grading standards are obtained. All labels, preset label orders, and preset grading standards are integrated into a complete labeling solution for subsequent performance feature marking.

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

[0049] The similarity index calculation formula is used to quantify the similarity between the target product performance label vector and the first product performance label vector, and 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 is, the higher the similarity between the two sets of performance characteristics is. It is used to measure the degree of performance matching between the two. The closer the value is to 1, the closer the performance of the two is, which helps to select the most suitable material solution.

[0052] Furthermore, the pressure distribution characteristic analysis is performed based on the multiple pressure data sequences to generate a pressure distribution characteristic analysis result, wherein the pressure distribution characteristic analysis result includes a plurality of pressure uneven areas, and the method includes:

[0053] Confidence feature calculation is performed based on the multiple pressure data sequences to generate a confidence pressure data sequence, wherein the confidence feature calculation includes normalization processing 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 degree sequence; a number of pressure uneven areas in the pressure deviation degree sequence that are greater than or equal to a preset pressure deviation degree are obtained; and the confidence pressure data sequence and the number of pressure uneven areas are added to the pressure distribution feature analysis result.

[0054] The multiple pressure data sequences are standardized to eliminate the dimensional differences between different data sources and improve the comparability of the data. For example, a Z-score standardization method is used, and the formula is as follows: Among them, Z is the standardized data, X is the original pressure data, μ is the mean of the pressure data, and σ is the standard deviation of the pressure data. The mean of the standardized pressure data is calculated as the basis of the confidence pressure data sequence, and the mean of each standardized data sequence is calculated to obtain the confidence pressure data sequence.

[0055] A predetermined pressure data sequence is extracted from a predetermined mold production plan, and 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, and the deviation of each data point is calculated to form a pressure deviation degree sequence.

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

[0058] The confidence pressure data sequence is integrated with the identified uneven pressure area to form a complete pressure distribution feature analysis result, which can effectively identify the pressure distribution problem in mold production and provide a basis for further mold optimization.

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

[0060] The initial one-piece septic tank is subjected to all-round image acquisition through a pre-deployed array of image acquisition devices to obtain a set of septic tank product images; the point cloud data of the septic tank product image set is analyzed in turn to obtain a set of septic tank product point cloud data; a registration fusion model is constructed based on the principle of random sampling consistency, and the septic tank product point cloud data set is analyzed through the registration fusion model to obtain the three-dimensional model of the septic tank product, which specifically includes: K1: extracting the first septic tank product point cloud data group of the septic tank product point cloud data set through the registration fusion model; K2: analyzing the first septic tank product point cloud data group 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 placed around the septic tank to form a full-range array of image acquisition devices. All image acquisition devices are calibrated to ensure that the shooting angle, focal length and lighting conditions of each device are consistent to ensure the quality of the acquired images. The initial one-piece septic tank is photographed from different angles to ensure that every angle is covered, and comprehensive image data is obtained to form an image collection of the septic tank product.

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

[0063] The random sampling consensus principle (RANSAC) is an iterative method for estimating mathematical model parameters. It can effectively deal with outliers and noise in the data. According to the geometric characteristics of the septic tank, a RANSAC model suitable for point cloud data registration is constructed. The point cloud data is preliminarily registered using the RANSAC model to determine the approximate registration parameters. Based on the initial registration results, the iterative closest point algorithm is used for fine registration to further improve the registration accuracy. The registered point cloud data is fused to eliminate overlapping areas and redundant data to ensure the continuity and integrity of the point cloud data. According to the registered and fused point cloud data, a surface reconstruction algorithm is used to generate a three-dimensional surface model of the septic tank product.

[0064] The registration fusion model built using RANSAC and ICP algorithms is used for preliminary and fine registration of point cloud data to ensure accurate alignment of point cloud data from different perspectives. Representative data points are selected from the point cloud data set as the initial data set, and these data points are processed by the registration fusion model to extract the first septic tank product point cloud data set.

[0065] For the first septic tank product point cloud data group, 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, and surface features such as protrusions and depressions are identified. The surface smoothness parameters are calculated through neighborhood statistical analysis, and the extracted surface parameters are summarized to form a surface parameter set of the first septic tank product. These parameters include but are not limited to normal vectors, curvature, smoothness, etc.

[0066] Traverse the entire point cloud data set, analyze each data point, extract the corresponding surface parameters for each data point, form a global surface parameter set, summarize the surface parameters extracted during the traversal process, and form a complete surface parameter set for septic tank products. Based on the complete surface parameter set, use surface reconstruction algorithms such as Poisson reconstruction and Delaunay triangulation to generate a three-dimensional model of the septic tank product, providing an accurate data basis for subsequent quality inspection and analysis.

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

[0068] Collect a set of historical defect records, wherein the set of historical defect records includes a set of historical pressure uneven records, a set of historical temperature uneven records, and a set of historical surface defect records; randomly extract a first historical defect record based on the set of historical defect records; analyze the first historical defect record by an expert group to determine a first defect factor of the first defect, and match a first mold production plan for the first defect factor; based on a first mapping relationship between the first defect and the first mold production plan, construct a defect-production plan list, and the defect-production plan list is used to provide a basis for optimizing the predetermined mold production plan.

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

[0071] Invite mold design experts, production engineers and quality control experts to form an analysis team, and conduct a detailed analysis of the first historical defect records through the expert group to identify the main factors leading to the first defect as the first defect factor. These factors include design defects, material problems, process parameter deviations, etc. According to the first defect factor, according to the nature and impact of the defect factor, match the relevant production plan from the preset mold production plan library as the first mold production plan.

[0072] According to the matching result 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 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, plan description, etc.

[0073] The defect-production plan list serves as basic data for optimizing the predetermined mold production plan, that is, the potential problems in the predetermined mold production plan can be analyzed and identified based on the defect-production plan list, and optimization suggestions can be put forward.

[0074] Furthermore, the method further comprises:

[0075] Perform similarity analysis on the history of the target production demand to extract similar mold production solutions; generate a first optimization direction based on the common characteristics of the similar mold production solutions; and optimize the predetermined mold production solution through the first optimization direction.

[0076] Collect historical production records, including production parameters, mold design plans, production results, and quality inspection data, etc. Extract key demand features from the target production requirements, such as performance requirements, specifications and dimensions, appearance features, etc. 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 records with the highest similarity as similar mold production plans.

[0077] Extract common features from similar mold production plans, including successful design plans, production parameter settings, quality control methods, etc., summarize all common features in similar mold production plans to form a common feature set. Analyze the key factors in the common feature set, determine the features that have the greatest impact on production quality and efficiency, and determine the first optimization direction based on the analysis results of the common features as a guiding direction for optimizing the predetermined mold production plan.

[0078] Based on the first optimization direction, clarify the optimization goals, such as increasing mold life, improving product quality, reducing production costs, etc., formulate specific optimization measures, including improving mold design, adjusting production parameters, optimizing process flow, etc., gradually implement optimization measures, adjust and improve the predetermined mold production plan, verify the effect of the optimized mold production plan through trial production and quality inspection, and further optimize and improve the mold production plan based on the verification results to form a closed loop of continuous improvement.

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

[0080] The target production requirements of the one-piece septic tank, including performance characteristics, specification characteristics and appearance requirements, are obtained and analyzed through the interactive user terminal, and the preset solution library is used for system matching, so that the selected predetermined mold production solution can fully meet the specific needs and improve the accuracy and rationality of the mold design; in the mold injection molding process, by arranging pressure sensor arrays and temperature sensor arrays in multiple predetermined areas, pressure data and temperature data are collected in real time to ensure that the key parameters in the production process are monitored in real time, and based on the collected pressure data and temperature data, pressure distribution characteristic analysis and temperature distribution characteristic analysis are performed to generate analysis results, including uneven pressure areas and uneven temperature areas; by constructing an initial three-dimensional model of the one-piece septic tank, and performing surface quality detection and analysis based on the model, surface defect areas are accurately identified; combined with the information of uneven pressure areas, uneven temperature areas and surface defect areas, the predetermined mold production solution is systematically optimized to obtain an optimized mold production solution and improve the overall quality of mold production; mold production is carried out according to the optimized mold production solution to ensure that various parameters and processes in the production process are optimally controlled. In summary, the mold optimization design method of the one-piece septic tank solves the problems of inaccurate mold design, insufficient defect control in the production process, lagging quality inspection and lack of systematic optimization methods in the prior art 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 one-piece septic tank, improves 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 implement or use the present application. Various modifications to these embodiments will be 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 the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A mold optimization design method for an integrated septic tank, characterized in that: The method comprises: The interactive user terminal obtains the target production demand of the one-piece septic tank, and determines a predetermined mold production plan based on the target production demand, wherein 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 a plurality of predetermined areas; Perform mold production based on the predetermined mold production plan to obtain an initial mold, use the initial mold to perform injection molding of plastic raw materials, and during the injection molding process, use a pressure sensor array and a temperature sensor array arranged in the plurality of predetermined areas to collect pressure data and temperature data of the plurality of predetermined areas at a preset collection frequency to obtain an initial one-piece molded septic tank, as well as a plurality of pressure data sequences and a plurality of temperature data sequences; Performing pressure distribution characteristic analysis based on the multiple pressure data sequences to generate a pressure distribution characteristic analysis result, wherein the pressure distribution characteristic analysis result includes a plurality of pressure uneven areas; Performing temperature distribution characteristic analysis based on the multiple temperature data sequences to generate temperature distribution characteristic analysis results, wherein the temperature distribution characteristic analysis results include a plurality of temperature uneven areas; Constructing a three-dimensional model of the initial one-piece septic tank, and performing surface quality detection and analysis of the plurality of predetermined areas based on the three-dimensional model to obtain a plurality of surface defect areas; Based on the plurality of uneven pressure regions, the plurality of uneven temperature regions, and the plurality of surface defect regions, the predetermined mold production plan is optimized to obtain an optimized mold production plan; The mold production of the one-piece septic tank is carried out according to the optimized mold production plan.

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

3. The mold optimization design method for an integrated septic tank according to claim 2, characterized in that: The method of matching the preset material selection solution library based on the performance characteristics of the target product to obtain the mold material selection solution with the highest matching degree includes: Extracting a first material selection scheme from the preset material selection scheme library, and extracting a first product performance feature of the first material selection scheme; Performing a similarity analysis on the target product performance feature and the first product performance feature to obtain a first performance similarity index, and using the first performance similarity index as a first matching degree; The preset material selection solution library is traversed to obtain a matching degree list sorted from high to low, and the mold material selection solution with the highest matching degree is obtained according to the matching degree list.

4. The mold optimization design method for an integrated septic tank as claimed in claim 3, characterized in that: The method of performing similarity analysis on the target product performance feature and the first product performance feature to obtain a first performance similarity index includes: Obtain product performance label information of the one-piece septic tank, the product performance label information including but not limited to wear resistance, corrosion resistance, strength, hardness, and thermal conductivity; Based on the product performance label information, combined with the preset label sequence and the preset grading standard, a preset label scheme is constructed; Label the target product performance feature and the first product performance feature based on the preset labeling scheme to obtain a target product performance label vector and a first product performance label vector; Using a similarity index calculation formula, a similarity index calculation is performed on the target product performance label vector and the first product performance label vector to obtain the first performance similarity index; The similarity index calculation formula is as follows: Wherein, C(A,B) is the first performance similarity index between the target product performance label vector and the first product performance label vector, A i is the i-th element of the target product performance label vector, B i is the ith element of the first product performance label vector, and n is the total number of elements of the performance label.

5. The mold optimization design method for an integrated septic tank according to claim 1, characterized in that: The method of performing pressure distribution characteristic analysis based on the multiple pressure data sequences to generate a pressure distribution characteristic analysis result, wherein the pressure distribution characteristic analysis result includes a plurality of pressure uneven areas, comprises: Performing confidence feature calculation according to the multiple pressure data sequences to generate a confidence pressure data sequence, wherein the confidence feature calculation includes standardization processing and mean value calculation; Loading a predetermined pressure data sequence of the predetermined mold production plan; Based on the predetermined pressure data sequence, performing deviation calculation on the confidence pressure data sequence to obtain a pressure deviation degree sequence; Acquire a plurality of pressure uneven regions in the pressure deviation sequence that are greater than or equal to a preset pressure deviation; The confidence pressure data sequence and the plurality of pressure uneven regions are added to the pressure distribution characteristic analysis result.

6. The mold optimization design method for an integrated septic tank according to claim 1, characterized in that: The method for constructing the three-dimensional model of the initial one-piece septic tank includes: Performing all-round image acquisition of the initial one-piece septic tank through a pre-deployed array of image acquisition devices to obtain a set of septic tank product images; Analyze the point cloud data of the septic tank product image set in sequence to obtain a septic tank product point cloud data set; A registration fusion model is constructed based on the principle of random sampling consistency, and the point cloud data set of the septic tank product is analyzed by the registration fusion model to obtain the three-dimensional model of the septic tank product, specifically including: K1: extracting the first septic tank product point cloud data set of the septic tank product point cloud data set through the registration fusion model; K2: Analyze the first septic tank product point cloud data group to obtain the surface parameters of the first septic tank product; K3: traverse the septic tank product point cloud data set, obtain the septic tank product surface parameter set, and generate the three-dimensional model based on the septic tank product surface parameter set.

7. The mold optimization design method for an integrated septic tank according to claim 1, characterized in that: The method of optimizing the predetermined mold production plan based on the plurality of pressure uneven areas, the plurality of temperature uneven areas, and the plurality of surface defect areas to obtain an optimized mold production plan includes: Collecting a historical defect record set, wherein the historical defect record set includes a historical pressure unevenness record set, a historical temperature unevenness record set, and a historical surface defect record set; Randomly extracting a first historical defect record based on the historical defect record set; By means of an expert group, the first historical defect record is analyzed to determine a first defect factor of the first defect, and a first mold production plan is matched with the first defect factor; Based on a first mapping relationship between the first defect and the first mold production plan, a defect-production plan list is constructed, and the defect-production plan list is used to provide a basis for optimizing the predetermined mold production plan.

8. The mold optimization design method for an integrated septic tank according to claim 1, characterized in that: The method also includes: Performing a historical similarity analysis on the target production demand and extracting similar mold production plans; Generate a first optimization direction through the common features of the similar mold production solutions; The predetermined mold production plan is optimized through the first optimization direction.

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