A research and development data processing system based on plastic products

Abstract

The present invention discloses a research and development data processing system based on plastic products. The research and development data processing system based on plastic products relates to the field of data processing technology, and includes: a research and development data module, a performance detection module, a comprehensive evaluation module and a plastic product research and development model construction module. The present invention obtains a performance classification result by classifying the collected plastic product research and development data, and then performs a performance test on the plastic product to be tested according to the performance classification result to obtain a performance evaluation result, and then performs a comprehensive evaluation on the plastic product to be tested according to the performance evaluation result to obtain a comprehensive evaluation result and obtain a grade classification result, and finally constructs a plastic product research and development model according to the comprehensive evaluation result of the plastic product to be tested and in combination with the grade classification result, thereby achieving the effect of improving the utilization rate of the research and development data of plastic products in performance analysis, and solving the problem of low utilization rate of the research and development data of plastic products in performance analysis in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a research and development data processing system based on plastic products. Background Art

[0002] With the development of modern industrialization, plastic products have become an indispensable part of people's lives. Plastic products are a general term for daily and industrial products made of plastic as the main raw material. It includes products made from all processes such as injection molding and blister molding using plastic as the raw material. Plastic is a type of synthetic polymer material with plasticity. Compared with metal, stone, and wood, plastic products have the advantages of low cost and strong plasticity, and are widely used in the national economy. The plastic industry occupies an extremely important position in the world today. For many years, the production of plastic products has developed rapidly around the world. It, synthetic rubber, and synthetic fibers form the three major synthetic materials that are indispensable in daily life. Specifically, plastic is a material that uses natural or synthetic resin as the main component, adds various additives, can be molded into a certain shape under certain conditions such as temperature and pressure, and maintains the same shape at room temperature.

[0003] The existing plastic product R&D data is processed by collecting various data related to plastic product R&D, then cleaning and organizing the collected data, using statistical analysis, machine learning or other data analysis methods to analyze the data, extract key information and rules, and then based on the data analysis results, establish mathematical models and prediction models, and finally apply the analysis and prediction results to the actual plastic product R&D.

[0004] For example, the invention patent announcement with announcement number: CN117313017B discloses a method and system for processing data on the development of colorful leaves, including: obtaining the chlorophyll content of each colorful leaf plant in each temperature zone at each moment in a preset time period; obtaining the temperature of the temperature zone; obtaining the height of the colorful leaf plant; dividing the preset time period into sub-time periods, and obtaining the chlorophyll accumulation of the colorful leaf plant in the sub-time period according to the change in the chlorophyll content of the colorful leaf plant in the sub-time period; obtaining the accumulation fluctuation degree of each temperature zone according to the chlorophyll accumulation of the colorful leaf plant in the sub-time period within the temperature zone and the difference with the chlorophyll accumulation in other sub-time periods; using the K-means clustering algorithm, taking the colorful leaf plant corresponding to the cluster center as the target plant; and The temperature weight between the colorful leaf plants and the target plants is obtained according to the difference in the degree of accumulation fluctuation between the colorful leaf plants and the target plants in the temperature zone; the change weight between the colorful leaf plants and the target plants is obtained according to the fluctuation difference in the chlorophyll accumulation between the colorful leaf plants and the target plants in the preset time period, and the temperature difference in the temperature zone where the colorful leaf plants and the target plants are located; the distance weight between the colorful leaf plants and the target plants is obtained according to the temperature weight and the change weight; the distance between the colorful leaf plants and the target plants is corrected according to the distance weight to obtain the final clustering clusters; the colorful leaf clustering clusters are obtained according to the chlorophyll content in each final clustering cluster; the height of each colorful leaf plant in the colorful leaf clustering cluster is detected through the local outlier factor algorithm to determine the abnormal colorful leaf plants.

[0005] For example, the FPGA-based data processing system disclosed in the invention patent with publication number CN110941585B includes: a data processing module; the data processing module includes a data transceiver unit and a data processing unit; the data transceiver unit includes a first FPGA chip; the data processing unit includes a first FPGA chipset; the first FPGA chipset includes a second FPGA chip and a third FPGA chip; the first FPGA chip is used to obtain the initial data collected by the sensor and send it to the second FPGA chip; the second FPGA chip is used to send the received initial data to the third FPGA chip; the third FPGA chip is used to process the initial data and obtain target data; the third FPGA chip includes a logic control unit; the logic control unit is used to process the initial data according to the set logic rules and obtain the target data; the logic control unit includes an initialization unit, a first judgment unit, a format conversion unit and an operation unit; the initialization unit is used to initialize the initial data and send it to the first judgment unit; the first judgment unit is used to determine whether the hardware configuration data in the initial data is consistent with the preset configuration data; the format conversion unit is used to convert the data type of the initial data into a set format and send it to the operation unit; the operation unit is used to operate and process the initial data in the set format and obtain the target data.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, the collected data on plastic product R&D include material properties, process parameters, experimental results, market demand, etc. The amount of data is huge and complicated, and there is a lack of effective data management strategies and processes, resulting in data dispersion, redundancy and inconsistency, which increases the difficulty of data integration and analysis. When performing performance analysis, it is necessary to integrate data from multiple sources. If the data is not managed well, this process will become difficult and time-consuming. The cost and time required for acquisition and processing are high, resulting in the difficulty in accurately utilizing the data on performance. There is a problem of low utilization of R&D data of plastic products in performance analysis. Summary of the invention

[0008] The embodiment of the present application solves the problem of low utilization rate of R&D data of plastic products in performance analysis in the prior art by providing a R&D data processing system based on plastic products, thereby improving the utilization rate of R&D data of plastic products in performance analysis.

[0009] The embodiment of the present application provides a research and development data processing system based on plastic products, including: a research and development data module, a performance detection module, a comprehensive evaluation module and a plastic product research and development model construction module; wherein the research and development data module is used to collect in real time the plastic product research and development data of the plastic products to be detected within a preset time interval, and classify the plastic product research and development data according to preset performance classification indicators to obtain performance classification results and store them, and the plastic product research and development data represents the information data generated during the research and development process of the plastic products to be detected; the performance detection module is used to perform performance testing on the plastic products to be detected according to the performance classification results to obtain performance evaluation results, and the performance evaluation results include impact resistance results and heat resistance results; the comprehensive evaluation module is used to perform comprehensive evaluation on the plastic products to be detected according to the performance evaluation results to obtain comprehensive evaluation results, and comprehensively classify the plastic products to be detected based on the comprehensive evaluation results to obtain a grade classification result; the plastic product research and development model construction module is used to construct a plastic product research and development model according to the comprehensive evaluation results of the plastic products to be detected, and the plastic product research and development model is used to visualize the grade classification results of the plastic products to be detected.

[0010] Furthermore, the R&D data module includes a R&D data acquisition unit, a R&D data processing unit and a R&D data storage unit; the R&D data acquisition unit is used to collect the initial plastic product R&D data of the plastic product to be tested in real time; the R&D data processing unit is used to pre-process the initial plastic product R&D data to obtain plastic product R&D data, and perform performance classification on the plastic product R&D data according to preset performance classification indicators; the R&D data storage unit is used to store and manage the obtained plastic product R&D data.

[0011] Furthermore, the performance detection module includes an impact resistance detection unit and a heat resistance detection unit; the impact resistance detection unit is used to detect the impact resistance of the plastic product to be detected to obtain an impact resistance performance evaluation index, and compare and analyze the impact resistance performance evaluation index with the corresponding threshold to obtain the impact resistance performance result of the plastic product to be detected, and the impact resistance performance evaluation index is used to describe the impact resistance of the plastic product to be detected; the heat resistance performance detection unit is used to detect the heat resistance of the plastic product to be detected to obtain a heat resistance performance evaluation index, and compare and analyze the heat resistance performance evaluation index with the corresponding threshold to obtain the heat resistance performance result of the plastic product to be detected, and the heat resistance performance evaluation index is used to describe the heat resistance of the plastic product to be detected.

[0012] Furthermore, the specific process of comprehensive evaluation of the plastic products to be tested is as follows: using a data analysis method to compare and verify the plastic product R&D data with the corresponding preset plastic product R&D data to extract the R&D performance data of the plastic products to be tested, the R&D performance data is used to describe the classification performance of the plastic products to be tested within a preset time interval, the R&D performance data includes impact resistance test data, heat resistance test data, corrosion resistance data and surface gloss data, the corrosion resistance data is used to measure the chemical properties of the plastic products to be tested, and the surface gloss data is used to measure the appearance quality of the plastic products to be tested; according to the calculated impact resistance performance evaluation index and heat resistance performance evaluation index, combined with the corrosion resistance data and the surface gloss data, a comprehensive evaluation index is obtained, and a comprehensive evaluation result is obtained according to the comprehensive evaluation index, and the comprehensive evaluation index is used to numerically describe the comprehensive performance of the plastic products to be tested.

[0013] Furthermore, the specific method of the comprehensive grade classification is as follows: the comprehensive evaluation index of the plastic product to be tested is graded according to the comprehensive performance matching index of the plastic product to be tested and combined with the preset performance grade, and the comprehensive performance matching index is used to describe the degree of matching between the comprehensive evaluation index and the preset performance grade.

[0014] Furthermore, the specific method for obtaining the comprehensive evaluation index is as follows: extracting the corrosion resistance data and surface glossiness data of the plastic product to be tested within a preset time interval from the R&D performance data, and obtaining the impact resistance performance evaluation index and the heat resistance performance evaluation index, and obtaining the corresponding thresholds and reference deviations; calculating the comprehensive evaluation index of the plastic product to be tested based on the acquired data, and the comprehensive evaluation index is calculated using the following formula:

[0015] ,

[0016] In the formula, f represents the serial number of the plastic product to be tested, , F is the total number of plastic products to be tested, is the comprehensive evaluation index of the fth plastic product to be tested, is the impact resistance evaluation index of the fth plastic product to be tested, is the threshold value of the impact resistance evaluation index, is the reference deviation of the impact resistance evaluation index, is the corrosion resistance data of the fth plastic product to be tested, is the threshold value of corrosion resistance data, is the reference deviation of the corrosion resistance data, is the heat resistance evaluation index of the fth plastic product to be tested, is the threshold value of the heat resistance evaluation index, is the reference deviation of the heat resistance performance evaluation index, is the surface glossiness data of the fth plastic product to be tested, is the threshold of the surface glossiness data, is the reference deviation of the surface glossiness data, It is the reference relative deviation of the impact resistance evaluation index.

[0017] Furthermore, a specific method for obtaining the impact resistance performance evaluation index is as follows: obtaining an impact resistance test data coefficient corresponding to the impact resistance test data of the plastic product to be tested within a preset time interval, and obtaining the impact resistance performance evaluation index of the plastic product to be tested according to the obtained impact resistance test data coefficient; the impact resistance test data coefficient includes an impact strength data coefficient, an impact absorption capacity data coefficient, a fracture toughness index coefficient and a breakdown strength data coefficient.

[0018] Furthermore, the specific method for obtaining the heat resistance performance evaluation index is as follows: extracting feature data from the heat resistance test data to obtain the heat resistance test data score of the plastic to be tested within a preset time interval, and obtaining the heat resistance performance evaluation index of the plastic product to be tested according to the obtained heat resistance test data score; the heat resistance test data score includes a heat deformation temperature index score, a transition temperature parameter score, a thermal expansion coefficient score, a thermal conductivity index score, an oxidation stability index score, and a thermal stabilizer content coefficient score; the heat resistance performance evaluation index is calculated using the following formula:

[0019] ,

[0020] In the formula, e is a natural constant, is the heat deformation temperature index score of the fth plastic product to be tested, is the reference deviation of the heat deformation temperature index score, is the transition temperature parameter score of the fth plastic product to be tested, is the reference deviation of the transition temperature parameter fraction, is the thermal conductivity index score of the fth plastic product to be tested, is the reference deviation of the thermal conductivity index score, is the oxidation stability index score of the fth plastic product to be tested, is the reference deviation of the oxidation stability index score, is the coefficient fraction of the heat stabilizer content of the fth plastic product to be tested, is the reference deviation of the thermal stabilizer content coefficient fraction, is the thermal expansion coefficient fraction of the fth plastic product to be tested, is the reference deviation of the thermal expansion coefficient fraction.

[0021] Furthermore, the specific method for obtaining the comprehensive performance matching index is as follows: extracting the R&D performance characteristic data of the plastic product to be tested from the R&D performance data, performing correlation analysis on the R&D performance characteristic data in combination with the preset performance level to obtain the correlation analysis result; and obtaining the comprehensive performance matching index according to the correlation analysis result.

[0022] Furthermore, the specific construction steps of the plastic product R&D model are as follows: constructing a plastic product R&D sub-model based on the R&D performance data, the plastic product R&D sub-model including an impact resistance sub-model, a heat resistance sub-model, a corrosion resistance sub-model and a surface gloss sub-model; performing sub-model integration on the constructed plastic product R&D sub-model to obtain the plastic product R&D model, the sub-model integration means fusing the plastic product R&D sub-models.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0024] 1. Performance classification results are obtained by classifying the collected plastic product research and development data, and then performance testing is performed based on the performance classification results to obtain performance evaluation results. Then, based on the performance evaluation results, a comprehensive evaluation is performed on the plastic products to be tested to obtain comprehensive evaluation results and obtain grade classification results. Finally, a plastic product research and development model is constructed based on the comprehensive evaluation results of the plastic products to be tested and combined with the grade classification results, thereby achieving efficient use of plastic product research and development data, and then achieving an improvement in the utilization rate of plastic product research and development data in performance analysis, effectively solving the problem of low utilization rate of plastic product research and development data in performance analysis in the prior art.

[0025] 2. By obtaining the impact resistance test data coefficient corresponding to the impact resistance test data of the plastic product to be tested within a preset time interval, and then obtaining the impact resistance performance evaluation index of the plastic product to be tested based on the obtained impact resistance test data coefficient, and finally evaluating the impact resistance of the plastic product to be tested through the obtained impact resistance performance evaluation index, the impact resistance of the plastic product to be tested is quantified, thereby achieving a more accurate evaluation of the impact resistance of the plastic product to be tested.

[0026] 3. The heat resistance test data score of the plastic to be tested within a preset time interval is obtained by extracting feature data from the heat resistance test data, and then the heat resistance performance evaluation index is obtained based on the obtained heat resistance test data score. Finally, the heat resistance of the plastic product to be tested is evaluated based on the calculated heat resistance performance evaluation index, thereby realizing the digitization of the heat resistance performance of the plastic product to be tested, and further realizing a more accurate evaluation of the heat resistance performance of the plastic product to be tested. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of the structure of a research and development data processing system based on plastic products provided in an embodiment of the present application;

[0028] Figure 2 A schematic diagram of the structure of a research and development data module in a research and development data processing system based on plastic products provided in an embodiment of the present application;

[0029] Figure 3 A schematic diagram of the structure of a performance detection module in a research and development data processing system based on plastic products provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiment of the present application solves the problem of low utilization rate of R&D data of plastic products in performance analysis in the prior art by providing a R&D data processing system based on plastic products. The system collects the plastic product R&D data of plastic products to be tested within a preset time interval in real time, classifies the plastic product R&D data according to preset performance classification indicators to obtain performance classification results, then performs performance testing on the plastic products to be tested based on the performance classification results to obtain performance evaluation results, then performs comprehensive evaluation on the plastic products to be tested based on the performance evaluation results to obtain comprehensive evaluation results, comprehensively grades the plastic products to be tested based on the comprehensive evaluation results to obtain grade classification results, and finally constructs a plastic product R&D model based on the comprehensive evaluation results of the plastic products to be tested, thereby improving the utilization rate of R&D data of plastic products in performance analysis.

[0031] The technical solution in the embodiment of the present application is to solve the problem of low utilization rate of the research and development data of the above-mentioned plastic products in performance analysis, and the overall idea is as follows:

[0032] The collected plastic product research and development data are classified by performance to obtain performance classification results, and then performance testing is performed based on the performance classification results to obtain performance evaluation results. Next, a comprehensive evaluation is performed on the plastic products to be tested based on the performance evaluation results to obtain comprehensive evaluation results and obtain grade classification results. Finally, a plastic product research and development model is constructed based on the comprehensive evaluation results of the plastic products to be tested and combined with the grade classification results, thereby achieving the effect of improving the utilization rate of plastic product research and development data in performance analysis.

[0033] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0034] like Figure 1As shown, it is a structural schematic diagram of a research and development data processing system based on plastic products provided in an embodiment of the present application. The research and development data processing system based on plastic products provided in an embodiment of the present application includes: a research and development data module, a performance detection module, a comprehensive evaluation module and a plastic product research and development model construction module; wherein the research and development data module is used to collect the plastic product research and development data of the plastic products to be detected within a preset time interval in real time, and classify the plastic product research and development data according to the preset performance classification index to obtain the performance classification results and store them, and the plastic product research and development data represents the information data generated during the research and development process of the plastic products to be detected; the performance detection module is used to perform performance detection on the plastic products to be detected according to the performance classification results to obtain performance evaluation results, and the performance evaluation results include impact resistance results and heat resistance results; the comprehensive evaluation module is used to perform comprehensive evaluation on the plastic products to be detected according to the performance evaluation results to obtain comprehensive evaluation results, and comprehensively classify the plastic products to be detected in combination with the comprehensive evaluation results to obtain a grade classification result; the plastic product research and development model construction module is used to construct a plastic product research and development model according to the comprehensive evaluation results of the plastic products to be detected, and the plastic product research and development model is used to visualize the grade classification results of the plastic products to be detected.

[0035] In this embodiment, preset performance classification indicators are used to classify the performance types of plastic product R&D data; after clarifying the preset performance classification indicators of plastic products, a corresponding classification model is established according to the preset performance classification indicators by using statistical methods, machine learning algorithms or rule engines, etc. The classification model can classify the plastic products to be tested in the R&D data into different categories according to their performance characteristics, and the plastic product R&D data is input into the classification model, and data classification operations are performed according to preset performance classification indicators and quantitative standards, and each plastic product is automatically classified into a corresponding performance category; the comprehensive evaluation result is used to describe the comprehensive performance of the plastic product to be tested; the R&D data module transmits the plastic product R&D data to the performance detection module, the performance detection module transmits the performance evaluation result to the comprehensive evaluation module, and the comprehensive evaluation module transmits the grade division result to the plastic product R&D model construction module; the comprehensive grade division is based on the preset performance grade; the utilization rate of the R&D data of plastic products in performance analysis is improved.

[0036] Further, such as Figure 2As shown, it is a structural schematic diagram of a R&D data module in a R&D data processing system based on plastic products provided in an embodiment of the present application, and the R&D data module includes a R&D data acquisition unit, a R&D data processing unit and a R&D data storage unit; the R&D data acquisition unit: used for collecting the initial plastic product R&D data of the plastic product to be tested in real time; the R&D data processing unit: used for pre-processing the initial plastic product R&D data to obtain the plastic product R&D data, and classifying the plastic product R&D data according to preset performance classification indicators; the R&D data storage unit: used for storing and managing the obtained plastic product R&D data.

[0037] In this embodiment, preprocessing includes data cleaning, data integration and data conversion to ensure data quality and accuracy. In data collection, data may be missing due to various reasons such as sensor failure and data transmission problems. If these missing values ​​are not processed, it will lead to information loss and degradation of model performance; outliers are usually caused by measurement errors, equipment failures or other unforeseen factors. These values ​​will seriously interfere with the accuracy of data analysis, so they need to be identified and removed through statistical methods or domain knowledge; storage and management of collected plastic product R&D data can include databases, file systems and cloud storage to ensure data security and reliability; the accuracy of obtaining plastic product R&D data of the plastic products to be tested is improved.

[0038] Further, such as Figure 3 As shown, it is a structural schematic diagram of a performance detection module in a research and development data processing system based on plastic products provided in an embodiment of the present application, the performance detection module includes an impact resistance detection unit and a heat resistance detection unit; the impact resistance detection unit: used to detect the impact resistance of the plastic product to be detected to obtain an impact resistance performance evaluation index, compare and analyze the impact resistance performance evaluation index with the corresponding threshold value to obtain the impact resistance performance result of the plastic product to be detected, and the impact resistance performance evaluation index is used to describe the impact resistance of the plastic product to be detected; the heat resistance performance detection unit: used to detect the heat resistance of the plastic product to be detected to obtain a heat resistance performance evaluation index, compare and analyze the heat resistance performance evaluation index with the corresponding threshold value to obtain the heat resistance performance result of the plastic product to be detected, and the heat resistance performance evaluation index is used to describe the heat resistance of the plastic product to be detected.

[0039] In this embodiment, impact resistance is to evaluate the resistance of the plastic product to be tested when subjected to external impact or collision, and is usually used to measure the ability of the plastic product to be tested to withstand impact and extrusion in actual use; heat resistance is to evaluate the stability and performance of the plastic product to be tested in a high temperature environment; and the different performances of the plastic products to be tested are classified and evaluated by using the research and development data of plastic products of different performance types.

[0040] Furthermore, the specific process of comprehensive evaluation of the plastic products to be tested is as follows: using data analysis methods to compare and verify the plastic product R&D data with the corresponding preset plastic product R&D data to extract the R&D performance data of the plastic products to be tested, the R&D performance data is used to describe the classification performance of the plastic products to be tested within a preset time interval, the R&D performance data includes impact resistance test data, heat resistance test data, corrosion resistance data and surface gloss data, the corrosion resistance data is used to measure the chemical properties of the plastic products to be tested, and the surface gloss data is used to measure the appearance quality of the plastic products to be tested; according to the calculated impact resistance performance evaluation index and heat resistance performance evaluation index, combined with the corrosion resistance data and the surface gloss data, a comprehensive evaluation index is obtained, and a comprehensive evaluation result is obtained based on the comprehensive evaluation index, and the comprehensive evaluation index is used to numerically describe the comprehensive performance of the plastic products to be tested.

[0041] In this embodiment, the data analysis methods generally include comparative analysis, grouping analysis, structural analysis, cross analysis, funnel plot analysis, matrix analysis, descriptive statistics, inferential statistics, data visualization and machine learning algorithms. One or more methods can be selected for data analysis according to actual conditions. The comparative analysis method mainly used is a method of comparing two or more plastic product R&D data to reveal the differences and regularities between them, so as to help us understand the changing trends, differences and reasons of plastic product R&D data, and finally screen out the required R&D performance data of the plastic products to be tested. The impact resistance test data includes impact strength data, impact absorption capacity data, fracture toughness data, etc. The heat resistance test data includes heat deformation temperature index, transition temperature parameter, thermal expansion coefficient, thermal conductivity index, oxidation stability index and thermal stabilizer content coefficient; the impact strength data indicates the maximum energy obtained when the plastic product to be tested is subjected to impact load, the impact absorption capacity data indicates the ability of the plastic product to be tested to absorb and disperse energy under impact loading, the fracture toughness index indicates the ability of the plastic product to be tested to resist crack propagation and fracture when subjected to impact and load, and the breakdown strength data indicates the ability of the plastic product to be tested to resist penetration when subjected to impact by sharp objects; the heat deformation temperature index indicates the maximum energy obtained when the plastic product to be tested is subjected to impact load, the impact absorption capacity data indicates the ability of the plastic product to be tested to absorb and disperse energy under impact loading, the fracture toughness index indicates the ability of the plastic product to be tested to resist crack propagation and fracture when subjected to impact and load, and the breakdown strength data indicates the ability of the plastic product to be tested to resist penetration when subjected to impact by sharp objects; the heat deformation temperature index indicates the maximum energy obtained when the plastic product to be tested is subjected to impact load after pre- The temperature at which continuous deformation occurs under load is set, the transition temperature parameter indicates the critical temperature at which the plastic product to be tested changes from solid to liquid during the heating process, the thermal expansion coefficient indicates the volume change rate of the plastic product to be tested when the temperature changes, the thermal conductivity index indicates the ability of the plastic product to be tested to conduct heat under a unit temperature gradient, the oxidation stability index indicates the antioxidant ability of the plastic product to be tested in a preset high temperature environment, and the heat stabilizer content coefficient indicates the heat stabilizer content of the plastic product to be tested in a preset high temperature environment; corrosion resistance is one of the key indicators for evaluating the chemical properties of plastic products, which reflects the corrosion resistance of the plastic product to be tested to various chemical media (such as acids, alkalis and solvents, etc.) Generally, the corrosion resistance data of plastics is obtained through immersion tests. The corresponding data can be obtained in combination with actual needs and experimental conditions. The corrosion resistance data indicates the corrosion resistance of the plastic products to be tested when subjected to chemical corrosion or electrochemical corrosion. The surface glossiness data indicates the ability of the surface of the plastic products to be tested to reflect light. Surface glossiness is one of the important indicators for evaluating the appearance quality of plastic products. It reflects the glossiness of the surface of the plastic products to be tested, which directly affects the appearance of the products. Generally, the surface glossiness data of plastic products is measured by a gloss meter. The corresponding data can be obtained in combination with actual needs and experimental conditions. It realizes the accurate comprehensive evaluation of the classification of plastic products to be tested.

[0042] Furthermore, the specific method of comprehensive grade classification is as follows: the comprehensive evaluation index of the plastic product to be tested is graded according to the comprehensive performance matching index of the plastic product to be tested and combined with the preset performance grade. The comprehensive performance matching index is used to describe the degree of matching between the comprehensive evaluation index and the preset performance grade.

[0043] In this embodiment, the general comprehensive grade classification methods include weighted average method, worst performance method, multi-factor analysis method, fuzzy comprehensive evaluation method, hierarchical analysis method and principal component analysis method. The applicable situations of each method are different. For example, the weighted average method is applicable to situations where there are clear priorities or importance differences between indicators. Specifically, one or more methods can be selected for comprehensive grade classification according to actual conditions, thereby achieving improved accuracy of comprehensive grade classification.

[0044] Furthermore, the specific method for obtaining the comprehensive evaluation index is as follows: extract the corrosion resistance data and surface glossiness data of the plastic product to be tested within a preset time interval from the R&D performance data, and obtain the impact resistance performance evaluation index and the heat resistance performance evaluation index, and at the same time obtain the corresponding threshold and reference deviation; calculate the comprehensive evaluation index of the plastic product to be tested based on the acquired data. The comprehensive evaluation index is used to describe the comprehensive performance. In addition to being directly obtained through sample testing, it can also be more accurately calculated through a formula. The comprehensive evaluation index is calculated using the following formula:

[0045] ,

[0046] In the formula, f represents the serial number of the plastic product to be tested, , F is the total number of plastic products to be tested, is the comprehensive evaluation index of the fth plastic product to be tested, is the impact resistance evaluation index of the fth plastic product to be tested, is the threshold value of the impact resistance evaluation index, is the reference deviation of the impact resistance evaluation index, is the corrosion resistance data of the fth plastic product to be tested, is the threshold value of corrosion resistance data, is the reference deviation of the corrosion resistance data, is the heat resistance evaluation index of the fth plastic product to be tested, is the threshold value of the heat resistance evaluation index, is the reference deviation of the heat resistance performance evaluation index, is the surface glossiness data of the fth plastic product to be tested, is the threshold of the surface glossiness data, is the reference deviation of the surface glossiness data, It is the reference relative deviation of the impact resistance evaluation index.

[0047] In this embodiment, the threshold value of the impact resistance performance evaluation index is obtained by analyzing and counting the impact resistance performance evaluation index of the plastic products to be tested based on a statistical method, the threshold value of the corrosion resistance data is obtained by analyzing and counting the corrosion resistance data of the plastic products to be tested based on a statistical method, the threshold value of the heat resistance performance evaluation index is obtained by analyzing and counting the heat resistance performance evaluation index of the plastic products to be tested based on a statistical method, and the threshold value of the surface gloss data is obtained by analyzing and counting the appearance quality evaluation data of the plastic products to be tested based on a statistical method; the reference deviation of the impact resistance performance evaluation index is obtained by analyzing and counting the impact resistance performance evaluation index of the plastic products to be tested through a reference deviation calculation formula, and the reference deviation of the corrosion resistance data is obtained by analyzing and counting the reference deviation calculation formula of the plastic products to be tested. The corrosion resistance data of the product is analyzed and counted, the reference deviation of the heat resistance performance evaluation index is obtained by analyzing and counting the heat resistance performance evaluation index of the plastic product to be tested through the reference deviation calculation formula, and the reference deviation of the surface gloss data is obtained by analyzing and counting the surface gloss data of the plastic product to be tested through the reference deviation calculation formula; when the impact resistance performance evaluation index is greater than the corresponding threshold, it has met the performance standards of qualified plastic products, so the comprehensive evaluation index is 1. When it is not greater than the corresponding threshold, a specific calculation is performed. The impact resistance performance evaluation index has the greatest impact, the heat resistance performance evaluation index has the second greatest impact, and the corrosion resistance data and surface gloss data have the least impact; the digitization of the comprehensive performance of the plastic products to be tested is realized, and the accuracy of the comprehensive performance of the plastic products to be tested is further improved.

[0048] Furthermore, a specific method for obtaining the impact resistance performance evaluation index is as follows: obtain the impact resistance test data coefficient corresponding to the impact resistance test data of the plastic product to be tested within a preset time interval, and obtain the impact resistance performance evaluation index of the plastic product to be tested according to the obtained impact resistance test data coefficient; the impact resistance test data coefficient includes the impact strength data coefficient, the impact absorption capacity data coefficient, the fracture toughness index coefficient and the breakdown strength data coefficient.

[0049] In this embodiment, the impact resistance evaluation index describes the impact resistance. In addition to being directly obtained through impact testing, it can also be more accurately calculated through a formula. The impact resistance evaluation index is calculated using the following formula:

[0050] ,

[0051] In the formula, e is a natural constant, is the impact strength data coefficient of the fth plastic product to be tested, is the reference deviation of the impact strength data coefficient, is the impact absorption capacity coefficient of the fth plastic product to be tested, is the reference deviation of the impact absorption capacity data coefficient, is the fracture toughness index coefficient of the fth plastic product to be tested, is the reference deviation of the fracture toughness index coefficient, is the breakdown strength coefficient of the fth plastic product to be tested, is the reference deviation of the breakdown strength data coefficient.

[0052] Specifically, the impact strength data coefficient is the absolute value of the ratio of the impact strength data to the corresponding reference value and the difference between the corresponding reference value, and the expression is: , is the impact strength data of the fth plastic product to be tested, is the reference impact strength data of the fth plastic product to be tested, and the reference impact strength data is obtained by analyzing and counting the impact strength data of the plastic product to be tested and taking the average value; the impact absorption capacity data coefficient is the absolute value of the ratio of the impact absorption capacity data to the corresponding reference value and the difference between the corresponding reference value, and the expression is: , is the impact absorption capacity data of the fth plastic product to be tested, is the reference impact absorption capacity data of the fth plastic product to be tested, and the reference impact absorption capacity data is obtained by analyzing and statistically analyzing the impact absorption capacity data of the plastic product to be tested and taking the average value; the fracture toughness index coefficient is the absolute value of the ratio of the fracture toughness index to the corresponding reference value and the difference between the corresponding reference value, and the expression is: , is the fracture toughness index of the fth plastic product to be tested, is the reference fracture toughness index of the fth plastic product to be tested. The reference fracture toughness index is obtained by analyzing and averaging the fracture toughness index of the plastic product to be tested. The breakdown strength data coefficient is the absolute value of the ratio of the breakdown strength data to the corresponding reference value and the difference between the corresponding reference value. The expression is: , is the breakdown strength data of the fth plastic product to be tested, is the reference breakdown strength data of the fth plastic product to be tested, and the reference breakdown strength data is obtained by analyzing and statistically analyzing the breakdown strength data of the plastic products to be tested and taking the average value; the impact strength data is usually expressed as the maximum impact energy or stress value in the impact test; good impact absorption capacity data can reduce the damage caused by the impact and protect the plastic products to be tested; the fracture toughness index reflects the ability of the plastic products to be tested to resist fracture; the breakdown strength data is usually used to evaluate the puncture resistance of the plastic products to be tested when hit by sharp objects; the impact strength data coefficient has the greatest influence, the breakdown strength data coefficient has the second greatest influence, and the impact absorption capacity data coefficient and the fracture toughness index coefficient have the least influence; the digitization of the impact resistance of the plastic products to be tested is realized, and the detection accuracy of the impact resistance of the plastic products to be tested is further improved.

[0053] Furthermore, the specific method for obtaining the heat resistance performance evaluation index is as follows: extract feature data from the heat resistance test data to obtain the heat resistance test data score of the plastic to be tested within a preset time interval, and obtain the heat resistance performance evaluation index of the plastic product to be tested according to the obtained heat resistance test data score; the heat resistance test data score includes the heat deformation temperature index score, the transition temperature parameter score, the thermal expansion coefficient score, the thermal conductivity index score, the oxidation stability index score and the thermal stabilizer content coefficient score; the heat resistance performance evaluation index describes the heat resistance performance. In addition to being directly obtained through heat resistance testing, it can also be more accurately calculated through formulas. The heat resistance performance evaluation index is calculated using the following formula:

[0054] ,

[0055] In the formula, e is a natural constant, is the heat deformation temperature index score of the fth plastic product to be tested, is the reference deviation of the heat deformation temperature index score, is the transition temperature parameter score of the fth plastic product to be tested, is the reference deviation of the transition temperature parameter fraction, is the thermal conductivity index score of the fth plastic product to be tested, is the reference deviation of the thermal conductivity index score, is the oxidation stability index score of the fth plastic product to be tested, is the reference deviation of the oxidation stability index score, is the coefficient fraction of the heat stabilizer content of the fth plastic product to be tested, is the reference deviation of the thermal stabilizer content coefficient fraction, is the thermal expansion coefficient fraction of the fth plastic product to be tested, is the reference deviation of the thermal expansion coefficient fraction.

[0056] In this embodiment, the heat deformation temperature index score is the absolute value of the ratio of the heat deformation temperature index to the corresponding reference value and the difference between the corresponding reference value, and the expression is: , is the heat deformation temperature index of the fth plastic product to be tested, is the reference heat deformation temperature index of the fth plastic product to be tested, and the reference heat deformation temperature index is obtained by analyzing and counting the heat deformation temperature index of the plastic product to be tested and taking the average value; the transition temperature parameter score is the absolute value of the ratio of the transition temperature parameter to the corresponding reference value and the difference between the corresponding reference value, and the expression is , is the transition temperature parameter of the fth plastic product to be tested, is the reference transition temperature parameter of the fth plastic product to be tested, and the reference transition temperature parameter is obtained by analyzing and counting the transition temperature parameters of the plastic product to be tested and taking the average value; the thermal expansion coefficient fraction is the absolute value of the ratio of the thermal expansion coefficient to the corresponding reference value and the difference between the corresponding reference value, and the expression is , is the thermal expansion coefficient of the fth plastic product to be tested, is the reference thermal expansion coefficient of the fth plastic product to be tested, which is obtained by analyzing and averaging the thermal expansion coefficients of the plastic products to be tested; the thermal conductivity index score is the absolute value of the ratio of the thermal conductivity index to the corresponding reference value and the difference between the corresponding reference value, and the expression is: , is the thermal conductivity index of the fth plastic product to be tested, is the reference thermal conductivity index of the fth plastic product to be tested, and the reference thermal conductivity index is obtained by analyzing and counting the thermal conductivity index of the plastic product to be tested and taking the average value; the oxidation stability index score is the absolute value of the ratio of the oxidation stability index to the corresponding reference value and the difference between the corresponding reference value, and the expression is , is the oxidation stability index of the fth plastic product to be tested, is the reference oxidation stability index of the fth plastic product to be tested, and the reference oxidation stability index is obtained by analyzing and statistically analyzing the oxidation stability index of the plastic product to be tested and taking the average value; the heat stabilizer content coefficient score is the absolute value of the ratio of the heat stabilizer content coefficient to the corresponding reference value and the difference between the corresponding reference value; the expression is , is the heat stabilizer content coefficient of the fth plastic product to be tested, is the reference heat stabilizer content coefficient of the fth plastic product to be tested, and the reference heat stabilizer content coefficient is obtained by analyzing and statistically analyzing the heat stabilizer content coefficients of the plastic products to be tested and taking the average value; the heat deformation temperature index is often used to evaluate the stability and load-bearing capacity of the tested plastic products in a high temperature environment; the transition temperature parameter is an important performance parameter of the tested plastic products, which affects the rigidity, elasticity and deformation behavior of the tested plastic products; for materials that need to be used in an environment with large temperature changes, understanding their thermal expansion coefficient can help design engineers predict and compensate for the dimensional changes of the tested plastic products under temperature changes; the tested plastic products with high thermal conductivity indicators can conduct heat faster, and in a high temperature environment, good thermal conductivity can help the tested plastic products maintain stability and performance; at high temperatures , plastic products under test are prone to oxidation reactions, resulting in performance degradation or even failure. The oxidation stability index of plastic products under test is crucial for applications in high temperature environments; for some plastic products under test that need to be used in high temperature environments, heat stabilizers can be added to improve their heat resistance. The heat stabilizer content coefficient is also one of the important indicators for evaluating the heat resistance of materials; the heat deformation temperature index score has the greatest influence, followed by the oxidation stability index score and the thermal expansion coefficient score, and the thermal expansion coefficient score is negatively correlated with the heat resistance evaluation index, and the transition temperature parameter score, thermal conductivity index score and thermal stabilizer content coefficient score have the least influence; the digitization of the heat resistance of the plastic products to be tested is realized, and the detection accuracy of the heat resistance of the plastic products to be tested is further improved.

[0057] Furthermore, a specific method for obtaining the comprehensive performance matching index is as follows: extracting the R&D performance characteristic data of the plastic product to be tested from the R&D performance data, performing a correlation analysis on the R&D performance characteristic data in combination with the preset performance level to obtain a correlation analysis result; and obtaining the comprehensive performance matching index based on the correlation analysis result.

[0058] In this embodiment, the collected R&D performance data is first cleaned by checking the completeness, accuracy, consistency and validity of the data to eliminate duplicate, erroneous or inconsistent data; then, relevant performance characteristic data is selected according to the needs and goals; then, the key features related to performance are extracted from the original data using machine learning methods and the key features are standardized or normalized to obtain the R&D performance characteristic data; association analysis, also known as association mining, is a technology for finding frequent patterns, associations, correlations or causal results between sets of items or sets of objects in transaction data, relational data or other information carriers; association analysis can be used for feature screening, including retaining features with a large correlation with the target variable and deleting highly correlated features, which helps to optimize in feature engineering Model performance; when the R&D performance characteristic data are subjected to correlation analysis in combination with the preset performance level, the purpose is to find out the potential relationship between the performance characteristic data and the preset performance level, so as to better understand the key factors affecting the performance of plastic products. The R&D performance characteristic data of the plastic products to be tested can be matched with the corresponding preset performance level and scored or classified to classify them into the corresponding performance level. According to the type and characteristics of the R&D performance characteristic data, a suitable correlation analysis method is selected. Common methods include correlation analysis, regression analysis and cluster analysis. The R&D performance characteristic data are processed using the selected correlation analysis method and a comprehensive performance matching index is further obtained. The matching degree of the R&D performance characteristic data is digitized, which further improves the intuitiveness of the matching degree.

[0059] Furthermore, the specific construction steps of the plastic product R&D model are as follows: construct a plastic product R&D sub-model based on the R&D performance data, the plastic product R&D sub-model includes an impact resistance sub-model, a heat resistance sub-model, a corrosion resistance sub-model and a surface gloss sub-model; perform sub-model integration on the constructed plastic product R&D sub-model to obtain the plastic product R&D model, and the sub-model integration means fusing the plastic product R&D sub-models.

[0060] In this embodiment, the impact resistance sub-model is used to monitor the impact resistance of the plastic products to be tested in real time, the heat resistance sub-model is used to monitor the heat resistance of the plastic products to be tested in real time, the corrosion resistance sub-model is used to obtain the chemical properties of the plastic products to be tested in real time, and the surface gloss sub-model is used to evaluate the appearance quality of the plastic products to be tested; the impact resistance test data, heat resistance test data, corrosion resistance data and surface gloss data can all be obtained through experimental tests, manufacturers, scientific literature, standards and specifications, and online databases. Combined with the four constructed sub-models, the accuracy of plastic product R&D model construction is improved.

[0061] It should be understood that the most common method of experimental testing is to obtain impact resistance test data of the plastic products to be tested through laboratory testing. These tests usually include tensile tests, compression tests, bending tests and hardness tests, etc., to evaluate the strength, stiffness and toughness of the plastic products to be tested. Experimental testing is usually conducted by professional institutions or laboratories and is carried out in accordance with standard test methods.

[0062] The technical scheme in the above-mentioned embodiment of the present application has at least the following technical effects or advantages: relative to a color leaf research and development data processing method and system in the invention patent announcement with announcement number: CN117313017B, the embodiment of the present application obtains the impact resistance detection data coefficient corresponding to the impact resistance detection data of the plastic product to be detected within a preset time interval, and then obtains the impact resistance performance evaluation index of the plastic product to be detected according to the obtained impact resistance detection data coefficient, and finally evaluates the impact resistance of the plastic product to be detected by the obtained impact resistance performance evaluation index, thereby realizing the digitization of the impact resistance of the plastic product to be detected, and further improving the impact resistance of the plastic product to be detected. A more accurate evaluation of the impact resistance of the plastic product to be tested is achieved; compared with the FPGA-based data processing system announced in the invention patent with announcement number: CN110941585B, the embodiment of the present application obtains the heat resistance test data score of the plastic to be tested within a preset time interval by performing feature data extraction on the heat resistance test data, and then obtains the heat resistance performance evaluation index based on the obtained heat resistance test data score, and finally evaluates the heat resistance of the plastic product to be tested based on the calculated heat resistance performance evaluation index, thereby realizing the digitization of the heat resistance performance of the plastic product to be tested, and further realizing a more accurate evaluation of the heat resistance performance of the plastic product to be tested.

[0063] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that performs the functions specified in one or more boxes.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 The performance specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the performance specified in one or more boxes.

[0067] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0068] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A research and development data processing system based on plastic products, characterized in that: It includes R&D data module, performance testing module, comprehensive evaluation module and plastic product R&D model building module; The R&D data module is used to collect the plastic product R&D data of the plastic product to be tested within a preset time interval in real time, classify the plastic product R&D data according to the preset performance classification index to obtain the performance classification result and store it, and the plastic product R&D data represents the information data generated during the R&D process of the plastic product to be tested; The performance detection module is used to perform performance detection on the plastic product to be detected according to the performance classification result to obtain a performance evaluation result, wherein the performance evaluation result includes an impact resistance performance result and a heat resistance performance result; The comprehensive evaluation module is used to comprehensively evaluate the plastic products to be tested according to the performance evaluation results to obtain a comprehensive evaluation result, and to comprehensively classify the plastic products to be tested based on the comprehensive evaluation results to obtain a classification result; The plastic product R&D model building module is used to build a plastic product R&D model according to the comprehensive evaluation results of the plastic products to be tested, and the plastic product R&D model is used to visualize the grade classification results of the plastic products to be tested; The R&D data module includes a R&D data acquisition unit, a R&D data processing unit and a R&D data storage unit; The R&D data collection unit is used to collect the initial plastic product R&D data of the plastic product to be tested in real time; The R&D data processing unit is used to pre-process the initial plastic product R&D data to obtain plastic product R&D data, and to classify the plastic product R&D data according to preset performance classification indicators; The R&D data storage unit is used to store and manage the obtained plastic product R&D data; The performance detection module includes an impact resistance detection unit and a heat resistance detection unit; The impact resistance detection unit is used to detect the impact resistance of the plastic product to be detected to obtain an impact resistance evaluation index, and compare and analyze the impact resistance evaluation index with a corresponding threshold to obtain an impact resistance result of the plastic product to be detected, wherein the impact resistance evaluation index is used to describe the impact resistance of the plastic product to be detected; The heat resistance performance detection unit is used to detect the heat resistance performance of the plastic product to be detected to obtain a heat resistance performance evaluation index, and compare and analyze the heat resistance performance evaluation index with the corresponding threshold to obtain the heat resistance performance result of the plastic product to be detected, wherein the heat resistance performance evaluation index is used to describe the heat resistance performance of the plastic product to be detected; The specific process of comprehensive evaluation of the plastic products to be tested is as follows: Using a data analysis method, the plastic product R&D data is compared and verified with the corresponding preset plastic product R&D data to extract R&D performance data of the plastic product to be tested, wherein the R&D performance data is used to describe the classification performance of the plastic product to be tested within a preset time interval, and the R&D performance data includes impact resistance test data, heat resistance test data, corrosion resistance data and surface gloss data, wherein the corrosion resistance data is used to measure the chemical properties of the plastic product to be tested, and the surface gloss data is used to measure the appearance quality of the plastic product to be tested; According to the calculated impact resistance evaluation index and heat resistance evaluation index, a comprehensive evaluation index is obtained in combination with the corrosion resistance data and the surface glossiness data, and a comprehensive evaluation result is obtained according to the comprehensive evaluation index, wherein the comprehensive evaluation index is used to numerically describe the comprehensive performance of the plastic product to be tested; The specific method of the comprehensive level classification is as follows: Performing level matching on the obtained comprehensive evaluation index of the plastic product to be tested according to the comprehensive performance matching index of the plastic product to be tested and in combination with the preset performance level, wherein the comprehensive performance matching index is used to describe the matching degree between the comprehensive evaluation index and the preset performance level; The specific method for obtaining the comprehensive evaluation index is as follows: Extract the corrosion resistance data and surface glossiness data of the plastic product to be tested within a preset time interval from the R&D performance data, obtain the impact resistance performance evaluation index and the heat resistance performance evaluation index, and obtain the corresponding threshold value and reference deviation; The comprehensive evaluation index of the plastic product to be tested is calculated based on the acquired data. The comprehensive evaluation index is calculated using the following formula: ， In the formula, f represents the serial number of the plastic product to be tested, , F is the total number of plastic products to be tested, is the comprehensive evaluation index of the fth plastic product to be tested, is the impact resistance evaluation index of the fth plastic product to be tested, is the threshold value of the impact resistance evaluation index, is the reference deviation of the impact resistance evaluation index, is the corrosion resistance data of the fth plastic product to be tested, is the threshold value of corrosion resistance data, is the reference deviation of the corrosion resistance data, is the heat resistance evaluation index of the fth plastic product to be tested, is the threshold value of the heat resistance evaluation index, is the reference deviation of the heat resistance performance evaluation index, is the surface glossiness data of the fth plastic product to be tested, is the threshold of the surface glossiness data, is the reference deviation of the surface glossiness data, It is the reference relative deviation of the impact resistance evaluation index; The specific method for obtaining the impact resistance evaluation index is as follows: Obtaining an impact resistance test data coefficient corresponding to the impact resistance test data of the plastic product to be tested within a preset time interval, and obtaining an impact resistance performance evaluation index of the plastic product to be tested according to the obtained impact resistance test data coefficient; The impact resistance test data coefficients include impact strength data coefficients, impact absorption capacity data coefficients, fracture toughness index coefficients and breakdown strength data coefficients; The specific method for obtaining the heat resistance evaluation index is as follows: Extract characteristic data from the heat resistance test data to obtain the heat resistance test data score of the plastic to be tested within a preset time interval, and obtain the heat resistance performance evaluation index of the plastic product to be tested based on the obtained heat resistance test data score; The heat resistance test data scores include heat deformation temperature index score, transition temperature parameter score, thermal expansion coefficient score, thermal conductivity index score, oxidation stability index score and thermal stabilizer content coefficient score; The heat resistance evaluation index is calculated using the following formula: ， In the formula, e is a natural constant, is the heat deformation temperature index score of the fth plastic product to be tested, is the reference deviation of the heat deformation temperature index score, is the transition temperature parameter score of the fth plastic product to be tested, is the reference deviation of the transition temperature parameter fraction, is the thermal conductivity index score of the fth plastic product to be tested, is the reference deviation of the thermal conductivity index score, is the oxidation stability index score of the fth plastic product to be tested, is the reference deviation of the oxidation stability index score, is the coefficient fraction of the heat stabilizer content of the fth plastic product to be tested, is the reference deviation of the thermal stabilizer content coefficient fraction, is the thermal expansion coefficient fraction of the fth plastic product to be tested, is the reference deviation of the thermal expansion coefficient fraction.

2. A plastic product-based R&D data processing system as claimed in claim 1, characterized in that: The specific method for obtaining the comprehensive performance matching index is as follows: The R&D performance characteristic data of the plastic product to be tested is extracted from the R&D performance data, and the R&D performance characteristic data is subjected to correlation analysis in combination with the preset performance level to obtain the correlation analysis result, and the comprehensive performance matching index is obtained according to the correlation analysis result.

3. A plastic product-based R&D data processing system as claimed in claim 1, characterized in that: The specific construction steps of the plastic product R&D model are as follows: Constructing a plastic product R&D sub-model according to the R&D performance data, wherein the plastic product R&D sub-model includes an impact resistance sub-model, a heat resistance sub-model, a corrosion resistance sub-model and a surface gloss sub-model; The constructed plastic product R&D sub-models are integrated to obtain a plastic product R&D model, wherein the sub-model integration represents fusing the plastic product R&D sub-models.

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