Method for detecting performance of heat stabilizer for PVC (polyvinyl chloride) product

Abstract

The invention relates to the technical field of material detection, in particular to a method for detecting the performance of a heat stabilizer for a PVC (polyvinyl chloride) product, which comprises the following steps: acquiring a multi-dimensional data sequence formed by production data and test data of the PVC product and a standard sequence corresponding to the data sequence of each dimension; according to the difference distribution between the data sequences of different dimensions of any batch of PVC products and the corresponding standard sequences, the trend influence degree of the data sequences of the PVC products under each dimension is obtained; obtaining the environmental condition influence degree of each batch of PVC products according to the aggregation characteristics of the trend influence degrees of the corresponding data sequences of all batches of PVC products under all dimensions; and analyzing the influence degrees of all environmental conditions of the PVC product by using a neural network so as to evaluate the performance of the stabilizer in the PVC product. The accuracy of the performance evaluation result of the heat stabilizer in the PVC product is improved.

Description

Technical Field

[0001] The invention relates to the technical field of material detection, and in particular to a method for detecting the performance of a heat stabilizer used for PVC products. Background Art

[0002] Polyvinyl chloride (PVC) is a commonly used thermoplastic resin with low price and good performance, so it is widely used in many industries. However, industrially produced PVC has some unstable structures, such as secondary chlorine, terminal allyl chloride, etc. These unstable structures will cause the PVC molecular chain to break when exposed to heat, causing the overall color of the product to darken and the mechanical properties to decrease. Thermal stabilizer modification is to slow down the degradation rate of PVC by adding an appropriate amount of stabilizer during production, mainly by replacing unstable Cl atoms and destroying the double bond conjugated system.

[0003] The performance test of heat stabilizers used for PVC products usually adopts experimental methods to test the content ratio of heat stabilizers and practical effects. There are many testing methods, including heat aging oven test, thermogravimetric test, etc. In the actual production environment, the performance of heat stabilizers should be obtained in a complex production environment, that is, it can effectively cope with the changes in complex factors in the production environment and achieve stable guarantee of the quality of finished PVC products. Summary of the invention

[0004] The invention provides a method for detecting the performance of a heat stabilizer for PVC products to solve the existing problems.

[0005] The method for detecting the performance of the heat stabilizer for PVC products of the present invention adopts the following technical scheme: One embodiment of the present invention provides a method for detecting the performance of a heat stabilizer for PVC products, the method comprising the following steps: Obtain data sequences formed by production data and test data of PVC products respectively, and use each data sequence as a data sequence of PVC products in a corresponding dimension, and obtain a standard sequence corresponding to the data sequence in each dimension; According to the difference distribution between the data series of different dimensions of any batch of PVC products and the corresponding standard series, the trend influence degree of the data series of the PVC products in each dimension is obtained; According to the clustering characteristics of the trend influence degree of the corresponding data series of all batches of PVC products in all dimensions, the influence degree of environmental conditions of each batch of PVC products is obtained; A neural network is used to analyze the impact of all environmental conditions on PVC products, thereby evaluating the performance of stabilizers in PVC products.

[0006] The specific method for obtaining the trend influence degree of the data sequence of the PVC products in each dimension according to the difference distribution between the data sequence of different dimensions of any batch of PVC products and the corresponding standard sequence is as follows: Obtaining trend difference parameters of data points in the data sequence of any dimension of any batch of PVC products according to differences between data points in the data sequence of any dimension of the PVC products and data points in the corresponding standard sequence; Obtaining a trend influence coefficient of a data point in a data sequence of any dimension for each batch of PVC products according to a trend difference parameter of all data points in a preset neighborhood range for the data point in the data sequence of any dimension for each batch of PVC products; According to the differences between the trend influence coefficients of all adjacent data points in a data sequence of any dimension, the trend influence degree of the data sequence of the dimension is obtained.

[0007] The specific method for obtaining the trend difference parameter is: For each data point in the data sequence of each dimension, the difference in value between the data point and the data points in the same order in the standard sequence is obtained, and recorded as the trend difference parameter of the data point in the data sequence of the corresponding dimension.

[0008] The method of obtaining the trend influence coefficient of the data points in the data sequence of any dimension of each batch of PVC products according to the trend difference parameters of all data points in the preset neighborhood range in the data sequence of any dimension of each batch of PVC products includes the following specific methods: For Batch of PVC products in the In the data sequence under the dimension data points, the specific calculation method of the corresponding trend influence coefficient is: in, Indicates Batch of PVC products in the In the data sequence under the dimension The trend influence coefficient of the data point is Indicates the preset range radius. Indicates The first batch of PVC products The first dimension of the data sequence within a preset neighborhood of data points The trend difference parameter corresponding to the data points is represents the linear normalization function.

[0009] The method of obtaining the trend influence degree of the data sequence of any dimension according to the difference between the trend influence coefficients of all adjacent data points in the data sequence of the dimension includes the following specific methods: For Batch of PVC products in the The specific calculation method for the trend impact degree of the data series under the dimension is: in, Indicates Batch of PVC products in the The degree of influence of the trend of the data series under the dimension, Indicates Batch of PVC products The number of data points in the data series of dimensions, Indicates Batch of PVC products In the data series of dimensions The trend influence coefficient of the data point is Indicates Batch of PVC products In the data series of dimensions The trend influence coefficient of the data point is Gets the absolute value.

[0010] The specific method for obtaining the degree of environmental condition influence of each batch of PVC products according to the aggregation characteristics of the trend influence degree of the corresponding data series of all batches of PVC products in all dimensions is as follows: All batches of PVC products are clustered using the trend influence of the corresponding data series under all dimensions to obtain several clusters and the largest cluster center; The standardization of the data sequence of any dimension of any batch of PVC products is obtained, and the degree of influence of environmental conditions in any dimension of the batch of PVC products is calculated based on the standardization and the relative distance between the cluster center of the cluster where the PVC products are located and the maximum cluster center.

[0011] The method of clustering all batches of PVC products by using the trend influence of corresponding data sequences under all dimensions to obtain several clusters and the maximum cluster center includes the following specific methods: Obtain a vector of trend influence degrees of data sequences in all dimensions of any batch of PVC products, record it as the trend influence vector of the corresponding batch of PVC products, cluster all batches of PVC products according to the distance between trend influence vectors of different batches of PVC products and use density clustering algorithm to obtain several clusters; The cluster center corresponding to the cluster with the largest number of data points in all clusters is recorded as the maximum cluster center.

[0012] The specific method for obtaining the standard degree of the data sequence of any dimension of any batch of PVC products includes: Get the first The cosine similarity between the data sequence of the dimension and the standard sequence is recorded as the first The standardization of a data series in one dimension.

[0013] The method of calculating the degree of influence of environmental conditions on the batch of PVC products in any dimension according to the standard degree and the relative distance between the cluster center of the cluster where the PVC products are located and the maximum cluster center includes: The environmental condition influencing factor of the batch of PVC products in any dimension is calculated according to the standard degree and the relative distance between the cluster center of the cluster where the PVC products are located and the maximum cluster center; The maximum and minimum normalization functions are used to normalize the environmental condition influencing factors to obtain the degree of environmental condition influence.

[0014] The specific method for obtaining the environmental condition influencing factor is: The first The ratio of the distance between the cluster center of the cluster where the PVC products of the batches belong and the largest cluster center to the average distance between the cluster center of all clusters and the largest cluster center is used as the first The relative distance between the cluster center of the cluster where the batches of PVC products are located and the maximum cluster center; According to the relative distance and Batch of PVC products The standard degree of the data sequence of the dimensions is used to obtain the environmental condition influencing factor, the relative distance is positively correlated with the environmental condition influencing factor, and the standard degree is negatively correlated with the environmental condition influencing factor.

[0015] The beneficial effect of the technical solution of the present invention is that the embodiment of the present invention can accurately identify the influence of the heat stabilizer in each batch of PVC products by analyzing the difference between the production data of PVC products and the standard sequence batch by batch and dimension by dimension, and through cluster analysis of the influence degree of the trend of the multi-dimensional data sequence of PVC products, different batches of PVC products can be classified according to the influence of environmental conditions, and the differences and influencing factors of performance can be identified, that is, cluster analysis can effectively group data and process them by classifying batches with relatively consistent environmental conditions into one category, avoiding subjective interference and noise in traditional methods, thereby obtaining a more accurate quantification of the influence degree of environmental conditions, and improving the accuracy of subsequent evaluation results of the performance of heat stabilizers in PVC products. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 It is a flow chart of the steps of the method for detecting the performance of the heat stabilizer for PVC products of the present invention; Figure 2 The basic process of the trend influence degree analysis method of the data sequence of PVC products in each dimension provided by one embodiment of the present invention; Figure 3 The basic process of the method for analyzing the degree of influence of environmental conditions on each batch of PVC products provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the heat stabilizer performance detection method for PVC products proposed by the present invention, its specific implementation, structure, characteristics and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] The specific scheme of the method for detecting the performance of the heat stabilizer for PVC products provided by the present invention is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a flow chart of the steps of a method for detecting the performance of a heat stabilizer for PVC products provided by an embodiment of the present invention, the method comprising the following steps: Step S001: obtaining data sequences respectively formed by production data and test data of PVC products, and taking each data sequence as the data sequence of PVC products in a corresponding dimension, and obtaining a standard sequence corresponding to the data sequence of each dimension.

[0022] It should be noted that, in the production environment of PVC products, the corresponding production process is usually carried out in the form of production batches, so the production data and test data of different batches of PVC products of the same specifications are extracted.

[0023] Specifically, in order to implement the heat stabilizer performance detection method for PVC products proposed in this embodiment, it is first necessary to collect production data and test data. The specific process is as follows: Firstly, a plurality of sensors are used to obtain production data at the same sampling frequency. The production data includes temperature data, pressure data and humidity data. The temperature data is sampled by a temperature sensor arranged on the production line and constitutes time series data according to a number of sampling moments. Similarly, the pressure data of the equipment in the production process of the heat stabilizer for PVC products and the humidity data of the production environment are obtained respectively by the pressure sensor and the humidity sensor.

[0024] It should be noted that the standard sequence is the expected standard data under idealized production or test conditions, which is obtained from the specific implementation environment. For example, the standard sequence of a thermogravimetric experiment is a data sequence of how the mass of a PVC product without adding a heat stabilizer changes with temperature.

[0025] Then, the test data includes sequence data obtained when performing heat aging oven test and thermogravimetric test on all batches of PVC product heat stabilizers, for example, the thermogravimetric test result is a data sequence of sample weight change with temperature change.

[0026] It should also be noted that, in this embodiment, for the production data and test data of each batch of PVC products, each type of data is used as a dimension, that is, one dimension corresponds to one data sequence.

[0027] Finally, obtain the standard sequence corresponding to the data sequence of each dimension.

[0028] It should be noted that the standard sequence is an expected standard data sequence under idealized production or test conditions, which is obtained from the specific implementation environment. For example, the standard sequence of a thermogravimetric experiment is a data sequence in which the mass of a PVC product without adding a heat stabilizer changes with temperature.

[0029] So far, the production data and test data of the heat stabilizer for PVC products have been obtained through the above method.

[0030] Step S002: According to the difference distribution between the data series of different dimensions of any batch of PVC products and the corresponding standard series, the trend influence degree of the data series of the PVC products in each dimension is obtained.

[0031] It should be noted that there is a correlation between production data and test data. Due to the sampling properties of test data, the data information in the real production environment is not accurately reflected. Therefore, in the data processing process, the data in clustered form should be extracted. The more it conforms to the clustering characteristics, the more it proves that the data reflects the impact of fluctuations in production environment conditions on the performance of thermal stabilizers, which is the improvement direction of thermal stabilizer addition.

[0032] Since the performance test of heat stabilizers for PVC products usually adopts experimental methods to test the content ratio of heat stabilizers and practical effects, and there are many testing methods, including heat aging oven test, thermogravimetric test, etc., the test results come from samples tested by sampling, while the production data of PVC products obtained are more actual environmental data and various quality indicators. In particular, there are large differences in the sample test results. For example, changes in some environmental data in the production environment have a great impact on the quality of PVC products, and the sampling results cannot effectively adjust for environmental differences.

[0033] Taking the results of thermogravimetric tests as an example, the thermal degradation of PVC is mainly divided into two stages: the first stage is at 180~380℃ (Ⅰ), which mainly releases HCl and gradually forms a conjugated polyene structure, and PVC will age and discolor; the second stage is at 380~520℃ (Ⅱ), when the carbon skeleton begins to break, and the conjugated polyene structure degrades to form carbon-containing residues. The single dimensional data of PVC product production and testing will itself have trend changes. Therefore, measuring the performance of heat stabilizers for the PVC product production process through the difference in single dimensional data values ​​will result in deviations, especially in the transition part of multiple stages of a single dimensional trend. Therefore, it is necessary to analyze the trend changes on both sides of the data point and adjust the dimensional data value difference to measure the quantitative accuracy of heat stabilizer performance.

[0034] See also Figure 2 , which shows the basic process of a method for analyzing the trend influence degree of a data sequence of PVC products in each dimension provided by an embodiment of the present invention.

[0035] Specifically, step S201 obtains trend difference parameters of data points in a data sequence of any dimension of any batch of PVC products according to differences between data points in a data sequence of any dimension of the PVC products and data points in a corresponding standard sequence.

[0036] As an embodiment, the specific method for obtaining the trend difference parameter is: For each data point in the data sequence of each dimension, the difference in value between the data point and the data points in the same order in the standard sequence is obtained, and recorded as the trend difference parameter of the data point in the data sequence of the corresponding dimension.

[0037] It should be noted that the value of the trend difference parameter can be a negative number. For each data point, the greater the cumulative change of the difference on both sides, the more obvious the trend change characteristics of the location of the data point are.

[0038] Step S202, obtaining the trend influence coefficient of the data points in the data sequence of any dimension of each batch of PVC products according to the trend difference parameters of all data points in the data sequence of any dimension of each batch of PVC products within a preset neighborhood.

[0039] As an example, for Batch of PVC products in the In the data sequence under the dimension data points, the specific calculation method of the corresponding trend influence coefficient is: in, Indicates Batch of PVC products in the In the data sequence under the dimension The trend influence coefficient of the data point is Indicates the preset range radius. Indicates The first batch of PVC products The first dimension of the data sequence within a preset neighborhood of data points The trend difference parameter corresponding to the data points is represents the linear normalization function.

[0040] It should be noted that The trend influence coefficient is actually a measure of the trend of the data point deviating from the standard sequence. The more obvious the deviation trend is, the more likely it is that the data point is affected by the thermal stabilizer or the corresponding production environment conditions, rather than random fluctuations.

[0041] It should be noted that based on experience , can be adjusted according to actual conditions, and this embodiment does not make any specific limitation.

[0042] Step S203, obtaining the trend influence degree of the data sequence of any dimension according to the difference between the trend influence coefficients of all adjacent data points in the data sequence of the dimension.

[0043] It should be noted that for data series in different dimensions, the correlation changes of the trend influence coefficients of all data points reflect the magnitude of the influence of the thermal stabilizer on the corresponding type of the dimension.

[0044] As an example, for Batch of PVC products in the The specific calculation method for the trend impact degree of the data series under the dimension is: in, Indicates Batch of PVC products in the The degree of influence of the trend of the data series under the dimension, Indicates Batch of PVC products The number of data points in the data series of dimensions, Indicates Batch of PVC products In the data series of dimensions The trend influence coefficient of the data point is Indicates Batch of PVC products In the data series of dimensions The trend influence coefficient of the data point is Gets the absolute value.

[0045] It should be noted that Indicates Batch of PVC products The difference in trend influence coefficients between two adjacent data points in a dimensional data sequence reflects the continuity of the trend influence on the dimensional data sequence, that is, the more consistent the trend changes between the data points in the sequence, the better the effect of the dimensional data on the performance of the thermal stabilizer.

[0046] So far, the trend influence degree of the data series of each dimension of each batch of PVC products is obtained through the above method.

[0047] Step S003: According to the aggregation characteristics of the trend influence degree of the corresponding data series of all batches of PVC products in all dimensions, the influence degree of the environmental conditions of each batch of PVC products is obtained.

[0048] It should be noted that since the trend influence degree of the obtained data of different dimensions is obtained from the trend of the data change of each dimension, the association between the data change and the type, and in fact, the change of environmental data in the production process of PVC products has a certain reaction difference with the difference of test data, but due to the limited sampling of test data, the correspondence between the difference between test data and the difference of corresponding production environment data is not obvious, so it needs to be extracted in the form of clustering.

[0049] See also Figure 3 , which shows the basic process of a method for analyzing the degree of influence of environmental conditions on each batch of PVC products provided by an embodiment of the present invention.

[0050] Specifically, in step S301, all batches of PVC products are clustered using the trend influence degree of corresponding data sequences under all dimensions to obtain a number of clusters and a maximum cluster center.

[0051] As an embodiment, the specific process is: Firstly, a vector consisting of the trend influence degree of the data sequence in all dimensions of any batch of PVC products is obtained, which is recorded as the trend influence vector of the corresponding batch of PVC products. According to the distance between the trend influence vectors of different batches of PVC products, all batches of PVC products are clustered using the density clustering algorithm to obtain several clusters.

[0052] Then, the cluster center corresponding to the cluster with the largest number of data points in all clusters is recorded as the maximum cluster center, and the distance between the cluster center of any cluster and the maximum cluster center is obtained.

[0053] It should be noted that, among several clusters, when the number of data points in the cluster (since a PVC product is regarded as a data point in the cluster space during the clustering process, the number of data points in the cluster is the number of PVC products contained in the cluster) is the largest, the corresponding cluster represents the general situation under the current production conditions, and other clusters can be regarded as significant differences that occur under the fluctuation of some environmental factors.

[0054] It should be noted that the distribution of data points in the clusters reflects the data differences in the actual production process under the same production conditions, that is, the clustering differences in production conditions. When there are also large differences in the test data, it means that the production data has a greater impact on the test results.

[0055] Step S302, obtaining the standardization of the data sequence of any dimension of any batch of PVC products, and calculating the degree of influence of environmental conditions in any dimension of the batch of PVC products based on the standardization and the relative distance between the cluster center of the cluster where the PVC products are located and the maximum cluster center.

[0056] As an example, a specific method for obtaining the degree of influence of environmental conditions on any batch of PVC products is as follows: First, obtain the first The cosine similarity between the data sequence of the dimension and the standard sequence is recorded as the first The standardization of a data series in one dimension.

[0057] Then, the specific calculation method of the impact of environmental conditions is: in, Indicates Batch of PVC products The degree of influence of environmental conditions in each dimension, Indicates Batch of PVC products The standardization of the data series in the dimension Indicates The distance between the cluster center of the cluster where the batches of PVC products are located and the maximum cluster center, Represents the average distance between the cluster centers of all clusters and the largest cluster center. Represents the maximum and minimum normalized function.

[0058] It should be noted that Indicates The relative distance between the cluster center of the cluster where the PVC products of each batch are located and the maximum cluster center, and the influencing factors of environmental conditions Indicates the impact of changes in production conditions on test results. The larger the value, the greater the impact of environmental conditions.

[0059] It should be noted that, since the ordinal numbers corresponding to the batches, dimensions, and data points in this embodiment are obtained by counting the discrete data in sequence, the ordinal numbers corresponding to the batches, dimensions, and data points in this embodiment are , and The values ​​of are all positive integers.

[0060] So far, the degree of influence of environmental conditions on production batches of different PVC products has been obtained through the above method.

[0061] Step S004: Analyze the influence of all environmental conditions on PVC products using a neural network, so as to evaluate the performance of the stabilizer in the PVC products.

[0062] Specifically, the degree of influence of environmental conditions on different production batches of PVC products is obtained, which is used to construct a neural network model for accurate analysis of the thermal stabilizer performance of subsequent PVC products. The specific steps are as follows: 1. Collect multiple batches of PVC product production and test data, and clean the data to handle missing values ​​and outliers to ensure data quality. Next, standardize the feature data to ensure that different features have the same scale. Finally, divide the data set into training and test sets, and ensure that each batch of data has a corresponding weight value for use in subsequent model training.

[0063] 2. Select the multi-layer perceptron (MLP) as the basic architecture of the neural network, which includes adding an input layer, several hidden layers (each layer contains a certain number of neurons and activation functions), and an output layer. This structure can capture the complex patterns and characteristics of the data, thereby effectively evaluating the performance of stabilizers in PVC products.

[0064] 3. In the process of building a neural network model, choose a suitable loss function, such as mean square error (MSE). In model training, introduce sample weights so that data from different batches can be adjusted according to the importance of their weights during the loss function calculation process, making the model more sensitive to data with larger weights.

[0065] 4. When compiling the model, select an appropriate optimizer (such as Adam) and specify the loss function and evaluation metric (such as mean square error or R²). Then, use the training set and sample weights to train the model, and monitor the loss and evaluation metrics during the training process in real time. After training is completed, use the test set data to evaluate the performance of the model, and verify the prediction effect of the model by calculating the loss value and other evaluation metrics.

[0066] 5. After initial training and evaluation, adjust hyperparameters (such as the number of layers, number of neurons, learning rate, etc.) according to model performance, and perform multiple training and evaluations to optimize the model. Further verify the stability and generalization ability of the model through cross-validation and other methods, and finally deploy the trained model to the production environment for real-time evaluation of the performance of stabilizers in PVC products, and optimize the production process in combination with real-time data to improve product quality.

[0067] At this point, this embodiment is completed.

[0068] It should be noted that the The model is only used to represent negative correlation and constrain the output of the model to be in In the specific implementation, it can be replaced by other models with the same purpose. This embodiment is only based on The model is described as an example without any specific limitation. is the input to the model.

[0069] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting the performance of heat stabilizers for PVC products, characterized in that: The method comprises the following steps: Obtain data sequences formed by production data and test data of PVC products respectively, and use each data sequence as a data sequence of PVC products in a corresponding dimension, and obtain a standard sequence corresponding to the data sequence in each dimension; According to the difference distribution between the data series of different dimensions of any batch of PVC products and the corresponding standard series, the trend influence degree of the data series of the PVC products in each dimension is obtained; According to the clustering characteristics of the trend influence degree of the corresponding data series of all batches of PVC products in all dimensions, the influence degree of environmental conditions of each batch of PVC products is obtained; A neural network is used to analyze the impact of all environmental conditions on PVC products, thereby evaluating the performance of stabilizers in PVC products.

2. The method for detecting the performance of heat stabilizers for PVC products according to claim 1, characterized in that: The specific method for obtaining the trend influence degree of the data sequence of the PVC products in each dimension according to the difference distribution between the data sequence of different dimensions of any batch of PVC products and the corresponding standard sequence is as follows: Obtaining trend difference parameters of data points in the data sequence of any dimension of any batch of PVC products according to differences between data points in the data sequence of any dimension of the PVC products and data points in the corresponding standard sequence; Obtaining a trend influence coefficient of a data point in a data sequence of any dimension for each batch of PVC products according to a trend difference parameter of all data points in a preset neighborhood range for the data point in the data sequence of any dimension for each batch of PVC products; According to the differences between the trend influence coefficients of all adjacent data points in a data sequence of any dimension, the trend influence degree of the data sequence of the dimension is obtained.

3. The method for detecting the performance of heat stabilizers for PVC products according to claim 2, characterized in that: The specific method for obtaining the trend difference parameter is: For each data point in the data sequence of each dimension, the difference in value between the data point and the data points in the same order in the standard sequence is obtained, and recorded as the trend difference parameter of the data point in the data sequence of the corresponding dimension.

4. The method for detecting the performance of heat stabilizers for PVC products according to claim 2, characterized in that: The method of obtaining the trend influence coefficient of the data points in the data sequence of any dimension of each batch of PVC products according to the trend difference parameters of all data points in the preset neighborhood range in the data sequence of any dimension of each batch of PVC products includes the following specific methods: For Batch of PVC products in the In the data sequence under the dimension data points, the specific calculation method of the corresponding trend influence coefficient is: in, Indicates Batch of PVC products in the In the data sequence under the dimension The trend influence coefficient of the data point is Indicates the preset range radius. Indicates The first batch of PVC products The first dimension of the data sequence within a preset neighborhood of data points The trend difference parameter corresponding to the data points is represents the linear normalization function.

5. The method for detecting the performance of heat stabilizers for PVC products according to claim 2, characterized in that: The method of obtaining the trend influence degree of the data sequence of any dimension according to the difference between the trend influence coefficients of all adjacent data points in the data sequence of the dimension includes the following specific methods: For Batch of PVC products in the The specific calculation method for the corresponding trend impact degree of the data series under the dimension is: in, Indicates Batch of PVC products in the The degree of influence of the trend of the data series under the dimension, Indicates Batch of PVC products The number of data points in the data series of dimensions, Indicates Batch of PVC products In the data series of dimensions The trend influence coefficient of the data point is Indicates Batch of PVC products In the data series of dimensions The trend influence coefficient of the data point is Gets the absolute value.

6. The method for detecting the performance of heat stabilizers for PVC products according to claim 1, characterized in that: The method of obtaining the degree of environmental condition influence of each batch of PVC products according to the aggregation characteristics of the trend influence degree of the corresponding data series of all batches of PVC products in all dimensions includes: All batches of PVC products are clustered using the trend influence of the corresponding data series under all dimensions to obtain several clusters and the largest cluster center; The standardization of the data sequence of any dimension of any batch of PVC products is obtained, and the degree of influence of environmental conditions in any dimension of the batch of PVC products is calculated based on the standardization and the relative distance between the cluster center of the cluster where the PVC products are located and the maximum cluster center.

7. The method for detecting the performance of heat stabilizers for PVC products according to claim 6, characterized in that: The method of clustering all batches of PVC products by using the trend influence of corresponding data sequences under all dimensions to obtain several clusters and the maximum cluster center includes the following specific methods: Obtain a vector of trend influence degrees of data sequences in all dimensions of any batch of PVC products, record it as the trend influence vector of the corresponding batch of PVC products, cluster all batches of PVC products according to the distance between trend influence vectors of different batches of PVC products and use density clustering algorithm to obtain several clusters; The cluster center corresponding to the cluster with the largest number of data points in all clusters is recorded as the maximum cluster center.

8. The method for detecting the performance of heat stabilizers for PVC products according to claim 6, characterized in that: The specific method for obtaining the standard degree of the data sequence of any dimension of any batch of PVC products includes: Get the first The cosine similarity between the data sequence of the dimension and the standard sequence is recorded as the first The standard degree of the data series in one dimension.

9. The method for detecting the performance of heat stabilizers for PVC products according to claim 6, characterized in that: The method of calculating the degree of influence of environmental conditions on the batch of PVC products in any dimension according to the standard degree and the relative distance between the cluster center of the cluster where the PVC products are located and the maximum cluster center includes: The environmental condition influencing factor of the batch of PVC products in any dimension is calculated according to the standard degree and the relative distance between the cluster center of the cluster where the PVC products are located and the maximum cluster center; The maximum and minimum normalization functions are used to normalize the environmental condition influencing factors to obtain the degree of environmental condition influence.

10. The method for detecting the performance of heat stabilizers for PVC products according to claim 9, characterized in that: The specific method for obtaining the environmental condition influencing factor is: The first The ratio of the distance between the cluster center of the cluster where the PVC products of the batches belong and the largest cluster center to the average distance between the cluster center of all clusters and the largest cluster center is used as the first The relative distance between the cluster center of the cluster where the batches of PVC products are located and the maximum cluster center; According to the relative distance and Batch of PVC products The standard degree of the data sequence of the dimensions is used to obtain the environmental condition influencing factor, the relative distance is positively correlated with the environmental condition influencing factor, and the standard degree is negatively correlated with the environmental condition influencing factor.

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