Data Management Method for Digital Twin Intelligent Factory

Abstract

The present invention relates to the field of electrical data processing, and particularly to a data management method for a digital twin intelligent factory. The method includes: obtaining relevant data of the digital twin intelligent factory, performing preprocessing and then clustering to obtain a clustering result, calculating the Gaussian density of each data point in each clustering cluster, obtaining the high-density preference degree of each data point according to the Gaussian density of all data points in each clustering cluster, and determining the high-density center point; counting the number of added real-time data points and classifying them into each clustering cluster according to the Euclidean distance, dynamically updating the average real-time distance of the data points in each clustering cluster, analyzing the necessity of adjustment according to the real-time distance, and determining whether to iterate the high-density center point to measure the uniformity of data distribution, so as to complete the data management of the digital twin intelligent factory. The present invention significantly improves the timeliness, accuracy and warning reliability of digital twin factory data classification by dynamically optimizing the clustering center.

Description

Technical Field

[0001] The present invention relates to the field of electrical data processing, and more particularly to a data management method based on a digital twin smart factory. Background Art

[0002] Amidst the surge in industrial digital transformation, digital twin technology, as an emerging technology, is bringing new insights and approaches to the development of smart factories, helping them achieve a higher level of intelligence. By building virtual models that accurately map physical factory elements, including equipment, production lines, and process flows, digital twins achieve a deep integration of virtual and real-world processes. Digital twins process factory data in real time, allowing managers to fully monitor operational status in a virtual space and provide precise insights for production decisions. This is crucial for smart factories to achieve a higher level of intelligence.

[0003] For smart factories, data is the lifeblood of the industry, carrying crucial information such as equipment operation, production progress, and quality control. Digital twins integrate sensor, equipment status, process, and business system data to form an efficient data network. They also establish a hierarchical early warning mechanism based on real-time data classification, accurately monitoring equipment, quality, energy consumption, and other anomalies, providing timely warnings and ensuring stable and efficient production. However, due to the wide variety of data, data classification faces technical bottlenecks, hindering overall data management efficiency.

[0004] Currently, the K-means algorithm is commonly used for data classification. However, digital twins require minute-by-minute data updates, and the K-means algorithm's iterative convergence speed cannot meet these requirements. When processing large-scale, highly dynamic data, the algorithm requires multiple iterations to determine cluster centers. This leads to data accumulation and classification lags in real-time scenarios, making it impossible to extract key features in a timely manner. This impacts the responsiveness and effectiveness of graded warnings, becoming a major bottleneck restricting the application of digital twins in smart factory data management. Summary of the Invention

[0005] In order to solve the problem that when the K-means algorithm is applied in data classification, the iterative convergence speed is difficult to match the minute-level data update requirements of digital twins, resulting in data classification lag and untimely extraction of key features, which affects the effectiveness of the hierarchical early warning mechanism, the present invention provides solutions in the following aspects.

[0006] A data management method for a digital twin intelligent factory includes: obtaining relevant data of the digital twin intelligent factory and performing preprocessing, where the relevant data includes: equipment temperature, vibration, rotation speed, and energy consumption; clustering based on the preprocessed relevant data to obtain a clustering result, calculating the Gaussian density of each data point in each clustering cluster, obtaining the high-density preference degree of each data point in each clustering cluster according to the Gaussian density of all data points in each clustering cluster, and obtaining the data point with the highest high-density preference degree in each clustering cluster as the high-density center point; counting the number of added real-time data points and classifying them into each clustering cluster according to the Euclidean distance, dynamically updating the average real-time distance of the data points in each clustering cluster, analyzing the necessity of adjustment according to the real-time distance, and judging whether to iterate the high-density center point according to the adjustment necessity to measure the uniformity of data distribution, completing the iteration of the high-density center point to obtain a classification result, and completing the data management of the digital twin intelligent factory according to the classification result.

[0007] By obtaining and preprocessing data such as equipment temperature, vibration, rotation speed, and energy consumption, using clustering analysis to determine the Gaussian density and high-density center point of each data point; by dynamically updating the average real-time distance of data points to analyze the necessity of adjustment, and iterating the high-density center point accordingly, data classification and management are completed. It can classify data in a timely and efficient manner, extract key features, improve the accuracy and reliability of data management, and support intelligent decision-making.

[0008] Preferably, the calculation method of the Gaussian density includes:

[0009] Taking any clustering cluster in the clustering result as the target clustering cluster, calculating the square of the distance between each data point in the target clustering cluster and other data points, and calculating the sum of the squares of the standard deviations of each item of data in the target clustering cluster;

[0010] Taking the ratio between the square of the distance and the sum of the squares of the standard deviations, performing exponential decay using the negative exponential function, and taking the average value of the sum of the exponential decay terms corresponding to all data points in the target clustering cluster to obtain the Gaussian density of each data point in the target clustering cluster.

[0011] By calculating the ratio of the square of the distance between each data point in the clustering cluster and the sum of the squares of the standard deviations of other data points, and performing exponential decay processing using the negative exponential function, summing and taking the average value, the Gaussian density of each data point in the target clustering cluster can be accurately evaluated. It enhances the quantization accuracy of the local density of data points, improves the accuracy and reliability of clustering analysis, and helps to more accurately identify the data distribution characteristics and high-density regions.

[0012] Preferably, the calculation method of the high-density preference degree includes:

[0013] Taking any clustering cluster as the target clustering cluster, the average value of the distances between all data points in the target clustering cluster and the center is used as the neighborhood radius. The sum of the Gaussian densities of the data points belonging to the target clustering cluster within the neighborhood radius of each data point in the target clustering cluster is statistically calculated, and a negative exponential function is used for mapping. Subtracting the mapped result from 1 gives the overall density degree of the data within the neighborhood radius. Calculate the difference between the Gaussian density of each data point and the average Gaussian density of all data points in the target clustering cluster, divide the difference by the standard deviation of the Gaussian densities of all data points in the target clustering cluster for normalization processing, and then use an exponential function for mapping to obtain the density difference coefficient. Multiply the overall density degree by the density difference coefficient to obtain the high-density preference degree of each data point in the target clustering cluster.

[0014] By calculating the sum of the Gaussian densities within the neighborhood radius of the data points and mapping to obtain the overall density degree, and at the same time calculating the density difference coefficient, and combining the two to obtain the high-density preference degree of each data point, the relative importance and density characteristics of the data points in the clustering cluster can be accurately evaluated, the key data points in the high-density area can be effectively identified, and the accuracy of clustering analysis and the efficiency of data management can be improved.

[0015] Preferably, the average real-time distance includes:

[0016] Taking any clustering cluster as the target clustering cluster, calculate the range of the data corresponding to each dimension of all data points in the target clustering cluster, divide the average value of all dimension ranges by the number of real-time data points, and obtain the average real-time distance of the target clustering cluster.

[0017] By evaluating the uniformity and overall characteristics of the data point distribution within the clustering cluster, a quantitative index is provided. Specifically, the average real-time distance reflects the distribution range of the data points within the clustering cluster in each feature dimension. Through normalization processing, it enables fair comparison of the data distributions of clustering clusters of different scales. It helps to timely detect changes in the data distribution, thereby optimizing the clustering results and enhancing the accuracy and adaptability of intelligent factory data management.

[0018] Preferably, the calculation method of the necessity for adjustment includes:

[0019] Taking any clustering cluster as the target clustering cluster, calculate the ratio of the average real-time distance of the target clustering cluster to the real-time distance after each real-time data is added to the target clustering cluster, and then take the absolute difference between 1 and the ratio as the deviation degree of the real-time data point. Calculate the average value of the deviation degrees of all real-time data points in each clustering cluster to obtain the necessity for adjustment of the target clustering cluster.

[0020] By quantitatively evaluating the uniformity of data point distribution within clusters, the system can sensitively capture changes in data distribution caused by the addition of real-time data. When the need for adjustment exceeds a preset threshold, iterative updates of high-density center points are triggered promptly to ensure that the cluster center remains at the center of the high-density area. This effectively improves the accuracy and stability of clustering results, enhancing the adaptability and reliability of the digital twin-based smart factory data management system.

[0021] Preferably, judging whether to iterate the high-density center point according to the necessity of adjustment includes:

[0022] In response to the adjustment necessity being greater than a preset threshold, the high-density preference of the data points in each cluster is recalculated, and the high-density center point is updated to ensure that the cluster center point is always in the center of the high-density area, thereby adapting to changes in data distribution and maintaining the accuracy of the clustering results.

[0023] Preferably, the high-density center point also includes:

[0024] In response to the presence of multiple data points in a cluster with similar high-density preference and exceeding a preset high-density threshold, the data points are marked as center point candidates, and the average distance and average density of each center candidate point and other high-density data points in the cluster are calculated. The data points with an average distance less than the preset distance threshold and an average density greater than the preset high-density threshold are selected as the final high-density center points.

[0025] Preferably, the obtaining of relevant data of the digital twin smart factory and preprocessing thereof include:

[0026] Check the missing values of the relevant data and fill them using interpolation, and eliminate data that is out of the reasonable range; standardize the data to eliminate dimensional differences, and align the data according to timestamps to complete the preprocessing of the relevant data.

[0027] The present invention has the following effects:

[0028] 1. The present invention can effectively adapt to the minute-level data update requirements in digital twin smart factories by dynamically updating high-density center points and iterating according to the necessity of adjustment. Compared with the K-means algorithm, it effectively avoids the data classification lag problem caused by insufficient iterative convergence speed, thereby completing data classification more efficiently and extracting key features in a timely manner, significantly improving the effectiveness and timeliness of the hierarchical early warning mechanism.

[0029] 2. By calculating Gaussian density and high-density preference, this invention accurately determines the center point of clusters, improving the accuracy of data classification and making data management more reliable. By continuously monitoring and analyzing the uniformity of data distribution and updating the center point when necessary, it can better reflect the actual distribution of data, reduce classification errors caused by changes in data distribution, and enhance the stability and credibility of smart factory data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a method flow chart of steps S1 to S3 in the data management method based on the digital twin smart factory in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0032] Reference Figure 1 The data management method based on the digital twin smart factory includes steps S1 to S3, which are as follows:

[0033] S1: Obtain relevant data of the digital twin smart factory and perform preprocessing. The relevant data includes: equipment temperature, vibration, rotation speed and energy consumption.

[0034] It should be noted that the relevant data include but are not limited to: real-time data such as equipment temperature, vibration, speed, energy consumption, etc., and the data collection period is set to , obtain all collected factory equipment data, and The first collection Item data is recorded as , recorded as historical data, the total number of historical data collected is recorded as , the number of data items collected each time is recorded as .

[0035] Preprocessing includes:

[0036] Check the missing values of the relevant data and fill them using interpolation, and eliminate data that is out of the reasonable range; standardize the data to eliminate dimensional differences, and align the data according to timestamps to complete the preprocessing of the relevant data.

[0037] Upon further analysis, during the process of intelligent factory data management, classifying the intelligent factory data can significantly improve the efficiency of data management. Although the K-means algorithm is often used for data classification management, the conventional K-means algorithm requires iterative data clustering and is vulnerable to abnormal or deviated data during the iterative data clustering process, resulting in inaccurate data clustering results.

[0038] S2: Perform clustering based on the preprocessed relevant data to obtain a clustering result, calculate the Gaussian density of each data point in each clustering cluster, obtain the high-density preference degree of each data point according to the Gaussian density of all data points in each clustering cluster, and obtain the data point with the highest high-density preference degree in each clustering cluster as the high-density center point.

[0039] The calculation method of Gaussian density includes:

[0040] Taking any clustering cluster in the clustering result as the target clustering cluster, calculate the square of the distance between each data point and other data points in the target clustering cluster, and calculate the sum of the squares of the standard deviations of each item of data in the target clustering cluster;

[0041] Take the ratio between the square of the distance and the sum of the squares of the standard deviations, perform exponential decay using the negative exponential function, and obtain the average value of the sum of the exponential decay terms corresponding to all data points in the target clustering cluster to get the Gaussian density of each data point in the target clustering cluster.

[0042] Specifically, the Gaussian density satisfies the following relationship:

[0043] ;

[0044] In the formula, represents the Gaussian density of the th data point in the th clustering cluster, represents the number of data points in the th clustering cluster, represents the square of the Euclidean distance between the th data point and the th data point, represents the standard deviation of the th item of data in the th clustering cluster, represents the exponential function with the natural number as the base.

[0045] That is to say, the Euclidean distance measures the straight-line distance between two data points in space. The square operation amplifies the difference in distances, causing the contribution of more distant data points to density to decrease significantly. It reflects the similarity between two data points. The smaller the distance, the higher the similarity and the greater the contribution to density. The standard deviation measures the degree of dispersion of data in each dimension. By adding the squares of the standard deviations of each dimension and taking the square root, a scalar is obtained that comprehensively reflects the degree of dispersion of data within the clustering cluster. It is used to normalize the Euclidean distance, making the distance metrics between different clustering clusters comparable and avoiding distance metric biases caused by large dimensionality or high dispersion degree of data in certain dimensions.

[0046] Further analysis shows that according to the Gaussian density of data points in each clustering cluster, the density preference degree of data points is obtained, so that data points with a higher Gaussian density itself and a Gaussian density around it higher than the average level of the Gaussian density of all data points in the clustering cluster have a higher high-density preference degree; the data point with the highest high-density preference degree in each clustering cluster is recorded as the high-density center point.

[0047] It should be noted that when selecting the high-density center point, to ensure that the selected center point has both a high local density and is located in the data aggregation area, the present invention calculates the high-density preference degree by comprehensively considering the Gaussian density of the data point itself and the density distribution of its neighboring data points. Specifically, the high-density preference degree of a data point not only depends on its own Gaussian density value but is also affected by the density distribution of its surrounding neighboring data points, so that the center point that truly represents the data distribution characteristics can be more accurately identified.

[0048] The calculation method of the high-density preference degree includes:

[0049] Taking any clustering cluster as the target clustering cluster, taking the average value of the distances between all data points in the target clustering cluster and the center as the neighborhood radius, counting the sum of the Gaussian densities of the data points belonging to the target clustering cluster within the neighborhood radius of each data point in the target clustering cluster, and using the negative exponential function for mapping, and taking 1 minus the mapped result as the overall density degree of the data within the neighborhood radius;

[0050] Calculating the difference between the Gaussian density of each data point and the average Gaussian density of all data points in the target clustering cluster, dividing the difference by the standard deviation of the Gaussian densities of all data points in the target clustering cluster for normalization processing, and then using the exponential function for mapping to obtain the density difference coefficient;

[0051] Multiplying the overall density degree by the density difference coefficient as the high-density preference degree of each data point in the target clustering cluster.

[0052] Specifically, the high-density preference degree satisfies the following relational expression:

[0053] ;

[0054] In the formula, represents the high-density preference degree of the th data point in the th clustering cluster. represents the number of data points within the neighborhood radius of the th data point in the th clustering cluster. represents the Gaussian density of the th data point in the th neighborhood data point of the th data point in the th clustering cluster. represents the Gaussian density of the th data point in the th clustering cluster. represents the average Gaussian density of the data points in the th clustering cluster. represents the standard deviation of the Gaussian densities of all data points in the th clustering cluster. represents the exponential function with the natural number

[0055] That is to say, is used to represent the density characteristics of the data points in the neighborhood as a whole, reflecting the density of the data points in the neighborhood. The more data points there are in its neighborhood and the greater their Gaussian density, the more likely it is that the data point is in a cluster of data points with a higher Gaussian density, and the higher the high-density preference degree of the data point is considered. Using the negative exponential function, the neighborhood density is transformed into a value within the range of , . However, to further illustrate, the complement of the mapping result is selected, so that the denser the data in the neighborhood, the closer the value is to 1; the sparser the data in the neighborhood, the closer the value is to 0.

[0056] represents the density level of the th data point in the th clustering cluster. The higher the value, the higher the density level of the data point in the clustering cluster and the higher the high-density preference degree.

[0057] The high-density center point further includes:

[0058] When there are multiple data points in the clustering cluster with similar high-density preference degrees and exceeding the preset high-density threshold, the data points are marked as center point candidates, and the average distance and average density between each center candidate point and other high-density data points in the clustering cluster are calculated. The data point with an average distance less than the preset distance threshold and an average density greater than the preset high-density threshold is selected as the final high-density center point.

[0059] Exemplarily, the preset high-density threshold is 0.8, and the preset distance threshold is 2.5 times the standard deviation of the data distribution. Implementers can adjust according to specific circumstances.

[0060] Furthermore, in the process of classifying time series data, due to the continuous addition of time series data, the distribution of data points in each clustering cluster changes, resulting in a change in the clustering center of the data points. Such changes need to be considered, and the position of the high-density center point is adjusted so that the high-density center point can always meet the distribution of smart factory data points.

[0061] S3: Count the number of real-time data points added and classify them into each clustering cluster according to the Euclidean distance. Dynamically update the average real-time distance of the data points in each clustering cluster. Analyze the necessity of adjustment according to the real-time distance, and judge whether to iterate the high-density center point according to the necessity of adjustment to measure the uniformity of data distribution. Complete the iteration of the high-density center point to obtain the classification result, and complete the intelligent factory data management of the digital twin according to the classification result.

[0062] When the number of real-time data points included in all clustering clusters is greater than 2, the necessity of adjustment is obtained according to the real-time distance of the real-time data points in all clustering clusters, as follows:

[0063] The average real-time distance includes:

[0064] Taking any clustering cluster as the target clustering cluster, calculate the range of the data corresponding to each dimension of all data points in the target clustering cluster, and divide the average value of all dimension ranges by the number of real-time data points to obtain the average real-time distance of the target clustering cluster.

[0065] Specifically, the average real-time distance satisfies the following relational expression:

[0066] ;

[0067] In the formula, represents the average real-time distance of the th clustering cluster, represents the number of real-time data points included in the th clustering cluster, represents the number of data items of each data point in the clustering cluster, represents the th item range of the data in the

[0068] That is to say, This measure reflects the distribution range of data points within the target cluster across each feature dimension. It comprehensively considers the range and number of data points in each feature dimension to measure the overall distribution of data points within the cluster. By calculating the average real-time distance for each cluster, it comprehensively reflects the distribution of data points within the cluster across each feature dimension. This helps assess the distribution uniformity and overall characteristics of data points within the cluster, providing a basis for subsequent cluster analysis and center point adjustment.

[0069] The calculation method for the necessity of adjustment includes:

[0070] Taking any cluster as the target cluster, calculate the ratio of the average real-time distance of the target cluster to the real-time distance of each real-time data after adding it to the target cluster, and then use the absolute difference between 1 and the ratio as the deviation degree of the real-time data point. Calculate the average deviation degree of all real-time data points in each cluster to obtain the necessity of adjustment of the target cluster.

[0071] Specifically, the necessity of adjustment satisfies the following relationship:

[0072] ;

[0073] Where, Indicates the need for adjustment. represents the total number of clusters, Indicates the The number of real-time data points contained in the clusters, Indicates the The average real-time distance of clusters, Indicates the The first cluster The real-time distance of real-time data points.

[0074] That is to say, Indicates the The average value of the real-time distance in the clusters is obtained according to the range of each data in the clusters, and the average level of the real-time distance of the real-time data points is obtained. Indicates the degree of deviation between the real-time distance of a data point and the average distance of the cluster. A larger deviation indicates a greater discrepancy between the real-time distance of the data point and the average distance, and a more unique position within the cluster indicates a more uneven distribution of the real-time data points within the cluster, necessitating greater adjustments. By calculating the average deviation value of all data points, we can determine the overall uniformity of the distribution of data points within the cluster. A large average deviation value indicates an uneven distribution of data points within the cluster, possibly with outliers or edge points. A small average deviation value indicates a relatively even distribution of data points.

[0075] It should be noted that in any clustering cluster, the more evenly the newly added real-time data points are distributed in the clustering cluster, the smaller the impact on the overall Gaussian density of all data points. Therefore, the necessity of adjustment is defined by the degree of uniformity of the real-time data points in the clustering cluster.

[0076] Determine whether to iterate the high-density center point according to the necessity of adjustment, including:

[0077] When the necessity of adjustment is greater than the preset threshold, recalculate the high-density preference degree of the data points in each clustering cluster and update the high-density center point to ensure that the clustering cluster center point is always at the center of the high-density area, so as to adapt to the change of data distribution and maintain the accuracy of the clustering result.

[0078] Exemplarily, the preset threshold is , and the implementer can adjust according to the actual implementation situation to complete the iteration of the high-density center point.

[0079] Classify and manage the intelligent factory data according to the data classification result, strengthen the real-time data classification management ability of digital twin, and make preparations for subsequent intelligent decision-making.

[0080] It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of this invention patent shall be subject to the appended claims.

Claims

1. A data management method for a digital twin intelligent factory, characterized in that Including: Obtain the relevant data of the digital twin intelligent factory and perform preprocessing. Among them, the relevant data includes: equipment temperature, vibration, rotation speed, and energy consumption. Cluster according to the preprocessed relevant data, obtain the clustering result, calculate the Gaussian density of each data point in each cluster, obtain the high-density preference degree of each data point according to the Gaussian density of all data points in each cluster, and obtain the data point with the highest high-density preference degree in each cluster as the high-density center point. Count the number of real-time data points added and classify them into each cluster according to the Euclidean distance, dynamically update the average real-time distance of the data points in each cluster, analyze the necessity of adjustment according to the real-time distance, and judge whether to iterate the high-density center point according to the necessity of adjustment to measure the uniformity of data distribution, complete the iteration of the high-density center point, obtain the classification result, and complete the data management of the digital twin intelligent factory according to the classification result.

2. The data management method based on a digital twin intelligent factory according to claim 1, wherein The calculation method of the Gaussian density includes: Taking any cluster in the clustering result as the target cluster, calculate the square of the distance between each data point in the target cluster and other data points, and calculate the sum of the squares of the standard deviations of each item of data in the target cluster. Taking the ratio between the square of the distance and the sum of the squares of the standard deviations, performing exponential decay using the negative exponential function, and calculating the average value of the exponential decay terms corresponding to all data points in the target cluster to obtain the Gaussian density of each data point in the target cluster.

3. The data management method based on a digital twin intelligent factory according to claim 1, characterized in that The calculation method of the high-density preference degree includes: Taking any cluster as the target cluster, taking the average value of the distances between all data points in the target cluster and the center as the neighborhood radius, counting the sum of the Gaussian densities of the data points belonging to the target cluster within the neighborhood radius of each data point in the target cluster, and performing mapping using the negative exponential function, and taking 1 minus the mapped result as the overall density degree of the data within the neighborhood radius; calculating the difference between the Gaussian density of each data point and the average Gaussian density of all data points in the target cluster, dividing the difference by the standard deviation of the Gaussian densities of all data points in the target cluster for normalization processing, and then performing mapping using the exponential function to obtain the density difference coefficient; multiplying the overall density degree by the density difference coefficient as the high-density preference degree of each data point in the target cluster.

4. The data management method based on the digital twin intelligent factory according to claim 1, characterized in that, The average real-time distance includes: Taking any cluster as the target cluster, calculating the range of each dimension data corresponding to all data points in the target cluster, and dividing the average value of all dimension ranges by the number of real-time data points to obtain the average real-time distance of the target cluster.

5. The data management method based on a digital twin intelligent factory according to claim 1, characterized in that, The calculation method of the necessity of adjustment includes: Taking any cluster as the target cluster, calculating the ratio of the average real-time distance of the target cluster to the real-time distance after each real-time data is added to the target cluster, and then taking the absolute difference between 1 and the ratio as the deviation degree of the real-time data point, and calculating the average value of the deviation degrees of all real-time data points in each cluster to obtain the necessity of adjustment of the target cluster.

6. The data management method based on the digital twin intelligent factory according to claim 1, characterized in that, The judgment of whether to iterate the high-density center point according to the necessity of adjustment includes: In response to the adjustment necessity being greater than a preset threshold, the high-density preference of the data points in each cluster is recalculated, and the high-density center point is updated to ensure that the cluster center point is always in the center of the high-density area, thereby adapting to changes in data distribution and maintaining the accuracy of the clustering results.

7. The data management method based on a digital twin intelligent factory according to claim 1, characterized in that The high-density center points also include: In response to the presence of multiple data points in a cluster with similar high-density preference and exceeding a preset high-density threshold, the data points are marked as center point candidates, and the average distance and average density of each center candidate point and other high-density data points in the cluster are calculated. The data points with an average distance less than the preset distance threshold and an average density greater than the preset high-density threshold are selected as the final high-density center points.

8. The data management method based on the digital twin intelligent factory according to claim 1, characterized in that The acquisition of relevant data of the digital twin smart factory and preprocessing thereof include: Check the missing values of the relevant data and fill them using interpolation, and eliminate data that is out of the reasonable range; standardize the data to eliminate dimensional differences, and align the data according to timestamps to complete the preprocessing of the relevant data.

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