Data Visualization Method, System and Medium Applicable to Digital Processing Workshop

Abstract

The present invention relates to the technical field of data processing, and particularly relates to a data visualization method, system and medium applicable to a digital processing workshop. The method includes: obtaining a sea cucumber daily output sequence and a sea cucumber daily raw material supply sequence within a predetermined time period; determining the raw material supply characteristic value and the sea cucumber daily output characteristic value for each day; determining the neighborhood radius in the clustering algorithm; correcting the neighborhood radius according to the Pearson correlation coefficient between the sea cucumber daily output sequence and the sea cucumber daily raw material supply sequence to obtain a corrected neighborhood radius, and clustering the positions of the feature groups in the coordinate system based on the corrected neighborhood radius to obtain multiple clusters; determining the data volume of the unit scale of the visualization coordinate system; and visually displaying the sea cucumber output based on a visualization graph formed by the data volume of the unit scale, the maximum value of the sea cucumber daily output, the minimum value of the sea cucumber daily output, and the number of grids of the scale. In this way, the present invention improves the accuracy of evaluating the increase and decrease of the sea cucumber output.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a data visualization method, system and medium applicable to a digital processing workshop. Background Art

[0002] A digital processing workshop refers to a new type of workshop model that uses digital technology, information technology, and automation technology to upgrade and transform traditional manufacturing workshops, thereby realizing digital management and control of the production process. Due to its advantages of high efficiency and high precision, it has been widely used in various industries. Among them, in the sea cucumber processing industry, effective monitoring and analysis of production data are crucial. Using a line chart in a visualization tool to display the daily output can intuitively present the changing trend of the output. Users can understand the increase and decrease of the sea cucumber output on different dates through the ups and downs of the line, and then reasonably arrange resource allocation work based on the trend of the sea cucumber output.

[0003] In some scenarios, the sea cucumber output has significant periodic characteristics. Affected by various factors such as seasons, the growth cycle of sea cucumbers, and fishing rules, the output of sea cucumbers varies greatly in different periods. Currently, a line chart of sea cucumber output is generally drawn with a fixed unit scale coordinate. There are obvious limitations in using this method. This is because during the high-yield period of sea cucumbers, due to the fixed vertical coordinate scale, the line may exceed the display range of the chart, resulting in poor data visualization effects and unable to clearly present the details of sea cucumber output data. While in the low-yield area of sea cucumbers, the line is almost close to the horizontal axis, making it difficult to observe the small fluctuations in sea cucumber output. This is also not conducive to accurately analyzing the sea cucumber output situation. Thus, it is difficult to clearly and accurately visualize the change details of sea cucumber output using the existing method, which further leads to a low accuracy in evaluating the increase and decrease of sea cucumber output and is not conducive to reasonably arranging resource allocation work based on the trend of sea cucumber output. Summary of the Invention

[0004] In order to solve the technical problem of being difficult to clearly and accurately visualize the change details of sea cucumber output, which further leads to a low accuracy in evaluating the increase and decrease of sea cucumber output, the purpose of the present invention is to provide a data visualization method, system and medium applicable to a digital processing workshop. The specific technical solutions adopted are as follows:

[0005] In a first aspect, an embodiment of the present invention provides a data visualization method applicable to a digital processing workshop, including: obtaining a sea cucumber daily production sequence and a sea cucumber daily raw material supply sequence within a predetermined time period; using a predetermined time window, the sea cucumber daily raw material supply sequence, and the sea cucumber daily production sequence to determine the daily raw material supply eigenvalue and the sea cucumber daily production eigenvalue; determining the neighborhood radius in a clustering algorithm according to the position of the feature group formed by the raw material supply eigenvalue and the sea cucumber daily production eigenvalue in a coordinate system; correcting the neighborhood radius according to the Pearson correlation coefficient between the sea cucumber daily production sequence and the sea cucumber daily raw material supply sequence to obtain a corrected neighborhood radius, and clustering the positions of the feature groups in the coordinate system based on the corrected neighborhood radius to obtain multiple clusters; determining the data volume of the unit scale of the visualization coordinate system according to the maximum value of the sea cucumber daily production, the minimum value of the sea cucumber daily production, and the number of grid cells of the scale in the time range of the cluster; visualizing the sea cucumber production based on the visualization graph formed by the data volume of the unit scale, the maximum value of the sea cucumber daily production, the minimum value of the sea cucumber daily production, and the number of grid cells.

[0006] Optionally, using a predetermined time window, the sea cucumber daily raw material supply sequence, and the sea cucumber daily production sequence to determine the daily raw material supply eigenvalue and the sea cucumber daily production eigenvalue includes: using the predetermined time window to divide the sea cucumber daily raw material supply sequence and the sea cucumber daily production sequence into first sequence segments and second sequence segments of multiple time lengths. Each first sequence segment includes the sea cucumber daily raw material supply of the day to be analyzed at the middle position of the first sequence segment and the sea cucumber daily raw material supply of the adjacent day of the day to be analyzed. Each second sequence segment includes the sea cucumber daily production of the day to be analyzed at the middle position of the second sequence segment and the sea cucumber daily production of the adjacent day of the day to be analyzed; determining the daily raw material supply eigenvalue of the day to be analyzed according to the first average value of all sea cucumber raw material supplies in the first sequence segment and the second average value of the sea cucumber raw material supplies of the adjacent days in the first sequence segment; determining the sea cucumber daily production eigenvalue of the day to be analyzed according to the third average value of all sea cucumber daily productions in the second sequence segment and the fourth average value of the sea cucumber daily productions of the adjacent days in the second sequence segment.

[0007] Optionally, determining the daily raw material supply eigenvalue of the day to be analyzed according to the first average value of all sea cucumber raw material supplies in the first sequence segment and the second average value of the sea cucumber raw material supplies of the adjacent days in the first sequence segment includes: determining the first product between the first average value and the second average value as the daily raw material supply eigenvalue of the day to be analyzed.

[0008] Optionally, determining the sea cucumber daily production eigenvalue of the day to be analyzed according to the third average value of all sea cucumber daily productions in the second sequence segment and the fourth average value of the sea cucumber daily productions of the adjacent days in the second sequence segment includes: determining the second product between the third average value and the fourth average value as the sea cucumber daily production eigenvalue of the day to be analyzed.

[0009] Optionally, determining the neighborhood radius in the clustering algorithm according to the position of the feature group composed of the raw material supply volume eigenvalue and the daily sea cucumber production eigenvalue in the coordinate system includes: mapping the raw material supply volume eigenvalue and the daily sea cucumber production eigenvalue as a feature group to the coordinate system, linearly fitting the positions of the daily feature groups in the coordinate system to obtain a fitting line; calculating the perpendicular distance between the daily feature group and the fitting line; determining the neighborhood radius in the clustering algorithm according to the fifth average value of each perpendicular distance and the ratio of the first quantity of the data points corresponding to the feature group on the fitting line to the second quantity of all the data points corresponding to the feature groups.

[0010] Optionally, determining the neighborhood radius in the clustering algorithm according to the fifth average value of each perpendicular distance and the ratio of the first quantity of the data points corresponding to the feature group on the fitting line to the second quantity of all the data points corresponding to the feature groups includes: calculating the first sum value between the ratio and a predetermined value, and performing normalization processing on the first sum value to obtain a normalized value; determining the third product between the fifth average value and the reciprocal of the normalized value as the neighborhood radius.

[0011] Optionally, correcting the neighborhood radius according to the Pearson correlation coefficient between the daily sea cucumber production sequence and the daily raw material supply volume sequence of sea cucumbers to obtain a corrected neighborhood radius includes: calculating the second sum value between a predetermined value and the reciprocal of the absolute value of the Pearson correlation coefficient; determining the fourth product between the neighborhood radius and the second sum value as the corrected neighborhood radius.

[0012] Optionally, determining the data volume of the unit scale of the visualization coordinate system according to the maximum daily sea cucumber production, the minimum daily sea cucumber production within the time range of the cluster, and the number of grids of the scale in the visualization coordinate system includes: taking the continuous dates in the cluster as a cluster group, and taking the time union of each cluster group as the time range of the cluster; calculating the third sum value between the maximum daily sea cucumber production and the anti-overflow parameter, and the first difference between the minimum daily sea cucumber production and the anti-overflow parameter; calculating the second difference between the third sum value and the first difference; determining the ratio between the second difference and the number of grids of the scale in the visualization coordinate system as the data volume of the unit scale.

[0013] In a second aspect, an embodiment of the present invention provides a data visualization system applicable to a digital processing workshop, including: a processor and a memory; wherein, the memory is used to store a computer program that can run on the processor; the processor is used to execute the program stored on the memory to implement the steps of the data visualization method applicable to the digital processing workshop as mentioned in the first aspect.

[0014] Thirdly, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the data visualization method applicable to a digital processing workshop as mentioned in the first aspect are implemented.

[0015] The present invention has the following beneficial effects: Firstly, obtain the daily sea cucumber production sequence and the daily raw material supply sequence of sea cucumbers within a predetermined time period; then use a predetermined time window, the daily raw material supply sequence of sea cucumbers, and the daily sea cucumber production sequence to determine the daily raw material supply characteristic value and the daily sea cucumber production characteristic value; and determine the neighborhood radius in the clustering algorithm according to the position of the feature group composed of the raw material supply characteristic value and the daily sea cucumber production characteristic value in the coordinate system; Secondly, correct the neighborhood radius according to the Pearson correlation coefficient between the daily sea cucumber production sequence and the daily raw material supply sequence of sea cucumbers to obtain a corrected neighborhood radius, and cluster the position of the feature group in the coordinate system based on the corrected neighborhood radius to obtain multiple clusters; Then, determine the data volume of the unit scale of the visualization coordinate system according to the maximum value of the daily sea cucumber production, the minimum value of the daily sea cucumber production, and the number of grid cells of the scale in the time range of the cluster; Finally, visually display the sea cucumber production based on the visualization graph composed of the data volume of the unit scale, the maximum value of the daily sea cucumber production, the minimum value of the daily sea cucumber production, and the number of grid cells of the scale.

[0016] In this way, the embodiment of the present invention can analyze the periodicity of sea cucumber production through clustering by constructing a feature group composed of the raw material supply characteristic value and the daily sea cucumber production characteristic value in the sea cucumber processing workshop, and finally adaptively adjust the coordinate scale according to the distribution of the maximum value, minimum value, number of grid cells, and data volume of the unit scale of the daily sea cucumber production in different time ranges of sea cucumbers in the visualization graph. By the way of adaptively adjusting the coordinate scale of the visualization graph, it can better adapt to the periodic changes of sea cucumber production. The line graph can accurately and clearly display the change details of sea cucumber production in different periods, improve the accuracy of evaluating the increase and decrease of sea cucumber production, provide more effective data visualization support for the production management of the sea cucumber processing workshop, and is more conducive to reasonably arranging resource allocation work based on the trend of sea cucumber production. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1Flowchart of a data visualization method applicable to a digital processing workshop provided by an embodiment of the present invention;

[0019] Figure 2 Schematic diagram showing the relationship between sea cucumber production and time provided by an embodiment of the present invention;

[0020] Figure 3 Schematic diagram showing the positional relationship of a feature group in a two-dimensional space provided by an embodiment of the present invention;

[0021] Figure 4 Schematic diagram showing the distribution relationship between the daily sea cucumber production and the daily raw material supply of sea cucumbers provided by an embodiment of the present invention;

[0022] Figure 5 Schematic diagram of the structure of a data visualization system applicable to a digital processing workshop provided by an embodiment of the present invention. Detailed implementation manners

[0023] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following describes in detail the specific implementation manners, structures, features, and effects of a data visualization method, system, and medium applicable to a digital processing workshop according to the present invention in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0025] The following specifically describes the specific solution of a data visualization method applicable to a digital processing workshop provided by the present invention in conjunction with the accompanying drawings.

[0026] Embodiment 1:

[0027] Please refer to Figure 1 , which shows the flowchart of a data visualization method applicable to a digital processing workshop provided by an embodiment of the present invention, including:

[0028] S101, obtaining the daily sea cucumber production sequence and the daily raw material supply sequence of sea cucumbers within a predetermined time period.

[0029] Specifically, in the embodiment of the present invention, the predetermined time period can be set according to the actual situation. In the embodiment of the present invention, the value is set to the past 5 years. The embodiment of the present invention obtains the daily sea cucumber production and the daily sea cucumber raw material supply in the past 5 years from the database of the processing workshop. The daily sea cucumber production sequence in the past 5 years is recorded as ,in, represents the daily production of sea cucumbers on the Tth day. And the daily raw material supply sequence of sea cucumbers in the past five years is recorded in the embodiment of the present invention as ,in, represents the daily raw material supply of sea cucumbers on day T. The daily sea cucumber production and the daily raw material supply of sea cucumbers can constitute the data set ,in, Represents the daily production of sea cucumbers and the daily supply of sea cucumber raw materials on day T.

[0030] S102, using a predetermined time window, a daily raw material supply sequence of sea cucumbers, and a daily sea cucumber production sequence to determine a daily raw material supply characteristic value and a daily sea cucumber production characteristic value.

[0031] Specifically, the daily production data of sea cucumbers has periodicity, so within a cycle, the change pattern of the daily production data of sea cucumbers and the change pattern of the related daily raw material supply of sea cucumbers will show similarity. For example, in the peak season of sea cucumber fishing, the daily production of sea cucumbers is high and the supply of raw materials is sufficient. In the off-season, the daily production of sea cucumbers and the supply of raw materials are both low. The clustering algorithm can naturally cluster data points in the same cycle stage based on the pattern of common changes in production and related factors in different cycle stages. Therefore, the present invention analyzes the periodicity of sea cucumber production through clustering results. Exemplary, such as Figure 2 As shown, Figure 2 A schematic diagram of the relationship between sea cucumber yield and time provided in one embodiment of the present invention. Figure 2 In the present invention, the output of sea cucumber has a periodic relationship with time every year. If only considering clustering from one-dimensional output data, it may be impossible to effectively capture the periodic characteristics of sea cucumber output, because the periodic variation of sea cucumber output is not very regular (such as being affected by factors such as climate change), such as the transition between off-season and peak season may be relatively vague, and the output peak in the peak season may not be too obvious due to some abnormal years. At this time, it is impossible to analyze the complex data changes through simple one-dimensional clustering, and it is impossible to accurately identify the real cycle. Therefore, there is a close causal relationship between the daily output of sea cucumber and the daily raw material supply of sea cucumber in the embodiment of the present invention, and the raw material supply is one of the key factors affecting the output of sea cucumber. In the peak season, the raw material supply is usually sufficient, and the output of sea cucumber is also high. In the off-season, the raw material supply is reduced, and the output of sea cucumber decreases thereupon. Therefore, the embodiment of the present invention constructs the output of sea cucumber and the raw material supply into a two-dimensional space to cluster data points, better reflects this causal relationship, and thus more accurately explains the periodic variation of the output of sea cucumber.

[0032] Furthermore, during the off-season, the demand for sea cucumbers is relatively small, so the daily production volume of sea cucumbers and the supply volume of raw materials are relatively low. The daily production volume of sea cucumbers and the supply volume of raw materials are dynamic data, which will change over time. The information of this dynamic change can be captured through the change amount. At the same time, during the off-season, the demand for the daily production volume of sea cucumbers is small, so the daily production volume of sea cucumbers and the supply volume of raw materials are relatively low. During the peak season, the demand for sea cucumbers is large, so the daily production volume of sea cucumbers and the supply volume of raw materials are relatively high. Based on the above analysis, the characteristic values of the daily raw material supply volume and the daily production volume of sea cucumbers are constructed through the change amount and the average amount of the daily production volume of sea cucumbers and the supply volume of raw materials in a time window.

[0033] Furthermore, as an optional embodiment of the present invention, determining the characteristic values of the daily raw material supply volume and the daily production volume of sea cucumbers by using a predetermined time window, the sequence of the daily raw material supply volume of sea cucumbers and the sequence of the daily production volume of sea cucumbers includes:

[0034] Using the predetermined time window to divide the sequence of the daily raw material supply volume of sea cucumbers and the sequence of the daily production volume of sea cucumbers into first sequence segments and second sequence segments of multiple time lengths. Each first sequence segment includes the daily raw material supply volume of sea cucumbers on the day to be analyzed at the middle position of the first sequence segment and the daily raw material supply volume of sea cucumbers on the adjacent day of the day to be analyzed. Each second sequence segment includes the daily production volume of sea cucumbers on the day to be analyzed at the middle position of the second sequence segment and the daily production volume of sea cucumbers on the adjacent day of the day to be analyzed; determining the characteristic value of the daily raw material supply volume of the day to be analyzed according to the first average value of all the raw material supply volumes of sea cucumbers in the first sequence segment and the second average value of the raw material supply volumes of sea cucumbers on the adjacent days in the first sequence segment; determining the characteristic value of the daily production volume of sea cucumbers on the day to be analyzed according to the third average value of all the daily production volumes of sea cucumbers in the second sequence segment and the fourth average value of the daily production volumes of sea cucumbers on the adjacent days in the second sequence segment.

[0035] Specifically, in the embodiment of the present invention, for the sequence of the daily raw material supply volume of sea cucumbers and the sequence of the daily production volume of sea cucumbers . A predetermined time window with a length of S is set, where the length of the predetermined time window can be determined according to the actual situation. In the embodiment of the present invention, S = 7. Taking any day in the sequence of the daily raw material supply volume of sea cucumbers and the sequence of the daily production volume of sea cucumbers as the analysis object, that is, the day to be analyzed, taking the day to be analyzed as the midpoint of the predetermined time window, and taking the length of the predetermined time window as S. At this time, the first sequence segment composed of the daily raw material supply volume of sea cucumbers every day and the second sequence segment composed of the daily production volume of sea cucumbers every day can be obtained.

[0036] Further, as an alternative embodiment of the present invention, determining the raw material supply quantity characteristic value of the day to be analyzed according to the first average value of all sea cucumber raw material supply quantities in the first sequence segment and the second average value of the sea cucumber raw material supply quantities on adjacent days in the first sequence segment includes: determining the first product between the first average value and the second average value as the raw material supply quantity characteristic value of the day to be analyzed.

[0037] Specifically, the embodiment of the present invention specifically calculates the raw material supply quantity characteristic value of the day to be analyzed using the following formula:

[0038]

[0039] In the above formula, represents the raw material supply quantity characteristic value. represents the first average value of the sea cucumber raw material supply quantities in the first sequence segment. represents the time length of the first sequence segment. represents the sea cucumber raw material supply quantity on the s-th day in the first sequence segment. represents the sea cucumber raw material supply quantity on the (s - 1)-th day in the first sequence segment. represents the second average value of the sea cucumber raw material supply quantities on adjacent days in the first sequence segment. represents the change amount between the sea cucumber raw material supply quantity on the s-th day and the sea cucumber raw material supply quantity on the adjacent (s - 1)-th day in the first sequence segment. represents the number of adjacent days in the first sequence segment.

[0040] Further, as an alternative embodiment of the present invention, determining the sea cucumber daily output characteristic value of the day to be analyzed according to the third average value of all sea cucumber daily outputs in the second sequence segment and the fourth average value of the sea cucumber daily outputs on adjacent days in the second sequence segment includes: determining the second product between the third average value and the fourth average value as the sea cucumber daily output characteristic value of the day to be analyzed.

[0041] Specifically, the embodiment of the present invention specifically calculates the sea cucumber daily output characteristic value of the day to be analyzed using the following formula:

[0042]

[0043] In the above formula, N represents the sea cucumber daily output characteristic value. represents the third average value of the sea cucumber daily outputs in the second sequence segment. represents the time length of the second sequence segment, which is the same as the time length of the first sequence segment. represents the sea cucumber daily output on the s-th day in the second sequence segment. represents the sea cucumber daily output on the (s - 1)-th day in the second sequence segment. represents the fourth average value of the sea cucumber daily outputs on adjacent days in the second sequence segment. It represents the change in the daily sea cucumber production on the s-th day in the second sequence segment compared to the daily sea cucumber production on the adjacent (s - 1)-th day. It represents the number of adjacent days in the second sequence segment.

[0044] S103. Determine the neighborhood radius in the clustering algorithm according to the position of the feature group composed of the raw material supply quantity eigenvalue and the daily sea cucumber production eigenvalue in the coordinate system.

[0045] Specifically, through the raw material supply quantity eigenvalue and the daily sea cucumber production eigenvalue calculated in the embodiments of the present invention, the feature group of each day can be obtained. . Exemplarily, as Figure 3 shown, Figure 3 is a schematic diagram of the positional relationship of a feature group in a two-dimensional space provided by an embodiment of the present invention. Figure 3 In it, the feature group is divided into two regions in the two-dimensional space. In the off-season, the demand for sea cucumbers is small, so both the daily sea cucumber production eigenvalue and the raw material supply quantity eigenvalue are small. Therefore, most of the data points in the off-season are in the lower left of the coordinate system. In the peak season, the demand for sea cucumbers is large, so both the daily sea cucumber production eigenvalue and the raw material supply quantity eigenvalue are large. Therefore, most of the data points in the peak season are in the upper right of the coordinate system. Based on the above analysis, the embodiments of the present invention use the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to classify all feature groups. Among them, the neighborhood radius of the DBSCAN clustering algorithm delimits the range around a data point, and other data points within this range are regarded as the neighbors of this point. The traditional method for determining the neighborhood radius uses a fixed empirical value, and at the same time does not consider the actual distribution of data points, such as the tightness of the data, whether there is a concentration trend in a specific direction, etc., resulting in an unreasonable setting of the neighborhood radius. Either it is too large, making the clustering too broad and unable to distinguish the relationship between the daily sea cucumber production and the daily sea cucumber raw material supply in different stages, or it is too small, resulting in an incomplete clustering and missing some relevant data points. Therefore, the embodiments of the present invention determine the neighborhood radius based on the position of the data points in the coordinate system, can accurately adapt to the distribution characteristics of the data, clearly divide the clusters with similar relationships between the daily sea cucumber production and the daily sea cucumber supply, help to identify the production modes in different stages, and improve the accuracy of clustering.

[0046] Further, as an optional embodiment of the present invention, determining the neighborhood radius in the clustering algorithm according to the position of the feature group composed of the raw material supply quantity eigenvalue and the daily sea cucumber production eigenvalue includes: mapping the raw material supply quantity eigenvalue and the daily sea cucumber production eigenvalue as a feature group to the coordinate system, linearly fitting the positions of the daily feature groups in the coordinate system to obtain a fitting line; calculating the perpendicular distance between the daily feature group and the fitting line; and determining the neighborhood radius in the clustering algorithm according to the fifth average value of each perpendicular distance and the ratio of the first quantity of the data points corresponding to the feature groups on the fitting line to the second quantity of all the data points corresponding to the feature groups.

[0047] Specifically, in the sea cucumber processing workshop, there is theoretically a certain correlation between the daily sea cucumber production and the daily raw material supply of sea cucumbers. It is shown in Figure 4 that whether it is off-season data points or peak-season data points, they are all distributed along a certain direction. Exemplarily, as shown in Figure 4 ... Figure 4 is a schematic diagram of the distribution relationship between the daily sea cucumber production and the daily raw material supply of sea cucumbers provided by an embodiment of the present invention. When there are more data points on the line, it indicates that the relationship between the daily feature groups in each cluster is closer, that is, the neighborhood radius is smaller. Therefore, the size of the neighborhood radius in the clustering algorithm is determined by the tightness between the daily feature groups. And in Figure 4 ..., the distance between the position of the daily feature group in the coordinate and the overall data points along a certain direction can intuitively reflect the deviation degree of the actual production situation represented by each data point from the theoretical relationship, that is, the above-mentioned tightness.

[0048] Further, in the embodiment of the present invention, the least squares method is first used to linearly fit the positions of the daily feature groups in the coordinate system to obtain the fitting line L after fitting. The fitting line L reflects the theoretical relationship between the raw material supply quantity eigenvalue and the daily sea cucumber production eigenvalue. Then, the perpendicular distance d between the position of the daily feature group in the coordinate system and the fitting line L is calculated. d reflects the deviation between the actual relationship and the theoretical relationship of the raw material supply quantity eigenvalue and the daily sea cucumber production eigenvalue. Finally, the neighborhood radius is represented by the average value of the perpendicular distances between the positions of the daily feature groups in the coordinate system and the fitting line L and the ratio of the number of data points on the fitting line L after fitting to the number of all data points. At this time, when the average distance of the perpendicular distance is smaller and the ratio of the number of data points on the fitting line L to the number of all data points is larger, it indicates that the position distribution of the data points in the two-dimensional coordinate is closer, that is, the neighborhood radius is smaller.

[0049] Further, as an optional embodiment of the present invention, determining the neighborhood radius in the clustering algorithm according to the fifth average value of each vertical distance and the ratio of the first number of data points corresponding to the feature group on the fitted line to the second number of data points corresponding to all feature groups includes: calculating the first sum value between the ratio and a predetermined value, and performing a normalization process on the first sum value to obtain a normalized value; determining the third product of the fifth average value and the reciprocal of the normalized value as the neighborhood radius.

[0050] Specifically, in the embodiment of the present invention, the predetermined value is taken as 1, and the following formula is specifically used in the embodiment of the present invention to calculate the neighborhood radius:

[0051]

[0052] In the above formula, represents the neighborhood radius. represents the fifth average value of the vertical distance between the position of the daily feature group in the coordinate system and the fitted line L. represents the vertical distance between the position of the feature group on the c-th day in the coordinate system and the fitted line L. C represents the second number of all data points. represents the first number of data points corresponding to the feature group on the fitted line L. represents the number of data points on the fitted line L after fitting The proportion of the overall data points C. The function is used to perform a normalization process.

[0053] S104. Modify the neighborhood radius according to the Pearson correlation coefficient between the sea cucumber daily production sequence and the sea cucumber daily raw material supply sequence to obtain a modified neighborhood radius, and cluster the positions of the feature groups in the coordinate system based on the modified neighborhood radius to obtain multiple clusters.

[0054] Specifically, in the above embodiment of the present invention, by analyzing the relationship between the position of the daily feature group in the coordinate system and the fitted line, the parameter size of the neighborhood radius in the clustering algorithm is determined, and the construction of the fitted line is based on the analysis of the positions of the daily feature groups in practice, that is, the relationship between the raw material supply characteristic value and the sea cucumber daily production characteristic value. In actual production, there will be a phenomenon of stockpiling of the supply volume, that is, the situation where the raw material supply is large but the sea cucumber daily production is small. At this time, the position of the daily feature group in the coordinate is Figure 4In the lower right middle, these data points are regarded as noise points. The noise points cause the fitting line to shift, resulting in an error in the relationship between the actually calculated eigenvalues of the raw material supply volume and the daily sea cucumber production volume. Eventually, the neighborhood radius cannot truly and accurately include the dense data points. The eigenvalues of the raw material supply volume and the daily sea cucumber production volume are calculated through the raw material supply volume and the daily sea cucumber production volume respectively. There is a strong or weak relationship between the raw material supply volume and the daily sea cucumber production volume, and the strong or weak relationship between the two can be used to reflect the relationship between the overall eigenvalues of the raw material supply volume and the daily sea cucumber production volume. The fact that there is a strong or weak relationship between the raw material supply volume and the daily sea cucumber production volume is the correlation between the two. Therefore, the size of the above neighborhood radius is corrected based on the correlation between the raw material supply volume and the daily sea cucumber production volume.

[0055] Further, as an optional embodiment of the present invention, the neighborhood radius is corrected according to the Pearson correlation coefficient between the daily sea cucumber production volume sequence and the daily raw material supply volume sequence of sea cucumbers. The corrected neighborhood radius obtained includes: calculating the second sum value between a predetermined value and the reciprocal of the absolute value of the Pearson correlation coefficient; determining the fourth product between the neighborhood radius and the second sum value as the corrected neighborhood radius.

[0056] Specifically, the embodiment of the present invention uses the Pearson correlation coefficient calculation to form a daily sea cucumber production volume sequence for the daily sea cucumber production volume in the past five years , and a daily raw material supply volume sequence of sea cucumbers formed by the daily raw material supply volume of sea cucumbers in the past five years to calculate the Pearson correlation coefficient, as shown in the following formula:

[0057]

[0058] In the above formula, represents the Pearson correlation coefficient between the daily sea cucumber production volume sequence and the daily raw material supply volume sequence. represents the relationship between the daily sea cucumber production volume sequence and the daily raw material supply volume sequence.

[0059] Further, at this time, when the correlation between the daily sea cucumber production volume and the daily raw material supply volume of sea cucumbers is stronger, it means that the relationship between the eigenvalues of the raw material supply volume and the daily sea cucumber production volume can more accurately reflect the relationship between the data points, that is, the neighborhood radius is more accurate. When the correlation between the daily sea cucumber production volume and the daily raw material supply volume of sea cucumbers is weaker, it means that there is a lot of noise in the data when calculating the neighborhood radius, that is, the neighborhood radius is inaccurate. Therefore, the correlation between the daily sea cucumber production volume and the daily raw material supply volume of sea cucumbers is used as the correction amount of the above neighborhood radius. In the embodiment of the present invention, the above predetermined value is taken as 1. The embodiment of the present invention specifically uses the following formula to calculate the corrected neighborhood radius:

[0060]

[0061] In the above formula, represents the size of the neighborhood radius before correction. represents the correction coefficient of the neighborhood radius. When the correlation between the daily sea cucumber production and the daily raw material supply of sea cucumbers is stronger, its correction coefficient is smaller, that is, the correction amount of the neighborhood radius is smaller. Conversely, the correction amount of the neighborhood radius is larger. represents the size of the corrected neighborhood radius.

[0062] Furthermore, after obtaining the corrected neighborhood radius, the embodiment of the present invention sets the minimum number of data points in the clustering algorithm to 200. The clustering process of the positions of the daily feature values in the two-dimensional coordinate includes: first calculating the number of data points within the corrected neighborhood range. Specifically, taking this data point as the center, count the number of data points within the circular area of the corrected neighborhood radius range. Then mark the data points. If the neighborhood of this data point contains at least data points, then this data point is marked as a core point. If the number of data points contained in the neighborhood of the data point is less than , but it is within the neighborhood of a certain core point, then this data point is marked as a boundary point. If a data point is neither a core point nor a boundary point, then it is marked as a noise point. Finally, starting from any unvisited core point, find all the data points that are density-connected to this core point, and these data points form a cluster. Repeat the above steps until all core points have been visited, and at this time, all clusters are obtained.

[0063] S105. Determine the data volume of the unit scale of the visualization coordinate system according to the maximum value of the daily sea cucumber production, the minimum value of the daily sea cucumber production, and the number of scale grids in the visualization coordinate system within the time range of the cluster.

[0064] Specifically, the above embodiment of the present invention can cluster the time dates of 5 years according to the daily sea cucumber production and the daily raw material supply. At this time, the daily sea cucumber production within the time of the same cluster has the same characteristic performance. Regarding the consecutive dates within the same cluster as a cluster group, the time difference of the cluster group is the production cycle of the sea cucumber, which is 1 year in the embodiment of the present invention. Exemplarily, the time range of a cluster is expressed as taking the union of the times of all cluster groups within the same cluster. For example, the time range of cluster group A is from March 4, 2024 to June 1, 2024, the time range of B is from March 2, 2024 to June 25, 2024, and the time range of C is from March 1, 2024 to June 2, 2024. Then the time range of this cluster group is from March 1, 2024 to June 25, 2024. In this way, the time range of the daily sea cucumber production in each season within 1 year is obtained.

[0065] Further, as an optional embodiment of the present invention, determining the data volume of the unit scale of the visualization coordinate system according to the maximum daily sea cucumber production, the minimum daily sea cucumber production within the time range of the cluster, and the number of scale grids in the visualization coordinate system includes: taking consecutive dates in the cluster as a cluster group, and taking the time union of each cluster group as the time range of the cluster; calculating the third sum between the maximum daily sea cucumber production and the anti-overflow parameter, and the first difference between the minimum daily sea cucumber production and the anti-overflow parameter; calculating the second difference between the third sum and the first difference; and determining that the ratio between the second difference and the number of scale grids in the visualization coordinate system is the data volume of the unit scale.

[0066] Specifically, in the embodiment of the present invention, the seafood production in the same cluster has similarity, and the maximum daily sea cucumber production in the same cluster is taken , the minimum daily sea cucumber production . At this time, in the embodiment of the present invention, the maximum value of the daily sea cucumber production displayed on the visualization coordinate scale within the time range of this cluster is , the minimum value . Among them, To prevent data overflow, it can be valued according to the actual situation. In the embodiment of the present invention, the value is 50. At this time, the data volume of the unit scale of the visualization coordinate system within the time range of this cluster can be specifically calculated by the following formula:

[0067]

[0068] In the above formula, represents the data volume of the unit scale of the visualization coordinate system. represents the maximum display amount of the visualization coordinate scale within the time range of this cluster. The number of scale grids in the visualization coordinate system. represents the maximum daily sea cucumber production. represents the minimum daily sea cucumber production.

[0069] S106. Visualize the sea cucumber production based on the visualization graph composed of the data volume of the unit scale, the maximum daily sea cucumber production, the minimum daily sea cucumber production, and the number of scale grids.

[0070] Specifically, after obtaining the data volume of the unit scale of the visualization coordinate system, the maximum daily sea cucumber production, and the minimum daily sea cucumber production, adapt the visualization graph of the number of scale grids in the visualization coordinate system to the daily sea cucumber production, that is, the scale and the data volume corresponding to the scale of this visualization graph can be adaptively adjusted, so as to more intuitively display the change details of the daily sea cucumber production.

[0071] The embodiments of the present invention can construct a feature group composed of the raw material supply volume eigenvalue and the daily sea cucumber production eigenvalue in the sea cucumber processing workshop, analyze the periodicity of the sea cucumber production through clustering with a determined neighborhood radius, and finally adaptively adjust the coordinate scale according to the distribution of the maximum value, minimum value, number of scale grids, and data volume per unit scale of the daily sea cucumber production in different time ranges in the visualization graph. By the way of adaptively adjusting the coordinate scale of the visualization graph, it can better adapt to the periodic changes of the sea cucumber production, enable the line graph to accurately and clearly display the change details of the sea cucumber production in different periods, improve the accuracy of evaluating the increase and decrease of the sea cucumber production, provide more effective data visualization support for the production management of the sea cucumber processing workshop, and is more conducive to reasonably arranging the resource allocation work based on the trend of the sea cucumber production.

[0072] Embodiment 2:

[0073] Corresponding to the data visualization method applicable to the digital processing workshop provided in the above embodiment, based on the same technical concept, the embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes the steps of the data visualization method applicable to the digital processing workshop mentioned in the above embodiment.

[0074] It should be noted that the computer-readable storage medium provided in the embodiments of the present invention and the data visualization method applicable to the digital processing workshop provided in the embodiments of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the data visualization method applicable to the digital processing workshop described above, and has the same or similar beneficial effects. The repeated parts will not be elaborated.

[0075] Embodiment 3:

[0076] Corresponding to the data visualization method applicable to the digital processing workshop provided in the above embodiment, based on the same technical concept, the embodiments of the present invention also provide a data visualization system applicable to the digital processing workshop. This data visualization system applicable to the digital processing workshop is used to execute the data visualization method applicable to the digital processing workshop. Figure 5 FIG. is a schematic structural diagram of a data visualization system applicable to the digital processing workshop provided in another embodiment of the present invention. As Figure 5 shown. The data visualization system applicable to the digital processing workshop may vary greatly due to configuration or performance, and may include one or more processors 501 and a memory 502. The memory 502 is used to store a computer program that can run on the processor 501. The processor 501 is used to execute the program stored in the memory 502 to implement the above Figure 1Each step in the method embodiment. Among them, the memory 502 can be transient storage or persistent storage. The application programs stored in the memory 502 can include one or more modules (not shown in the figure), and each module can include a series of computer-executable instructions applicable to the data visualization system in the digital processing workshop.

[0077] Furthermore, the processor 501 can be set to communicate with the memory 502 and execute a series of computer-executable instructions in the memory 502 on the data visualization system applicable to the digital processing workshop. The data visualization system applicable to the digital processing workshop can also include one or more power supplies 503, one or more wired or wireless network interfaces 504, one or more input / output interfaces 505, and one or more keyboards 506.

[0078] Specifically in this embodiment, the data visualization system applicable to the digital processing workshop includes a processor, a communication interface, a memory, and a communication bus; among them, the processor, the communication interface, and the memory complete communication with each other through the bus; the memory is used to store computer programs; the processor is used to execute the programs stored on the memory to implement the above Figure 1 Each step in the method embodiment, and has the beneficial effects of the above method embodiment. To avoid repetition, the embodiments of the present invention will not be described in detail here.

[0079] It should be noted that the data visualization system applicable to the digital processing workshop provided by the embodiments of the present invention and the data visualization method applicable to the digital processing workshop provided by the embodiments of the present invention are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned data visualization method applicable to the digital processing workshop, and has the same or similar beneficial effects. The repeated parts will not be described again.

[0080] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. A data visualization method suitable for a digital processing workshop, characterized in that: The data visualization method applicable to a digital processing workshop includes: Obtaining the daily production sequence of sea cucumbers and the daily raw material supply sequence of sea cucumbers within a predetermined time period; Determining daily raw material supply characteristic values ​​and daily sea cucumber production characteristic values ​​using a predetermined time window, the sea cucumber daily raw material supply sequence, and the sea cucumber daily production sequence includes: Dividing the sea cucumber daily raw material supply sequence and the sea cucumber daily production sequence into a first sequence segment and a second sequence segment of a plurality of time lengths by using a predetermined time window, each of the first sequence segments including the sea cucumber daily raw material supply of the day to be analyzed at the middle position of the first sequence segment and the sea cucumber daily raw material supply of the day adjacent to the day to be analyzed, and each of the second sequence segments including the sea cucumber daily production of the day to be analyzed at the middle position of the second sequence segment and the sea cucumber daily production of the day adjacent to the day to be analyzed; Determine the raw material supply characteristic value of the day to be analyzed according to a first average value of all sea cucumber raw material supplies in the first sequence segment and a second average value of the sea cucumber raw material supplies on adjacent days in the first sequence segment; Determine the characteristic value of the daily production of sea cucumbers on the day to be analyzed according to the third average value of the daily production of all sea cucumbers in the second sequence segment and the fourth average value of the daily production of sea cucumbers on adjacent days in the second sequence segment; Determine the neighborhood radius in the clustering algorithm according to the position of the feature group composed of the raw material supply feature value and the sea cucumber daily output feature value in the coordinate system; The neighborhood radius is corrected according to the Pearson correlation coefficient between the daily sea cucumber production sequence and the daily sea cucumber raw material supply sequence to obtain a corrected neighborhood radius, and the positions of the feature groups in the coordinate system are clustered based on the corrected neighborhood radius to obtain a plurality of clusters; Determine the data volume of a unit scale of the visualization coordinate system according to the maximum value of the daily sea cucumber production within the time range of the cluster, the minimum value of the daily sea cucumber production and the number of grids of the scale in the visualization coordinate system; The sea cucumber production is visualized based on a visualization graph formed based on the data volume of the unit scale, the maximum daily sea cucumber production, the minimum daily sea cucumber production, and the number of grids of the scale.

2. The data visualization method applicable to a digital processing workshop according to claim 1 is characterized in that: Determining the characteristic value of the raw material supply on the day to be analyzed according to the first average value of all sea cucumber raw material supplies in the first sequence segment and the second average value of the sea cucumber raw material supplies on adjacent days in the first sequence segment comprises: A first product between the first average value and the second average value is determined as a characteristic value of the raw material supply quantity on the day to be analyzed.

3. The data visualization method applicable to a digital processing workshop according to claim 1 is characterized in that: Determining the characteristic value of the daily production of sea cucumbers on the day to be analyzed based on the third average value of the daily production of all sea cucumbers in the second sequence segment and the fourth average value of the daily production of sea cucumbers on adjacent days in the second sequence segment comprises: Determine the second product between the third average value and the fourth average value as the daily production characteristic value of sea cucumbers on the day to be analyzed.

4. The data visualization method applicable to a digital processing workshop according to claim 1 is characterized in that: Determining the neighborhood radius in the clustering algorithm according to the position of the feature group formed by the raw material supply feature value and the sea cucumber daily output feature value in the coordinate system includes: Mapping the raw material supply characteristic value and the sea cucumber daily output characteristic value as a characteristic group to a coordinate system, performing linear fitting on the position of the daily characteristic group in the coordinate system to obtain a fitting straight line; Calculating the vertical distance between the feature group and the fitting straight line on a daily basis; A neighborhood radius in a clustering algorithm is determined according to a fifth average value of each of the vertical distances and a ratio of a first number of data points corresponding to the feature group on the fitting straight line to a second number of data points corresponding to all feature groups.

5. The data visualization method applicable to a digital processing workshop according to claim 4 is characterized in that: Determining the neighborhood radius in the clustering algorithm according to the fifth average values ​​of the vertical distances and the ratio of the first number of data points corresponding to the feature groups on the fitting straight line to the second number of data points corresponding to all feature groups includes: Calculating a first sum value between the ratio and a predetermined value, and normalizing the first sum value to obtain a normalized value; A third product between the fifth average value and the reciprocal of the normalized value is determined as the neighborhood radius.

6. The data visualization method applicable to a digital processing workshop according to claim 1 is characterized in that: The neighborhood radius is corrected according to the Pearson correlation coefficient between the sea cucumber daily production sequence and the sea cucumber daily raw material supply sequence to obtain the corrected neighborhood radius, which includes: calculating a second sum between a predetermined value and the reciprocal of the absolute value of the Pearson correlation coefficient; A fourth product between the neighborhood radius and the second sum is determined as the modified neighborhood radius.

7. The data visualization method applicable to a digital processing workshop according to any one of claims 1 to 6, characterized in that: The method of determining the data volume of the unit scale of the visualization coordinate system according to the maximum value of the daily sea cucumber production within the time range of the cluster, the minimum value of the daily sea cucumber production and the number of grids of the scale in the visualization coordinate system includes: Taking the continuous dates in the cluster as a cluster group, and taking the time union of each cluster group as the time range of the cluster; Calculating a third sum value between the maximum value of the daily sea cucumber production and the anti-overflow parameter, and a first difference value between the minimum value of the daily sea cucumber production and the anti-overflow parameter; calculating a second difference between the third sum and the first difference; The ratio between the second difference and the number of grids of the scale in the visualization coordinate system is determined as the data amount of the unit scale.

8. A data visualization system suitable for a digital processing workshop, characterized in that: include: A processor and a memory; wherein the memory is used to store a computer program that can be run on the processor; A processor is used to execute the program stored in the memory to implement the steps of the data visualization method applicable to a digital processing workshop as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the data visualization method applicable to a digital processing workshop as described in any one of claims 1 to 7 are implemented.

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