Data visualization method and system suitable for digital processing workshop and medium

Abstract

The invention relates to the technical field of data processing, in particular to a data visualization method and system suitable for a digital processing workshop and a medium, and the method comprises the steps: obtaining a sea cucumber daily output sequence and a sea cucumber daily raw material supply sequence in a preset time period; determining a daily raw material supply eigenvalue and a sea cucumber daily output eigenvalue; determining a neighborhood radius in a clustering algorithm; correcting the neighborhood radius according to a 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 a plurality of class clusters; determining the data volume of the unit scale of the visual coordinate system; and visually displaying the sea cucumber yield based on a visual graph formed by the data volume of the unit scale, the maximum daily yield of the sea cucumber, the minimum daily yield of the sea cucumber and the grid number of the scale. Therefore, the accuracy of evaluating the increase and decrease conditions of the sea cucumber yield is improved.

Description

Technical Field

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

[0002] Digital processing workshop refers to a new 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. It has been widely used in various industries due to its high efficiency and high precision. Among them, in the sea cucumber processing industry, effective monitoring and analysis of production data is crucial. Using the line chart in the visualization tool to show the daily output can intuitively present the trend of production changes. Users can understand the increase and decrease of sea cucumber production on different dates through the fluctuations of the line, and then reasonably arrange resource allocation based on the trend of sea cucumber production.

[0003] In some scenarios, the production of sea cucumbers has significant cyclical characteristics. Affected by various factors such as seasons, sea cucumber growth cycles, and fishing rules, the production of sea cucumbers in different periods varies greatly. At present, fixed unit scale coordinates are generally used to draw a line chart of sea cucumber production, which has obvious limitations. This is because in the high-yield period of sea cucumbers, due to the fixed vertical coordinate scale, the broken line may exceed the display range of the chart, resulting in poor data visualization and unable to clearly present the details of sea cucumber production data. In low-yield areas of sea cucumbers, the broken line is almost close to the horizontal axis, making it difficult to observe small fluctuations in sea cucumber production. It is also not conducive to accurately analyzing the production of sea cucumbers. In this way, it is difficult to clearly and accurately visualize the details of changes in sea cucumber production using existing methods, which leads to low accuracy in evaluating the increase or decrease in sea cucumber production, which is not conducive to the reasonable arrangement of resource allocation based on the trend of sea cucumber production. Summary of the invention

[0004] In order to solve the technical problem that it is difficult to clearly and accurately visualize the details of the changes in sea cucumber production, which leads to low accuracy in evaluating the increase or decrease in sea cucumber production, the purpose of the present invention is to provide a data visualization method, system and medium suitable for a digital processing workshop. The technical solutions adopted are as follows: In a first aspect, an embodiment of the present invention provides a data visualization method suitable for 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; determining daily raw material supply characteristic values ​​and sea cucumber daily production characteristic values ​​using a predetermined time window, a sea cucumber daily raw material supply sequence and a sea cucumber daily production sequence; determining a neighborhood radius in a clustering algorithm according to the position of a feature group consisting of the raw material supply characteristic values ​​and the sea cucumber daily production characteristic values ​​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, clustering the position of the feature group in the coordinate system based on the corrected neighborhood radius to obtain a plurality of clusters; determining the data volume per 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 grids of the scale in the visualization coordinate system within the time range of the cluster; and visually displaying the sea cucumber production based on a visualization graph consisting of the data volume per unit scale, the maximum value of the sea cucumber daily production, the minimum value of the sea cucumber daily production and the number of grids of the scale.

[0005] Optionally, using a predetermined time window, a sea cucumber daily raw material supply sequence and a sea cucumber daily production sequence to determine a daily raw material supply characteristic value and a sea cucumber daily production characteristic value includes: using a predetermined time window to divide 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 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 day adjacent to the day to be analyzed, and 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 day adjacent to the day to be analyzed; determining the raw material supply characteristic value of the day to be analyzed based on a first average value of all sea cucumber raw material supplies in the first sequence segment and a second average value of sea cucumber raw material supplies on adjacent days in the first sequence segment; determining the sea cucumber daily production characteristic value of the day to be analyzed based on a third average value of all sea cucumber daily productions in the second sequence segment and a fourth average value of sea cucumber daily productions on adjacent days in the second sequence segment.

[0006] Optionally, determining the characteristic value of the raw material supply for the day to be analyzed based on 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 includes: determining a first product between the first average value and the second average value as the characteristic value of the raw material supply for the day to be analyzed.

[0007] Optionally, 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 includes: determining the second product between the third average value and the fourth average value as the characteristic value of the daily production of sea cucumbers on the day to be analyzed.

[0008] Optionally, determining the neighborhood radius in the clustering algorithm based on the position of the feature group composed of the characteristic values ​​of raw material supply and the daily sea cucumber output in the coordinate system includes: mapping the characteristic values ​​of raw material supply and the daily sea cucumber output as feature groups to the coordinate system, performing linear fitting on the position of the daily feature group in the coordinate system to obtain a fitted straight line; calculating the vertical distance between the daily feature group and the fitted straight line; determining the neighborhood radius in the clustering algorithm based on the fifth average of each vertical distance and the ratio of the first number of data points corresponding to the feature group on the fitted straight line to the second number of data points corresponding to all feature groups.

[0009] Optionally, determining the neighborhood radius in the clustering algorithm based on 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 fitting straight line to the second number of data points corresponding to all feature groups includes: calculating a first sum between the ratio and a predetermined value, and normalizing the first sum to obtain a normalized value; and determining the third product between the fifth average value and the reciprocal of the normalized value as the neighborhood radius.

[0010] Optionally, 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 the corrected neighborhood radius, including: calculating the second sum between a predetermined value and the reciprocal of the absolute value of the Pearson correlation coefficient; and determining the fourth product between the neighborhood radius and the second sum as the corrected neighborhood radius.

[0011] Optionally, determining the data volume of a unit scale of the visualization coordinate system based on the maximum daily sea cucumber production, the minimum daily sea cucumber production and the number of grids of the scale in the visualization coordinate system within the time range of the cluster 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 the ratio between the second difference and the number of grids of the scale in the visualization coordinate system as the data volume of a unit scale.

[0012] In a second aspect, an embodiment of the present invention provides a data visualization system suitable for a digital processing workshop, comprising: a processor and a memory; wherein the memory is used to store a computer program that can be run on the processor; and the processor is used to execute the program stored in the memory to implement the steps of the data visualization method suitable for a digital processing workshop as mentioned in the first aspect.

[0013] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. 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.

[0014] The present invention has the following beneficial effects: firstly, a sea cucumber daily production sequence and a sea cucumber daily raw material supply sequence within a predetermined time period are obtained; then, a daily raw material supply characteristic value and a sea cucumber daily production characteristic value are determined using a predetermined time window, a sea cucumber daily raw material supply sequence and a sea cucumber daily production sequence; and a neighborhood radius in a clustering algorithm is determined according to the position of a feature group composed of the raw material supply characteristic value and the sea cucumber daily production characteristic value in a coordinate system; secondly, 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 a corrected neighborhood radius, and the position of the feature group in the coordinate system is clustered based on the corrected neighborhood radius to obtain a plurality of clusters; then, the data volume of a unit scale of a visualization coordinate system is determined according to the maximum value of the sea cucumber daily production, the minimum value of the sea cucumber daily production and the number of grids of the scale in the visualization coordinate system within the time range of the cluster; finally, a visualization diagram composed of 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 grids of the scale is used to visualize the sea cucumber production.

[0015] In this way, the embodiment of the present invention can construct a feature group consisting of the characteristic values ​​of the raw material supply and the daily output of sea cucumbers in the sea cucumber processing workshop, and analyze the periodicity of the sea cucumber production through clustering analysis of the determined neighborhood radius, and finally adaptively adjust the coordinate scale according to the maximum value, minimum value, number of scale grids and data volume of the daily output of sea cucumbers in different time ranges of sea cucumbers in the visualization diagram. By adaptively adjusting the coordinate scale of the visualization diagram, it is possible to better adapt to the periodic changes in sea cucumber production, and the line graph can accurately and clearly display the details of the changes in sea cucumber production in different periods, thereby improving the accuracy of evaluating the increase and decrease in sea cucumber production, providing more effective data visualization support for the production management of the sea cucumber processing workshop, and being more conducive to the reasonable arrangement of resource allocation work based on the trend of sea cucumber production. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0017] Figure 1A flow chart of a data visualization method applicable to a digital processing workshop provided by one embodiment of the present invention; Figure 2 A schematic diagram of the relationship between sea cucumber production and time provided by one embodiment of the present invention; Figure 3 A schematic diagram of the position relationship of a feature group in a two-dimensional space provided by an embodiment of the present invention; Figure 4 A schematic diagram of the distribution relationship between the daily output of sea cucumbers and the daily supply of sea cucumber raw materials provided by one embodiment of the present invention; Figure 5 A schematic structural diagram of a data visualization system suitable for a digital processing workshop is provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a data visualization method, system and medium suitable for a digital processing workshop proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

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

[0020] The following specifically describes a data visualization method applicable to a digital processing workshop provided by the present invention in conjunction with the accompanying drawings.

[0021] Embodiment 1: See also Figure 1 , which shows a flow chart of a data visualization method applicable to a digital processing workshop provided by an embodiment of the present invention, including: S101, obtaining a sequence of daily sea cucumber production and a sequence of daily sea cucumber raw material supply within a predetermined time period.

[0022] 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 raw material supply of sea cucumbers on day T.

[0023] 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.

[0024] 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.

[0025] Furthermore, in the off-season, the demand for sea cucumbers is small, so the daily output of sea cucumbers and the supply of raw materials are low. The daily output of sea cucumbers and the supply 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, in the off-season, the demand for daily output of sea cucumbers is small, so the daily output of sea cucumbers and the supply of raw materials are low. In the peak season, the demand for sea cucumbers is large, so the daily output of sea cucumbers and the supply of raw materials are high. Based on the above analysis, the daily raw material supply characteristic value and the daily output characteristic value of sea cucumbers are constructed by the change and average amount of daily output of sea cucumbers and raw material supply in a time window.

[0026] Further, as an optional embodiment of the present invention, determining the daily raw material supply characteristic value and the daily sea cucumber output characteristic value by using the predetermined time window, the sea cucumber daily raw material supply sequence and the sea cucumber daily output sequence includes: The daily raw material supply sequence and the daily production sequence of sea cucumbers are divided into a first sequence segment and a second sequence segment of multiple time lengths by using a predetermined time window, each first sequence segment includes the daily raw material supply of sea cucumbers on the day to be analyzed which is in the middle of the first sequence segment and the daily raw material supply of sea cucumbers on the adjacent days of the day to be analyzed, and each second sequence segment includes the daily production of sea cucumbers on the day to be analyzed which is in the middle of the second sequence segment and the daily production of sea cucumbers on the adjacent days of the day to be analyzed; the characteristic value of the raw material supply of the day to be analyzed is determined according to the first average value of all sea cucumber raw material supplies in the first sequence segment and the second average value of sea cucumber raw material supplies on the adjacent days in the first sequence segment; the characteristic value of the daily production of sea cucumbers on the day to be analyzed is determined according to the third average value of all sea cucumber daily productions in the second sequence segment and the fourth average value of sea cucumber daily productions on the adjacent days in the second sequence segment.

[0027] Specifically, the embodiment of the present invention is for the sequence of daily raw material supply of sea cucumber and sea cucumber daily production series . Set a predetermined time window of length S, where the length of the predetermined time window can be determined according to actual conditions. In the embodiment of the present invention, S=7. and sea cucumber daily production series Any day in is taken as the analysis object, that is, the day to be analyzed, the day to be analyzed is taken as the midpoint of the predetermined time window, and the length of the predetermined time window is taken as S. At this time, the first sequence segment composed of the daily raw material supply of sea cucumbers and the second sequence segment composed of the daily output of sea cucumbers can be obtained.

[0028] Further, as an optional embodiment of the present invention, determining the characteristic value of the raw material supply for the day to be analyzed based on 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 includes: determining a first product between the first average value and the second average value as the characteristic value of the raw material supply for the day to be analyzed.

[0029] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the characteristic value of the raw material supply quantity on the day to be analyzed: In the above formula, Represents the characteristic value of raw material supply. Represents the first average value of the sea cucumber raw material supply in the first sequence segment. Indicates the time length of the first sequence segment. Represents the supply of sea cucumber raw materials on the sth day in the first sequence segment. Represents the supply of sea cucumber raw materials on day s-1 in the first sequence segment. Represents the second average value of the sea cucumber raw material supply on adjacent days in the first sequence segment. Represents the change between the sea cucumber raw material supply on the s-th day in the first sequence segment and the sea cucumber raw material supply on the adjacent s-1 day. Indicates the number of consecutive days in the first sequence segment.

[0030] Further, as an optional embodiment of the present invention, 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 includes: determining the second product between the third average value and the fourth average value as the characteristic value of the daily production of sea cucumbers on the day to be analyzed.

[0031] Specifically, the embodiment of the present invention specifically uses the following formula to calculate the characteristic value of the daily output of sea cucumbers on the day to be analyzed: In the above formula, N represents the characteristic value of daily sea cucumber production. Represents the third average value of daily sea cucumber production in the second sequence segment. Indicates the time length of the second sequence segment, which is the same as the time length of the first sequence segment. Represents the daily production of sea cucumbers on the sth day in the second sequence segment. Represents the daily production of sea cucumbers on day s-1 in the second sequence segment. Represents the fourth average value of daily sea cucumber production on adjacent days in the second sequence segment. It represents the change between the daily production of sea cucumbers on the sth day in the second sequence segment and the daily production of sea cucumbers on the adjacent s-1th day. Indicates the number of adjacent days in the second sequence segment.

[0032] S103, determining a neighborhood radius in a clustering algorithm according to the position of a feature group formed by the feature value of raw material supply and the feature value of sea cucumber daily output in the coordinate system.

[0033] Specifically, the daily characteristic values ​​of raw material supply and sea cucumber production calculated by the embodiment of the present invention can be used to obtain the daily characteristic values ​​of the sea cucumber production. For example, Figure 3 As shown, Figure 3 A schematic diagram of the position relationship of a feature group in a two-dimensional space provided by an embodiment of the present invention, Figure 3 In the figure, the feature group is divided into two areas in the two-dimensional space. In the off-season, the demand for sea cucumbers is small, so the characteristic value of the daily output of sea cucumbers and the characteristic value of the raw material supply are both small, so 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 the characteristic value of the daily output of sea cucumbers and the characteristic value of the raw material supply are both large, so most of the data points in the peak season are in the upper right of the coordinate system. Based on the above analysis, the embodiment of the present invention adopts a density-based clustering algorithm (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) to classify all feature groups. Among them, the neighborhood radius of the DBSCAN clustering algorithm defines the range around a data point, and other data points within this range are regarded as neighbors of the point. The traditional method of determining the neighborhood radius adopts a fixed empirical value to determine it, and does not consider the actual distribution of data points, such as the compactness of the data, whether there is a central tendency in a specific direction, etc., which leads to unreasonable setting of the neighborhood radius, either too large to make the clustering too broad, unable to distinguish the relationship between the daily output of sea cucumbers in different stages and the daily raw material supply of sea cucumbers, or too small to cause incomplete clustering, missing some relevant data points. Therefore, the embodiment of the present invention determines 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 divides the clustering with the relationship between the daily output of similar sea cucumbers and the daily supply of sea cucumbers, helps to identify the production mode of different stages, and improves the accuracy of clustering.

[0034] 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 characteristic values ​​of raw material supply and the daily sea cucumber output in the coordinate system includes: mapping the characteristic values ​​of raw material supply and the daily sea cucumber output as feature groups to the coordinate system, performing linear fitting on the position of the daily feature group in the coordinate system to obtain a fitted straight line; calculating the vertical distance between the daily feature group and the fitted straight line; 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 straight line to the second number of data points corresponding to all feature groups.

[0035] Specifically, in the sea cucumber processing workshop, there is a certain correlation between the daily output of sea cucumbers and the daily supply of sea cucumber raw materials in theory. Figure 4 In the above figure, it is indicated that no matter it is a low season data point or a peak season data point, it is distributed along a certain direction. For example, Figure 4 As shown, Figure 4 A schematic diagram of the distribution relationship between the daily production of sea cucumbers and the daily supply of sea cucumber raw materials provided by an embodiment of the present invention. When there are more data points on the straight line, it means 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 closeness between the daily feature groups, and Figure 4 In the figure, the distance between the position of the daily feature group in the coordinates and the overall data points along a certain direction can intuitively reflect the degree of deviation between the actual production situation represented by each data point and the theoretical relationship, that is, the above-mentioned closeness.

[0036] Further, the embodiment of the present invention first uses the least square method to perform linear fitting on the position of the daily feature group in the coordinate system to obtain the fitted straight line L after fitting, and the fitted straight line L reflects the theoretical relationship between the characteristic value of the raw material supply and the characteristic value of the daily output of sea cucumbers. Then the vertical distance d between the position of the daily feature group in the coordinate system and the fitted straight line L is calculated, and d reflects the deviation between the actual relationship and the theoretical relationship between the characteristic value of the raw material supply and the characteristic value of the daily output of sea cucumbers. Finally, the neighborhood radius is represented by the average value of the vertical distance between the position of the daily feature group in the coordinate system and the fitted straight line L and the proportion of the number of data points on the fitted straight line L to the number of overall data points. At this time, when the average distance of the vertical distance is smaller and the proportion of the number of data points on the fitted straight line L to the number of overall data points is larger, it means that the position distribution of the data points in the two-dimensional coordinates is more compact, that is, the neighborhood radius is smaller.

[0037] Further, as an optional embodiment of the present invention, determining the neighborhood radius in the clustering algorithm based on 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 fitting straight line to the second number of data points corresponding to all feature groups includes: calculating a first sum between the ratio and a predetermined value, and normalizing the first sum 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.

[0038] Specifically, in the embodiment of the present invention, the predetermined value is 1, and the embodiment of the present invention specifically uses the following formula to calculate the neighborhood radius: In the above formula, Represents the neighborhood radius. Represents the fifth average of the vertical distances between the positions of the daily feature groups in the coordinates and the fitting straight line L. It represents the vertical distance between the position of the feature group on the cth day in the coordinate system and the fitting line L. C represents the second number of all data points. Indicates the first number of data points corresponding to the feature group on the fitting line L. Indicates the number of data points on the fitted line L after fitting The proportion of the total data points C. Function used to Perform normalization.

[0039] S104, correcting the neighborhood radius 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 clustering the positions of the feature groups in the coordinate system based on the corrected neighborhood radius to obtain multiple clusters.

[0040] Specifically, the above embodiment of the present invention determines the parameter size of the neighborhood radius in the clustering algorithm by analyzing the relationship between the position of the daily feature group in the coordinate system and the fitted straight line, and the construction of the fitted straight line is based on the actual position analysis of the daily feature group, that is, the relationship between the characteristic value of the raw material supply and the characteristic value of the daily output of sea cucumbers. In actual production, there will be a phenomenon of supply hoarding, that is, the supply of raw materials is large but the daily output of sea cucumbers is small. At this time, the position of the daily feature group in the coordinate system is Figure 4In the lower right middle, these data points are regarded as noise points. The noise points cause the fitting straight line to shift, resulting in errors in the relationship between the actual calculated characteristic value of raw material supply and the characteristic value of daily sea cucumber production, and ultimately making the neighborhood radius unable to truly and accurately include dense data points. The characteristic value of raw material supply and the characteristic value of daily sea cucumber production are calculated by raw material supply and daily sea cucumber production respectively, and there is a strong and weak relationship between raw material supply and daily sea cucumber production. The strong and weak relationship between the two can be used to reflect the relationship between the overall characteristic value of raw material supply and the characteristic value of daily sea cucumber production. The strong and weak relationship between raw material supply and daily sea cucumber production is the correlation between the two. Therefore, the size of the above neighborhood radius is corrected by the correlation between raw material supply and daily sea cucumber production.

[0041] Furthermore, 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 sequence and the daily sea cucumber raw material supply sequence to obtain the corrected neighborhood radius, including: calculating the second sum between a predetermined value and the inverse of the absolute value of the Pearson correlation coefficient; and determining the fourth product between the neighborhood radius and the second sum as the corrected neighborhood radius.

[0042] Specifically, the embodiment of the present invention uses the Pearson correlation coefficient to calculate the daily production of sea cucumbers in the past five years to form a daily production sequence of sea cucumbers. , and the daily raw material supply of sea cucumbers in the past five years constitute the daily raw material supply sequence of sea cucumbers The Pearson correlation coefficient is calculated as follows: In the above formula, Represents the Pearson correlation coefficient between the daily sea cucumber production series and the daily sea cucumber raw material supply series. Represents the relationship between the daily sea cucumber production series and the daily sea cucumber raw material supply series.

[0043] Further, at this time, when the correlation between the daily output of sea cucumbers and the daily raw material supply of sea cucumbers is stronger, it means that the relationship between the characteristic value of the raw material supply and the characteristic value of the daily output of sea cucumbers is more accurate in reflecting the relationship between the data points, that is, the neighborhood radius is more accurate. When the correlation between the daily output of sea cucumbers and the daily raw material supply of sea cucumbers is weaker, it means that when calculating the neighborhood radius, the noise in the data is large, that is, the neighborhood radius is inaccurate. Therefore, the correlation between the daily output of sea cucumbers and the daily raw material supply of sea cucumbers is used as the correction amount of the above-mentioned neighborhood radius. In the embodiment of the present invention, the above-mentioned predetermined value is 1, and the embodiment of the present invention specifically adopts the following formula to calculate the corrected neighborhood radius: In the above formula, Indicates the size of the neighborhood radius before correction. Represents the correction coefficient of the neighborhood radius. The stronger the correlation between the daily sea cucumber output and the daily raw material supply of sea cucumbers, the smaller the correction coefficient, that is, the smaller the correction amount of the neighborhood radius. Conversely, the larger the correction amount of the neighborhood radius, Indicates the size of the corrected neighborhood radius after correction.

[0044] Further, after obtaining the modified neighborhood radius, the embodiment of the present invention sets the minimum number of data points in the clustering algorithm Set to 200. The clustering process of the daily feature value position in the two-dimensional coordinates includes: first calculate The number of data points in the corrected neighborhood is calculated. Specifically, the number of data points in the circular area within the corrected neighborhood radius is calculated with the data point as the center. Then the data point is marked. The neighborhood contains at least data point, then the data point is marked as a core point. The number of data points contained in the neighborhood is less than , but it is at a core point If a data point is neither a core point nor a boundary point, it is marked as a noise point. Finally, starting from any unvisited core point, find all data points that are densely connected to the core point. These data points form a cluster. Repeat the above steps until all core points have been visited, and all clusters are obtained.

[0045] S105, determining the data volume of a unit scale of the visualization coordinate system according to the maximum and minimum daily sea cucumber production values ​​within the time range of the cluster and the number of grids of the scale in the visualization coordinate system.

[0046] Specifically, the above embodiment of the present invention can cluster the time dates of 5 years according to the daily output of sea cucumbers and the daily supply of raw materials. At this time, the daily output of sea cucumbers in the same cluster has the same characteristic performance. The continuous dates in the same cluster are regarded as a cluster group. At this time, the time difference of the cluster group is the production cycle of sea cucumbers, which is 1 year in the embodiment of the present invention. Exemplarily, the time range of a cluster is expressed as the time union of all cluster groups in the same cluster, such as the time range of cluster group A is 2024.3.4-2024.6.1, the time range of B is 2024.3.2-2024.6.25, and the time range of C is 2024.3.1-2024.6.2, then the time range of the cluster group is 2024.3.1-2024.6.25. In this way, the time range of the daily output of sea cucumbers in each season in 1 year is obtained.

[0047] Further, as an optional embodiment of the present invention, determining the data volume of a unit scale of the visualization coordinate system according to the maximum daily production of sea cucumbers, the minimum daily production of sea cucumbers and the number of grids of the scale in the visualization coordinate system within the time range of the cluster 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 production of sea cucumbers and the anti-overflow parameter, and the first difference between the minimum daily production of sea cucumbers and the anti-overflow parameter; calculating the second difference between the third sum and the first difference; and 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 a unit scale.

[0048] Specifically, in the embodiment of the present invention, the seafood production in the same cluster is similar, and the maximum daily production of sea cucumbers in the same cluster is taken. , minimum daily production of sea cucumbers At this time, the maximum value of the daily output of sea cucumbers displayed by the visual coordinate scale within the time range of this cluster in the embodiment of the present invention is , minimum .in, In order to prevent data overflow, the value can be set 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 the cluster can be calculated by the following formula: In the above formula, The amount of data representing the unit scale of the visualization coordinate system. Indicates the maximum display amount of the visualization coordinate scale within the time range of this cluster. The number of ticks in the visualization coordinate system. Indicates the maximum daily production of sea cucumbers. Indicates the minimum daily production of sea cucumbers.

[0049] S106, visually displaying the sea cucumber production based on a visualization diagram consisting 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 grids of the scale.

[0050] Specifically, after obtaining the data volume of the unit scale of the visualization coordinate system, the maximum and minimum daily sea cucumber production, the visualization graph of the daily sea cucumber production is adapted according to the number of grids of the scale in the visualization coordinate system. That is, the scale of the visualization graph and the data volume corresponding to the scale can be adaptively adjusted, so as to more intuitively display the details of the changes in the daily sea cucumber production.

[0051] The embodiment of the present invention can construct a feature group consisting of the characteristic values ​​of the raw material supply and the daily output of sea cucumbers in the sea cucumber processing workshop, and analyze the periodicity of the sea cucumber output through clustering analysis of the determined neighborhood radius, and finally adaptively adjust the coordinate scale according to the maximum value, minimum value, number of scale grids and data volume of the daily output of sea cucumbers in different time ranges of sea cucumbers in the visualization diagram. By adaptively adjusting the coordinate scale of the visualization diagram, it is possible to better adapt to the periodic changes in sea cucumber production, so that the line graph can accurately and clearly display the details of the changes in 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 sea cucumber processing workshops, and be more conducive to the reasonable arrangement of resource allocation based on the trend of sea cucumber production.

[0052] Embodiment 2: Corresponding to the data visualization method applicable to a digital processing workshop provided in the above embodiment, based on the same technical concept, an embodiment of the present invention also 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 mentioned in the above embodiment are implemented.

[0053] It should be noted that the computer-readable storage medium provided in the embodiment of the present invention and the data visualization method applicable to a digital processing workshop provided in the embodiment 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 a digital processing workshop, and has the same or similar beneficial effects, and the repeated parts will not be repeated.

[0054] Embodiment three: Corresponding to the data visualization method applicable to a digital processing workshop provided in the above embodiment, based on the same technical concept, an embodiment of the present invention further provides a data visualization system applicable to a digital processing workshop, and the data visualization system applicable to a digital processing workshop is used to execute the data visualization method applicable to a digital processing workshop. Figure 5 A structural diagram of a data visualization system suitable for a digital processing workshop provided by another embodiment of the present invention is as follows: Figure 5 The data visualization system applicable to the digital processing workshop may have relatively large differences due to different configurations or performances, and may include one or more processors 501 and memory 502, the memory 502 is used to store computer programs that can be run on the processor 501, and the processor 501 is used to execute the program stored in the memory 502 to achieve the above Figure 1The memory 502 may be a temporary storage or a permanent storage. The application stored in the memory 502 may include one or more modules (not shown in the figure), each of which may include a series of computer executable instructions in a data visualization system suitable for a digital processing workshop.

[0055] Furthermore, the processor 501 can be configured to communicate with the memory 502, and execute a series of computer executable instructions in the memory 502 on the data visualization system suitable for the digital processing workshop. The data visualization system suitable for 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 and output interfaces 505, and one or more keyboards 506.

[0056] Specifically in this embodiment, the data visualization system suitable for a digital processing workshop includes a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other through the bus; the memory is used to store computer programs; the processor is used to execute the programs stored in the memory to achieve the above Figure 1 The various steps in the method embodiment are similar to those in the method embodiment, and have the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present invention will not be described in detail here.

[0057] It should be noted that the data visualization system applicable to a digital processing workshop provided in an embodiment of the present invention and the data visualization method applicable to a digital processing workshop provided in an embodiment 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 a digital processing workshop, and has the same or similar beneficial effects, and the repetitive parts will not be repeated.

[0058] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0059] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on 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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