CN108280191B - Multi-region MR L standard contrast visual analysis method and system - Google Patents
Multi-region MR L standard contrast visual analysis method and system Download PDFInfo
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Abstract
The invention discloses a multi-region maximum pesticide residue limit (MR L) standard comparison visual analysis method and a system, wherein an agricultural product classification tree is constructed by MR L standard data from top to bottom, association comparison analysis, detail comparison analysis, index evaluation comparison analysis and overall comparison analysis are realized by combining an interaction technology, the system comprises a user interaction module, a user exploration module, an index calculation module and an index visualization module, L PM indexes comprise classification layer times L of agricultural products in data sets, related pesticide numbers P and record numbers M of pesticide limit values, L PM indexes are calculated in a weighting and quantitative mode, the precision of comparison analysis is improved, and nested circles, radar maps, parallel coordinates, label clouds, broken line-column maps and interaction technologies are adopted to rapidly find the difference of the strictness degree formulated by coverage ranges of different regions in agricultural product classification conditions and the MR L standard.
Description
Technical Field
The invention belongs to the technical field of information visualization and food safety, and mainly relates to a comparison visual analysis method and system for the MR L standard of multiple organizations (hereinafter referred to as multiple regions) in multiple countries and regions.
Background
The maximum limit of pesticide Residue (Maxmum Residue L evits, abbreviated as MR L) refers to the legal maximum allowable concentration of pesticide Residue in an agricultural product, and is calculated by milligrams of pesticide Residue per kilogram of agricultural product (mg/kg). A record of MR L standard refers to the maximum limit of pesticide Residue in an agricultural product.MR L standard reflects the management level of pesticide use by a country, region and organization to some extent.China mainland, hong Kong, USA, Japan, European Union organization (hereinafter EUR) and International food Committee organization (hereinafter CAC) and the like have strict regulations on the standard of pesticide Residue in agricultural products.the MR L standard in various regions has the following differences due to geographical location or national conditions:
(1) the agricultural products of each country or regional organization are different, such as mainland China, hong Kong China, and Amelanchier CAC, but the EUR, Japan and United states do not have the agricultural products; this product is available in mainland china, hong kong china, CAC, EUR and japan, except for the absence of figs in the united states.
(2) The classification mode of each country or region organization to the same agricultural product is different, for example, the classification mode of potatoes in mainland China, hong Kong region China and CAC is vegetables, root vegetables and yam vegetables; classification in EUR as fresh or frozen vegetables, root and tuber vegetables; classification in japan as tubers; in the united states, the classification is vegetable, root and tuber vegetables 1 group, tuber and bulb vegetables 1C.
(3) The quantity of pesticide limit values given to the same agricultural products in the MR L standard of each national or regional organization is different, for example, the limit values of 7 pesticides are given in the CAC MR L standard for spinach, the limit values of 17 pesticides are given in the China hong Kong MR L standard, the limit values of 24 pesticides are given in the US MR L standard, the limit values of 31 pesticides are given in the China mainland MR L standard, the limit values of 315 pesticides are given in the Japan MR L standard, and the limit values of 485 pesticides are given in the EUR MR L standard.
(4) The maximum residual limit of the same pesticide in the same agricultural product varies from country to country or from region to region, e.g., the limit for the diazinon pesticide in spinach is specified as EUR 0.01mg/kg, japan 0.1mg/kg, mainland china 0.5mg/kg, CAC 0.5mg/kg, usa 0.7mg/kg, and the maximum limit for the pesticide residue in spinach is not given in hong kong of china.
The multi-region MR L standard data has the characteristics of multiple dimensions, discrete distribution of attribute values and complex hierarchical structure, different visual coding modes are adopted to express that different attributes are not suitable for use, firstly, the visual coding modes are limited, secondly, the excessively complex visual coding can cause visual confusion to users and reduce the readability of a visualization scheme.
Disclosure of Invention
To overcome the above-mentioned deficiencies of the prior art, the present invention provides a method and system for comparative visual analysis of the multi-regional MR L standard.
The invention provides a contrast visual analysis method of a multi-region MR L standard, which comprises correlation contrast analysis, detail contrast analysis, index evaluation contrast analysis and overall contrast analysis, wherein firstly, MR L standard data is constructed from top to bottom to form a classification tree, nested circles are used for visualization, agricultural products in one region are circled in a main view of the nested circles, agricultural products in the other region are highlighted in an auxiliary view, parallel coordinates and label clouds are generated, detail comparison can be carried out on 6 regions on the basis of the correlation analysis, and the MR L standard value of a specific agricultural product or pesticide is compared.
The technical scheme provided by the invention is as follows:
a multi-region MR L standard comparison visual analysis method comprises the steps of constructing a classification tree from top to bottom of a data set (specifically, constructing an agricultural product classification tree according to MR L standard data of pesticide residue limit), using a nested circle for visualization, and performing association comparison analysis, detail comparison analysis, index evaluation comparison analysis and overall comparison analysis by combining an interaction technology, and comprises the following steps:
A. preprocessing the maximum pesticide residue limit original data in food, converting the maximum pesticide residue limit original data into a JSON format with a hierarchical inclusion relationship according to the classification mode of agricultural products, and constructing a classification tree from top to bottom, wherein the method specifically comprises the following steps:
A1. data preprocessing is carried out to delete data of non-agricultural products and non-fruits in original data, meanwhile, the maximum residual limit of pesticide is defined in a one-to-many relationship with agricultural products, the data are separated into a one-to-one relationship, for example, pesticide A is defined in the maximum residual limit of agricultural products 1, 2 and 3, and after separation, the data are defined in the maximum residual limit of pesticide A on agricultural products 1, the maximum residual limit of pesticide A on agricultural products 2 and the maximum residual limit of pesticide A on agricultural products 3; converting the preprocessed data into a JSON format, wherein the JSON format is as follows:
{‘name’:’XXX’,’children’:[{‘name’:’XXX’}]}
A2. and constructing a classification tree by using the converted data in the A1, and visualizing by using nested circles.
B. Performing correlation contrast analysis, detail contrast analysis, index evaluation contrast analysis and overall contrast analysis by combining an interaction technology;
the method specifically comprises the following steps:
B1. agricultural products in one region are selected from the main view of the nested circle, relevant agricultural product MR L standard data in the database are searched under the condition of the name of the agricultural product to generate an association table, agricultural products associated with the other region are highlighted in the auxiliary view, meanwhile, the agricultural products, the pesticide toxicity types and the quantity related to the association table are counted to generate label clouds and parallel coordinates, and association comparison analysis is achieved.
B2. And comparing a specific MR L standard value of an agricultural product or pesticide in the tag cloud generated by B1 in a mouse click interaction mode, generating data of an SQ L statement query association table according to a selection condition, updating parallel coordinates, and displaying a result of detail comparison.
B3. For data selected by a user, counting pesticides in an MR L standard as a value P by using a Count function provided by a database, counting all records in an MR L standard as a value M, and summing the classification layer times of each agricultural product by using a Sum function as a value L, quantitatively calculating the score of a L PM index for a partial data set by adopting a weighting mode, wherein three conditions are considered, namely all data of the partial data set, data after duplication removal in the partial data set, data which only appears in one region in the partial data set, and the calculation mode is as follows:
S(L)＝u1∑Xi+u2∑Yi+u3∑Zi
wherein S is(L)Score representing L index, XiRepresenting the number of classification levels of each agricultural product in all data; y isiRepresenting the classification layer times of each agricultural product in the data after the duplication removal; ziRepresenting the number of classification levels of each agricultural product in the data that occurs in only one region; u. of1,u2,u3All represent weight coefficients, and the default value is 1;
the pesticide number related in the MR L standard and the record number of the pesticide limit value in the MR L standard also use the same calculation mode, the result of obtaining 3 indexes through calculation is the final comprehensive score, and a user can automatically adjust the weight coefficient of any index to carry out analysis with emphasis, so that index comparative analysis is realized.
B4. The results of the L PM indicators for the 6 regions statistically in B3 are shown by a broken line-bar graph, and are macroscopically compared for analysis.
On the other hand, the specific generation steps of the above-mentioned visualization result are:
A. the generated classification tree is visualized in a nested circle mode, and a user selects an interested part through a lasso tool of a toolbar, and the method specifically comprises the following steps:
A1. and visualizing the generated data of the classification tree with the hierarchical structure by using a nested circle algorithm, wherein the interface view consists of a data screening box and two nested circles. The size of the circular area of the sub-node represents the number of all records of the node and is arranged from large to small in a spiral manner. Then calculating the circumscribed circles of the circular areas of all the child nodes, wherein the diameter of the circular area of the father node is the diameter of the circumscribed circle, and the size of the circular area of the father node is the sum of the record numbers of the child nodes; and performing bottom-up recursive computation until the circle drawing of the root node is completed.
A2. The user may use the lasso tool of the toolbar to circle the data of interest. The first option in the toolbar is a rectangular selection box, the second option is a selection box with a self-defined graph, the third option can select data for multiple times, and the fourth option is to clear the selected data.
A3. The user can also use a mouse to highlight the classification condition of a certain attribute, and after the user selects interested data, a prompt box displays the MR L standard record number of each agricultural product in the data which has been selected by the user, so as to generate an association table.
B. And B, combining the data of the association table generated in the step A with the tag cloud and the parallel coordinates to perform detail comparison analysis, and mapping the data of the character to the parallel coordinates by clicking the character in the tag cloud, wherein the method specifically comprises the following steps:
B1. the label clouds are generated by the nested circles in a linkage mode, the number of the label clouds is the type of user circle selected data in the nested circles, and the size of the label clouds is the number of specific attributes.
B2. The parallel coordinates represent different attributes by drawing n parallel coordinate axes, and curves crossing the n coordinate axes are drawn from left to right according to each attribute value recorded on each coordinate axis. The user can use a mouse to click on the tag clouds to select to view the attribute information of a certain tag cloud.
C. The statistical and calculated L PM index value and the score after weighted calculation are displayed by adopting a radar chart and a polyline-column chart, and the method specifically comprises the following steps:
C1. the upper half of the radar chart represents the rating of the attribute on the whole data set, the lower half represents the rating of the user circled data, starting clockwise with the number of pesticides involved in the MR L standard, the number of records of pesticide limit values in the MR L standard, and the number of classification levels of the agricultural product, respectively1,u2,u3The values of (a) are respectively used for carrying out weighted calculation on L PM indexes, and a user can carry out adjustment by clicking a mouse1,u2,u3Is in the range of 0.1 to 10, with a default value of 1.
C2. The broken line graph represents the L PM index statistical value of 6 regions in the whole data set, the bar graph represents the L PM index statistical value of 6 regions in the data selected by the user, the number of classification layers of agricultural products, the number of pesticides involved in the MR L standard and the number of records of pesticide limit values in the MR L standard are sequentially represented from top to bottom, and when the user hovers over a certain point on the bar graph or the broken line graph, a prompt box displays the specific value.
D. User interaction visualization techniques. The invention provides an interactive mode of data screening box, mouse click, mouse hovering, filtering, circle selection and highlight display for facilitating exploration and analysis tasks of users, and the interactive mode specifically comprises the following aspects:
(1) a user selects data to be checked through the screening box, and a result is displayed in the nesting circle;
(2) the user may further circle the portion of interest within the nested circle;
(3) mouse-over can be used to view specific property values in all visualization charts.
(4) The weighting coefficient of the L PM index and the selection of the category in the label cloud can be adjusted by clicking a user through a mouse, so that parallel coordinates are generated in a linkage mode to perform detail comparison analysis.
The invention also provides a multi-region MR L standard comparison visual analysis system which comprises a user interaction module, a user exploration module, an index calculation module and an index visualization module, wherein the user interaction module consists of a data screening frame and mouse clicking, mouse hovering, filtering, circle selecting and highlight displaying interaction modes in the user exploration module, the exploration module consists of a nested circle view, a parallel coordinate and a label cloud view, the index calculation module queries a database from a background according to a condition system of a user, counts L PM index values by using a sum () function and a count () function, performs weighted summation calculation by combining weight coefficients set by the user for each index, and the index visualization module consists of a radar map, a thermodynamic map view and a broken line-column map view.
The upper half part of the system comprises a data screening frame, a nested circle view, a thermodynamic diagram and a radar map view, a user can select two regions for comparison through the data screening frame, the nested circle view shows the classification tree generated in the first step, the thermodynamic diagram can adjust the weight coefficient of indexes, the radar map shows the calculation results of L PM indexes in the two regions selected by the user, the lower half part of the system comprises a label cloud and a parallel coordinate view and a broken line-column diagram view, the label cloud is used for showing types and quantities of agricultural products and pesticide toxicity related in data selected by the user, the parallel coordinate is used for showing the distribution conditions of MR L standard values of agricultural products in different regions, the broken line-column diagram shows the statistical results of L PM indexes, the user screens the data through an interaction module and sends out a visualization request, an exploration module generates corresponding nested circles, parallel coordinates and cloud labels, the statistical values of L PM indexes corresponding to the index calculation module, and the radar map and the broken line-column diagram are generated through interaction operation of the user in combination with the setting of the index weight coefficient in the exploration module and the thermodynamic diagram to evaluate the radar map.
The invention relates to a comparison visual analysis method and a comparison visual analysis system aiming at the MR L standard of multiple regions, in the specific embodiment of the invention, the standard values of the names of agricultural products, the classification modes of the agricultural products, the names of the agricultural chemicals, the toxicity of the agricultural chemicals and the pesticide residue limit MR L of the continental China, the hong Kong China, the EUR, the United states, the Japan and the CAC are set, the correlation comparison analysis, the detail comparison analysis, the index evaluation comparison analysis and the overall comparison analysis are carried out by combining the interaction technology, the classification tree of the agricultural products is displayed by adopting a nested circle, the calculation result of the PM index is displayed by a radar map L, the data of user selection is displayed by parallel coordinates and label clouds in a linkage mode, the upper part of the label clouds represent the types of the agricultural products contained in the data, the size represents the number, the lower part of the label clouds represent the types of the pesticides in the data of the user selection, the agricultural product names and the number of multiple regions MR L standard (comprising the continental China, the hong Kong China, the US, the China, the United states, the EUR, the CAC, the toxicity of the agricultural chemicals and the name of the PM data of the whole region 36.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a multi-region MR L standard comparison visual analysis method and a multi-region MR L standard comparison visual analysis system, wherein a classification tree is constructed from top to bottom according to the classification condition of agricultural products, and association comparison analysis, detail comparison analysis, index evaluation comparison analysis and overall comparison analysis are carried out on the multi-region MR L standard by combining an interaction mode and a visual chart.
The multi-region MR L standard comparison visual analysis system developed by the invention comprises a user interaction module, a user exploration module, an index calculation module and an index visualization module, and various visualization methods such as nested circles, label clouds, parallel coordinates, broken line-column diagrams, radar maps and the like are adopted to help a user carry out all-around multi-layer comparison visual analysis on MR L standard data, so that the difference of two regions in MR L standard formulation and management is quantitatively analyzed.
Drawings
FIG. 1 is a block diagram of a method and system for comparative visual analysis of a multi-site MR L standard according to an embodiment of the present invention;
wherein (a) is a flow diagram of a method; (b) is a structural block diagram of the system.
Fig. 2 is a schematic diagram of constructing a classification tree of continental china from MR L standard data in the embodiment of the present invention.
FIG. 3 is an interface of a nested circle visualization for data meeting requirements according to a user's screening condition in an embodiment of the present invention;
the method comprises the steps of (a) selecting a selection box for a user, (b) adopting a nested circle visual interface for a classification tree of Chinese mainland, (c) adopting a nested circle visual interface for a classification tree of Chinese hong Kong area, wherein the dotted line part represents the data hierarchy, the circle at the innermost layer represents an agricultural product, and the size of the circle represents the MR L standard record number of the agricultural product in the area.
FIG. 4 is an interface showing the names of agricultural products, the number of MR L standards for a plurality of regions (including mainland China, hong Kong region China, USA, Japan, EUR, CAC), the names of agricultural chemicals, and the toxicity of agricultural chemicals using parallel coordinates in an embodiment of the present invention;
the user can check the distribution condition of a certain agricultural product or select to check specific information of certain pesticide toxicity by clicking the tag cloud on the left side through the mouse.
Fig. 5 is an interface for generating parallel coordinates in a linkage manner when a user clicks a winter squash tag in an agricultural product tag cloud in the embodiment of the present invention.
FIG. 6 is a screenshot of parallel coordinates generated in a linkage manner when a user clicks a highly toxic label in a pesticide toxic label cloud in the embodiment of the invention.
FIG. 7 is an interface showing the MR L index using a polyline-bar graph in an embodiment of the present invention;
the agricultural product quality index statistical method comprises the following steps of (a) statistical results of classification layer times of agricultural products, (b) statistical results of pesticide numbers involved in an MR L standard, and (c) statistical results of recorded pesticide limit values in an MR L standard, wherein a line graph represents statistical numerical values of L PM indexes of 6 regions in a whole data set, and a bar graph represents statistical numerical values of L PM indexes of 6 regions in data selected by a user.
Fig. 8 is a visual interface of scores obtained by calculating L PM indexes of different regions (mainland china and hong kong region china) by using a radar map in the embodiment of the present invention.
Detailed Description
The invention will be further described by way of examples, without in any way limiting the scope of the invention, with reference to the accompanying drawings.
The invention provides a contrast visual analysis method and a contrast visual analysis system of a multi-region MR L standard, which are combined with an interaction technology to perform correlation contrast analysis, detail contrast analysis, index contrast analysis and overall contrast analysis, the contrast visual analysis system of the multi-region MR L standard performs contrast visual analysis on nested circles representing a hierarchical structure, parallel coordinates and radar maps displaying multi-dimensional data, label clouds representing the size of attribute values, a broken line-column diagram combined with a linkage technology, and simultaneously provides an interaction technology to help a user drill down to dig deep knowledge, and can perform contrast visual analysis on multi-dimensional hierarchical data for pesticide residue limited MR L standard data, library book catalog data, enterprise organization structure data and the like in the field of food safety.
The following examples are the comparative analysis process of pesticide residue limit MR L standard data of agricultural products in mainland china and hong kong china using the multi-region MR L standard comparative visual analysis method and system provided by the present invention, fig. 2 is a flow chart of the multi-region MR L standard comparative visual analysis system in the practice of the present invention, the original data are shown in tables 1 and 2:
TABLE 1 original data of pesticide residue limit MR L standard for agricultural products in mainland China
TABLE 2 original data of pesticide residue limit MR L standard for agricultural products in hong Kong area of China
The invention provides a multi-region MR L standard comparison visual analysis method and a multi-region MR L standard comparison visual analysis system for performing comparison analysis on the pesticide residue limit MR L standard data, wherein the method comprises the following specific steps:
A. preprocessing the original data, converting the preprocessed original data into a JSON format with a hierarchical inclusion relationship according to the classification mode of agricultural products, constructing a classification tree from top to bottom, and generating a result as shown in figure 1. According to the operation of a user on a system data screening module, screening original data, and extracting the data from a database;
in a specific implementation, a user can select two broad categories of vegetables and fruits; the mainland china, hong kong china, the united states, EUR, japan and CAC can be selected in both the main view and the auxiliary view. After the selection is finished, the data after the screening can be visually displayed by clicking a visual button by using a mouse.
B. Visualizing a spanning tree structure in a visual analysis method in a nested circle mode, and selecting an interested part by a user through a lasso tool of a toolbar for correlation comparison analysis;
according to the task of comparing the quantity of pesticide residues of agricultural products in mainland China and hong Kong regions in MR L standards, two nested circles can be generated through the steps, the nested circle on the left side is the type of the vegetable detected by a sampling point of mainland China and the classification of each vegetable, and correspondingly, the data of the hong Kong region China is on the right side, the size of a leaf node in each nested circle represents the MR L standard record quantity of the agricultural products, as shown in figure 3, the classification mode of a user circle selection region in a main view is vegetables, melon vegetables, mini-melons/big melons, the mini melons comprise pumpkin, luffa and joint gourds, the big melons comprise pumpkin, pumpkin and wax gourd, the circle selection region is classified into a cucumber, pumpkin, a joint gourd, luffa, bamboo shoot, watermelon and pumpkin, a pumpkin, and a layered mode of the agricultural products, wax gourd, luffa, bamboo shoot, watermelon, pumpkin and gourd, the hierarchical mode of the agricultural products, the MR classification mode of the vegetable, the MR 6335 record of the relevant MR standard record quantity of the agricultural products in the Chinese mainland China vegetable can be more clearly compared with the MR record of the MR standard record of the MR L classification mode of the agricultural products in the Chinese Hongkong vegetables in the China Hongkong regions.
Table 3 shows the number of records of the MR L standard for agricultural products in mainland China and hong Kong region China in the example
C. Combining the data of the association table generated in the step B with the tag cloud and the parallel coordinates to perform visual analysis, and mapping the data of the character to the parallel coordinates by clicking the character in the tag cloud to perform detail comparison analysis;
in a specific example, the parallel coordinates show the name of the agricultural product, the number of MR L standards in a plurality of regions (including mainland China, hong Kong region China, USA, Japan, EUR, CAC), the name of the pesticide and the toxicity of the pesticide.
FIG. 4 shows the result of user circled data in B by using the above parallel coordinates and label cloud, from which it can be seen that there are a small number of MR L standard records and a large number of records with low toxicity for most agricultural products in mainland China, hong Kong area China, USA, Japan, CAC and EUR, there are many MR L standard records for individual agricultural products in the above six areas, and there are a small number of MR L standard records for agricultural products of EUR, and it is presumed that there are defects in the limited quantity management of pesticide residue by EUR.
Fig. 5 is a parallel coordinate generated by linkage after a user clicks the cucurbita pepo tag cloud, only 3 MR L standard records are recorded in the data selected by the user, wherein the residual limit values of the 3 pesticides in the usa, japan and EUR in the data selected by the user are not specified, the 3 pesticides are all specified in the continental china, and the supervision of the cucurbita pepo in the continental china is presumed to be relatively comprehensive.
FIG. 6 shows that the user clicks the highly toxic label and then generates parallel coordinates in a linkage manner. It can be seen from the figure that the five high-toxicity pesticides are respectively applied to the pumpkin, the jigua and the cucumber, and the high-toxicity pesticides can be seen in the rightmost coordinate axis by tracing, and are specifically triazophos, bayanphos, abamectin and oxamyl. In the data selected by the users, only China defines the residual limit value of the triazophos pesticide, only EUR and CAC define the residual limit value of the baoban pesticide, the data selected by the users does not define the residual limit value of the avermectin pesticide, and the residual limit values of the Wicolistin pesticide in the mainland China, the hong Kong district China, the United states of America, Japan and CAC have the same defined size. The application of pesticides in 6 areas can be further analyzed by using parallel coordinates.
D. L PM indexes of the data selected by the user in the whole data set and the B are calculated and counted, and the calculation result of L PM indexes is displayed by adopting a radar map;
in a specific example, a broken line graph represents the value of L PM index statistics for 6 regions in the entire data set, a bar graph represents the value of L PM index statistics for 6 regions in the data circled by the user, a radar graph uses the number of classification levels of agricultural products, the number of pesticides involved in the MR L standard, and the number of records of pesticide limit values in the MR L standard for comparative analysis fig. 7 shows the statistics for the user circled data in nested circles using a broken line-bar graph, the broken line-bar graph of (a) is the statistics for classification of agricultural products for 6 regions, the broken line graph represents the statistics for classification of agricultural products for that region in the entire data set, the bar graph represents the statistics for classification of agricultural products for that region in the user circled data, (b) the statistics for the number of pesticides applied for 6 regions, the broken line-bar graph of (c) is the statistics for the MR L standard records of agricultural products for 6 regions, the specific values of three broken line-bar graphs are shown in the table, wherein the left side of the nested data set is the statistics for agricultural products in the entire data set, and the left side of the nested data set is the statistics for 4 data of the user data set:
TABLE 4 specific values of the L PM index in the broken line-bar chart
FIG. 8 is a visual interface of a score calculated from L PM indicator in mainland China and hong Kong province using radar mapping, weight coefficient of L PM indicator (u1,u2,u3) The number of records of the MR L standard of the agricultural products in the mainland and the hong Kong area in China is not much different for the whole data set, the mainland in China is slightly finer than the hong Kong area in the classification mode of the agricultural products, and the quantity of the agricultural products applied in the hong Kong area in China is more than that in the mainland in China.
E. Through interactive operation of tools such as interface buttons, a filter box and the like, a user can arbitrarily adjust the weighting coefficient of the L PM index in the thermodynamic diagram, wherein the value ranges from 0.1 to 10, so that different aspects of data are analyzed and compared.
And obtaining a final visualization result through the operation of the steps. In the embodiment of the invention, the final visualization result is shown as follows: the upper left corner is a data screening box; the middle part is a contrast view of two nested circles; the upper right corner is a radar chart; the lower left corner is a label cloud and parallel coordinates; the lower right hand corner is a broken line-column diagram.
The visual analysis method and the visual analysis system for the multi-region MR L standard can compare the advantages of different organizational relationships from different angles so as to make correct decisions.
It is noted that the disclosed embodiments are intended to aid in further understanding of the invention, but those skilled in the art will appreciate that: various substitutions and modifications are possible without departing from the spirit and scope of the invention and appended claims. Therefore, the present invention should not be limited to the disclosure of the embodiments, and the scope of the present invention is defined by the appended claims.
Claims (4)
1. A method for comparing and visually analyzing maximum limit quantity MR L standards of pesticide residues in multiple regions comprises the following steps:
the method comprises the following steps of firstly, constructing a classification tree from top to bottom by using a data set of a pesticide residue limit MR L standard, counting the value of L PM indexes in the data set, and calculating the score of each region L PM index in a weighting mode, wherein the L PM indexes comprise the number of classification layers of agricultural products in the data set L and the number of records M of pesticide limit values in a pesticide number P, MR L standard related in an MR L standard, and comprises the following steps:
A. preprocessing original data of pesticide residue limit MR L standard, converting the data into JSON format with hierarchical inclusion relation according to the classification mode of agricultural products, and constructing a classification tree from top to bottom, and executing the following operations:
A1. converting the preprocessed data into a JSON format:
{‘name’:’XXX’,’children’:[{‘name’:’XXX’}]}
A2. constructing a classification tree from the converted JSON format data in A1;
B. the method comprises the following steps of quantitatively calculating the score of the L PM index of each region in a weighting mode by L PM index values in a statistical data set, wherein the method specifically comprises the following steps:
B1. calculating and counting the data set to obtain the number of pesticides in the MR L standard as the value of P, all records in the MR L standard as the value of M, and the sum of the times of classification layers of each agricultural product as the value of L;
B2. quantitatively calculating L PM index score for part of data set by weighting method;
l index values in L PM indexes are calculated by adopting the following formula:
S(L)＝u1∑Xi+u2∑Yi+u3∑Zi
in the formula, S(L)Score representing L index, XiRepresenting the number of classification levels of each agricultural product in all data; y isiRepresenting the classification layer times of each agricultural product in the data after the duplication removal; ziRepresenting the number of classification levels of each agricultural product in the data that occurs in only one region; u. of1,u2,u3All represent weights, and are 1 by default;
obtaining the index value of the pesticide number P related in the MR L standard and the index value of the record number M of the pesticide limit value in the MR L standard by using the same calculation mode, and taking the index values as final comprehensive scores;
secondly, visualizing the statistical calculation result, and performing correlation contrast analysis, detail contrast analysis, index evaluation contrast analysis and overall contrast analysis by combining an interaction technology; the following operations are performed:
C. visualizing the classification tree generated in the first step in a nesting circle mode, designing the nesting circle visualization mode as a lasso tool, and selecting an interested part by a user through the lasso tool of a toolbar, wherein the method specifically comprises the following steps:
C1. visualizing classification tree data with a hierarchical structure by using a nested circle algorithm, wherein the view comprises a data screening box and two nested circles; the size of the circular area of the classification tree child node represents the number of all records of the node, and the records are arranged from large to small in a spiral manner; then calculating the circumscribed circles of the circular areas of all the child nodes, wherein the diameter of the circular area of the parent node of the classification tree is the diameter of the circumscribed circle, and the diameter is the sum of the record numbers of the child nodes of the parent node; performing bottom-up recursive computation until the circle of the root node is drawn;
C2. the user uses the lasso tool of the toolbar to select the interested data; the circle selection mode comprises a rectangular circle selection frame, a circle selection frame for customizing a graph and a plurality of circles;
C3. the user can also use a mouse to highlight the classification condition of a certain attribute, and after the user selects interested data, a prompt box displays the MR L standard record number of each agricultural product in the data selected by the user;
D. combining the generated data of the association table with the tag cloud and the parallel coordinates to perform detail comparison analysis, and mapping the data of the character to the parallel coordinates by clicking the character in the tag cloud; the method specifically comprises the following steps:
D1. specifically, agricultural products in one area are circled in a main view of the nested circle, relevant agricultural product MR L standard data in a database are retrieved by taking the name of the agricultural product as a condition to generate an association table, the agricultural products associated with the other area are highlighted in an auxiliary view, and meanwhile, the agricultural products, the pesticide toxicity types and the quantity related in the association table are counted to generate label clouds and parallel coordinates so as to realize association comparison and analysis;
D2. the parallel coordinates represent different attributes by drawing n parallel coordinate axes, and curves crossing the n coordinate axes are drawn from left to right according to each attribute value recorded on each coordinate axis; clicking the tag cloud through a mouse to select and view the attribute information of a certain tag cloud; inquiring data of the association table according to the selection condition, updating the parallel coordinates, and displaying a result of detail comparison;
E. and realizing index comparison analysis according to the value of L PM index and the score of each region, and displaying by adopting a radar chart and a broken line-column chart, wherein the method comprises the following steps:
E1. the upper half part of the radar chart shows the score of L PM indexes in the whole data set, the lower half part of the radar chart shows the score of L PM indexes in user circled data, and the number of classification layers of agricultural products, the number of pesticides related in the MR L standard and the number of records of pesticide limit values in the MR L standard are respectively counted from clockwise;
E2. the view sequentially represents the times of classification layers of agricultural products, the number of pesticides related in the MR L standard and the number of records of pesticide limit values in the MR L standard from top to bottom;
F. visualization of user interaction: adopting a data screening box, a mouse click mode, a mouse hover mode, a filtering mode, a circle selection mode and a highlight display interaction mode for carrying out comparison analysis; the method comprises the following steps:
F1. selecting data to be checked through a screening box, and displaying the result in a nesting circle;
F2. the interesting part can be further selected in the nested circle;
F3. specific attribute values can be checked in all the visual charts by using mouse hovering;
F4. the weighting coefficient of the L PM index is adjusted through mouse clicking and the category in the tag cloud is selected, so that the parallel coordinates are generated in a linkage mode to compare specific attributes, and detail comparison analysis is conducted.
2. A comparative visual analysis method according to claim 1, wherein the weighting factor has a value of 0.1 to 10 and a default value of 1.
3. A contrast visual analysis system of a multi-region pesticide residue maximum limit MR L standard for realizing the contrast visual analysis method according to claim 1 or 2, wherein a data set of the pesticide residue limit MR L standard is represented as a classification tree from top to bottom, the value of a L PM index in the data set is counted, and the score of each region L PM index is calculated in a weighting mode, the L PM index comprises the number of times of classification layers of agricultural products in the data set L and the recorded number M of pesticide limit values in a pesticide number P, MR L standard related in an MR L standard, the system comprises a user interaction module, a user exploration module, an index calculation module and an index visualization module, and the system is used for realizing correlation contrast analysis, detail contrast analysis, evaluation contrast analysis and overall contrast analysis on the multi-region pesticide residue maximum MR L standard data;
the user interaction module consists of a data screening box and mouse clicking, mouse hovering, filtering, circle selecting and highlight displaying interaction modes in the user exploration module; a user screens data through an interaction module and sends a visualization request;
the exploration module consists of a nested circle view, a parallel coordinate and a label cloud view;
the index calculation module is used for inquiring a database from a background according to a condition system of a user, counting L PM indexes, and performing weighted summation calculation by using the weight set by the user for each index;
the index visualization module consists of a radar chart, a thermodynamic chart view and a broken line-column chart view; wherein, the nested circle is used for showing the data hierarchical structure; the parallel coordinates and the radar chart show multidimensional attributes; the label cloud and the broken line-column graph show the size of the specific attribute value; and the user can drill down to dig deep knowledge for comparative visual analysis through interaction.
4. The comparative visual analysis system according to claim 3, wherein the upper half part of the view of the system is composed of a data screening box, a nested circle view, a thermodynamic diagram and a radar diagram view, a user can select two regions for comparison through the data screening box, the nested circle view is displayed in a classification tree, the thermodynamic diagram is used for adjusting the weight of indexes, the radar diagram is used for displaying the calculation result of L PM indexes in the two regions selected by the user, the lower half part of the view of the system is composed of a label cloud, a parallel coordinate view and a polyline-column diagram view, the label cloud is used for displaying the types and the quantities of agricultural products and pesticide toxicity involved in the data selected by the user, the parallel coordinates are used for displaying the distribution situation of MR L standard values of the agricultural products in different regions, and the polyline-column diagram is used for displaying the statistical result of L PM indexes.
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