CN112100271A - EOS consensus mechanism utility visualization method based on workload ranking difference - Google Patents
EOS consensus mechanism utility visualization method based on workload ranking difference Download PDFInfo
- Publication number
- CN112100271A CN112100271A CN202010934181.0A CN202010934181A CN112100271A CN 112100271 A CN112100271 A CN 112100271A CN 202010934181 A CN202010934181 A CN 202010934181A CN 112100271 A CN112100271 A CN 112100271A
- Authority
- CN
- China
- Prior art keywords
- node
- ranking
- workload
- eos
- mapping
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/219—Managing data history or versioning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
Abstract
The invention discloses a workload ranking difference-based EOS consensus mechanism utility visualization method, which comprises the steps of collecting EOS super node ranking data and EOS node historical daily income data, and calculating the node workload and workload ranking difference when node ranking versions are changed each time; designing node ranking visual mapping, carrying out visual coding on super node voting ranking, workload ranking difference, alternation version and the like, and representing an evolution mode of the overall utility of the EOS consensus mechanism along with time; designing node workload comparison visualization mapping, extracting single node information, and comparing differences existing between single nodes under the influence of a consensus mechanism; and realizing the visual layout of the node ranking visual view and the node workload comparison view based on the visual mapping by combining the multi-view linkage and the interaction means. The method can help analysts to sense the overall situation of the utility of the EOS consensus mechanism, mine potential useful information and perform accurate analysis on the utility of the consensus mechanism.
Description
Technical Field
The invention relates to the field of information visualization and visual analysis, in particular to an EOS consensus mechanism utility visualization method based on workload ranking difference.
Background
With the rapid development of digital currency and blockchain technology, more and more researchers and practitioners are beginning to have a strong interest in this area. Among them, the consensus mechanism is one of the most important underlying technologies of the blockchain technology. The so-called 'consensus mechanism' is that the verification and confirmation of the transaction are completed in a short time through the voting of a special node; if several nodes with irrelevant interests can agree on one transaction, it can be considered that the whole network can agree on this.
EOS (commercially available operating system) is a relatively novel blockchain architecture intended to extend the performance of commercially available distributed software, referred to as third generation blockchains. The consensus mechanism adopted by the EOS currently stipulates that 21 super nodes and 100 standby nodes exist in the network, and the super nodes are 'block producers', and are mainly responsible for recording and verifying transaction information on the chain and commonly maintaining the security, effectiveness and stability of the whole block chain network. Only EOS nodes that meet a set of criteria may qualify for contention. In the voting process, all EOS coin holders have voting right with corresponding weight of coin holding quantity, and 30 nodes are selected for voting from 100 alternative nodes in each account. The first 21 nodes in the final voting result become supernodes that are allowed to earn accounting rights and thus be allowed to block out on the chain and receive a corresponding number of EOS token awards depending on the completion of the job. If 21 super nodes can not fulfill their duties, for example, the system removes the name if the block cannot be generated in time within the working time limit, and the community selects a new super node to replace. Therefore, the EOS super node ranking is continuously changed according to the change of the user voting, and the completion condition of the super node work can be represented by the number of the obtained rewards in the corresponding time interval.
Over the past few years, researchers have conducted multi-angle analyses of the utility of consensus mechanisms using different research approaches. Most researches focus on the aspects of safety, expansibility, performance efficiency, resource consumption and the like of the block chain consensus mechanism, and focus on analyzing the advantages and the disadvantages of various consensus mechanisms. In terms of methods, the current research on the consensus mechanism is mainly researched by a statistical method and a method for theoretically deducing or establishing a virtual block chain, and an improvement method under the visual angle of a researcher is provided.
According to the research background, the research on the utility of the current block chain consensus mechanism still has the following defects:
1) the utility analysis of a consensus mechanism based on real block chain data is lacked, and the practicability is left to be examined;
2) EOS appears relatively late, data attributes and structures are unique, and a consensus mechanism utility analysis method aiming at EOS block chain characteristics is lacked in the existing method;
3) the analysis angle of the existing consensus mechanism utility analysis method is limited, and the attention degree of the super node workload ranking and the actual voting difference is weak;
4) the research adopting the visualization and visual analysis methods is less, the existing research has the defects of simple analysis problem, single visualization view, lack of interactive operation and linkage between views and the like, and the analysis effect is not visual enough.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a visualization method for EOS consensus mechanism utility based on workload ranking difference, based on online EOS main chain real data, two data sets of EOS super node ranking and super node daily income are fused, the workload ranking of the super nodes is calculated when the voting ranking is overlapped every time, and an analyst is helped to sense the overall situation of the EOS consensus mechanism utility from the perspective of the difference between the node workload ranking and the actual voting ranking, and potential useful information is mined and accurate consensus mechanism utility analysis is made.
In order to solve the technical problems, the invention adopts the technical scheme that:
an EOS consensus mechanism utility visualization method based on workload ranking differences comprises the following steps:
s1: data acquisition
Two types of data are acquired: EOS super node ranking data and EOS node historical daily income data;
s2: data processing
Processing the data collected in the step S1, wherein the processing comprises two parts of node workload calculation and workload ranking difference calculation; the node workload calculation comprises three parts of state marking, daily average income calculation and version interval benefit-in-profit calculation; the calculation of the workload ranking difference is to calculate the difference between the income ranking and the actual voting ranking of the nodes in the node version interval;
s3: visualization mapping
Performing visual mapping on the data processed in the step S2 through a visual channel; designing node ranking visual mapping, carrying out visual coding on voting ranking, workload ranking difference and alternation version of the super nodes, and representing an evolution mode of the overall utility of the EOS consensus mechanism along with time; designing node workload comparison visualization mapping, extracting single node information, and comparing differences existing between single nodes under the influence of a consensus mechanism;
s4: visual layout
Carrying out specific visual layout and drawing realization on the mapping rule defined in the step S3; for the node ranking view, firstly determining whether a global view or a local view is adopted according to the version alternation number in a time span, calculating the coordinate position and the size attribute of the shape representing the node according to the data format, drawing the node in the view, traversing the shapes belonging to the same super node and connecting the nodes by straight lines; for the node workload comparison view, the version span is confirmed and the super node list is selected, and the shape coordinate position is calculated and drawn in the view.
Further, in step S1, the data acquisition specifically includes:
s11: querying an EOS historical ranking snapshot page URL by using a BP Information tool;
s12: analyzing a network position and compiling a crawler program, and acquiring EOS super node ranking snapshot data when ranking of each time is changed historically, wherein original fields of the data comprise super node names, vote numbers, states, change versions and change time;
s13: and downloading historical daily income data of each super node, wherein the original data field comprises the name of the super node, the income type, the income amount and the income acquisition date.
Further, in step S2, the data processing specifically includes:
s2 a: firstly, calculating the working time of the node in each day, then dividing the working income of the day by the working time to obtain the average income of each day, and finally adding the average income of the time period included by each interval of ranking change according to the time weight to obtain the working income of the time period;
s2 b: the workload ranking difference calculation comprises four processes of workload ranking calculation before version alternation, workload ranking calculation after version alternation, voting ranking calculation and ranking difference calculation; calculating the workload ranking before and after the version alternation of the super node according to the profits in the version interval in two time periods before and after the version alternation; calculating the voting ranking of the super node in the current version according to the actually obtained voting number; and obtaining the difference value of the voting ranking and the workload ranking to obtain the workload ranking difference data before and after version alternation.
Further, in step S2a, the node workload calculation is specifically:
s2a 1: and (3) state marking: traversing the whole ranking data table, inserting a state field for each piece of data to indicate the state, marking the newly appeared node as '1', marking the node falling out of the first 21 names as '1', marking the node always existing in the first 21 names as '0', and marking the node which disappears after only appearing once as '2';
s2a 2: calculating the average daily gain: after the state is marked, calculating the working time of each node every day according to the marked state of the node, traversing the data of each node every day, adding the time between the mark '1' and the '-1' in one day and the later period of time of the mark '2' to obtain the working time of each node every day, and then dividing the working time of each day by the working time of each day to obtain the average benefit of each day;
s2a 3: calculate the yield within the version interval: and decomposing the working time of the node in each version interval according to different dates, and then carrying out weighted addition on the working time of each segment according to the average income of the current day to calculate the income value of the node in each version interval, wherein the income value represents the working completion degree of the node in the current period.
Further, in step S3, visualizing the mapping includes: the method comprises the steps of node ranking view visualization mapping under a local visual angle, node ranking view visualization mapping under a global visual angle, node workload comparison view visualization mapping under the local visual angle and node workload comparison view visualization mapping under the global visual angle.
Further, in step S3, the node ranking view visualization mapping under the local perspective is specifically: each node under a local view angle is coded by using two circles which are arranged side by side, the circle on the left represents the difference between the workload ranking of the node and the current ranking before the current ranking is changed, the circle on the right represents the difference between the workload ranking of the node and the current ranking after the current ranking is changed, the filling color of the circles represents the difference degree between the workload ranking and the actual ranking, the deeper red represents the higher estimated degree of the node in the ranking, conversely, the deeper green represents the workload ranking of the node is far higher than the current actual ranking, and the white represents the workload ranking of the node is the same as the actual voting ranking; circles represented by the same nodes in different versions are connected by gray line segments, if the nodes fall out of the first 21 nodes, connecting lines to the lowest horizontal line, and otherwise, connecting lines from the lower horizontal line when a certain node protrudes into the ranking.
Further, in step S3, the node ranking view visualization mapping under the global perspective is specifically: and removing line segments connecting the nodes, displaying the ranking change condition of a certain node by using interaction instead, reserving a group of data before or after the ranking change, and selecting to display the ranking difference before the ranking change or after the ranking change by a user through an interaction means.
Further, in step S3, the node workload comparison view visualization mapping under the global perspective is specifically: under a local visual angle, the visual mapping of the node workload comparison view is divided into three parts, namely shape, color and position; in terms of shape, circles represent an EOS super node, and the connecting lines between circles indicate that the node in the second alternate version does not enter the top 21 ranked names; in the aspect of color, the round color keeps the color mapping of the node ranking view, and in addition, the color of a small round point in front of the node name encodes the country to which the node belongs; in terms of position, the horizontal position encodes different super nodes selected by the user, and the vertical position encodes the number of alternate versions of the node from top to bottom.
Further, in step S3, the node workload comparison view visualization mapping under the global perspective is specifically: under the global visual angle, the same color mapping, connection line mapping and position mapping as the local visual angle are adopted, and the shape mapping of the nodes is changed into rectangular mapping.
Compared with the prior art, the invention has the beneficial effects that:
1) aiming at the problems that the existing research lacks of common identification mechanism utility analysis based on real online operation block chain data, lacks of EOS common identification mechanism utility research aiming at EOS data characteristics and the like, the method disclosed by the invention is based on the online EOS main chain real data, pays attention to block chain data common characteristics and EOS data characteristics, and improves the effectiveness and the practicability of the analysis to a certain extent.
2) Aiming at the problem that attention to the difference between the super node workload ranking and the actual voting in the existing research is weak, the method mainly focuses on the difference between the super node workload ranking and the actual voting ranking, provides a node workload calculation method, and expands the common recognition mechanism utility research view angle.
3) Aiming at the defects that the research adopting visualization and visual analysis methods is less, the existing research has simple analysis problems, single visual view, lack of interactive operation and linkage between views and the like, the method provided by the invention constructs a novel visual view through an effective visual analysis method, and enhances the intuitiveness and the attractiveness of the analysis by combining a multi-view linkage strategy and a flexible interactive means.
Drawings
FIG. 1 is a framework of a method for visualizing utility of an EOS consensus mechanism based on workload ranking differences.
FIG. 2 is a schematic diagram of a node ranking visualization method under a local view angle.
FIG. 3 is a schematic diagram of a node ranking visualization method under a global view.
FIG. 4 is a schematic diagram of a method for visualizing comparison of node workload under a local view angle.
FIG. 5 is a node ranking view interaction diagram.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
The invention realizes the EOS consensus mechanism utility visualization method based on workload ranking difference by an effective information visualization method in combination with a multi-view linkage strategy and a flexible interaction means, and the method comprises the following steps: the method comprises the following steps of data acquisition and preprocessing, visual mapping, visual layout implementation, interaction and linkage design, and specifically comprises the following steps:
firstly, data acquisition and preprocessing
1) The data acquisition process is as follows:
a) querying the network position of the EOS historical ranking snapshot page by using a super node information tool;
b) analyzing the network position and compiling a crawler program, acquiring EOS super node ranking snapshot data when ranking changes each time in history, and acquiring 33474 data of 1596 changed versions in total, wherein original fields of the data comprise super node names, vote numbers, states, changed version numbers and changed time.
c) And downloading historical daily income data of each super node, wherein the original data field comprises the name of the super node, the income type, the income amount and the income acquisition date.
2) The data processing process mainly comprises two parts of node workload calculation and workload ranking difference calculation, and the following is detailed specifically:
a) the node workload calculation method comprises three processes of state marking, calculation of daily average income and calculation of income in a version interval. The working time of the node in each day is calculated, then the working income of the node in the day is divided by the working time to obtain the average income of each day, and finally the average income of the time period included by each interval of ranking change is added according to the time weight to obtain the working income of the time period. The actual processing procedure is as follows:
i. and (3) state marking: because the node ranking in one day is not fixed, the nodes may be within the first 21 and fall out of the first 21, the time when the nodes appear and disappear needs to be marked, and the daily working time of the nodes is convenient to calculate. And traversing the whole ranking data table, inserting a state field for each piece of data to indicate the state, marking the newly appeared node as '1', marking the node falling out of the first 21 names as '1', marking the node which always exists in the first 21 names as '0', and marking the node which disappears after only one time of appearance as '2'.
Calculating the average daily gain: after the states are marked, the working time of each node every day is calculated according to the states marked by the nodes, data of each node every day is traversed, the time between the marks of '1' and '-1' in one day and the later period of time of the mark of '2' are added to obtain the working time of each day (taking seconds as a unit) of the node, and then the daily income is divided by the working time of each day to obtain the daily average income.
Calculate yield over version interval: and decomposing the working time of the node in each version interval according to different dates, and then performing weighted addition on the working time of each segment according to the average income of the current day to calculate the income value of the node in each version interval, wherein the income value represents the working completion degree of the node in the current period.
b) The workload ranking difference calculation comprises four processes of workload ranking calculation before version alternation, workload ranking calculation after version alternation, voting ranking calculation and ranking difference calculation. Calculating the workload ranking before and after the version alternation of the super node according to the profits in the version interval in two time periods before and after the version alternation; calculating the voting ranking of the super node in the current version according to the actually obtained voting number; and obtaining the difference value of the voting ranking and the workload ranking to obtain the workload ranking difference data before and after version alternation.
Two, visual mapping
1) Node ranking view visualization mapping under local view
As shown in fig. 2, each node under a local view angle is encoded by using two circles side by side, the left circle represents the difference between the workload ranking of the node and the current ranking before the current ranking changes, the right circle represents the difference between the workload ranking of the node and the current ranking after the current ranking changes, the filling color of the circles represents the difference between the workload ranking and the actual ranking, the deeper red represents the higher estimated degree of the node in the ranking, whereas the darker green represents the workload ranking of the node is far higher than the current actual ranking, and the white represents that the workload ranking of the node is the same as the actual voting ranking, so that the voting is proved to be fair and accurate, and the voting effectiveness of the EOS consensus mechanism is very high.
The local view angle is also combined with the idea of a line graph, circles represented by the same nodes in different versions are connected by gray line segments, if the nodes fall out of the first 21 nodes, connecting lines are connected to the lowest horizontal line, otherwise, the node which is highlighted into the rank is also connected by the connecting lines from the lower horizontal line, and a user can clearly see how the voting ranking positions of the nodes change along with time.
2) Node ranking view visualization mapping under global perspective
As shown in FIG. 3, in order to avoid element coverage and visual confusion, the global view of the node ranking view removes the line segment connecting the nodes, and instead, displays the ranking change condition of a certain node by using interaction, and only retains a set of data before or after the ranking change, so that a user can select to display the ranking difference before the ranking change or the ranking difference after the ranking change by using an interactive means. The global view ignores the details of ranking change to a certain extent, but retains the color coding mode same as that of the local view, and the visual sense of the user is strongly impacted by the color coding, so that the user can clearly observe the overall level and abnormal conditions of the utility of the consensus mechanism.
3) Node workload comparison view visualization mapping under local view
As shown in fig. 4, the visualization mapping of the node workload comparison view is divided into three parts, namely shape, color and position, in the local view. In terms of shape, circles represent an EOS supernode, and the lines between circles indicate that the node in the second alternate version does not go into the top 21 ranked names. In terms of color, the circle color maintains the color mapping of the node ranking view, and the color of the small circle point in front of the node name encodes the country to which the node belongs, red represents china, green represents the united states, blue represents the united kingdom, and so on. In terms of position, the horizontal position encodes different super nodes selected by the user, and the vertical position encodes the number of alternate versions of the node from top to bottom.
4) Node workload comparison view visualization mapping under global view
Under the global visual angle, the same color mapping, connection line mapping and position mapping as the local visual angle are adopted, and only the shape mapping of the nodes is changed into rectangular mapping.
Third, visual layout implementation
a) The node ranking view visualization layout implementation comprises the following steps:
step 1: set the view height to height1, view width to width1, and lateral boundary length to padhLongitudinal boundary length of padw；
Step 2: a color interpolator is set. Mapping "-20", "0" and "20" to red, white and green, respectively, the mapping method being a linear mapping method;
and step 3: and calculating the scale of the node ranking data. The number of vertical ranks is fixed to 21, the horizontal time span is set to N versions, and the number of versions of each node is defined as i (i belongs to [0, N ]):
and 4, step 4: defining the node voting Rank name of the ith version as RankiThe difference value of the workload ranking before change is upValueiThe difference value of the workload ranking after change is downlink valuei；
And 5: and judging whether the view selected by the user is a local view or a global view. If the local visual angle is the local visual angle, performing the step 5-9; if the view is the global view, performing the step 10-12;
step 6: calculating the circular radius R1 of the representative node according to the following formula:
step 6: and calculating the coordinate Y of the vertical axis of the circle center coordinate by the following method:
and 7: calculating the center of a circle before changeupAnd the coordinate X of the horizontal axis of the circle center after changedownThe calculation method is as follows:
and 8: mapping the workload ranking difference values before and after change into corresponding colors, and drawing a circle in a view by combining the obtained circle center coordinate and radius;
and step 9: traversing all circles from the same node, calculating the coordinates of the head and the tail of the connection, and drawing the connection line in a view;
step 10: calculating the rectangle width RectwAnd height RecthThe calculation method is as follows:
step 11: calculating a rectangular drawing start point coordinate value (X)r,Yr) The calculation method is as follows:
step 12: mapping the workload ranking difference values before and after change into corresponding colors, and drawing a rectangular shape in a view by combining the obtained rectangular coordinate and width and height;
b) the node workload comparison view visualization layout implementation comprises the following steps:
step 1: setting the view height to 2, the view width to 2, the transverse boundary length to pad _ h, and the longitudinal boundary length to pad _ w;
step 2: a color interpolator is set. Mapping "-20", "0" and "20" to red, white and green, respectively, the mapping method being a linear mapping method;
and step 3: and acquiring a super node list selected by a user, wherein the number of the super nodes in the list is M. Traversing all super nodes, and acquiring the ith version state and workload ranking difference value Rank of the nodei. Wherein i ∈ [0, N ∈ >]；
And 4, step 4: and calculating the transverse coordinate center X of the jth super node according to the number M of the super nodes in the list, wherein the calculation method comprises the following steps:
and 5: according to the total number N of node versions, calculating the longitudinal coordinate center Y of the ith version super node, wherein the calculation method comprises the following steps:
step 6: and judging whether the view selected by the user is a local view or a global view. If the local visual angle is the local visual angle, performing the step 7-8; if the view angle is the global view angle, performing step 9;
and 7: a circle radius R2 is calculated. The calculation method is as follows:
and 8: mapping the workload ranking difference values into corresponding colors, and drawing a circle in a view by taking (X, Y) as a circle center;
and step 9: mapping the workload ranking difference value into corresponding colors, and drawing a rectangular shape in a view by taking the average width and height as the width and height of the rectangle and (X, Y) as a drawing starting point;
step 10: for the super nodes which do not appear in the first 21, connecting lines are used for representation. Calculating the initial position of the connecting line according to the parameters, and drawing the connecting line in a view;
four, interaction and linkage
a) Integral interaction: the method can set the research time range through time or version number; a toggle switch is provided, and the user can actively select which data to display by interactive means. The time or version number setting interaction is realized through an input box, and the selection before and after the version is realized through a switch.
b) View linkage: a circle (or rectangle) representing a super node is selected in the node ranking view, the graph and the connecting line related to the node are highlighted, and meanwhile the workload performance of the node in a selected time period is displayed in the node workload comparison view. Clicking on the selected node again in the node ranking view or clicking on the delete in the node workload comparison view may cancel the selected state of the node and refresh the view.
Claims (9)
1. An EOS consensus mechanism utility visualization method based on workload ranking differences is characterized by comprising the following steps of:
s1: data acquisition
Two types of data are acquired: EOS super node ranking data and EOS node historical daily income data;
s2: data processing
Processing the data collected in the step S1, wherein the processing comprises two parts of node workload calculation and workload ranking difference calculation; the node workload calculation comprises three parts of state marking, daily average income calculation and version interval benefit-in-profit calculation; the calculation of the workload ranking difference is to calculate the difference between the income ranking and the actual voting ranking of the nodes in the node version interval;
s3: visualization mapping
Performing visual mapping on the data processed in the step S2 through a visual channel; designing node ranking visual mapping, carrying out visual coding on voting ranking, workload ranking difference and alternation version of the super nodes, and representing an evolution mode of the overall utility of the EOS consensus mechanism along with time; designing node workload comparison visualization mapping, extracting single node information, and comparing differences existing between single nodes under the influence of a consensus mechanism;
s4: visual layout
Carrying out specific visual layout and drawing realization on the mapping rule defined in the step S3; for the node ranking view, firstly determining whether a global view or a local view is adopted according to the version alternation number in a time span, calculating the coordinate position and the size attribute of the shape representing the node according to the data format, drawing the node in the view, traversing the shapes belonging to the same super node and connecting the nodes by straight lines; for the node workload comparison view, the version span is confirmed and the super node list is selected, and the shape coordinate position is calculated and drawn in the view.
2. The method for visualizing the utility of the EOS consensus mechanism based on workload ranking differences as claimed in claim 1, wherein in step S1, the data collection specifically comprises:
s11: querying EOS historical ranking snapshot page resource positioning by using a super node information tool;
s12: analyzing a network position and compiling a crawler program, and acquiring EOS super node ranking snapshot data when ranking of each time is changed historically, wherein original fields of the data comprise super node names, vote numbers, states, change versions and change time;
s13: and downloading historical daily income data of each super node, wherein the original data field comprises the name of the super node, the income type, the income amount and the income acquisition date.
3. The method for visualizing the utility of the EOS consensus mechanism based on workload ranking differences as claimed in claim 1, wherein in step S2, the data processing specifically comprises:
s2 a: firstly, calculating the working time of the node in each day, then dividing the working income of the day by the working time to obtain the average income of each day, and finally adding the average income of the time period included by each interval of ranking change according to the time weight to obtain the working income of the time period;
s2 b: the workload ranking difference calculation comprises four processes of workload ranking calculation before version alternation, workload ranking calculation after version alternation, voting ranking calculation and ranking difference calculation; calculating the workload ranking before and after the version alternation of the super node according to the profits in the version interval in two time periods before and after the version alternation; calculating the voting ranking of the super node in the current version according to the actually obtained voting number; and obtaining the difference value of the voting ranking and the workload ranking to obtain the workload ranking difference data before and after version alternation.
4. The method for visualizing the utility of the EOS consensus mechanism based on workload ranking differences as claimed in claim 3, wherein in step S2a, the node workload calculation specifically is:
s2a 1: and (3) state marking: traversing the whole ranking data table, inserting a state field for each piece of data to indicate the state, marking the newly appeared node as '1', marking the node falling out of the first 21 names as '1', marking the node always existing in the first 21 names as '0', and marking the node which disappears after only appearing once as '2';
s2a 2: calculating the average daily gain: after the state is marked, calculating the working time of each node every day according to the marked state of the node, traversing the data of each node every day, adding the time between the mark '1' and the '-1' in one day and the later period of time of the mark '2' to obtain the working time of each node every day, and then dividing the working time of each day by the working time of each day to obtain the average benefit of each day;
s2a 3: calculate the yield within the version interval: and decomposing the working time of the node in each version interval according to different dates, and then carrying out weighted addition on the working time of each segment according to the average income of the current day to calculate the income value of the node in each version interval, wherein the income value represents the working completion degree of the node in the current period.
5. The method for visualizing the utility of the EOS consensus mechanism based on workload ranking differences as claimed in claim 1, wherein in step S3, the visualization mapping comprises: the method comprises the steps of node ranking view visualization mapping under a local visual angle, node ranking view visualization mapping under a global visual angle, node workload comparison view visualization mapping under the local visual angle and node workload comparison view visualization mapping under the global visual angle.
6. The method for visualizing the utility of the EOS consensus mechanism based on workload ranking differences as claimed in claim 5, wherein in step S3, the node ranking view visualization mapping under the local perspective is specifically: each node under a local view angle is coded by using two circles which are arranged side by side, the circle on the left represents the difference between the workload ranking of the node and the current ranking before the current ranking is changed, the circle on the right represents the difference between the workload ranking of the node and the current ranking after the current ranking is changed, the filling color of the circles represents the difference degree between the workload ranking and the actual ranking, the deeper red represents the higher estimated degree of the node in the ranking, conversely, the deeper green represents the workload ranking of the node is far higher than the current actual ranking, and the white represents the workload ranking of the node is the same as the actual voting ranking; circles represented by the same nodes in different versions are connected by gray line segments, if the nodes fall out of the first 21 nodes, connecting lines to the lowest horizontal line, and otherwise, connecting lines from the lower horizontal line when a certain node protrudes into the ranking.
7. The method for visualizing the utility of the EOS consensus mechanism based on workload ranking differences as claimed in claim 6, wherein in step S3, the node ranking view visualization mapping under the global perspective is specifically: and removing line segments connecting the nodes, displaying the ranking change condition of a certain node by using interaction instead, reserving a group of data before or after the ranking change, and selecting to display the ranking difference before the ranking change or after the ranking change by a user through an interaction means.
8. The method for visualizing the utility of the EOS consensus mechanism based on workload ranking differences as claimed in claim 7, wherein in step S3, the node workload comparison view visualization mapping under the global perspective is specifically: under a local visual angle, the visual mapping of the node workload comparison view is divided into three parts, namely shape, color and position; in terms of shape, circles represent an EOS super node, and the connecting lines between circles indicate that the node in the second alternate version does not enter the top 21 ranked names; in the aspect of color, the round color keeps the color mapping of the node ranking view, and in addition, the color of a small round point in front of the node name encodes the country to which the node belongs; in terms of position, the horizontal position encodes different super nodes selected by the user, and the vertical position encodes the number of alternate versions of the node from top to bottom.
9. The method for visualizing the utility of the EOS consensus mechanism based on workload ranking differences as claimed in claim 8, wherein in step S3, the node workload comparison view visualization mapping under the global perspective is specifically: under the global visual angle, the same color mapping, connection line mapping and position mapping as the local visual angle are adopted, and the shape mapping of the nodes is changed into rectangular mapping.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010934181.0A CN112100271B (en) | 2020-09-08 | 2020-09-08 | EOS consensus mechanism utility visualization method based on workload ranking difference |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010934181.0A CN112100271B (en) | 2020-09-08 | 2020-09-08 | EOS consensus mechanism utility visualization method based on workload ranking difference |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112100271A true CN112100271A (en) | 2020-12-18 |
| CN112100271B CN112100271B (en) | 2022-07-15 |
Family
ID=73751115
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010934181.0A Active CN112100271B (en) | 2020-09-08 | 2020-09-08 | EOS consensus mechanism utility visualization method based on workload ranking difference |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112100271B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116596174A (en) * | 2023-04-28 | 2023-08-15 | 北京大数据先进技术研究院 | Path planning method, device, equipment and storage medium for integrating cost and benefit |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107622132A (en) * | 2017-10-09 | 2018-01-23 | 四川大学 | A kind of association analysis method for visualizing towards online Ask-Answer Community |
| WO2019021107A1 (en) * | 2017-07-24 | 2019-01-31 | nChain Holdings Limited | Computer-implemented system and method for managing a large distributed memory pool in a blockchain network |
| US20190295079A1 (en) * | 2018-03-23 | 2019-09-26 | Graph Blockchain Limited | Control system and method for controlling private blockchain system |
| CN111327602A (en) * | 2020-01-21 | 2020-06-23 | 腾讯科技（深圳）有限公司 | Equipment access processing method, equipment and storage medium |
| CN111343001A (en) * | 2020-02-07 | 2020-06-26 | 复旦大学 | Social data sharing system based on block chain |
| CN111355799A (en) * | 2020-02-27 | 2020-06-30 | 百度在线网络技术（北京）有限公司 | Voting method, device, equipment and storage medium based on block chain |
| WO2020143183A1 (en) * | 2019-01-11 | 2020-07-16 | 平安科技（深圳）有限公司 | Blockchain consensus method based on delegated proof of stake, and related device |
| CN111563822A (en) * | 2020-05-14 | 2020-08-21 | 上海简苏网络科技有限公司 | DPOS (distributed data processing System) consensus method and system realized by intelligent contracts |
-
2020
- 2020-09-08 CN CN202010934181.0A patent/CN112100271B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019021107A1 (en) * | 2017-07-24 | 2019-01-31 | nChain Holdings Limited | Computer-implemented system and method for managing a large distributed memory pool in a blockchain network |
| CN107622132A (en) * | 2017-10-09 | 2018-01-23 | 四川大学 | A kind of association analysis method for visualizing towards online Ask-Answer Community |
| US20190295079A1 (en) * | 2018-03-23 | 2019-09-26 | Graph Blockchain Limited | Control system and method for controlling private blockchain system |
| WO2020143183A1 (en) * | 2019-01-11 | 2020-07-16 | 平安科技（深圳）有限公司 | Blockchain consensus method based on delegated proof of stake, and related device |
| CN111327602A (en) * | 2020-01-21 | 2020-06-23 | 腾讯科技（深圳）有限公司 | Equipment access processing method, equipment and storage medium |
| CN111343001A (en) * | 2020-02-07 | 2020-06-26 | 复旦大学 | Social data sharing system based on block chain |
| CN111355799A (en) * | 2020-02-27 | 2020-06-30 | 百度在线网络技术（北京）有限公司 | Voting method, device, equipment and storage medium based on block chain |
| CN111563822A (en) * | 2020-05-14 | 2020-08-21 | 上海简苏网络科技有限公司 | DPOS (distributed data processing System) consensus method and system realized by intelligent contracts |
Non-Patent Citations (4)
| Title |
|---|
| ISENBERG P等: "Exploring entity behavior on the bitcoin blockchain", 《IEEE CONFERENCE ON VISUALIZATION》 * |
| JIAQI WANG等: "Study of Visual Traceability System based on Blockchain", 《INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING, INFORMATION SCIENCE & APPLICATION TECHNOLOGY》 * |
| 王缵: "区块链共识机制分析与设计", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 郝悦: "区块链共识算法性能仿真及可视化平台的设计与实现", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116596174A (en) * | 2023-04-28 | 2023-08-15 | 北京大数据先进技术研究院 | Path planning method, device, equipment and storage medium for integrating cost and benefit |
| CN116596174B (en) * | 2023-04-28 | 2023-10-20 | 北京大数据先进技术研究院 | Path planning method, device, equipment and storage medium for integrating cost and benefit |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112100271B (en) | 2022-07-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bertini et al. | Quality metrics in high-dimensional data visualization: An overview and systematization | |
| Ankerst et al. | Circle segments: A technique for visually exploring large multidimensional data sets | |
| Haining et al. | Exploratory spatial data analysis in a geographic information system environment | |
| Chrisman | The accuracy of map overlays: a reassessment | |
| CN104217002B (en) | A kind of road condition information filling method obtained based on quality data | |
| CN111241085B (en) | Natural partition visualization method based on meteorological data | |
| CN110264238A (en) | A kind of visualization marketing management system based on big data | |
| CN112100271B (en) | EOS consensus mechanism utility visualization method based on workload ranking difference | |
| JP2003085194A (en) | Process analyzing method and information system | |
| CN107820214A (en) | A kind of user trajectory analysis system based on time suboptimal control | |
| CN111324659A (en) | Visual recommendation method and system for time-series medical data | |
| JP2022189946A (en) | Work information analysis device and work information analysis method | |
| Zhou et al. | Visual abstraction of geographical point data with spatial autocorrelations | |
| Lei et al. | A visual analytics system for financial time-series data | |
| CN104239319A (en) | Data analysis method | |
| Bimonte et al. | Definition and analysis of new agricultural farm energetic indicators using spatial OLAP | |
| CN112231386A (en) | Visual interaction method, system, equipment and storage medium for railway scientific research data | |
| US20060223523A1 (en) | Method of analyzing the operation of a cellular mobile telecommunications network | |
| CN106250676B (en) | Element geochemistry survey data preferred method based on information gain-ratio | |
| Wascher | Overview on agricultural landscape indicators across OECD countries | |
| Fuchs | Glyph design for temporal and multi-dimensional data: Design considerations and evaluation | |
| CN110263069B (en) | Method and system for extracting and depicting implicit factors of time sequence characteristics of new energy use behaviors | |
| Vico et al. | Business Intelligence in Agriculture–A Practical Approach | |
| Liu et al. | DiffusionInsighter: Visual analysis of traffic diffusion flow patterns | |
| CN114491188B (en) | Visualization method, system and computer readable storage medium for data capacity |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |