CN111178945A - Visual analysis method and device for time-space aggregation of fruit prices - Google Patents
Visual analysis method and device for time-space aggregation of fruit prices Download PDFInfo
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Abstract
The embodiment of the invention provides a visual analysis method and a visual analysis device for time-space aggregation of fruit prices, wherein the method comprises the following steps: acquiring position information and fruit price of a fruit market; acquiring a position point with abnormal fruit price fluctuation; and scanning all abnormal position points in the analysis area in time and/or space by using a space-time cylinder which is enlarged along with the time and space, obtaining a space-time aggregation area of the abnormal event of the fruit price by using the log-likelihood analysis of a Bernoulli model, and drawing on a map. The method also comprises the steps of determining the edge of the network according to the producing area and the selling area, determining the weight of the edge according to the difference between the producing price and the selling price, constructing a directed producing price space transmission network, and visualizing the edge, the weight of the edge and the price transmission direction of the producing price space transmission network. The method can obtain an accurate fruit price time-space gathering area, can visually display the transmission process between the fruit price production and marketing markets, and provides scientific basis for fruit sales and market circulation.
Description
Technical Field
The invention relates to the field of fruit price analysis, in particular to a visual analysis method and device for time-space aggregation of fruit prices.
Background
In recent years, the market price of fruits gradually enters a high-level operation stage, the fluctuation range of the market price is enhanced, and the fluctuation frequency is gradually accelerated. For example, in 6 months in 2019, the data published by the bureau of statistics shows that the fresh fruit price rises by 42.7% on a par, and the price level is at a high level in history. The qualified fruit price is related to the stable and healthy development of the fruit industry, and simultaneously, the planting income of fruit growers and consumers is also influenced. Therefore, the exploration of the fluctuation rule of the fruit price and the conduction path thereof has important significance for promoting the stable and healthy development of the fruit industry.
Due to the production and marketing characteristics of fruits and the need of long-distance transportation and marketing, the fruit wholesale market is divided into two major types, namely a production place market and a marketing place market. The fruit production and marketing is characterized by decentralized production and decentralized consumption, and along with the regionalization of agricultural production and the urbanization of social structure development, the production places and the consumption places of fruits are more and more concentrated. The double-dispersion and double-concentration production and marketing pattern requires that the fruit circulation must adopt a flow from dispersion to concentration and then from concentration to dispersion, namely, a circulation mode taking the wholesale market as the core. The characteristic of fruit production and selling in different places determines the main position of long-distance transportation and selling in circulation for solving fruit displacement. In order to continuously reduce the cost of long-distance transportation, reduce intermediate links and shorten the circulation time, the fruit is generally required to be directly transported to the fruit wholesale market from a production place in batches when possible. Therefore, in addition to local production and marketing, the fruit "distribution" process must be performed separately by both production and marketing, that is, both production and marketing markets are indispensable and equally important.
At present, the content of the analysis of the fruit price comprises time comparison, area comparison, category comparison or combination comparison of various indexes of the fruit price, such as price trend of single variety single/multiple markets, price comparison of multiple product single markets, price comparison of multiple areas of single product, and the like. The common visual analysis of the fruit price mainly aims at visualizing the time attribute and the space attribute of the fruit price, the visual technical means based on the time trend comprise statistical charts such as a line graph, a bar graph and a scatter diagram, and the visual technical means based on the space position comprise various thematic maps. However, these traditional methods cannot analyze the aggregation degree of the abnormal fluctuation phenomenon of the fruit price, so that staff such as fruit production, sales and management cannot accurately master the temporal-spatial characteristics and laws of the fruit price.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a method and an apparatus for visually analyzing temporal and spatial aggregation of fruit prices.
In a first aspect, an embodiment of the present invention provides a method for visually analyzing spatiotemporal aggregation of fruit prices, including: acquiring position information and fruit prices of all fruit markets in an analysis area of an analysis time period; acquiring a position point with abnormal fruit price fluctuation; scanning all abnormal position points in an analysis area of an analysis time period in time and/or space by using a space-time cylinder which is enlarged along with the time space, and obtaining a space-time area with a log-likelihood ratio meeting analysis conditions by using the log-likelihood analysis of a Bernoulli model, wherein the space-time area is used as a space-time area with space-time aggregation; and drawing a space-time area meeting space-time aggregation on a map through GIS software.
Further, the acquiring the location information and the fruit prices of all fruit markets in the analysis area of the analysis time period includes: acquiring related data of a fruit market through a network, wherein the related data comprises time, a production place price, a selling place price, a production place market and a position text of the selling place market; and according to the position text, obtaining the position information of a third-level division corresponding to the position text from a standard library of the third-level administration division as the position information of the fruit market.
Further, the acquiring of the position point of the abnormal fruit price fluctuation includes: and acquiring a position point of which the difference between the highest-price logarithm and the lowest-price logarithm of the fruit is greater than a preset threshold value in the analysis time period as a position point of abnormal price fluctuation.
Further, the scanning of all abnormal position points in the analysis region of the analysis time period in time and/or space by using a space-time cylinder which is increased along with the time and space comprises the following steps: dividing the analysis area into a plurality of sub-areas by taking a preset sub-area as a basic unit; the space-time column takes the central point of each subregion as the center of a circle, the distance between the radius and the maximum adjacent point is changed, the time is changed from the shortest preservation period of the fruits to the analysis time period, and scanning is carried out; the maximum adjacent point distance is the distance which takes each position point as a starting point to ensure that all the position points have at least N adjacent points, and N is a preset value which is more than or equal to 1. Further, the obtaining a space-time region with a log-likelihood ratio satisfying an analysis condition by using the log-likelihood analysis of the bernoulli model includes: for each spatial window, null hypothesis H0The distribution of the abnormal events of the fruit price in space and time is completely random, and the likelihood function L0Comprises the following steps:
L0＝Πp(xi)＝(A/B)A(1-A/B)B-A；
alternative hypothesis H1For example, the abnormal fruit price event has space-time aggregative distribution in the scanning window and likelihood function L1Comprises the following steps:
L1＝Πp(xi)＝pa(1-p)b-aqA-a(1-q)(B-b)-(A-a)；
taking a space-time window satisfying LLR >0 and p > q as a window satisfying space-time aggregativity:
wherein, p (x)i) The probability of the abnormal price event is shown, A is the total number of the abnormal fruit price event, and B is the total number of the fruit price records in the analysis area; a is the number of abnormal fruit price events in the scanning window, and b is the total number of the fruit price records in the scanning window; p is a/b is scanningThe proportion of the abnormal events of the prices of the fruits in the window; and q is (A-a)/(B-B) which is the proportion of the abnormal fruit price events outside the scanning window.
Further, after the spatio-temporal regions meeting the spatio-temporal aggregations are drawn on the map by the GIS software, the method further includes: determining the sides of the network according to the production place and the sales place, determining the weight of the sides according to the difference value of the price of the production place and the price of the sales place, constructing a production and sales price space transmission network with the direction, and visualizing the sides, the weight of the sides and the price transmission direction of the production and sales price space transmission network.
Further, the determining the edge of the network according to the production place and the selling place comprises: and selecting the selling place and the producing place as the sides of the network, wherein the difference value between the quotation time of the selling place and the quotation time of the producing place is smaller than a preset time threshold value, and the spatial distance between the selling place and the producing place is smaller than a preset spatial threshold value.
In a second aspect, an embodiment of the present invention provides a device for visualizing and analyzing temporal and spatial aggregation of fruit prices, including: the first acquisition module is used for acquiring the position information and the fruit price of all fruit markets in an analysis area of an analysis time period; the second acquisition module is used for acquiring position points with abnormal fruit price fluctuation; the processing module is used for scanning time and/or space of all abnormal position points in an analysis area of an analysis time period by utilizing a space-time cylinder which is increased along with the time space, and acquiring a space-time area with a log-likelihood ratio meeting analysis conditions by adopting the log-likelihood analysis of a Bernoulli model as the space-time area with space-time aggregation; and the visualization module is used for drawing the space-time area meeting the space-time aggregation on a map through GIS software.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method for visually analyzing the spatiotemporal aggregation of the prices of fruits according to the first aspect of the present invention.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method for spatiotemporal aggregation visualization analysis of fruit prices of the first aspect of the present invention.
According to the time-space aggregation visualization analysis method and device for the fruit prices, provided by the embodiment of the invention, time and/or space scanning is carried out on all abnormal position points in an analysis area of an analysis time period, and a time-space area with a log-likelihood ratio meeting analysis conditions is obtained by adopting the log-likelihood analysis of a Bernoulli model, so that an accurate time-space aggregation time-space area can be obtained. The spatial-temporal distribution rule stored in the fruit price text data is displayed through the visual chart, market analysis personnel can be helped to acquire the spatial characteristics and the temporal characteristics of the occurrence of the abnormal fluctuation event of the fruit production and sale price, the spatial-temporal discovery of the abnormal fruit price event is realized, the capability of discovering the market quotation rule of the fruit is improved, and scientific basis is provided for fruit sales, market circulation and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for visual analysis of spatiotemporal aggregation of fruit prices provided by an embodiment of the present invention;
FIG. 2 is a flow chart of a method for visual analysis of spatiotemporal aggregation of fruit prices according to another embodiment of the present invention;
FIG. 3 is a schematic diagram of a price network for delivering fruit products and sales according to an embodiment of the present invention;
FIG. 4 is a block diagram of a visual analysis apparatus for temporal and spatial aggregation of fruit prices according to an embodiment of the present invention;
fig. 5 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a visualization method for the spatial-temporal aggregation distribution of the price of a fruit product and a price of a selling place and the spatial transmission process of the price of the producing and selling place. It should be noted that the method can also be applied to agricultural crops other than fruits.
The distribution difference of the main producing area and the main selling area of different fruits is obvious, the storage resistance of the fruits is different, the fruit price has close relation with the trans-regional transportation distance, and the prior art method does not combine the characteristics of the spatial position information of the fruits, the producing area/selling area information of the fruit price and the like, and carries out visual analysis on the fluctuation distribution condition of the fruit price in each region and the market space transmission process. Therefore, a visual view needs to be designed by combining factors such as time and spatial attributes of fruit prices, the price rising and falling processes and the transfer relation of the fruit prices among various markets are modeled, the internal rules and modes hidden in fruit market data are mined, data exploration and knowledge discovery are carried out by fruit production and market dealers in a visual analysis mode, visual cognition is formed on fruit production and marketing price abnormal fluctuation event distribution and a transfer network, and scientific basis is provided for reducing fruit price fluctuation and improving the smoothness of a price transfer path.
Fig. 1 is a flowchart of a method for visually analyzing spatiotemporal aggregation of fruit prices according to an embodiment of the present invention, and as shown in fig. 1, the embodiment of the present invention provides a method for visually analyzing spatiotemporal aggregation of fruit prices, including:
101. and acquiring the position information and the fruit prices of all fruit markets in an analysis area of the analysis time period.
The analysis time period and the analysis region are respectively the time period length and the analysis region of the space-time aggregative property analysis, and the analysis region can be a region formed by a plurality of provinces or a national region. The following description is made on the premise of an analysis time period, and the position information and the fruit prices of all fruit markets in an analysis area can be acquired through network data.
102. And acquiring a position point with abnormal fruit price fluctuation.
In 102, finding all the fruit markets with abnormal price fluctuation, that is, the location points of the abnormal price fluctuation, can be realized by judging through a preset threshold.
103. And scanning all abnormal position points in the analysis area of the analysis time period in time and/or space by using a space-time cylinder which is enlarged along with the time space, and acquiring a space-time area with the log-likelihood ratio meeting the analysis condition by using the log-likelihood analysis of the Bernoulli model, wherein the space-time area is used as the space-time area with space-time aggregation.
In the embodiment, a space-time rearrangement scanning method is adopted to analyze the space-time aggregation of the abnormal fluctuation event of the fruit price. Spatio-temporal rescheduling scan statistics comprehensively consider temporal and spatial factors and are often used to explore the spatio-temporal aggregations of events. The time-space rearrangement scanning statistics mainly adopts a moving window method (moving windows), a movable cylindrical scanning window is established, the window is a cylinder, the space size is the cylinder bottom, the time length corresponds to the cylinder height, the occurrence rate of the abnormal fruit price events is scanned, and the number of the abnormal price events of each cylindrical scanning window is calculated.
For example, a fruit price of a certain region c is set in an analysis region in a certain time period with the region as a basic space unit
The number of the fruit price abnormal events is Ac,tOver the entire analysis areaThe abnormal event quantity A of the fruit price in all the time ranges t is
A scanning window is defined in the analysis area, the bottom surface represents a space aggregation area, the height represents a time aggregation section, a space-time cylinder is formed, and the space-time cylinder is continuously expanded along with the time until the upper limit set by the window.
The circle center of the window changes along the center of the county on the map, and in the changing process, the difference of the abnormal fruit price events between the inner area and the outer area of the window is calculated. The fruit price abnormal event belongs to binary data, only normal and abnormal events occur, therefore, scanning statistics is constructed based on probability distribution of the event, a Bernoulli (Bernoulli) model is adopted to carry out scanning statistical analysis, a space-time region with space-time aggregation is selected, and a Log Likelihood Ratio (LLR) is adopted in the detection method.
104. And drawing a space-time area meeting space-time aggregation on a map through GIS software.
For example, GIS software such as Satscan 9.3 is applied, and pure spatial scanning statistical analysis is performed by taking a district as a spatial scanning unit; taking day, week, ten days, month and the like as time scanning units, and carrying out pure time scanning statistical analysis; or, taking week, month and the like as time scanning units and taking district as space scanning units, carrying out space-time scanning statistical analysis according to the method, extracting the space-time aggregation area of the abnormal fruit price events with statistical significance, drawing on a map by using GIS software, and intuitively reflecting the space-time distribution condition of the aggregation of the abnormal fruit price events.
The time-space aggregation visualization analysis method for the fruit prices provided by the embodiment scans time and/or space of all abnormal position points in an analysis region of an analysis time period, and obtains a time-space region with a log-likelihood ratio meeting analysis conditions by using the log-likelihood analysis of a bernoulli model, so that an accurate time-space aggregation time-space region can be obtained. The spatial-temporal distribution rule stored in the fruit price text data is displayed through the visual chart, market analysis personnel can be helped to acquire the spatial characteristics and the temporal characteristics of the occurrence of the abnormal fluctuation event of the fruit production and sale price, the spatial-temporal discovery of the abnormal fruit price event is realized, the capability of discovering the market quotation rule of the fruit is improved, and scientific basis is provided for fruit sales, market circulation and the like.
Based on the content of the foregoing embodiment, as an optional embodiment, the obtaining of the location information and the fruit prices of all fruit markets in the analysis area of the analysis time period includes: acquiring related data of a fruit market through a network, wherein the related data comprises time, a production place price, a selling place price, a production place market and a position text of the selling place market; and obtaining the position information of the third level division corresponding to the position text from the standard library of the third level administration division according to the position text, wherein the position information is used as the position information of the fruit market.
First, the relevant data of the market quotation of the fruit, including the category, the variety, the time, the price of the production place, the price of the sales place, the market of the production place, the market of the sales place, and the like, can be captured from the market quotation website of the market of the fruit on the internet by using a tool such as a web crawler and the like. Both the production market and the sales market are address texts, and the address texts need to be converted into spatial position information. In consideration of the subsequent visualization process, the price data of the fruit market needs to be converted into point elements and face elements of the map, so that two methods can be respectively adopted to perform space conversion on the text information of the fruit market.
(1) And (3) performing address conversion on the text information of the fruit producing area market and the sales area market by using an address resolution tool of map software, and obtaining longitude and latitude information of the fruit producing area market and the sales area market in batches, so as to position the information on the map and generate a fruit price point diagram layer element. If the information cannot be obtained, manual judgment is carried out, and the space range of the lowest administrative division level is located according to the administrative division information of the market. If the position information cannot be judged manually, the position information is considered to be missing, and the missing position information is removed.
(2) Spatial position matching: the fruit wholesale market name generally consists of provincial/direct prefecture city, grade city, district/county and market name, but as the reported fruit market information has no unified requirement, partial data information is incomplete, and the problems of information missing and non-uniform spatial scale exist. For example, the cangzhou red date trading market contains information at the regional level and does not contain information at the provincial level, and the Gansu Jingyuan melon, fruit and vegetable wholesale market contains information at the provincial level and the county level and does not contain information at the city level.
Firstly, establishing a three-level administrative division place name standard library of provinces, local cities and counties according to the national administrative place name standard. And then analyzing the character string structure of the fruit wholesale market name, defining corresponding regular expressions according to different administrative divisions and wholesale market name combination methods, and matching the market name character strings one by one. In the regular expression, division is mainly performed according to characteristic words, the administrative division is divided into province or direct administration cities at the first level, prefecture cities at the second level and district or county cities at the third level, and the end words of the wholesale market comprise wholesale market/trading market/center/trading center/limited company/limited responsibility company/big market/cooperative society and the like.
Through the steps, the address text of the fruit wholesale market is converted into the spatial position, and the spatial position comprises the longitude and latitude information of the wholesale market and the central point position of the administrative division of the lowest level where the wholesale market is located. So far, the fruit price can be positioned at a point element of a fruit wholesale market, and also can be positioned at a surface element of a lowest-level administrative division, which is generally at a district and county level.
According to the visual analysis method for the time-space aggregation of the fruit prices, price data and a position text of a fruit market are obtained through a network, and position information of a third-level division corresponding to the position text is obtained from a standard library of the third-level administrative division according to the position text, so that the position information of the fruit market is beneficial to visual accurate positioning.
Based on the content of the foregoing embodiment, as an optional embodiment, the obtaining a location point of the fruit price fluctuation exception includes: and acquiring a position point of which the difference between the highest-price logarithm and the lowest-price logarithm of the fruit is greater than a preset threshold value in the analysis time period as a position point of abnormal price fluctuation.
The problem of abnormal fluctuation of the fruit price is always a hot topic concerned by all social circles, whether the fruit price is abnormally increased or not is evaluated, whether the abnormal fluctuation event of the fruit price is aggregated in time, space and time and space or not is researched, whether the abnormal price event is randomly distributed or not is checked, the method is helpful for analyzing the difference of the fruit price at different time and areas from an objective angle, and searching areas where the abnormal price event is possibly aggregated is found.
In the embodiment of the invention, the price fluctuation range is adopted to define the price fluctuation of the fruit market, namely the difference between the highest-price logarithm and the lowest-price logarithm in a period of time:
Rt＝lnht-lnlt；
Rt、ht、ltthe price fluctuation range, the highest price and the lowest price in the t-th time period are respectively. Setting sigmapFor price fluctuation range threshold, when Rt>σpAnd (4) considering that the abnormal fluctuation event of the market price of the fruit occurs.
Based on the content of the foregoing embodiments, as an alternative embodiment, the time and/or space scanning is performed on all abnormal position points in the analysis area of the analysis time period by using a spatiotemporal cylinder which increases with the time and space, and the method includes: dividing an analysis area into a plurality of sub-areas by taking a preset sub-area as a basic unit; the space-time column takes the central point of each subregion as the center of a circle, the distance between the radius and the maximum adjacent point is changed, and the time is changed from the shortest preservation period of the fruits to the analysis time period, so as to scan; the maximum adjacent point distance is the distance which takes each position point as a starting point to ensure that all the position points have at least N adjacent points, and N is a preset value which is more than or equal to 1. .
The sub-area may be set to a district or county, i.e., a third-level administrative division. The maximum space upper limit of the space scanning window adopts the maximum adjacent point distance of the positions of all the abnormal price fluctuation events, namely, each abnormal event is taken as a starting point, and all the events are ensured to have the distance of at least N adjacent points. For example, when N is 1, the maximum neighboring point distance may be the maximum of the neighboring point distances of all the abnormal position points. The maximum value of the time scanning window does not exceed the length of the total analysis time period, preferably does not exceed 80 percent of the length of the total analysis time period, and the lower limit of the time window is set and is determined according to the shortest fresh-keeping period of the fruits.
The circle center of the window changes at the center position of the map along the county, the scanning radius changes from 0 to the maximum near point distance as the upper limit, and in the changing process, the difference of the abnormal fruit price events between the inner area and the outer area of the window is calculated.
Based on the content of the foregoing embodiment, as an optional embodiment, obtaining a space-time region with a log-likelihood ratio satisfying an analysis condition by using log-likelihood analysis of a bernoulli model includes: for each spatial window, null hypothesis H0The distribution of the abnormal events of the fruit price in space and time is completely random, and the likelihood function L0Comprises the following steps:
L0＝Πp(xi)＝(A/B)A(1-A/B)B-A；
alternative hypothesis H1For example, the abnormal fruit price event has space-time aggregative distribution in the scanning window and likelihood function L1Comprises the following steps:
L1＝Πp(xi)＝pa(1-p)b-aqA-a(1-q)(B-b)-(A-a)；
taking a space-time window satisfying LLR >0 and p > q as a window satisfying space-time aggregativity:
wherein, p (x)i) The probability of the abnormal price event is shown, A is the total number of the abnormal fruit price event, and B is the total number of the fruit price records in the analysis area; a is the number of abnormal fruit price events in the scanning window, and b is the total number of the fruit price records in the scanning window; p is a/b is the proportion of the abnormal fruit price events in the scanning window; and q is (A-a)/(B-B) which is the proportion of the abnormal fruit price events outside the scanning window.
The LLR reflects the possibility of event aggregation in the window, and is calculated by using the LLR formula, namely the higher the LLR of the space position (circle center coordinate and circle radius) and the time interval is, the higher the possibility of the event aggregation with abnormal fruit price is. The window satisfying LLR >0 and p > q has space-time aggregation.
Fig. 2 is a flowchart of a method for visually analyzing spatio-temporal aggregation of fruit prices according to another embodiment of the present invention, as shown in fig. 2, in consideration that the conventional method cannot analyze a delivery process of a price of a place of production and a place of sale, based on the contents of the above embodiment, as an optional embodiment, after a spatio-temporal region satisfying spatio-temporal aggregation is drawn on a map by GIS software, the method further includes: determining the sides of the network according to the production place and the sales place, determining the weight of the sides according to the difference value of the price of the production place and the price of the sales place, constructing a production and sales price space transmission network with the direction, and visualizing the sides, the weights of the sides and the price transmission direction of the production and sales price space transmission network.
Data model representation of fruit production and marketing price space delivery network: the network consists of several nodes (i.e. the origin market and the sales market) and several edges (the price relationship between the origin and the sales). The set of nodes is denoted V ═ V1,V2,...,VnWhere n is the number of nodes and the set of edges is denoted E ═ E1,E2,...,EmM is the number of edges.
Constructing a fruit production and sale price space transmission network: take a certain variety of fruit, such as red Fuji apple. The setting is carried out in a way that,
representing the ith fruit-producing wholesale market,
the price of the fruit in the production place wholesale market is the price of the fruit in the production place,
in order to report the time of the place price,
and
respectively the geographic position coordinates of the wholesale market of the production place;
representing the jth fruit wholesale market at the market place,
the selling price of the fruit in the selling wholesale market of the selling place,
in order to report the date of the price of the sales floor,
and
respectively the geographical location coordinates of the wholesale market of the production place.
The production and marketing price transmission network is constructed by taking the wholesale market of a certain variety of fruit production places as a starting point, taking the price of the fruit production places as an end point, and forming an edge by a line connecting the starting point with the end point, wherein the weight of the edge is determined by the price difference of two nodes.
According to the steps, a fruit production and sale price transmission network is constructed, the directionality of the network reflects the direction of the fruit production and sale price transmission, the weight of connecting edges among the nodes reflects the amplitude of the production and sale price change, the direction and the change amplitude among the nodes are analyzed, and the space transmission process of the production and sale price can be quantitatively analyzed and visualized.
The method comprises the following steps of visualizing a fruit production and sale price space transmission network by using Gephi software, and specifically:
1) constructing a data set: according to the fruit production and sale price transfer network modeling method, a network data set is constructed, and data fields comprise Source, Target, Weight and the like, wherein the Source is a production place wholesale market, the Target is a sale place wholesale market, namely nodes forming the network point to the Target from the Source to form an edge of the network, and the Weight is the Weight of the edge. And after finishing the data sorting, obtaining N rows of data, and storing the N rows of data into a csv format.
2) Drawing network nodes and node degrees: and importing the constructed fruit production and sale price transmission network data set into Gephi software to form a directed weighted graph, and calculating the degree of each node. The degree is used for representing the number of edges directly connected with the nodes, reflects the mutual connection condition between the nodes, and is an important statistical index for reflecting the network topological characteristic.
The degree of each node is rendered by adjusting the node color and size. The lines connected among all the nodes are edges of the fruit production and marketing price transmission network, and the weight of each edge is represented by adjusting the thickness of the edge. The method is characterized in that Gephi software is applied with GeoLayout layout, longitude and latitude information longitude and latitude of a producing area wholesale market and a selling area wholesale market are introduced, network nodes are positioned according to geographical position information, accordingly, map visualization of a fruit producing and selling price transmission space network is achieved, the space composition of the fruit producing and selling price transmission network can be intuitively mastered, and the transmission relation of fruit prices among the producing and selling markets is quantitatively obtained.
The visual analysis method for the time-space aggregation of the fruit prices, provided by the embodiment, is used for modeling the transmission process of the fruit market price data in the production and marketing places, designing a visual representation method for the transmission process, realizing the display of the fruit price transmission process on a map, further contributing to improving the capability of finding the fruit market quotation rules, and providing scientific basis for fruit sales, market circulation and the like. The method can visually display the transfer process between the production and marketing markets of the fruit price, so that an analyst can more easily understand the spatial-temporal characteristic distribution and the transfer process of the fruit price. The analysis result covers time, space and multidimensional information related to the market, and is helpful for mastering the time-space distribution characteristics and the price transmission rule of the fruit price.
Based on the content of the foregoing embodiments, as an alternative embodiment, determining the edge of the network according to the production place and the selling place includes: and selecting the selling place and the producing place as the sides of the network, wherein the difference value between the quotation time of the selling place and the quotation time of the producing place is smaller than a preset time threshold value, and the spatial distance between the selling place and the producing place is smaller than a preset spatial threshold value.
Since fruit transportation needs to take into account circulation cost and time consumption, especially fresh fruits, the connection between the production-place wholesale market and the sales-place wholesale market, namely the construction of edges, needs to satisfy certain space-time proximity relations. Use of
The distance in time is represented by a distance in time,
represents the spatial distance, i.e.:
in principle, the time of the selling place price is later than the producing place price, and the European distance calculation is used between the selling place market and the producing place market. Time distance between selling price and producing price
Less than a predetermined time threshold thetatAnd is and
less than a predetermined spatial threshold θdThen the price of the fruit in the production area
Price of selling with
And satisfying the space-time proximity relation, connecting the 2 market nodes to form an edge, wherein the direction of the edge points to the market of the market from the market of the origin, and the weight of the edge is calculated as follows:
wherein the content of the first and second substances,
to meet the price of producing area
Average of all the price of the sales ground of the spatio-temporal proximity relation. Fig. 3 is a schematic diagram of the delivery of the price of the fruit production and sale network according to the embodiment of the present invention, as shown in fig. 3.
According to the visual analysis method for the spatio-temporal aggregation of the fruit prices, the selling place and the producing place, where the difference between the quotation time of the selling place and the quotation time of the producing place is smaller than the preset time threshold and the spatial distance between the selling place and the producing place is smaller than the preset spatial threshold, are selected as the edges of the network, and the visualization of more accurate price transmission relation can be realized.
Fig. 4 is a structural diagram of a fruit price spatiotemporal aggregation visualization analysis apparatus according to an embodiment of the present invention, and as shown in fig. 3, the fruit price spatiotemporal aggregation visualization analysis apparatus includes: a first acquisition module 401, a second acquisition module 402, a processing module 403 and a visualization module 404. The first obtaining module 401 is configured to obtain location information and fruit prices of all fruit markets in an analysis area of an analysis time period; the second obtaining module 402 is configured to obtain a location point where the price fluctuation of the fruit is abnormal; the processing module 403 is configured to perform time and/or space scanning on all abnormal position points in an analysis region of an analysis time period by using a space-time cylinder which increases with time and space, and obtain a space-time region with a log-likelihood ratio satisfying an analysis condition by using a log-likelihood analysis of a bernoulli model, as the space-time region with space-time aggregation; the visualization module 404 is configured to draw a spatiotemporal region satisfying spatiotemporal aggregations on a map through GIS software.
The device embodiment provided in the embodiments of the present invention is for implementing the above method embodiments, and for details of the process and the details, reference is made to the above method embodiments, which are not described herein again.
The time-space aggregation visualization analysis device for the fruit price, provided by the embodiment of the invention, scans time and/or space of all abnormal position points in an analysis area of an analysis time period, adopts the log-likelihood analysis of a Bernoulli model to obtain a time-space area with a log-likelihood ratio meeting analysis conditions, and can obtain an accurate time-space aggregation time-space area. The spatial-temporal distribution rule stored in the fruit price text data is displayed through the visual chart, market analysis personnel can be helped to acquire the spatial characteristics and the temporal characteristics of the occurrence of the abnormal fluctuation event of the fruit production and sale price, the spatial-temporal discovery of the abnormal fruit price event is realized, the capability of discovering the market quotation rule of the fruit is improved, and scientific basis is provided for fruit sales, market circulation and the like.
Fig. 5 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 5, the electronic device may include: a processor (processor)501, a communication Interface (Communications Interface)502, a memory (memory)503, and a bus 504, wherein the processor 501, the communication Interface 502, and the memory 503 are configured to communicate with each other via the bus 504. The communication interface 502 may be used for information transfer of an electronic device. The processor 501 may call logic instructions in the memory 503 to perform a method comprising: acquiring position information and fruit prices of all fruit markets in an analysis area of an analysis time period; acquiring a position point with abnormal fruit price fluctuation; scanning all abnormal position points in an analysis area of an analysis time period in time and/or space by using a space-time cylinder which is enlarged along with the time space, and obtaining a space-time area with a log-likelihood ratio meeting analysis conditions by using the log-likelihood analysis of a Bernoulli model, wherein the space-time area is used as a space-time area with space-time aggregation; and drawing a space-time area meeting space-time aggregation on a map through GIS software.
In addition, the logic instructions in the memory 503 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-described method embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the transmission method provided in the foregoing embodiments when executed by a processor, and for example, the method includes: acquiring position information and fruit prices of all fruit markets in an analysis area of an analysis time period; acquiring a position point with abnormal fruit price fluctuation; scanning all abnormal position points in an analysis area of an analysis time period in time and/or space by using a space-time cylinder which is enlarged along with the time space, and obtaining a space-time area with a log-likelihood ratio meeting analysis conditions by using the log-likelihood analysis of a Bernoulli model, wherein the space-time area is used as a space-time area with space-time aggregation; and drawing a space-time area meeting space-time aggregation on a map through GIS software.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A visual analysis method for the time-space aggregation of fruit prices is characterized by comprising the following steps:
acquiring position information and fruit prices of all fruit markets in an analysis area of an analysis time period;
acquiring a position point with abnormal fruit price fluctuation;
scanning all abnormal position points in an analysis area of an analysis time period in time and/or space by using a space-time cylinder which is enlarged along with the time space, and obtaining a space-time area with a log-likelihood ratio meeting analysis conditions by using the log-likelihood analysis of a Bernoulli model, wherein the space-time area is used as a space-time area with space-time aggregation;
and drawing a space-time area meeting space-time aggregation on a map through GIS software.
2. The method for visual analysis of spatiotemporal aggregation of fruit prices according to claim 1, wherein said obtaining location information and fruit prices of all fruit markets within an analysis area of an analysis time period comprises:
acquiring related data of a fruit market through a network, wherein the related data comprises time, a place of production price, a place of sale price and position texts of the place of production market and the place of sale market;
and according to the position text, obtaining the position information of a third-level division corresponding to the position text from a standard library of the third-level administration division as the position information of the fruit market.
3. The method for visually analyzing the spatiotemporal aggregation of fruit prices according to claim 1, wherein the obtaining of the position points of the fruit price fluctuation anomaly comprises:
and acquiring a position point of which the difference between the highest-price logarithm and the lowest-price logarithm of the fruit is greater than a preset threshold value in the analysis time period as a position point of abnormal price fluctuation.
4. The method for spatiotemporal aggregate visual analysis of fruit prices according to claim 1, characterized in that said scanning in time and/or space of all the anomaly location points within the analysis area of the analysis time segment with spatiotemporal cylinders increasing in time and space comprises:
dividing the analysis area into a plurality of sub-areas by taking a preset sub-area as a basic unit;
the space-time column takes the central point of each subregion as the center of a circle, the distance between the radius and the maximum adjacent point is changed, the time is changed from the shortest preservation period of the fruits to the analysis time period, and scanning is carried out;
the maximum adjacent point distance is the distance which takes each position point as a starting point to ensure that all the position points have at least N adjacent points, and N is a preset value which is more than or equal to 1.
5. The method of claim 4, wherein the log-likelihood analysis using Bernoulli model obtains spatiotemporal regions whose log-likelihood ratios satisfy analysis conditions, including
For each spatial window, null hypothesis H0Is prepared from fruitThe distribution of the product price abnormal events in space and time is completely random, and the likelihood function L0Comprises the following steps:
L0＝Πp(xi)＝(A/B)A(1-A/B)B-A；
alternative hypothesis H1For example, the abnormal fruit price event has space-time aggregative distribution in the scanning window and likelihood function L1Comprises the following steps:
L1＝Πp(xi)＝pa(1-p)b-aqA-a(1-q)(B-b)-(A-a)；
taking a space-time window satisfying LLR >0 and p > q as a window satisfying space-time aggregativity:
wherein, p (x)i) The probability of the abnormal price event is shown, A is the total number of the abnormal fruit price event, and B is the total number of the fruit price records in the analysis area; a is the number of abnormal fruit price events in the scanning window, and b is the total number of the fruit price records in the scanning window; p is a/b is the proportion of the abnormal fruit price events in the scanning window; and q is (A-a)/(B-B) which is the proportion of the abnormal fruit price events outside the scanning window.
6. The method for visual analysis of spatiotemporal aggregation of fruit prices according to claim 1, characterized in that after said spatiotemporal regions satisfying spatiotemporal aggregations are mapped on a map by GIS software, it further comprises:
determining the sides of the network according to the production place and the sales place, determining the weight of the sides according to the difference value of the price of the production place and the price of the sales place, constructing a production and sales price space transmission network with the direction, and visualizing the sides, the weight of the sides and the price transmission direction of the production and sales price space transmission network.
7. The method for visual analysis of spatiotemporal aggregation of fruit prices of claim 6, wherein said determining edges of a network from a place of production and a place of sale comprises:
and selecting the selling place and the producing place as the sides of the network, wherein the difference value between the quotation time of the selling place and the quotation time of the producing place is smaller than a preset time threshold value, and the spatial distance between the selling place and the producing place is smaller than a preset spatial threshold value.
8. A visual analytical equipment of the temporal-spatial aggregation of the fruit price, characterized by comprising:
the first acquisition module is used for acquiring the position information and the fruit price of all fruit markets in an analysis area of an analysis time period;
the second acquisition module is used for acquiring position points with abnormal fruit price fluctuation;
the processing module is used for scanning time and/or space of all abnormal position points in an analysis area of an analysis time period by utilizing a space-time cylinder which is increased along with the time space, and acquiring a space-time area with a log-likelihood ratio meeting analysis conditions by adopting the log-likelihood analysis of a Bernoulli model as the space-time area with space-time aggregation;
and the visualization module is used for drawing the space-time area meeting the space-time aggregation on a map through GIS software.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for spatiotemporal aggregate visual analysis of fruit prices according to any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of a method for spatiotemporal aggregate visual analysis of fruit prices according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112749619A (en) * | 2020-11-20 | 2021-05-04 | 北京林业大学 | Method for detecting space-time aggregation of pine wilt disease |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7315835B1 (en) * | 1999-07-08 | 2008-01-01 | Sony Corporation | Price fluctuation predicting device and predicting method, price fluctuation warning device and method, and program providing medium |
| CN107798482A (en) * | 2017-11-16 | 2018-03-13 | 中国农业科学院农业信息研究所 | A kind of market for farm products unusual fluctuations risk monitoring method and system |
| CN108230016A (en) * | 2017-12-22 | 2018-06-29 | 北京农业信息技术研究中心 | A kind of market for farm products Price pass-through analysis method and analytical equipment |
-
2019
- 2019-12-16 CN CN201911296338.5A patent/CN111178945B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7315835B1 (en) * | 1999-07-08 | 2008-01-01 | Sony Corporation | Price fluctuation predicting device and predicting method, price fluctuation warning device and method, and program providing medium |
| CN107798482A (en) * | 2017-11-16 | 2018-03-13 | 中国农业科学院农业信息研究所 | A kind of market for farm products unusual fluctuations risk monitoring method and system |
| CN108230016A (en) * | 2017-12-22 | 2018-06-29 | 北京农业信息技术研究中心 | A kind of market for farm products Price pass-through analysis method and analytical equipment |
Non-Patent Citations (1)
| Title |
|---|
| 高风华 等: "时空统计量在感染性钉螺分布预警中的应用研究", 《中国血吸虫病防治杂志》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112749619A (en) * | 2020-11-20 | 2021-05-04 | 北京林业大学 | Method for detecting space-time aggregation of pine wilt disease |
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