CN111178945B - Visual analysis method and device for space-time aggregation of fruit prices - Google Patents
Visual analysis method and device for space-time aggregation of fruit prices Download PDFInfo
- Publication number
- CN111178945B CN111178945B CN201911296338.5A CN201911296338A CN111178945B CN 111178945 B CN111178945 B CN 111178945B CN 201911296338 A CN201911296338 A CN 201911296338A CN 111178945 B CN111178945 B CN 111178945B
- Authority
- CN
- China
- Prior art keywords
- space
- time
- price
- fruit
- analysis
- 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.)
- Active
Links
- 235000013399 edible fruits Nutrition 0.000 title claims abstract description 233
- 238000004458 analytical method Methods 0.000 title claims abstract description 137
- 230000002776 aggregation Effects 0.000 title claims abstract description 76
- 238000004220 aggregation Methods 0.000 title claims abstract description 76
- 230000000007 visual effect Effects 0.000 title claims abstract description 32
- 230000002159 abnormal effect Effects 0.000 claims abstract description 79
- 238000004519 manufacturing process Methods 0.000 claims abstract description 51
- 238000000034 method Methods 0.000 claims abstract description 50
- 230000005540 biological transmission Effects 0.000 claims abstract description 38
- 238000009826 distribution Methods 0.000 claims description 17
- 238000012800 visualization Methods 0.000 claims description 17
- 230000006870 function Effects 0.000 claims description 8
- 238000003860 storage Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 230000005856 abnormality Effects 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 238000012546 transfer Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 4
- 238000007619 statistical method Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 239000006185 dispersion Substances 0.000 description 3
- 235000013569 fruit product Nutrition 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 235000021022 fresh fruits Nutrition 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008707 rearrangement Effects 0.000 description 2
- 244000241257 Cucumis melo Species 0.000 description 1
- 235000015510 Cucumis melo subsp melo Nutrition 0.000 description 1
- 244000141359 Malus pumila Species 0.000 description 1
- 235000010724 Wisteria floribunda Nutrition 0.000 description 1
- FJJCIZWZNKZHII-UHFFFAOYSA-N [4,6-bis(cyanoamino)-1,3,5-triazin-2-yl]cyanamide Chemical compound N#CNC1=NC(NC#N)=NC(NC#N)=N1 FJJCIZWZNKZHII-UHFFFAOYSA-N 0.000 description 1
- 238000012271 agricultural production Methods 0.000 description 1
- 235000021016 apples Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000019771 cognition Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013499 data model Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 235000012055 fruits and vegetables Nutrition 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000007794 visualization technique Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0283—Price estimation or determination
- G06Q30/0284—Time or distance, e.g. usage of parking meters or taximeters
Landscapes
- Business, Economics & Management (AREA)
- Development Economics (AREA)
- Strategic Management (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Economics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention provides a space-time aggregation visual analysis method and device for fruit prices, wherein the method comprises the following steps: acquiring position information and price of the fruit market; acquiring position points with abnormal price fluctuation of fruits; and (3) scanning time and/or space by using a space-time column body which is increased along with the time space, carrying out log likelihood analysis of a Bernoulli model on all abnormal position points in the analysis area, obtaining a fruit price abnormal event space-time aggregation area, and drawing on a map. The method also comprises the steps of determining the edges of the network according to the production places and the sales places, determining the weights of the edges according to the difference of the production and sales prices, constructing a directional production and sales price space transmission network, and visualizing the edges, the weights of the edges and the price transmission direction of the production and sales price space transmission network. The method can obtain accurate fruit price space-time aggregation areas, intuitively display the transmission process between fruit price production and marketing markets, and provide 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 space-time aggregation visual analysis method and device for fruit prices.
Background
In recent years, the market price of fruits gradually enters a high-order operation stage, the fluctuation range of the market price is strengthened, and the fluctuation frequency is gradually increased. For example, 6 months 2019, the statistical bureau published data shows that fresh fruit prices rise 42.7% over the same price, with price levels at a high historical level. The acceptable fruit price is related to the stable and healthy development of the fruit industry, and also affects the planting yields of fruit farmers and consumers. 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 characteristics of fruit marketing and long-distance marketing, fruit wholesale markets are divided into two major categories, namely, the market at the origin and the market at the marketing site. The fruit marketing is characterized by decentralized production and decentralized consumption, and along with regional and social structure development of agricultural production, the production and consumption areas of the fruit are more and more concentrated. This dual-dispersion, dual-centralization production and marketing architecture requires that fruit circulation must take a flow from dispersion to centralization, and then from centralization to dispersion, i.e., a circulation mode with wholesale market as the core. The characteristics of fruit yield and different selling places determine the main status of long-distance transportation and marketing in circulation, which is used for knowing the displacement of fruits. In order to continuously reduce the cost of long-distance transportation, reduce intermediate links and shorten circulation time, the fruit is generally required to be transported to the fruit wholesale market in batches from the production place when possible. Thus, in addition to local marketing, the "distributed" process of fruit must be performed separately by both the source and the marketing, that is, both the source and the marketing market are indispensable and equally important.
The current analysis content of the fruit price comprises time comparison, region comparison, category comparison or combination comparison of various indexes and the like of the fruit price, such as price trend of single variety/multiple markets, price comparison of single markets of multiple products, price comparison of multiple regions of single product and the like. The common visual analysis of the fruit price is mainly used for visualizing the time attribute and the space attribute of the fruit price, the visual technical means based on the time trend comprise line diagrams, bar graphs, scatter diagrams and other statistical charts, and the visual technical means based on the space position comprise various thematic maps. However, the traditional methods cannot analyze the aggregation degree of abnormal fluctuation of the price of the fruits, so that the time-space characteristics and rules of the price of the fruits cannot be accurately mastered by personnel such as production, sales, operation and the like of the fruits.
Disclosure of Invention
In order to solve the problems, the embodiment of the invention provides a space-time aggregation visual analysis method and device for fruit prices.
In a first aspect, an embodiment of the present invention provides a method for space-time aggregation visualization analysis of fruit prices, including: acquiring position information and fruit prices of all fruit markets in an analysis area of an analysis time period; acquiring position points with abnormal price fluctuation of fruits; using a space-time column body which increases along with the time space to scan the time and/or space of all abnormal position points in an analysis area of an analysis time period, and adopting the log likelihood analysis of a Bernoulli model to obtain a space-time area with the log likelihood ratio meeting the analysis condition as the space-time area with space-time aggregation; and drawing the space-time region meeting the space-time aggregation on a map through GIS software.
Further, in the analysis area for acquiring the analysis time period, the position information and the price of all fruits in the market include: acquiring related data of a fruit market through a network, wherein the related data comprises time, price of a production place, price of a sales place, market of the production place and position text of the market of the sales place; and obtaining the position information of the third-level region corresponding to the position text from the standard library of the third-level administrative region according to the position text, and taking the position information as the position information of the fruit market.
Further, the step of obtaining the position point of the abnormal price fluctuation of the fruit comprises the following steps: and acquiring a position point with the difference between the highest price logarithm and the lowest price logarithm of the fruits being larger than a preset threshold value in the analysis time period as a position point with abnormal price fluctuation.
Further, the scanning of all abnormal position points in the analysis area of the analysis time period in time and/or space by using the space-time column which increases with time and space comprises the following steps: dividing the analysis area into a plurality of subareas by taking a preset subarea as a basic unit; the space-time column takes the center point of each sub-area as the center of a circle, the distance of the radius from 0 to the maximum adjacent point is changed, and the time is changed from the shortest fresh-keeping period of the fruits to the analysis time period, so that scanning is performed; wherein the maximum adjacent point distance is a distance which takes each position point as a starting point to ensure that all the position points have at least N adjacent pointsN is a preset value greater than or equal to 1. Further, the log-likelihood analysis using the bernoulli model, to obtain a space-time region in which the log-likelihood ratio satisfies the analysis condition, includes: null hypothesis H for each spatial window 0 For the fruit price abnormal event is distributed completely randomly in space and time, likelihood function L 0 The method comprises the following steps:
L 0 ＝Πp(x i )＝(A/B) A (1-A/B) B-A ；
alternative hypothesis H 1 For the fruit price anomaly event to exist space-time aggregation distribution in the scanning window, likelihood function L 1 The method comprises the following steps:
L 1 ＝Πp(x i )＝p a (1-p) b-a q A-a (1-q) (B-b)-(A-a) ；
a space-time window satisfying LLR >0 and p > q is taken as a window satisfying space-time aggregation:
wherein p (x) i ) The probability of the abnormal price event is that A is the total number of the abnormal price events of the fruits, and B is the total number of the price records of the fruits in the analysis area; a is the abnormal number of the prices of the fruits in the scanning window, b is the total number of the price records of the fruits in the scanning window; p=a/b is the proportion of abnormal events of the price of fruits in the scanning window; q= (a-a)/(B-B) is the proportion of abnormal fruit price events outside the scanning window.
Further, after the space-time area satisfying the space-time aggregation is drawn on the map by the GIS software, the method further includes: determining the edges of the network according to the producing places and selling places, determining the weights of the edges according to the difference between the producing places and selling places, constructing a producing and selling price space transmission network with directions, and visualizing the edges of the producing and selling price space transmission network, the weights of the edges and the price transmission directions.
Further, the determining the edge of the network according to the place of origin and the place of sale includes: and selecting the selling places and the producing places with the quotation time difference value of the selling places and the quotation time of the producing places smaller than a preset time threshold value and the space distance of the selling places and the producing places smaller than the preset space threshold value as the edges of the network.
In a second aspect, an embodiment of the present invention provides a space-time aggregation visual analysis device for fruit prices, including: the first acquisition module is used for acquiring the position information and the price of the fruits in all the fruit markets in the analysis area of the analysis time period; the second acquisition module is used for acquiring position points with abnormal price fluctuation of the fruits; the processing module is used for scanning time and/or space by utilizing a space-time column body which is increased along with the space of time, and acquiring a space-time area with space-time aggregation by adopting the log likelihood analysis of the Bernoulli model, wherein the space-time area has the log likelihood ratio meeting the analysis condition; and the visualization module is used for drawing the space-time area meeting the space-time aggregation on the map through GIS software.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the steps of the spatio-temporal aggregation visualization analysis method for fruit prices according to the first aspect of the present invention.
In a fourth aspect, embodiments of the present invention provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the spatio-temporal aggregation visualization analysis method of fruit prices of the first aspect of the present invention.
According to the method and the device for the visual analysis of the space-time aggregation of the fruit prices, provided by the embodiment of the invention, all abnormal position points in the analysis area of the analysis time period are scanned in time and/or space, the space-time area with the log-likelihood ratio meeting the analysis condition is obtained by adopting the log-likelihood analysis of the Bernoulli model, and the accurate space-time aggregation space-time area can be obtained. The space-time distribution rule which is stored in the fruit price text data is displayed through the visual chart, so that market analysts can be helped to obtain the space characteristics and the time characteristics of the occurrence of abnormal fluctuation events of the fruit price, the space-time discovery of the abnormal events of the fruit price is realized, the capability of discovering the fruit market quotation rule 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 of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a visual analysis method for space-time aggregation of fruit prices provided by an embodiment of the invention;
FIG. 2 is a flow chart of a visual analysis method for space-time aggregation of fruit prices according to another embodiment of the present invention;
FIG. 3 is a schematic diagram of network delivery of fruit product marketing prices according to an embodiment of the present invention;
FIG. 4 is a block diagram of a visual analysis device for space-time aggregation of fruit prices according to an embodiment of the present invention;
fig. 5 is a schematic entity structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a visualization method for space-time aggregation distribution of fruit producing and selling price and space transfer process of producing and selling price, by which the space-time distribution characteristics of the fruit market producing and selling price can be visually displayed, the space-time aggregation degree of abnormal price events can be detected, and the transfer process relation of the fruit price between producing and selling markets can be further obtained, so that the space-time distribution characteristics of the fruit price and the market transfer rule can be revealed. It should be noted that the method can also be applied to other agricultural crops besides fruits.
The main producing areas and the main marketing areas of different fruits are obviously different, the storage endurance of the fruits is different, the fruit price is closely related to 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/marketing area information of the fruit price and the like, and performs visual analysis on the fluctuation distribution condition of the fruit price in each area and the market space transmission process. Therefore, visual views are designed by combining factors such as time and space attributes of the fruit price, the price fluctuation process and the transmission relation of the fruit price among various markets are modeled, the hidden internal rules and modes in the fruit market data are mined, the fruit production, market dealers and the like are helped to perform data exploration and knowledge discovery in a visual analysis mode, visual cognition is formed for abnormal fluctuation event distribution and transmission network of the fruit production and marketing price, and scientific basis is provided for reducing fruit price fluctuation and improving smoothness of a price transmission path.
Fig. 1 is a flowchart of a method for space-time aggregation visual analysis 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 space-time aggregation visual analysis of fruit prices, including:
101. and acquiring position information and fruit prices of all fruit markets in an analysis area of the analysis time period.
The analysis period and the analysis region are a period length and an analysis region of the space-time aggregation analysis, respectively, and the analysis region may be a region constituted by a plurality of provinces or a nationwide region. The following description is made on the premise of analysis time period, and the position information and the fruit price of all fruit markets in the analysis area can be obtained through network data.
102. And obtaining position points with abnormal price fluctuation of the fruits.
In 102, finding all the fruit markets with abnormal price fluctuation, namely the position points with abnormal price fluctuation, can be realized through the judgment of a preset threshold value.
103. And (3) scanning time and/or space by using a space-time column body which is increased along with the time space, and acquiring a space-time area with a log likelihood ratio meeting analysis conditions as a space-time area with space-time aggregation by adopting the log likelihood analysis of the Bernoulli model.
In this embodiment, a space-time rearrangement scanning method is used to analyze the space-time aggregation of abnormal fluctuation events of the fruit price. Space-time rebinning scan statistics comprehensively consider time and space factors and are commonly used for discussing the space-time aggregation of events. The space-time 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 height of the cylinder, the occurrence rate of abnormal price events of fruits is scanned, and the abnormal price event number of each cylindrical scanning window is calculated.
For example, a certain fruit price in a certain county c is set in an analysis area by taking the county as a basic space unit, and a certain time periodThe number of the abnormal events of the fruit price is A c,t The abnormal event quantity A of the fruit price in all time ranges t in the whole analysis area is +.>
A scanning window is defined in the analysis area, the bottom surface represents a space aggregation area, the high represents a time aggregation section, a space-time column is formed, and the space is continuously expanded along with time until the window reaches the set upper limit.
The circle center of the window is changed at the center position of the map along the county, and in the changing process, the abnormal event difference of the fruit price between the inner area and the outer area of the window is calculated. The fruit price abnormal event belongs to two kinds of classified data, namely normal and abnormal occurrence, so that scanning statistics are constructed based on probability distribution of the event, a Bernoulli (Bernoulli) model is adopted for scanning statistical analysis, a space-time area with space-time aggregation is selected, and a log-likelihood ratio (Log Likehood Ratio, LLR) is adopted for a detection method.
104. And drawing the space-time region meeting the space-time aggregation on a map through GIS software.
For example, by using GIS software such as Satscan 9.3 and the like, simple space scanning statistical analysis is performed by taking county as a space scanning unit; taking the day, week, ten days, month and the like as time scanning units, and performing simple time scanning statistical analysis; or, the time-space scanning statistical analysis is carried out by taking weeks, months and the like as time scanning units and counties as space scanning units according to the method, the space-time aggregation area of the abnormal fruit price events with statistical significance is extracted, and the space-time distribution condition of the abnormal fruit price event aggregation can be intuitively reflected by drawing on a map by using GIS software.
According to the visual analysis method for the space-time aggregation of the fruit prices, which is provided by the embodiment, all abnormal position points in the analysis area of the analysis time period are scanned in time and/or space, the space-time area with the log likelihood ratio meeting the analysis condition is obtained by adopting the log likelihood analysis of the Bernoulli model, and the accurate space-time area with the space-time aggregation can be obtained. The space-time distribution rule which is stored in the fruit price text data is displayed through the visual chart, so that market analysts can be helped to obtain the space characteristics and the time characteristics of the occurrence of abnormal fluctuation events of the fruit price, the space-time discovery of the abnormal events of the fruit price is realized, the capability of discovering the fruit market quotation rule is improved, and scientific basis is provided for fruit sales, market circulation and the like.
Based on the content of the above embodiment, as an alternative embodiment, acquiring the position information and the fruit prices of all the fruit markets in the analysis area of the analysis period includes: acquiring related data of a fruit market through a network, wherein the related data comprise time, price of a production place, price of a sales place, market of the production place and position text of the market of the sales place; and obtaining the position information of the third-level region corresponding to the position text from the standard library of the third-level administrative region according to the position text, and taking the position information as the position information of the fruit market.
First, the related data of fruit market quotation including fruit category, variety, time, price of producing place, price of selling place, market of producing place, market of selling place, etc. can be captured from fruit market quotation website of internet by utilizing tools such as web crawler. Both the origin market and the sales market are address texts, and the address texts need to be converted into spatial position information. Considering the subsequent visualization process, the fruit market price data needs to be converted into point elements and surface elements of the map, so that two methods can be respectively adopted to spatially convert the fruit market text information.
(1) And (3) address conversion is carried out on text information of the fruit producing place market and the selling place market by using an address analysis tool of map software, and longitude and latitude information of the fruit producing place market and the selling place market is obtained in batches, so that the information is positioned on a map, and a fruit price point map layer element is generated. If the information cannot be obtained, the manual judgment is carried out, and the information is positioned in the space range of the lowest administrative division level according to the administrative division information of the market. If the position information is still not judged manually, the position information is considered to be missing, and the position information is removed.
(2) Spatial position matching: the fruit wholesale market name generally consists of 'province/direct administration city+district level city+district/county+market name', but the reported fruit market information has no unified requirement, partial data information is incomplete, and the problem of information missing and non-uniform spatial scale exists. For example, "Cangzhou red date trading market" contains regional level information, no provincial level information, and "Gansu Jinguangjin county melon, fruit and vegetable wholesale market" contains provincial level and county level information, and no municipal level information.
Firstly, a third-level administrative division place name standard library of provinces, places and cities and counties is established according to national administrative place name standards. And then analyzing the character string structure of the wholesale market names of the fruits, defining corresponding regular expressions according to different administrative division and wholesale market name combination methods, and matching the character strings of the market names one by one. In the regular expression, the division is mainly performed according to characteristic words, the first level of administrative division is province or direct administration city, the second level is district level city, the third level is district level city or county level city, and the ending words of wholesale market comprise wholesale market/trade market/center/trade center/finite company/finite responsibility company/large market/cooperation company and the like.
Through the steps, the address text of the fruit wholesale market is converted into a spatial position, which not only comprises longitude and latitude information of the wholesale market, but also comprises the central point position of the administrative division of the lowest level where the wholesale market is located. The price of the fruit can be positioned at the point element of the wholesale market of the fruit, or at the surface element of the administrative division at the lowest level, generally at the county level.
According to the space-time aggregation visual analysis method for the fruit prices, provided by the embodiment, the price data and the position text of the fruit market are obtained through the network, and the position information of the third-level region corresponding to the position text is obtained from the standard library of the third-level administrative region according to the position text, so that the position information of the fruit market is favorable for visual accurate positioning.
Based on the content of the above embodiment, as an alternative embodiment, obtaining the location point of the abnormality of the price fluctuation of the fruit includes: and acquiring a position point with the difference between the highest price logarithm and the lowest price logarithm of the fruits being larger than a preset threshold value in the analysis time period as a position point with abnormal price fluctuation.
The problem of abnormal fluctuation of the price of the fruit is always a hot topic concerned by all communities of society, whether the price of the fruit is abnormally increased is evaluated, whether the abnormal fluctuation event of the price of the fruit is aggregated in time, space and space is researched, whether the abnormal price event occurs in space and time is randomly distributed is checked, the difference of different times and areas of the price of the fruit is analyzed from an objective angle, and the area where the abnormal price event is possibly aggregated is searched.
In the embodiment of the invention, the price fluctuation amplitude is used for defining 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:
R t ＝lnh t -lnl t ；
R t 、h t 、l t respectively within the t time periodPrice fluctuation amplitude, maximum price and minimum price. Setting sigma p For the price fluctuation range threshold, when R t >σ p When the market price of the fruits is abnormal, the abnormal fluctuation event of the market price of the fruits is considered to occur.
Based on the foregoing embodiment, as an alternative embodiment, using a spatiotemporal cylinder that increases with time space, scanning in time and/or space all abnormal location points in an analysis area of an analysis period includes: dividing an analysis area into a plurality of subareas by taking a preset subarea as a basic unit; the space-time column takes the center point of each sub-area as the center of a circle, the distance of the radius from 0 to the maximum adjacent point is changed, and the time is changed from the shortest fresh-keeping period to the analysis time period of the fruits, so that scanning is performed; the maximum adjacent point distance is a 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-region may be set as a 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 position of all the abnormal price fluctuation events, namely, each abnormal event is taken as a starting point, and the distances of at least N adjacent points of all the events are ensured. For example, when n=1, the maximum adjacent point distance may take the maximum value of the adjacent point distances of all the abnormality position points. The maximum value of the time scanning window is not more than the total analysis time period length, preferably not more than 80% of the total analysis time period length, and the lower limit of the time window is set according to the shortest fresh-keeping period of the fruits.
The circle center of the window is changed at the center position of the map border county, the scanning radius is changed from 0 to the distance value of the maximum adjacent point according to the maximum adjacent point distance as the upper limit, and in the changing process, the abnormal event difference of the fruit price between the inner area and the outer area of the window is calculated.
Based on the content of the foregoing embodiment, as an alternative embodiment, using log-likelihood analysis of the bernoulli model, obtaining a spatiotemporal region in which the log-likelihood ratio satisfies the analysis condition includes: null hypothesis H for each spatial window 0 For the purpose, the distribution of the abnormal events of the fruit price in space and time is thatCompletely random likelihood function L 0 The method comprises the following steps:
L 0 ＝Πp(x i )＝(A/B) A (1-A/B) B-A ；
alternative hypothesis H 1 For the fruit price anomaly event to exist space-time aggregation distribution in the scanning window, likelihood function L 1 The method comprises the following steps:
L 1 ＝Πp(x i )＝p a (1-p) b-a q A-a (1-q) (B-b)-(A-a) ；
a space-time window satisfying LLR >0 and p > q is taken as a window satisfying space-time aggregation:
wherein p (x) i ) The probability of the abnormal price event is that A is the total number of the abnormal price events of the fruits, and B is the total number of the price records of the fruits in the analysis area; a is the abnormal number of the prices of the fruits in the scanning window, b is the total number of the price records of the fruits in the scanning window; p=a/b is the proportion of abnormal events of the price of fruits in the scanning window; q= (a-a)/(B-B) is the proportion of abnormal fruit price events outside the scanning window.
The LLR reflects the likelihood of event aggregation within the window and is calculated using the above-described LLR formula using the formula, i.e., the greater the LLR at the spatial location (center coordinates and radius of circle) and time interval, the higher the likelihood of occurrence of fruit price anomaly event aggregation. The LLR >0 is satisfied and the window of p > q has spatio-temporal aggregations.
Fig. 2 is a flowchart of a method for visualizing and analyzing fruit prices by space-time aggregation according to another embodiment of the present invention, as shown in fig. 2, considering that a conventional method cannot analyze a delivery process of a price of a place of origin, based on the content of the foregoing embodiment, as an alternative embodiment, after a space-time area satisfying space-time aggregation is drawn on a map by GIS software, the method further includes: determining the edges of the network according to the producing places and selling places, determining the weights of the edges according to the difference between the producing places and selling places, constructing a producing and selling price space transmission network with directions, and visualizing the edges of the producing and selling price space transmission network, the weights of the edges and the price transmission directions.
Data model representation of fruit product marketing price space delivery network: the network is made up of several nodes (i.e., production and sales markets) and several edges (production and sales price relationships). The set of nodes is denoted as v= { V 1 ,V 2 ,...,V n Where n is the number of nodes and the set of edges is denoted as e= { E 1 ,E 2 ,...,E m M is the number of edges.
Constructing a fruit production and marketing price space transmission network: for example, a fruit of a certain variety, such as red Fuji apples. The setting is carried out such that,represents the ith fruit producing place wholesale market,/->For the price of the fruit in the wholesale market of the place of origin, < >>For reporting the time of the price of the place of origin, +.>And->Geographic position coordinates of the wholesale market of the producing area are respectively; />Represents the j-th fruit market for wholesale, the +.>For the market price of the fruit in the market of wholesale at the market of the market, the +.>To report the date of the price of the sales place, +.>And->Geographic location coordinates of the origin wholesale market are respectively given.
The production and marketing price transfer network is constructed by taking the wholesale market of a fruit producing place of a certain variety as a starting point, taking the price of the fruit at the marketing place as an end point, forming the edge of the network by a line connecting the starting point and the end point, and determining the weight of the edge by the price difference of two nodes.
According to the steps, a fruit production and marketing price transmission network is constructed, the directionality of the network reflects the transmission direction of the fruit production and marketing price, the weight of the connecting edges between the nodes reflects the change amplitude of the production and marketing price, the direction and the change amplitude between the nodes are analyzed, and the space transmission process of the production and marketing price can be quantitatively analyzed and visualized.
The Gephi software is used for visualizing the fruit production and marketing price space transmission network, and the method comprises the following specific steps:
1) Constructing a data set: according to the modeling method of the fruit production and marketing price transfer network, a network data set is constructed, the data field comprises Source, target, weight and the like, wherein Source is a production place wholesale market, target is a marketing place wholesale market, namely, nodes forming the network are pointed to Target by Source, edges forming the network are formed, and Weight is the Weight of the edges. And after finishing the data arrangement, 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 marketing price transmission network data set into Gephi software to form a directional weighted graph, and calculating the degree of each node. The degree is used for representing the number of edges directly connected with the nodes, reflecting the interconnection condition between the nodes and is an important statistical index reflecting the network topology characteristics.
The degree of each node is rendered by adjusting the node color and size. The lines connected between all 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 edges. The Gephi software is used for importing latitude and longitude information longitude, latitude of the wholesale market of the production place and the wholesale market of the sales place by using GeoLayout layout, and the network nodes are positioned according to the geographical position information, so that map visualization of a fruit production and sales price transmission space network is realized, the space composition of the fruit production and sales price transmission network can be intuitively mastered, and the transmission relation of the fruit price between the production and sales markets is quantitatively acquired.
The space-time aggregation visual analysis method for the fruit prices provided by the embodiment models the transmission process of the fruit market price data in the production place and sales place, designs a visual representation method for the transmission process, realizes the display of the fruit price transmission process on a map, is further beneficial to improving the capability of discovering the fruit market quotation rule, and provides scientific basis for fruit sales, market circulation and the like. The method can intuitively display the transmission process between the fruit price production and marketing markets, so that an analyst can more easily understand the space-time characteristic distribution and transmission process of the fruit price. The analysis result covers time, space and multidimensional information related to markets, and is helpful to grasp the time-space distribution characteristics and price transfer rules of the price of the fruits.
Based on the content of the above embodiment, as an alternative embodiment, determining the edge of the network according to the place of origin and the place of sale includes: and selecting the selling places and the producing places with the quotation time difference value of the selling places and the quotation time of the producing places smaller than a preset time threshold value and the space distance of the selling places and the producing places smaller than the preset space threshold value as the edges of the network.
Since the transportation of fruits needs to take into account the circulation cost and time consumption, especially fresh fruits, the connection of the origin wholesale market and the sales wholesale market, namely the construction of edges, needs to satisfy a certain space-time proximity relation. UsingIndicating the time distance>Representing the spatial distance, namely:
in principle, the time of the market price is later than the price of the place of origin, and the market of the place of origin use Euclidean distance calculation. Distance in time between the price of the place of sale and the price of the place of productionLess than a preset time threshold value theta t And->Less than a preset spatial threshold value theta d When the fruit is in the same place, the price of the fruit is +.>Price->The space-time proximity relation is satisfied, the 2 market nodes are connected to form edges, the direction of the edges points to the market of the selling places from the market of the producing places, and the weight of the edges is calculated as follows:
wherein,to meet the price of the place of origin->Average of all the market prices of the space-time proximity relationship. Fig. 3 is a schematic diagram of network delivery of fruit product marketing prices according to an embodiment of the present invention, as shown in fig. 3.
According to the space-time aggregation visualization analysis method for the fruit prices, provided by the embodiment, the difference value between the quotation time of the sales place and the quotation time of the production place is selected to be smaller than the preset time threshold, and the spatial distance between the sales place and the production place is smaller than the preset spatial threshold, so that the sales place and the production place are used as the edges of the network, and the more accurate visualization of the price transfer relationship can be realized.
Fig. 4 is a structural diagram of a space-time aggregation visual analysis device for fruit prices according to an embodiment of the present invention, and as shown in fig. 3, the space-time aggregation visual analysis device for fruit prices 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 position information and price of all fruit markets in an analysis area in an analysis period; the second obtaining module 402 is configured to obtain a location point with abnormal price fluctuation of the fruit; the processing module 403 is configured to perform temporal and/or spatial scanning on all abnormal location points in the analysis area of the analysis period by using a spatiotemporal column that increases with time space, and acquire a spatiotemporal area with a log likelihood ratio meeting an analysis condition as a spatiotemporal area with spatiotemporal aggregation by using log likelihood analysis of a bernoulli model; the visualization module 404 is configured to draw, by GIS software, a space-time region that satisfies the space-time aggregation on a map.
The embodiment of the device provided by the embodiment of the present invention is for implementing the above embodiments of the method, and specific flow and details refer to the above embodiments of the method, which are not repeated herein.
The visual analysis device for the space-time aggregation of the fruit prices provided by the embodiment of the invention scans time and/or space of all abnormal position points in the analysis area of the analysis time period, adopts the log likelihood analysis of the Bernoulli model to obtain the space-time area with the log likelihood ratio meeting the analysis condition, and can obtain the accurate space-time area with the space-time aggregation. The space-time distribution rule which is stored in the fruit price text data is displayed through the visual chart, so that market analysts can be helped to obtain the space characteristics and the time characteristics of the occurrence of abnormal fluctuation events of the fruit price, the space-time discovery of the abnormal events of the fruit price is realized, the capability of discovering the fruit market quotation rule is improved, and scientific basis is provided for fruit sales, market circulation and the like.
Fig. 5 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention, where, 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 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 invoke 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 position points with abnormal price fluctuation of fruits; using a space-time column body which increases along with the time space to scan the time and/or space of all abnormal position points in an analysis area of an analysis time period, and adopting the log likelihood analysis of a Bernoulli model to obtain a space-time area with the log likelihood ratio meeting the analysis condition as the space-time area with space-time aggregation; and drawing the space-time region meeting the space-time aggregation on a map through GIS software.
Further, the logic instructions in the memory 503 described above may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform 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, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, embodiments of the present invention further provide a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor is implemented to perform the transmission method provided in the above embodiments, for example, including: acquiring position information and fruit prices of all fruit markets in an analysis area of an analysis time period; acquiring position points with abnormal price fluctuation of fruits; using a space-time column body which increases along with the time space to scan the time and/or space of all abnormal position points in an analysis area of an analysis time period, and adopting the log likelihood analysis of a Bernoulli model to obtain a space-time area with the log likelihood ratio meeting the analysis condition as the space-time area with space-time aggregation; and drawing the space-time region meeting the space-time aggregation on a map through GIS software.
The apparatus embodiments described above are merely illustrative, wherein elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the various embodiments or methods of some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A visual analysis method for fruit price space-time aggregation 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 position points with abnormal price fluctuation of fruits;
using a space-time column body which increases along with the time space to scan the time and/or space of all abnormal position points in an analysis area of an analysis time period, and adopting the log likelihood analysis of a Bernoulli model to obtain a space-time area with the log likelihood ratio meeting the analysis condition as the space-time area with space-time aggregation;
drawing a space-time region meeting space-time aggregation on a map through GIS software;
the method for scanning time and/or space by utilizing a space-time column which increases with time and space comprises the following steps:
dividing the analysis area into a plurality of subareas by taking a preset subarea as a basic unit;
the space-time column takes the center point of each sub-area as the center of a circle, the distance of the radius from 0 to the maximum adjacent point is changed, and the time is changed from the shortest fresh-keeping period of the fruits to the analysis time period, so that scanning is performed;
the maximum adjacent point distance is a distance which takes each position point as a starting point to ensure that all the position points have at least N adjacent points, wherein N is a preset value which is more than or equal to 1;
the log likelihood analysis using the Bernoulli model, the acquisition of the space-time region in which the log likelihood ratio satisfies the analysis condition, includes:
null hypothesis H for each spatial window 0 For the fruit price abnormal event is distributed completely randomly in space and time, likelihood function L 0 The method comprises the following steps:
L 0 ＝Πp(x i )＝(A/B) A (1-A/B) B-A ；
alternative hypothesis H 1 For the fruit price anomaly event to exist space-time aggregation distribution in the scanning window, likelihood function L 1 The method comprises the following steps:
L 1 ＝Πp(x i )＝p a (1-p) b-a q A-a (1-q) (B-b)-(A-a) ；
a space-time window satisfying LLR >0 and p > q is taken as a window satisfying space-time aggregation:
wherein p (x) i ) The probability of the abnormal price event is that A is the total number of the abnormal price events of the fruits, and B is the total number of the price records of the fruits in the analysis area; a is the abnormal number of the prices of the fruits in the scanning window, b is the total number of the price records of the fruits in the scanning window; p=a/b is the proportion of abnormal events of the price of fruits in the scanning window; q= (a-a)/(B-B) is the proportion of abnormal fruit price events outside the scanning window;
after the space-time area meeting the space-time aggregation property is drawn on the map by GIS software, the method further comprises the following steps:
determining the edges of the network according to the production places and the sales places, determining the weights of the edges according to the difference value of the price of the production places and the price of the sales places, constructing a production and sales price space transmission network with directions, and visualizing the edges of the production and sales price space transmission network, the weights of the edges and the price transmission directions;
wherein the production and marketing price space transmission network with the direction consists of nodes and edges;
the visualizing includes: the degree of each node is rendered by adjusting the color and size, and the size of the weight of the edge is represented by adjusting the thickness of the edge.
2. The method for space-time aggregation visualization analysis of fruit prices according to claim 1, wherein the acquiring of the location information and the fruit prices of all fruit markets within the analysis area of the analysis period includes:
acquiring related data of a fruit market through a network, wherein the related data comprise time, price of a production place, price of a sales place and position texts of the production place market and the sales place market;
and obtaining the position information of the third-level region corresponding to the position text from the standard library of the third-level administrative region according to the position text, and taking the position information as the position information of the fruit market.
3. The method for space-time aggregation visualization analysis of fruit prices according to claim 1, wherein the acquiring the location point of the abnormality of the fluctuation of the fruit prices comprises:
and acquiring a position point with the difference between the highest price logarithm and the lowest price logarithm of the fruits being larger than a preset threshold value in the analysis time period as a position point with abnormal price fluctuation.
4. The method for spatiotemporal aggregation visualization analysis of fruit prices according to claim 1, wherein the determining of edges of a network according to origin and sales locations comprises:
and selecting the selling places and the producing places with the quotation time difference value of the selling places and the quotation time of the producing places smaller than a preset time threshold value and the space distance of the selling places and the producing places smaller than the preset space threshold value as the edges of the network.
5. A visual analysis device for fruit price space-time aggregation, comprising:
the first acquisition module is used for acquiring the position information and the price of the fruits in all the fruit markets in the analysis area of the analysis time period;
the second acquisition module is used for acquiring position points with abnormal price fluctuation of the fruits;
the processing module is used for scanning time and/or space by utilizing a space-time column body which is increased along with the space of time, and acquiring a space-time area with space-time aggregation by adopting the log likelihood analysis of the Bernoulli model, wherein the space-time area has the log likelihood ratio meeting the analysis condition;
the visualization module is used for drawing the space-time area meeting the space-time aggregation on the map through GIS software;
the processing module is further configured to:
the method for scanning time and/or space by utilizing a space-time column which increases with time and space comprises the following steps:
dividing the analysis area into a plurality of subareas by taking a preset subarea as a basic unit;
the space-time column takes the center point of each sub-area as the center of a circle, the distance of the radius from 0 to the maximum adjacent point is changed, and the time is changed from the shortest fresh-keeping period of the fruits to the analysis time period, so that scanning is performed;
the maximum adjacent point distance is a distance which takes each position point as a starting point to ensure that all the position points have at least N adjacent points, wherein N is a preset value which is more than or equal to 1;
the log likelihood analysis using the Bernoulli model, the acquisition of the space-time region in which the log likelihood ratio satisfies the analysis condition, includes:
null hypothesis H for each spatial window 0 For the fruit price abnormal event is distributed completely randomly in space and time, likelihood function L 0 The method comprises the following steps:
L 0 ＝Πp(x i )＝(A/B) A (1-A/B) B-A ；
alternative hypothesis H 1 For the fruit price anomaly event to exist space-time aggregation distribution in the scanning window, likelihood function L 1 The method comprises the following steps:
L 1 ＝Πp(x i )＝p a (1-p) b-a q A-a (1-q) (B-b)-(A-a) ；
a space-time window satisfying LLR >0 and p > q is taken as a window satisfying space-time aggregation:
wherein p (x) i ) The probability of the abnormal price event is that A is the total number of the abnormal price events of the fruits, and B is the total number of the price records of the fruits in the analysis area; a is the abnormal number of the prices of the fruits in the scanning window, b is the total number of the price records of the fruits in the scanning window; p=a/b is the proportion of abnormal events of the price of fruits in the scanning window; q= (a-a)/(B-B) is the proportion of abnormal fruit price events outside the scanning window;
the visualization module is further configured to:
determining the edges of the network according to the production places and the sales places, determining the weights of the edges according to the difference value of the price of the production places and the price of the sales places, constructing a production and sales price space transmission network with directions, and visualizing the edges of the production and sales price space transmission network, the weights of the edges and the price transmission directions;
wherein the production and marketing price space transmission network with the direction consists of nodes and edges;
the visualizing includes: the degree of each node is rendered by adjusting the color and size, and the size of the weight of the edge is represented by adjusting the thickness of the edge.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the spatiotemporal aggregation visualization analysis method of fruit prices according to any of claims 1 to 4 when the program is executed by the processor.
7. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the spatiotemporal aggregation visualization analysis method of fruit prices according to any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911296338.5A CN111178945B (en) | 2019-12-16 | 2019-12-16 | Visual analysis method and device for space-time aggregation of fruit prices |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911296338.5A CN111178945B (en) | 2019-12-16 | 2019-12-16 | Visual analysis method and device for space-time aggregation of fruit prices |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111178945A CN111178945A (en) | 2020-05-19 |
| CN111178945B true CN111178945B (en) | 2023-12-05 |
Family
ID=70650158
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911296338.5A Active CN111178945B (en) | 2019-12-16 | 2019-12-16 | Visual analysis method and device for space-time aggregation of fruit prices |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111178945B (en) |
Families Citing this family (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 |
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 |
|---|
| 时空统计量在感染性钉螺分布预警中的应用研究;高风华 等;《中国血吸虫病防治杂志》;第25卷(第04期);第353-356页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111178945A (en) | 2020-05-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11748379B1 (en) | Systems and methods for generating and implementing knowledge graphs for knowledge representation and analysis | |
| US11188935B2 (en) | Analyzing consumer behavior based on location visitation | |
| US7774227B2 (en) | Method and system utilizing online analytical processing (OLAP) for making predictions about business locations | |
| US20170097963A1 (en) | Data Analysis | |
| US9043302B1 (en) | Campaign and competitive analysis and data visualization based on search interest data | |
| Guan et al. | Analyzing massive data sets: an adaptive fuzzy neural approach for prediction, with a real estate illustration | |
| US20130226539A1 (en) | Method and system for statistical analysis of customer movement and integration with other data | |
| Nilakanta et al. | Dimensional issues in agricultural data warehouse designs | |
| Jain et al. | Pricing high-dimensional Bermudan options using the stochastic grid method | |
| CN108256032B (en) | Method and device for visualizing co-occurrence mode of time-space data | |
| Melo et al. | Voronoi diagrams and spatial analysis of crime | |
| WO2012150107A1 (en) | Network analysis tool | |
| Xiao et al. | Can street segments indexed for accessibility form the basis for housing submarket delineation? | |
| Zhou et al. | Point grid map: A new type of thematic map for statistical data associated with geographic points | |
| CN105824872A (en) | Search-based detection, link, and acquisition of data | |
| Berrahou et al. | A quality-aware spatial data warehouse for querying hydroecological data | |
| Desjardins et al. | A space–time parallel framework for fine-scale visualization of pollen levels across the Eastern United States | |
| Tan et al. | Urban spatial organization, multifractals, and evolutionary patterns in large cities | |
| CN111178945B (en) | Visual analysis method and device for space-time aggregation of fruit prices | |
| Gür et al. | GeoSemOLAP: Geospatial OLAP on the Semantic Web made easy | |
| Ilhan et al. | Collaboration in design research: An analysis of co-authorship in 13 design research journals, 2000–2015 | |
| CN114693367A (en) | Agricultural product price big data monitoring and visualization system and method | |
| Halpern et al. | Dimensions of uncertainty: a spatiotemporal review of five COVID-19 datasets | |
| Brida et al. | Analyzing the performance of the South Tyrolean hospitality sector: a dynamic approach | |
| US11810001B1 (en) | Systems and methods for generating and implementing knowledge graphs for knowledge representation and analysis |
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 |