CN116151693A

CN116151693A - Public resource transaction interest and cheat assessment method and system for purchasing

Info

Publication number: CN116151693A
Application number: CN202310417342.2A
Authority: CN
Inventors: 周维
Original assignee: Nanjing Public Resources Trading Center Jiangbei New Area Sub Center
Current assignee: Nanjing Public Resources Trading Center Jiangbei New Area Sub Center
Priority date: 2023-04-19
Filing date: 2023-04-19
Publication date: 2023-05-23
Anticipated expiration: 2043-04-19
Also published as: CN116151693B

Abstract

The invention provides a public resource transaction interest and cheat assessment method and system for purchasing, which relate to the technical field of public resource transactions and comprise a commodity analysis module, a user analysis module and a terminal processor, wherein the commodity analysis module and the user analysis module are in communication connection with the terminal processor; the commodity analysis module is used for analyzing historical transaction conditions, defects and historical transaction areas of the transacted public resources; the user analysis module is used for analyzing historical transaction conditions of the former holder; the terminal processor comprises an evaluation unit and a storage unit, and is used for comprehensively evaluating the transaction commodity; the invention improves the existing public resource transaction system for purchasing, starts from the historical transaction condition of the public resource and the historical transaction condition of the former holder, so that the public resource to be purchased is better known, and the evaluation of the public resource transaction is more accurate.

Description

Public resource transaction interest and cheat assessment method and system for purchasing

Technical Field

The invention relates to the technical field of public resource transactions, in particular to a public resource transaction interest and cheating assessment method and system for purchasing.

Background

Public resource transaction refers to public resources of public resource management departments of public utility franchise, monopoly and speciality for carrying out transaction and providing consultation, service and the like, wherein the public resources are controlled by the public resource management departments of public benefit, monopoly and speciality, such as property leases, office building waste material treatment and the like of municipal utility franchises, administrative public institution logistics social service management rights, outdoor advertising board management rights, anti-smuggling fine public material auction, real estate and office building and the like.

In order to facilitate purchasing public resources during public resource transaction, the conventional public resource transaction system for purchasing only analyzes the value of the public resources, judges whether to purchase the public resources according to the analysis result, and judges whether to purchase the public resources according to the transaction value.

Disclosure of Invention

Aiming at the defects existing in the prior art, the invention improves the public resource transaction system for purchasing, and starts from the history transaction condition of the public resource and the history transaction condition of the former holder, so that the public resource to be purchased is better understood, the interest and cheat evaluation of the public resource transaction is more accurate, and the problem that the public resource purchased is inconsistent with the expected public resource caused by the fact that the public resource cannot be completely understood when the public resource is purchased is solved.

In order to achieve the above object, in a first aspect, the present invention provides a public resource transaction interest and fraud assessment system for purchasing, including a commodity analysis module, a user analysis module and a terminal processor, where the commodity analysis module and the user analysis module are in communication connection with the terminal processor;

the commodity analysis module is used for analyzing historical transaction conditions, characteristic keywords and historical transaction areas of the transacted public resources, marking the transacted public resources based on analysis results and obtaining high-frequency commodity scores, and marking the transacted public resources as transaction commodities;

the user analysis module is used for analyzing historical transaction conditions of the former holder and obtaining a low reputation score, and marking the transaction commodity based on the analysis result;

The terminal processor comprises an evaluation unit and a storage unit, and is used for comprehensively evaluating the trade commodity and dividing the purchasing condition of the commodity based on the evaluation result;

the evaluation unit analyzes the transaction commodity based on the analysis results of the commodity analysis module and the user analysis module to obtain commodity scores, regional scores and user scores, analyzes the transaction commodity based on the regional scores, the high-frequency commodity scores, the low reputation scores, the commodity scores and the user scores, and divides the purchase condition of the transaction commodity;

the storage unit stores historical transaction conditions of the transaction commodity, historical transaction conditions of a front holder and credit investigation points of the front holder.

Further, the commodity analysis module is configured with a historical transaction analysis policy, the historical transaction analysis policy comprising:

acquiring transaction times of the transaction commodity after leaving the factory in a transaction system;

when the number of transactions is greater than the first transaction amount and less than or equal to the second transaction amount, marking the transaction commodity as a low transaction commodity;

when the number of transactions is greater than the second transaction amount, the transaction item is marked as a high transaction item.

Further, the commodity analysis module is further configured with a low transaction analysis policy, the low transaction analysis policy comprising:

Obtaining the transaction time of each transaction of low-transaction commodities, marking the difference value of the transaction time of two adjacent transactions as low-transaction commodity interval time, arranging all the low-transaction commodity interval time on a low-transaction time axis, and sequentially marking the low-transaction commodity interval time from the earliest low-transaction commodity interval time as first low-transaction commodity interval time to the Nth low-transaction commodity interval time, wherein N is a positive integer;

comparing the first to Nth low-trade commodity interval times with the standard interval time, marking the low-trade commodity interval time smaller than the standard interval time as short interval time, marking the short interval time on a low-trade time axis, and marking the low-trade commodity as high-frequency trade commodity when the number of times of short interval time in the first conventional trade time is larger than the first interval number of times;

making a high-frequency commodity score according to the times of short interval time in the first conventional transaction time;

placing the low-transaction commodity into a search engine for searching, selecting a first number of characteristic keywords, comparing the characteristic keywords of the low-transaction commodity with the characteristic keywords of the standard commodity, marking commodity characteristics with different comparison between the low-transaction commodity and the standard commodity as low-flaw characteristics, and calculating the low flaw rate of the low-transaction commodity by using a low-flaw algorithm;

The low-flaw algorithm is as follows:

wherein, C1 is low flaw rate, m1 is the number of low flaw features, and n1 is the first number;

when the low defect rate is greater than the standard low defect rate, the low transaction merchandise is marked as defective merchandise.

Further, the commodity analysis module is further configured with a high transaction analysis policy, the high transaction analysis policy comprising:

acquiring the transaction time of each transaction of the high-transaction commodity, arranging the transaction time on a high-transaction time axis, starting from the earliest transaction time and marking the transaction time as a first high-transaction commodity transaction time to an Mth high-transaction commodity transaction time, acquiring the adjacent time interval of each high-transaction commodity transaction time, and eliminating the Mth 1 high-transaction commodity transaction time in the time axis when the adjacent time interval of the Mth 1 high-transaction commodity transaction time is larger than the second interval time;

rearranging the transaction time on the removed high transaction time axis, starting from the transaction time of the earliest transaction to be marked as a first high transaction commodity transaction time to a P high transaction commodity transaction time, marking the transaction time interval between the first high transaction commodity transaction time and the adjacent two times in the P high transaction commodity transaction time as a first high transaction commodity interval time to a P-1 high transaction commodity interval time, comparing the first interval time to the P-1 interval time with a standard interval time, marking the interval time smaller than the standard interval time as a short interval time, marking the short interval time on the time axis, and marking the high transaction commodity as a high frequency transaction commodity when the number of times of the short interval time is larger than that of the second interval time in the second conventional transaction time;

Making a high-frequency commodity score according to the times of short interval time in the second conventional transaction time;

placing the high-transaction commodity into a search engine for searching, selecting a second number of characteristic keywords, comparing the commodity characteristics represented by the characteristic keywords of the high-transaction commodity with the commodity characteristics represented by the characteristic keywords of the standard commodity, marking different commodity characteristics of the high-transaction commodity and the standard commodity as high flaw characteristics, and calculating the high flaw rate of the high-transaction commodity by using a high flaw algorithm;

the Gao Xiaci algorithm is as follows:

wherein, C2 is high flaw rate, alpha is a first coefficient, A1 is transaction times, m2 is the number of high flaw features, and n2 is a second number;

and when the high flaw rate is larger than the standard high flaw rate, marking the high-transaction commodity as a flaw commodity.

Further, the commodity analysis module is configured with a historical transaction area analysis policy, the historical transaction area analysis policy comprising:

acquiring the place of the trade commodity in each trade in the historical trade, and acquiring the trade radius through a price conversion algorithm according to the trade price of the trade commodity;

the price conversion algorithm is as follows:

wherein L is the transaction radius, gamma is the first radius coefficient, and K is the price of the transaction commodity;

Drawing a circle by taking the place where the trade commodity is located as the circle center and the trade radius to obtain a trade influence circle, and setting the trade influence circle as a trade influence area;

acquiring the quantity of the same type of transaction commodities which are transacted in the transaction influence area when the transaction commodities are transacted or the area of an overlapping area which overlaps with the transaction influence area of the same type of commodities;

calculating a transaction influence value through a transaction influence algorithm, wherein the transaction influence algorithm is as follows:

wherein Q is a transaction influence value, delta 1 is a quantity influence coefficient, delta 2 is an area influence coefficient, W1 is the quantity of the same type of commodities which are transacted in the transaction influence area, and W2 is the overlapping area of the transaction influence area of the same type of commodities and the transaction influence area of the transacted commodities;

when the transaction influence value is larger than the standard influence value, marking the transaction commodity as a commodity in a high transaction area;

and when the transaction influence value is smaller than or equal to the standard influence value, marking the transaction commodity as a commodity in a low transaction area.

Further, the user analysis module is configured with a user analysis policy, the user analysis policy comprising:

acquiring historical transaction conditions of a front holder, wherein the historical transaction conditions comprise historical transaction quantity, historical transaction success quantity and historical transaction dispute quantity, and the historical transaction dispute quantity is disputes between the front holder and a transactor in historical transaction;

Dividing the number of the historical trade disputes into the number of the forward trade disputes and the number of the reverse trade disputes, wherein the forward trade disputes are disputes which are not resolved by the two parties, and the reverse trade disputes are disputes which are not resolved by the two parties, so as to obtain a historical trade score through a historical trade algorithm;

the historical transaction algorithm is as follows:

wherein K is historical transaction score, Z is historical transaction quantity, Z1 is historical transaction success quantity, and B1 is forward transactionThe number of disputes is easy, B2 is the number of disputes in reverse transaction, X1 is a first transaction coefficient, X2 is a second transaction coefficient, and X3 is a third transaction coefficient;

comparing the historical transaction score with the standard transaction score, and marking the transaction commodity as a good commodity of the historical credit when the historical transaction score is larger than the standard transaction score;

when the historical transaction score is smaller than or equal to the standard transaction score, marking the transaction commodity as a historical credit bad commodity;

acquiring credit score of a front holder and change time of the credit score;

marking the change of credit score reduction as negative change, obtaining the latest transaction time from the change time of the negative change, marking the transaction corresponding to the transaction time as low-credit transaction when the change time of the negative change is different from the latest transaction time by less than the standard change influence time, obtaining the transaction quantity of the low-credit transaction, and marking the former holder as a low-credit transaction user when the transaction quantity of the low-credit transaction is greater than the third quantity;

Formulating a low reputation user score based on the transaction quantity of the low reputation transaction;

comparing the credit score of the front holder with the social average credit score, and marking the front holder as a credit bad user when the credit score of the current holder is smaller than or equal to the social average credit score;

when the credit score of the current holder is greater than the social average credit score, the previous holder is marked as a credit-good user.

Further, the evaluation unit is configured with a commodity evaluation policy including:

acquiring the marking condition of the trade commodity in a historical trade analysis strategy, and marking the commodity grading of the trade commodity as a first commodity grading value when the trade commodity is marked as a low trade commodity and is a flaw commodity;

when the trade commodity is marked as a low trade commodity and is not marked as a flaw commodity, marking the commodity score of the trade commodity as a second commodity score value;

when the trade commodity is marked as a high trade commodity and is a defective commodity, marking the commodity score of the trade commodity as a third commodity score value;

when the trade commodity is marked as a high trade commodity and is not marked as a flaw commodity, marking the commodity score of the trade commodity as a fourth commodity score value;

When the trade commodity is marked as a high trade area commodity, marking the area score of the trade commodity as a first area score value;

when the transaction item is marked as a low transaction area item, the area score for the transaction item is marked as a second area score value.

Further, the evaluation unit is configured with a user evaluation policy, the user evaluation policy comprising:

acquiring the marking condition of the transaction commodity in a user analysis strategy and the marking condition of a former holder in the user analysis strategy;

when the transaction good is marked as a historical credit bad good and the previous holder is marked as a credit bad user, marking the user score of the transaction good as a first user score value;

when the transaction good is marked as a historical credit good and the previous holder is marked as a credit bad user, marking the user score of the transaction good as a second user score value;

when the transaction good is marked as a historical credit bad good and the previous holder is marked as a credit good user, marking the user score of the transaction good as a third user score value;

when the transaction good is marked as a historical credit good and the previous holder is marked as a credit good user, the user score for the transaction good is marked as a fourth user score value.

Further, the evaluation unit is configured with a comprehensive evaluation policy comprising:

obtaining commodity scores and user scores of the trade commodities, and obtaining a comprehensive score value through a comprehensive evaluation algorithm, wherein the comprehensive evaluation algorithm is as follows:

wherein V is the integrated score value, and β1 is the firstThe comprehensive coefficient, beta 2 is the second comprehensive coefficient, beta 3 is the third comprehensive coefficient, beta 4 is the fourth comprehensive coefficient, beta 5 is the fifth comprehensive coefficient, T1 is commodity grading, T2 is user grading, T5 is regional grading, T3 is high-frequency commodity grading, and T4 is low-reputation user grading; />

When the comprehensive grading value is larger than the first standard grading value, marking the trade commodity as a purchasable commodity;

when the comprehensive average value is smaller than or equal to the first standard grading value and larger than the second standard grading value, marking the trade commodity as a general commodity;

and when the comprehensive grading value is smaller than or equal to the second standard grading value, marking the trade commodity as a non-purchasable commodity.

The invention has the beneficial effects that:

1. according to the invention, through analyzing the historical transaction condition, defect and historical transaction area of the transacted public resource, the public resource is marked with the proper mark through the analysis result, so that the public resource is convenient to accurately evaluate at last, and the value of each step of analysis can be reflected;

2. The method has the advantages that the historical transaction times of public resources are analyzed, transaction commodities are divided into low transaction commodities and high transaction commodities, the low transaction commodities and the high transaction commodities are respectively analyzed, the historical transaction time is analyzed, the transaction commodities which are subjected to multiple transactions in a short time are recorded as high-frequency transaction commodities, the method has the advantages that unstable commodities transferred by multiple hands can be distinguished, flaws of the transaction commodities are analyzed, the transaction commodities with higher standard flaw rate are divided into flaw commodities, the method has the advantages that the integrity of the commodities can be better analyzed, the incomplete commodities are distinguished, and the finally obtained comprehensive scores are more comprehensive;

3. the invention also analyzes the historical transaction area of the transaction commodity, has the advantages that commodity overflow is prevented from being too serious due to the fact that a plurality of transaction positions exist in the same area of the commodity, the actual value of the commodity can be better evaluated, the probability of buying the high-frequency transaction commodity is reduced, and the probability of buying the commodity with proper price is improved;

4. the invention also analyzes the historical transaction condition of the former holder, and evaluates the historical transaction credit of the former holder by analyzing the transaction times, the transaction success quantity and the historical transaction dispute quantity of the former holder, so that the invention has the advantages of effectively avoiding purchasing public resources of bad sellers and leading the purchased transaction commodity to have value more in line with the self;

5. The invention also carries out comprehensive analysis on the analysis results, calculates the evaluation results of the trade commodity through the comprehensive evaluation algorithm, and analyzes the purchasing condition of the commodity based on the results of the comprehensive evaluation algorithm.

Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:

FIG. 1 is a schematic block diagram of a common resource trading interest and fraud assessment system for purchasing of the present invention;

FIG. 2 is a flow chart of steps of a method for evaluating the trading of public resources for purchasing of the present invention;

FIG. 3 is a schematic diagram of a historical transaction region according to the present invention.

Description of the embodiments

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention.

Embodiments of the invention and features of the embodiments may be combined with each other without conflict.

Referring to fig. 1, the present invention provides a public resource transaction interest and fraud assessment system for purchasing, including: the system comprises a commodity analysis module, a user analysis module and a terminal processor, wherein the commodity analysis module and the user analysis module are in communication connection with the terminal processor;

the commodity analysis module is configured with a historical transaction analysis policy comprising:

in the specific implementation process, the transaction times acquired in the transaction system cannot represent the actual transaction times, private transaction possibly exists but is not registered in the system, and certain errors exist;

when the number of transactions is greater than the second transaction amount, marking the transaction merchandise as a high transaction merchandise;

dividing the commodities based on the transaction times of the commodities, wherein the commodities with higher transaction times and the commodities with lower transaction times have different emphasis points in analysis, and the commodity is divided at the step so as to be beneficial to subsequent analysis;

in the specific implementation process, the first transaction quantity is set to be 2, the second transaction quantity is set to be 10, the transaction times of the transaction commodity are detected to be 5, and the transaction commodity is marked as a low transaction commodity; the number of times of transaction of the transaction commodity is 15, and the transaction commodity is marked as a high transaction commodity;

the commodity analysis module is also configured with a low transaction analysis policy comprising:

The method has the advantages that the transaction interval time of the transaction commodity can be obtained more intuitively, the commodity experience change time can be obtained, whether the commodity is subjected to multiple transactions in a short time can be judged according to the transaction interval time, and the problem that different damages occur when hands are turned over to some transaction commodities is solved, so that the analysis mode can distinguish high-frequency transaction commodities, and plays a role in comprehensive analysis;

in the specific implementation process, the standard interval time is set to 15 days, the first interval times are 5 times, the first conventional transaction time is 1 year, the interval time from the first low transaction commodity to the Nth low transaction commodity is detected to be 1 year, 2 years, 10 days, 12 days, 4 days, 3 days, 15 days, 14 days and 3 years, 6 transactions with short interval time in one year are detected, the transaction commodity is marked as a high-frequency transaction commodity, and the high-frequency commodity score is set to 6;

When the two transaction time is less than or equal to 15 days, the trade commodity is usually sold by multiple hands through one intermediate manufacturer, the trade commodity has a problem or a price problem, and the standard interval time is set to 15 days, so that whether the trade commodity is sold by multiple hands due to the problem can be more intuitively judged;

the method comprises the steps that low-transaction commodity is placed in a search engine to search, a first number of characteristic keywords are selected, the characteristic keywords are words which are possessed by the transaction commodity and have the largest searching times, the words can describe the commodity to a certain extent, such as the material or processing mode of a table, the characteristic keywords of the low-transaction commodity are compared with the characteristic keywords of standard commodity, the characteristic keywords of the standard commodity are more representative, and the transaction commodity is placed in an objective judgment angle;

marking commodity features with different comparison between the low-transaction commodity and the standard commodity as low-flaw features, and calculating the low flaw rate of the low-transaction commodity by using a low flaw algorithm;

the low-flaw algorithm is as follows:

when the low flaw rate is greater than the standard low flaw rate, marking the low-transaction commodity as a flaw commodity;

In the specific implementation process, the standard commodity is set as a first hand commodity produced from a qualified factory, the standard low flaw rate is set as 30%, the first number is set as 5, 3 low flaw features are detected, the low flaw rate is calculated to be 60%, and the transaction commodity is marked as a flaw commodity;

when the low defect rate is lower than 30%, it is judged that the commodity is damaged to some extent due to the plurality of transactions, but the low defect rate cannot be too high because of the low transaction commodity, and when the defect rate is higher than 30%, more defects of the commodity are likely to occur due to external factors;

the commodity analysis module is also configured with a high transaction analysis policy comprising:

The high-transaction commodity usually undergoes tens of transactions, and the transaction time which is too long from the front transaction to the back transaction is removed, so that the high-transaction commodity is more efficient in transaction, and excessive invalid transaction records are avoided because of analysis;

in the specific implementation process, the second interval time is set to be 5 years, the transaction time from the first high transaction commodity to the Mth high transaction commodity is detected to be 10 years, 4 years, 3 years, 6 years, 2 years and 0.5 years in sequence, and after the transaction time of the high transaction commodity higher than 5 years is removed, the transaction time from the first high transaction commodity to the Mth high transaction commodity is 4 years, 3 years, 2 years and 0.5 years in sequence;

The standard interval time for the high-transaction commodity is consistent with that of the low-transaction commodity, and the standard interval time of the high-transaction commodity cannot be widened or shortened because of excessive transaction times;

in the specific implementation process, the second conventional transaction time is set to be 3 years, the second interval times are set to be 8 times, the short interval time from the first high transaction commodity interval time to the P-1 high transaction commodity interval time of 4 years, 3 years, 2 years and 0.5 year is detected, the short interval time more than 8 times in 3 years is not met, the transaction commodity is not marked in the link, and the high-frequency commodity score defaults to 1;

the Gao Xiaci algorithm is as follows:

In the specific implementation process, the standard high flaw rate is set to 40%, the second number is set to 10, alpha is set to 0.05, the number of detected transactions is 20, the number of high flaw features is 2, the calculated high flaw rate is 20%, and the transaction commodity is not marked in the link with the high flaw rate smaller than the standard high flaw rate;

the commodity analysis module is configured with a historical transaction area analysis strategy, the historical transaction area analysis strategy comprising:

the price conversion algorithm is as follows:

in the specific implementation process, gamma is set to be 10, the price of the transaction commodity transaction is detected to be 50, and L is calculated to be 500m;

the value range of the commodity is regulated to different degrees, and judgment can be more objective in judgment;

Referring to fig. 3, a transaction influence area of a transaction commodity is determined by taking a place where the transaction commodity is located as a center and a transaction radius as a radius, and the number of the same type of transaction commodity or the overlapping area overlapping with the transaction influence area of the same type of commodity in the transaction influence area when the transaction commodity is transacted is obtained;

whether the transaction quantity of the commodities of the same type or the overlapping area of the transaction influence areas of the commodities of the same type can reflect whether the transaction commodities are commodities which are transacted in batches in the transaction influence areas or not, and the transaction commodities with different transaction prices from actual prices can be effectively prevented from being purchased;

In the specific implementation process, the standard influence value is set to be 5, delta 1 is set to be 0.8, delta 2 is set to be 0.2, W1 is detected to be 3, W2 is detected to be 0.3k square meter, Q is calculated to be 2.46 and smaller than the standard influence value, and the trade commodity is marked as a commodity in a low trade area;

the user analysis module is used for analyzing historical transaction conditions of a front holder and the front holder and obtaining a low reputation score, and marking transaction commodities based on analysis results, wherein the front holder is a holder holding transaction commodities before the transaction commodities are transacted;

the user analysis module is configured with a user analysis policy comprising:

The transaction disputes of the front holder can be analyzed to judge whether the front holder has excessive problems in transaction, and the transaction conditions of the front holder can be more perfectly analyzed by combining the division of the transaction disputes, so that the transaction with bad sellers is avoided;

the historical transaction algorithm is as follows:

wherein K is historical transaction score, Z is historical transaction quantity, Z1 is historical transaction success quantity, B1 is forward transaction dispute quantity, B2 is reverse transaction dispute quantity, X1 is first transaction coefficient, X2 is second transaction coefficient, and X3 is third transaction coefficient;

the historical transaction score can reflect whether the previous holder has good transaction habits during the transaction;

in the specific implementation process, the standard transaction score is set to 200, X1 is set to 3, X2 is set to 4, X3 is set to 5, Z is detected to be 10, Z1 is detected to be 8, B1 is detected to be 1, B2 is detected to be 1, K is calculated to be 230 and is larger than the standard transaction score, and the transaction commodity is marked as a good historical credit commodity;

Acquiring credit score of a front holder and change time of the credit score;

the specified value of the credit score is 0-1000, the credit condition of a person can be reflected, the change time of the credit score is analyzed, whether a former holder sells or transacts due to personal credit problem can be judged, the transaction risk can be reduced, and bad goods can be prevented from being purchased by a bad seller;

in the specific implementation process, the standard variation influence time is set to be 5 days, the third quantity is set to be 3 times, the transaction appears after the sign information of the former holder is detected to be negatively varied for two days, the transaction is marked as low-credit transaction, in the historical transaction process of the former holder, the 5 times of low-credit transaction appears, and the former holder is marked as a low-credit transaction user;

in the implementation process, the score of the low-reputation user is initially 5, and is reduced by 1 and is at least 1 every two times when the score exceeds the third quantity;

when the credit score of the current holder is greater than the social average credit score, the previous holder is marked as a credit good user;

the evaluation unit is configured with a commodity evaluation policy including:

when the trade commodity is marked as a low trade area commodity, marking the area score of the trade commodity as a second area score value;

in the specific implementation process, the first commodity grading value to the fourth commodity grading value are set to be 20, 40, 10 and 30, the first area grading value is set to be 10, and the second area grading value is set to be 40;

the evaluation unit is configured with a user evaluation policy comprising:

when the transaction good is marked as a historical credit good and the previous holder is marked as a credit good user, marking the user score of the transaction good as a fourth user score value;

in the implementation process, the first user scoring values to the first user scoring values are set to be 10, 20, 30 and 40;

the evaluation unit is configured with a comprehensive evaluation strategy comprising:

Wherein V is a comprehensive grading value, beta 1 is a first comprehensive coefficient, beta 2 is a second comprehensive coefficient, beta 3 is a third comprehensive coefficient, beta 4 is a fourth comprehensive coefficient, beta 5 is a fifth comprehensive coefficient, T1 is commodity grading, T2 is user grading, T5 is region grading, T3 is high-frequency commodity grading, and T4 is low-reputation user grading;

when the comprehensive grading value is smaller than or equal to the second standard grading value, marking the trade commodity as a non-purchasable commodity;

in the implementation process, the first standard score value is set to 100, the second standard score value is set to 80, the third standard score value is set to 60, beta 1 is set to 0.5, beta 2 is set to 0.4, beta 3 is set to 0.5, beta 4 is set to 10, beta 5 is set to 0.1, the commodity score is detected to be 30, the user score is 20, the high-frequency commodity score is 6, the low-reputation user score is 2, the region score is 40, the comprehensive score value is calculated to be 101, and the transaction commodity is marked as a purchasable commodity;

In a second embodiment, referring to fig. 2, the present invention provides a method for evaluating the trading interest and cheating of a public resource for purchasing, including:

step S1, analyzing historical transaction conditions, defects and historical transaction areas of the transacted public resources, marking the transacted public resources based on analysis results, and marking the transacted public resources as transaction commodities;

step S1 comprises the following sub-steps:

step S101, acquiring the transaction times of the transaction commodity after leaving the factory in a transaction system;

step S102, obtaining the transaction time of each transaction of the low-transaction commodity, marking the difference value of the transaction time of two adjacent transactions as low-transaction commodity interval time, arranging all the low-transaction commodity interval time on a low-transaction time axis, and marking the low-transaction commodity interval time from the earliest low-transaction commodity interval time to the Nth low-transaction commodity interval time in sequence, wherein N is a positive integer;

placing low-transaction commodity into a search engine to search, selecting a first number of characteristic keywords which are words of the transaction commodity and have the largest searching times, comparing the characteristic keywords of the low-transaction commodity with the characteristic keywords of the standard commodity, marking commodity characteristics with different comparison between the low-transaction commodity and the standard commodity as low flaw characteristics, and calculating the low flaw rate of the low-transaction commodity by using a low flaw algorithm;

the low-flaw algorithm is as follows:

Step S103, acquiring the transaction time of each transaction of the high-transaction commodity, arranging the transaction time on a high-transaction time axis, starting from the earliest transaction time to be recorded as a first high-transaction commodity transaction time to an Mth high-transaction commodity transaction time, acquiring the adjacent time interval of each high-transaction commodity transaction time, and eliminating the Mth 1 high-transaction commodity transaction time in the time axis when the adjacent time interval of the Mth 1 high-transaction commodity transaction time is larger than the second interval time;

the Gao Xiaci algorithm is as follows:

when the high flaw rate is larger than the standard high flaw rate, marking the high-transaction commodity as a flaw commodity;

step S104, the place where the trade commodity is located in each trade in the historical trade is obtained, and the trade radius is obtained through a price conversion algorithm according to the trade price of the trade commodity;

the price conversion algorithm is as follows:

determining a trade influence area of trade commodity by taking a place where the trade commodity is located as a circle center and a trade radius as a radius, and acquiring the quantity of the trade commodity of the same type or the area of an overlapping area overlapping with the trade influence area of the commodity of the same type in the trade influence area when the trade commodity is traded;

when the transaction influence value is smaller than or equal to the standard influence value, marking the transaction commodity as a commodity in a low transaction area;

step S2, analyzing the former holder and the historical transaction situation of the former holder, and marking the transaction commodity based on the analysis result, wherein the former holder is the holder holding the transaction commodity before the transaction commodity is transacted;

step S2 comprises the following sub-steps:

step S201, acquiring historical transaction conditions of a former holder, wherein the historical transaction conditions comprise historical transaction quantity, historical transaction success quantity and historical transaction disputes, and the historical transaction disputes are disputes between the former holder and a transactor in the historical transaction;

The historical transaction algorithm is as follows:

wherein K is historical transaction score, Z is historical transaction quantity, Z1 is historical transaction success quantity, B1 is forward transaction dispute quantity, B2 is reverse transaction dispute quantity, and X1 is first transactionThe coefficient of ease, X2 is the second transaction coefficient, X3 is the third transaction coefficient;

step S202, acquiring credit score of a former holder and change time of the credit score;

s3, comprehensively evaluating the trade commodity, and dividing the purchasing condition of the commodity based on the evaluation result;

the step S3 comprises the following sub-steps:

step S301, storing historical price and product parameter data related to the related commodity;

step S302, analyzing the trade commodity based on the analysis results of the commodity analysis module and the user analysis module to obtain commodity scores, regional scores and user scores;

said step S302 comprises the sub-steps of:

step S3021, obtaining a marking condition of the transaction commodity in the historical transaction analysis strategy, and marking the commodity score of the transaction commodity as a first commodity score value when the transaction commodity is marked as a low transaction commodity and is a defective commodity;

step S3022, obtaining the marking condition of the transaction commodity in the user analysis policy and the marking condition of the front holder in the user analysis policy;

step S303, analyzing the trade commodity based on the regional score, the high-frequency commodity score, the low reputation score, the commodity score and the user score, and dividing the purchase condition of the trade commodity;

step S303 comprises the sub-steps of,

step S3031, commodity scores of the trade commodities and user scores are obtained, and a comprehensive score value is obtained through a comprehensive evaluation algorithm, wherein the comprehensive evaluation algorithm is as follows:

step S3032, when the comprehensive score value is greater than the first standard score value, marking the transaction commodity as a purchasable commodity;

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied therein. The storage medium may be implemented by any type or combination of volatile or nonvolatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM), electrically erasable Programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

The above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. The public resource transaction interest and cheat assessment system for purchasing is characterized by comprising a commodity analysis module, a user analysis module and a terminal processor, wherein the commodity analysis module and the user analysis module are in communication connection with the terminal processor;

2. The system for evaluating the equity of a common resource transaction for purchasing of claim 1, wherein said commodity analysis module is configured with a historical transaction analysis strategy comprising:

3. The system for evaluating the equity trading in a common resource for purchases of claim 2, wherein the commodity analysis module is further configured with a low-transaction analysis strategy comprising:

the low-flaw algorithm is as follows:

4. A public resource trading fraud assessment system for purchasing as defined in claim 3, wherein the commodity analysis module is further configured with a high trading analysis strategy comprising:

The Gao Xiaci algorithm is as follows:

5. The system for evaluating the equity of a common resource transaction for purchasing of claim 4, wherein said commodity analysis module is configured with a historical transaction area analysis policy comprising:

the price conversion algorithm is as follows:

6. The system for evaluating the equity trading of resources for purchasing according to claim 5, wherein said user analysis module is configured with a user analysis policy comprising:

The historical transaction algorithm is as follows:

acquiring credit score of a front holder and change time of the credit score;

7. The system for evaluating the equity trading in a common resource for purchases according to claim 6, wherein the evaluating unit is configured with a commodity evaluation policy including:

8. The system for evaluating the equity trading of resources for purchases according to claim 7, wherein the evaluation unit is configured with a user evaluation policy comprising:

9. The system for evaluating the equity trading of resources for purchases according to claim 7, wherein the evaluation unit is configured with a comprehensive evaluation strategy comprising: