A performance risk early warning method and system based on transaction data

Abstract

The present invention discloses a performance risk early warning method and system based on transaction data, which belongs to the field of performance risk early warning. The present invention obtains post-transaction comment data of customers of similar transactions and imports the transaction data into an evaluation of the authenticity of transaction performance to obtain a real performance transaction, imports the evaluated real performance transaction information and the transaction information data of this transaction into a transaction performance risk assessment strategy to conduct a transaction performance risk assessment, and performs a transaction process early warning based on the obtained transaction performance risk assessment result. The present application firstly accurately obtains similar transactions through transaction data, and then accurately obtains the real performance transactions in similar transactions based on the data features in the post-transaction comments, and finally comprehensively analyzes the transaction risks based on the transaction data, transaction similarity values ​​and performance risk values ​​of the real performance transactions, thereby improving the efficiency and accuracy of performance risk early warning.

Description

Technical Field

[0001] The present invention belongs to the field of contract performance risk early warning, and specifically relates to a contract performance risk early warning method and system based on transaction data. Background Art

[0002] Shopping risks mainly refer to the risks faced by consumers due to various uncertainties during the consumption process. These risks may come from the product itself, the shopping process, after-sales service and other aspects. Specifically, shopping risks can include the following types: Product risk: This mainly involves product quality, performance, safety and other aspects. If the product has defects or does not meet consumer expectations, consumers may face risks such as inconvenience in use, damage, or even health damage. Transaction risk: This mainly includes payment security, transaction fraud and other aspects. For example, when shopping online, consumers need to provide personal information and bank account information, which may be stolen or abused by criminals. In addition, false transactions, fraudulent behavior, etc. may also cause losses to consumers, so it is necessary to analyze transaction data during transactions and then issue performance risk warnings;

[0003] However, when the existing technology conducts risk warning and data analysis, the transaction data is usually high-dimensional and complex, involving multiple relevant factors, such as transaction amount, time, location, participants, etc., which makes data analysis complicated and prone to errors. At the same time, the existing technology cannot accurately obtain similar transactions through transaction data, and it also leads to the inability to accurately obtain the actual performance transactions in similar transactions, resulting in low efficiency and accuracy in comprehensive analysis and warning of transaction risks. Most of the existing technologies have the above problems;

[0004] For example, a Chinese patent with application publication number CN117333287A discloses a performance risk early warning method and system based on user data, the method comprising receiving a credit application form for pre-loan review and generating pre-loan review results; obtaining industry information to which the user belongs based on identity information, obtaining industry assessment data corresponding to the industry information based on a preset sampling period, and fitting an industry development trend curve based on several items of industry assessment data; obtaining asset valuation data corresponding to the user's declared asset information based on a sampling period, and fitting an asset value trend curve based on several items of asset valuation data; predicting user income and declared asset value based on the industry development trend curve and the asset value trend curve, and updating the performance risk assessment results; the application facilitates the assessment and early warning of the performance risk of credit users;

[0005] The above patents all have the problems raised by this background technology. In order to solve the problems raised by this background technology, this application designs a performance risk warning method and system based on transaction data. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention proposes a contract performance risk early warning method and system based on transaction data.

[0007] To achieve the above object, the present invention provides the following technical solution: a performance risk early warning method based on transaction data, which comprises the following specific steps:

[0008] Obtain the customer's transaction history data on the platform, and also obtain the transaction information data of this transaction;

[0009] Obtain the customer's transaction history data on the platform and the transaction information data of this transaction and import it into the similar transaction acquisition strategy to obtain similar transactions;

[0010] Obtain post-transaction review data of customers with similar transactions and import transaction data into the evaluation of transaction fulfillment authenticity to obtain the actual fulfillment transaction;

[0011] Import the evaluated actual transaction information of the contract and the transaction information data of this transaction into the transaction performance risk assessment strategy to conduct transaction performance risk assessment;

[0012] Issue early warning of the transaction process based on the transaction performance risk assessment results.

[0013] As a preferred technical solution for a performance risk early warning method based on transaction data, the specific content of obtaining the historical transaction data of the customer on the platform and obtaining the transaction information data of this transaction is:

[0014] Acquire historical transaction data of transactions of the customer on the platform, wherein the historical transaction data includes transaction commodity type, transaction commodity quantity, transaction price and transaction commodity evaluation data of the transaction commodity, and store it in the first storage component;

[0015] The transaction data of the transaction process is obtained, wherein the transaction data includes the type of the transaction commodity, the quantity of the transaction commodity and the transaction price data, and is stored in the second storage component.

[0016] As a preferred technical solution of a performance risk early warning method based on transaction data, the acquisition of similar transactions by importing the transaction information data of the customer in the platform transaction history data and the current transaction into a similar transaction acquisition strategy includes the following specific steps:

[0017] Obtain the transaction commodity type, transaction commodity quantity and transaction price of the transaction commodity in the historical transaction data, and simultaneously obtain the transaction commodity type, transaction commodity quantity and transaction price data of the current transaction commodity;

[0018] The commodity type and quantity of the transaction commodities in the historical transaction data and the commodity type and quantity of the current transaction are introduced into the commodity type similarity value calculation formula to calculate the commodity type similarity value. The commodity type similarity value calculation formula between the i-th historical transaction data and the current transaction data is: , where n is the number of commodity types in this transaction data, Kj is the unit price of the jth commodity type in this transaction, cj is the number of the jth commodity type in this transaction, cji is the number of the jth commodity type in the ith historical transaction data, ni is the number of commodity types in the ith historical transaction, Hv is the unit price of the vth commodity type in the ith historical transaction, dv is the number of the vth commodity type in this transaction, and dvi is the number of the vth commodity type in the ith historical transaction;

[0019] The calculated commodity category similarity value between the ith historical transaction data and the current transaction data is obtained, and the transaction price of the ith historical transaction and the transaction price of the current historical transaction are obtained at the same time. The obtained commodity category similarity value, the transaction price of the ith historical transaction and the transaction price of the current historical transaction are introduced into the transaction similarity value calculation formula to calculate the transaction similarity value. Among them, the transaction similarity value calculation formula between the ith historical transaction data and the current transaction data is: , where a is the commodity category ratio, Q is the current transaction price, and Qi is the transaction price of the i-th historical transaction;

[0020] The calculated transaction similarity value between the historical transaction data and the current transaction data is obtained and compared with the set transaction similarity threshold. If the transaction similarity value between the historical transaction data and the current transaction data is greater than or equal to the set transaction similarity threshold, the historical transaction data is set as a similar transaction; if the transaction similarity value between the historical transaction data and the current transaction data is less than the set transaction similarity threshold, the historical transaction data is not set as a similar transaction.

[0021] This step conducts a comprehensive analysis of the transaction similarity values ​​between historical transaction data and current transaction data by transaction commodity type, unit price and quantity, obtains similar transactions of current transaction data, and then uses similar transactions to evaluate the actual transaction performance, thereby improving the accuracy of transaction performance judgment.

[0022] As a preferred technical solution of a performance risk early warning method based on transaction data, the acquisition of post-transaction review data of customers with similar transactions and the evaluation of transaction performance authenticity imported into the transaction data to obtain the performance real transaction includes the following specific steps:

[0023] Obtain the evaluation information and return and change data of customers with similar transactions after the transaction, obtain the text similarity and evaluation time interval data of the text data of any two similar transaction evaluations, and substitute them into the performance anomaly value calculation formula to calculate the performance anomaly value, where the performance anomaly value calculation formula is: , exp() is the power of the natural constant e, zt is the average time between comments, ztx is the time between two comments, cd is the text similarity of text data, where L(0or1) means: if the transaction corresponding to the evaluation is cancelled or changed, L(0or1) takes 1, if the transaction corresponding to the evaluation is not cancelled or changed, L(0or1) takes 0;

[0024] Obtain the performance abnormality value of any similar transaction and all other similar transactions, calculate the average value of the performance abnormality value and set it as the performance risk value of the corresponding similar transaction;

[0025] The calculated performance risk value of the corresponding similar transaction is obtained and compared with the set performance risk threshold. If the performance risk value of the corresponding similar transaction is less than or equal to the set performance risk threshold, it is set as a real performance transaction; if the performance risk value of the corresponding similar transaction is greater than the set performance risk threshold, it is not set as a real performance transaction.

[0026] In this step, the real transactions are analyzed and evaluated through comprehensive analysis of the comment data and transaction data after similar transactions, and the real transactions that fulfilled the contract are obtained.

[0027] As a preferred technical solution of a performance risk early warning method based on transaction data, the method of importing the evaluated performance real transaction information and the transaction information data of this transaction into the transaction performance risk assessment strategy for transaction performance risk assessment includes the following specific contents:

[0028] Obtain the transaction score data, transaction similarity value and performance risk value data of the actual performance transaction, and substitute them into the transaction performance evaluation value calculation formula to calculate the transaction performance evaluation value, where the transaction performance evaluation value calculation formula is: , where R is the number of real fulfillment transactions, Ct is the transaction score data of the t-th real fulfillment transaction, Cm is the average transaction score, Tt is the transaction similarity value of the t-th real fulfillment transaction, Tm is the transaction similarity threshold, Sm is the fulfillment risk threshold, and St is the fulfillment risk value of the t-th real fulfillment transaction;

[0029] The obtained transaction performance evaluation value is compared with the set transaction performance evaluation threshold. If the transaction performance evaluation value is greater than or equal to the set transaction performance evaluation threshold, the corresponding transaction will not be set as a risky transaction. If the transaction performance evaluation value is less than the set transaction performance evaluation threshold, the corresponding transaction will be set as a risky transaction.

[0030] As a preferred technical solution of a performance risk early warning method based on transaction data, the transaction process early warning according to the obtained transaction performance risk assessment results includes the following specific contents:

[0031] Obtain the results of risky transaction judgment. If it is judged to be a risky transaction, issue a transaction process warning to the transaction party and the management end.

[0032] A performance risk early warning system based on transaction data, which is implemented based on the above-mentioned performance risk early warning method based on transaction data, and specifically includes a data collection module, a similar transaction acquisition module, a performance real transaction evaluation module, a performance risk evaluation module and a transaction process early warning module;

[0033] The data collection module is used to obtain the historical transaction data of the customer on the platform and the transaction information data of the current transaction;

[0034] The similar transaction acquisition module is used to obtain the transaction history data of the customer on the platform and the transaction information data of the current transaction and import them into the similar transaction acquisition strategy to obtain similar transactions;

[0035] The real transaction performance evaluation module is used to obtain the post-transaction review data of customers with similar transactions and the transaction data to evaluate the authenticity of the transaction performance, and obtain the real transaction performance;

[0036] The performance risk assessment module is used to import the assessed performance real transaction information and the transaction information data of this transaction into the transaction performance risk assessment strategy to conduct transaction performance risk assessment;

[0037] The transaction process early warning module is used to issue a transaction process early warning based on the obtained transaction performance risk assessment result;

[0038] It also includes a control module, which is used to control the operation of the data collection module, the similar transaction acquisition module, the performance real transaction evaluation module, the performance risk evaluation module and the transaction process early warning module.

[0039] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0040] The processor executes the above-mentioned performance risk warning method based on transaction data by calling the computer program stored in the memory.

[0041] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute a performance risk early warning method based on transaction data as described above.

[0042] Compared with the prior art, the beneficial effects of this application are:

[0043] This application first obtains the customer's transaction history data on the platform, and at the same time obtains the transaction information data of this transaction, obtains the customer's transaction history data on the platform and the transaction information data of this transaction, imports them into the similar transaction acquisition strategy to obtain similar transactions, obtains the customer's post-transaction comment data and transaction data of similar transactions, and imports them into the transaction performance authenticity assessment to obtain the real transaction performance, and imports the assessed real transaction performance information and the transaction information data of this transaction into the transaction performance risk assessment strategy to conduct transaction performance risk assessment, and issues a transaction process warning based on the obtained transaction performance risk assessment results. This application first accurately obtains similar transactions through transaction data, and then accurately obtains the real performance transactions in similar transactions based on the data features in the comments after the transaction, and finally conducts a comprehensive analysis of transaction risks based on the transaction data of the real performance transactions, transaction similarity values ​​and performance risk values, thereby producing a technical effect of improving the efficiency and accuracy of performance risk warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the overall process of a performance risk early warning method based on transaction data of the present invention;

[0045] Figure 2 This is a schematic diagram of step S2 of a performance risk early warning method based on transaction data of the present invention;

[0046] Figure 3 This is a schematic diagram of step S3 of a performance risk early warning method based on transaction data of the present invention;

[0047] Figure 4 The figure is a schematic diagram of the overall framework of a contract performance risk early warning system based on transaction data of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present application and its application or use.

[0049] Example 1

[0050] In order to solve the technical problems raised in the background technology, the present invention provides a preferred embodiment: Figure 1-Figure 3 As shown, a performance risk early warning method based on transaction data includes the following specific steps:

[0051] S1. Obtain the historical transaction data of the customer on the platform, and at the same time obtain the transaction information data of this transaction;

[0052] In this step, the specific content of step S1 is:

[0053] S11, obtaining historical transaction data of transactions of the customer on the platform, wherein the historical transaction data includes transaction commodity type, transaction commodity quantity, transaction price and transaction commodity evaluation data of the transaction commodity, and storing the data in the first storage component;

[0054] In this step, the types of traded goods, the quantity of traded goods, the traded prices and the traded goods evaluation data are only used for calculation in the system and do not involve private information such as name and age. Therefore, there is no need to consider confidentiality issues in detail.

[0055] S12, obtaining the transaction data of the transaction process, wherein the transaction data includes the type of the transaction commodity, the quantity of the transaction commodity and the transaction price data, and storing the data in the second storage component;

[0056] S2. Obtain the customer's transaction history data on the platform and the transaction information data of this transaction and import them into the similar transaction acquisition strategy to obtain similar transactions;

[0057] In this step, the acquisition of the customer's transaction history data on the platform and the transaction information data of this transaction is imported into the similar transaction acquisition strategy to acquire similar transactions, including the following specific steps:

[0058] S21. Obtain the transaction commodity type, transaction commodity quantity and transaction price of the transaction commodity in the historical transaction data, and simultaneously obtain the transaction commodity type, transaction commodity quantity and transaction price data of the current transaction commodity;

[0059] In this step, you need to obtain the commodity type, quantity, and price in the historical transaction data and the current transaction data. The following is a sample code showing how to retrieve the historical transaction data and the current transaction data from the SQLite database:

[0060] Python

[0061] import sqlite3

[0062] # Connect to the SQLite database

[0063] conn = sqlite3.connect('transaction_data.db')

[0064] cursor = conn.cursor()

[0065] # Get historical transaction data

[0066] cursor.execute('''

[0067] SELECT product_type, quantity, price FROM transactions

[0068] ''')

[0069] historical_transactions = cursor.fetchall()

[0070] current_transaction_data = {

[0071] 'product_type': 'Electronic products',

[0072] 'quantity': 5,

[0073] 'price': 99.99

[0074] }

[0075] # Print out historical transaction data and current transaction data

[0076] print("Historical transaction data:")

[0077] for transaction in historical_transactions:

[0078] print(f"Product type: {transaction[0]}, quantity: {transaction[1]}, price: {transaction[2]}")

[0079] print(" \ n This transaction data: ")

[0080] print(f"Product type: {current_transaction_data['product_type']}, Quantity: {current_transaction_data['quantity']}, Price: {current_transaction_data['price']}"

[0081] # Close the database connection

[0082] conn.close()

[0083] This example assumes that there is already a database containing historical transaction data. In addition, the way to obtain the current transaction data depends on the specific implementation of your application - it may come from user input, API calls or other data sources;

[0084] S22, importing the transaction commodity type and transaction commodity quantity of the transaction commodity in the historical transaction data and the type and transaction commodity quantity of the current transaction commodity into the commodity type similarity value calculation formula to calculate the commodity type similarity value, wherein the commodity type similarity value calculation formula between the i-th historical transaction data and the current transaction data is: , where n is the number of commodity types in this transaction data, Kj is the unit price of the jth commodity type in this transaction, cj is the number of the jth commodity type in this transaction, cji is the number of the jth commodity type in the ith historical transaction data, ni is the number of commodity types in the ith historical transaction, Hv is the unit price of the vth commodity type in the ith historical transaction, dv is the number of the vth commodity type in this transaction, and dvi is the number of the vth commodity type in the ith historical transaction;

[0085] In this step, similar transactions are evaluated by calculating the similarity between the historical transaction data and the types and quantities of the transaction commodities;

[0086] At the same time in this step, In order to calculate the difference between the historical transaction data and the current transaction data in the number of the jth commodity category, the unit price of the jth commodity category in the previous transaction is multiplied to allocate weights so that the ones with higher prices are more representative.

[0087] S23, obtaining the calculated commodity category similarity value between the ith historical transaction data and the current transaction data, and simultaneously obtaining the transaction price of the ith historical transaction and the transaction price of the current historical transaction, and importing the obtained commodity category similarity value, the transaction price of the ith historical transaction, and the transaction price of the current historical transaction into a transaction similarity value calculation formula to calculate the transaction similarity value, wherein the transaction similarity value calculation formula between the ith historical transaction data and the current transaction data is: , where a is the commodity category ratio, Q is the current transaction price, and Qi is the transaction price of the i-th historical transaction;

[0088] In this step, the similarity of transactions is comprehensively evaluated by the similarity of transaction commodities and transaction prices;

[0089] S24, obtaining the calculated transaction similarity value between the historical transaction data and the current transaction data and comparing it with the set transaction similarity threshold. If the transaction similarity value between the historical transaction data and the current transaction data is greater than or equal to the set transaction similarity threshold, the historical transaction data is set as a similar transaction; if the transaction similarity value between the historical transaction data and the current transaction data is less than the set transaction similarity threshold, the historical transaction data is not set as a similar transaction;

[0090] This step comprehensively analyzes the transaction similarity values ​​of historical transaction data and current transaction data by transaction commodity type, unit price and quantity, obtains similar transactions of current transaction data, and then uses similar transactions to evaluate the actual transaction of fulfillment, thereby improving the accuracy of judgment on transaction fulfillment;

[0091] S3, obtaining post-transaction review data of customers with similar transactions and importing transaction data into the evaluation of transaction fulfillment authenticity to obtain the fulfillment real transaction;

[0092] In this step, the post-transaction review data of customers with similar transactions and the transaction data are obtained to evaluate the authenticity of the transaction performance. The following specific steps are included to obtain the actual transaction performance:

[0093] S31. Obtain the evaluation information and return and change data of customers with similar transactions after the transaction, obtain the text similarity and evaluation time interval data of the text data of any two similar transaction evaluations, and substitute them into the performance abnormality calculation formula to calculate the performance abnormality, where the performance abnormality calculation formula is: , exp() is the power of the natural constant e, zt is the average time between comments, ztx is the time between two comments, cd is the text similarity of text data, where L(0or1) means: if the transaction corresponding to the evaluation is cancelled or changed, L(0or1) takes 1, if the transaction corresponding to the evaluation is not cancelled or changed, L(0or1) takes 0;

[0094] In this step, there are many methods to calculate the text similarity of two text data. The following are some common methods:

[0095] 1. Cosine similarity: measures the similarity between two text vectors by calculating the angle between them. First, convert the text into word vector representation, such as using the Bag of Words model or Word Embeddings. Then, calculate the cosine similarity of the two text vectors. The value is between -1 and 1. The closer it is to 1, the higher the similarity.

[0096] 2. Jaccard similarity: This is a set-based similarity measurement method, often used to compare the vocabulary overlap of texts. It calculates the similarity by calculating the ratio of the common terms in the two texts to the total number of terms;

[0097] 3. Similarity calculation based on word embedding: Use the pre-trained word embedding model to convert the text into word vector representation, and calculate the similarity of the word vectors between two texts. You can use measurement methods such as cosine similarity or Euclidean distance;

[0098] 4. TF-IDF similarity: The similarity of texts is measured by calculating the TF-IDF weights of the words in the text. TF-IDF combines the word frequency and the inverse document frequency to highlight words that appear frequently in one document but less frequently in other documents.

[0099] In the present application, cosine similarity is preferably used to calculate the text similarity of text data, which is a conventional technical means used by those skilled in the art and will not be described in detail here;

[0100] S32, obtaining the performance abnormality value of any similar transaction and all other similar transactions, and calculating the average value of the performance abnormality value and setting it as the performance risk value of the corresponding similar transaction;

[0101] S33. Obtain the calculated performance risk value of the corresponding similar transaction and compare it with the set performance risk threshold. If the performance risk value of the corresponding similar transaction is less than or equal to the set performance risk threshold, it is set as a real performance transaction; if the performance risk value of the corresponding similar transaction is greater than the set performance risk threshold, it is not set as a real performance transaction.

[0102] In this step, the real transaction is analyzed and evaluated through comprehensive analysis of the comment data and transaction data after similar transactions, and the real transaction of performance is obtained;

[0103] S4. Importing the evaluated real transaction information of performance and the transaction information data of this transaction into the transaction performance risk assessment strategy to conduct transaction performance risk assessment;

[0104] In this step, the evaluated real transaction information and the transaction information data of this transaction are imported into the transaction performance risk assessment strategy for transaction performance risk assessment, which includes the following specific contents:

[0105] S41. Obtain transaction score data, transaction similarity value and performance risk value data of the actual performance transaction, and substitute them into the transaction performance evaluation value calculation formula to calculate the transaction performance evaluation value, wherein the transaction performance evaluation value calculation formula is: , where R is the number of real fulfillment transactions, Ct is the transaction score data of the t-th real fulfillment transaction, Cm is the average transaction score, Tt is the transaction similarity value of the t-th real fulfillment transaction, Tm is the transaction similarity threshold, Sm is the fulfillment risk threshold, and St is the fulfillment risk value of the t-th real fulfillment transaction;

[0106] S42, comparing the acquired transaction performance evaluation value with the set transaction performance evaluation threshold, if the transaction performance evaluation value is greater than or equal to the set transaction performance evaluation threshold, the corresponding transaction is not set as a risky transaction, if the transaction performance evaluation value is less than the set transaction performance evaluation threshold, the corresponding transaction is set as a risky transaction;

[0107] The method for determining the value of the commodity category proportion coefficient, transaction performance evaluation threshold, performance risk threshold and transaction performance evaluation threshold in this embodiment is explained in detail herein: 500 sets of transaction data are obtained, and the transaction performance evaluation value calculation formula is substituted to calculate the transaction performance evaluation value, and the performance status in the 500 sets of transaction data is obtained, and the transaction performance evaluation value and the performance status are imported into the fitting software, and the values ​​of the commodity category proportion coefficient, transaction performance evaluation threshold, performance risk threshold and transaction performance evaluation threshold that meet the maximum performance status judgment accuracy are output.

[0108] S5. Provide early warning of the transaction process based on the transaction performance risk assessment results;

[0109] In this step, the transaction process warning is carried out according to the transaction performance risk assessment results, including the following specific contents:

[0110] Obtain the results of risky transaction judgment. If it is judged to be a risky transaction, issue a transaction process warning to the transaction party and the management end.

[0111] The benefits of this embodiment are: first, the customer's transaction history data on the platform is obtained, and the transaction information data of the current transaction is obtained at the same time, the customer's transaction history data on the platform and the transaction information data of the current transaction are imported into the similar transaction acquisition strategy to obtain similar transactions, the customer's post-transaction comment data and transaction data of similar transactions are obtained and imported into the transaction performance authenticity assessment to obtain the real transaction performance, the evaluated real transaction performance information and the transaction information data of this transaction are imported into the transaction performance risk assessment strategy to conduct transaction performance risk assessment, and the transaction process warning is performed according to the obtained transaction performance risk assessment results. This application firstly accurately obtains similar transactions through transaction data, and then accurately obtains the real performance transactions in similar transactions according to the data features in the comments after the transaction, and finally conducts a comprehensive analysis of transaction risks according to the transaction data of the real performance transactions, transaction similarity values ​​and performance risk values, thereby producing a technical effect of improving the efficiency and accuracy of performance risk warning.

[0112] Example 2

[0113] like Figure 4As shown, a performance risk early warning system based on transaction data is implemented based on the above-mentioned performance risk early warning method based on transaction data, which specifically includes a data collection module, a similar transaction acquisition module, a performance real transaction evaluation module, a performance risk evaluation module and a transaction process early warning module;

[0114] Among them, the data collection module is used to obtain the historical transaction data of the customer on the platform, and at the same time obtain the transaction information data of this transaction;

[0115] The similar transaction acquisition module is used to obtain the customer's transaction history data on the platform and the transaction information data of the current transaction and import it into the similar transaction acquisition strategy to obtain similar transactions;

[0116] The real transaction evaluation module is used to obtain the post-transaction comment data of customers with similar transactions and the transaction data to evaluate the authenticity of transaction performance, and obtain the real transaction performance;

[0117] The performance risk assessment module is used to import the assessed performance real transaction information and the transaction information data of this transaction into the transaction performance risk assessment strategy to conduct transaction performance risk assessment;

[0118] The transaction process early warning module is used to issue a transaction process early warning based on the transaction performance risk assessment results;

[0119] It also includes a control module, which is used to control the operation of a data collection module, a similar transaction acquisition module, a fulfillment real transaction evaluation module, a fulfillment risk evaluation module and a transaction process early warning module.

[0120] Example 3

[0121] An embodiment of the present invention also provides an electronic device, including a memory for storing a computer program; and a processor for executing the computer program to implement a performance risk warning method based on transaction data as in any of the above embodiments.

[0122] Specifically, the electronic device may include: at least one processor, at least one memory, a power supply, a communication interface, an input / output interface, and a communication bus. The memory is used to store a computer program, and the computer program is loaded and executed by the processor to implement the relevant steps in a performance risk warning method based on transaction data disclosed in any of the above embodiments. In addition, the electronic device in the embodiments of the present invention may be an electronic computer.

[0123] In an embodiment of the present invention, a power supply is used to provide operating voltage for various hardware devices on the electronic device; the communication interface can create a data transmission channel between the electronic device and an external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present invention, and is not specifically limited here; the input and output interface is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.

[0124] In addition, the memory as a carrier for resource storage can be a read-only memory, random access memory, disk or CD, etc. The resources stored thereon can include an operating system, computer programs, etc., and the storage method can be temporary storage or permanent storage.

[0125] The operating system is used to manage and control the hardware devices and computer programs on the electronic device, and may be Windows Server, Netware, Unix, Linux, etc. In addition to the computer program that can be used to complete the performance risk early warning method based on transaction data performed by the electronic device disclosed in any of the aforementioned embodiments, the computer program may further include computer programs that can be used to complete other specific tasks.

[0126] Example 4

[0127] An embodiment of the present invention further provides a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements a performance risk warning method based on transaction data as in any of the above embodiments.

[0128] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

Claims

1. A performance risk early warning method based on transaction data, characterized in that: It includes the following specific steps: Obtain the customer's transaction history data on the platform, and also obtain the transaction information data of this transaction; Obtain the customer's transaction history data on the platform and the transaction information data of this transaction and import it into the similar transaction acquisition strategy to obtain similar transactions; Obtain post-transaction review data of customers with similar transactions and import transaction data into the evaluation of transaction fulfillment authenticity to obtain the actual fulfillment transaction; Import the evaluated actual transaction information of the contract and the transaction information data of this transaction into the transaction performance risk assessment strategy to conduct transaction performance risk assessment; According to the obtained transaction performance risk assessment results, a transaction process warning is performed; the acquisition of the customer's transaction history data on the platform and the transaction information data of this transaction is imported into the similar transaction acquisition strategy to acquire similar transactions, including the following specific steps: Obtain the transaction commodity type, transaction commodity quantity and transaction price of the transaction commodity in the historical transaction data, and simultaneously obtain the transaction commodity type, transaction commodity quantity and transaction price data of the current transaction commodity; The commodity type and quantity of the transaction commodities in the historical transaction data and the commodity type and quantity of the current transaction are introduced into the commodity type similarity value calculation formula to calculate the commodity type similarity value. The commodity type similarity value calculation formula between the i-th historical transaction data and the current transaction data is: , where n is the number of commodity types in this transaction data, Kj is the unit price of the jth commodity type in this transaction, cj is the number of the jth commodity type in this transaction, cji is the number of the jth commodity type in the ith historical transaction data, ni is the number of commodity types in the ith historical transaction, Hv is the unit price of the vth commodity type in the ith historical transaction, dv is the number of the vth commodity type in this transaction, and dvi is the number of the vth commodity type in the ith historical transaction; The calculated commodity category similarity value between the ith historical transaction data and the current transaction data is obtained, and the transaction price of the ith historical transaction and the transaction price of the current historical transaction are obtained at the same time. The obtained commodity category similarity value, the transaction price of the ith historical transaction and the transaction price of the current historical transaction are introduced into the transaction similarity value calculation formula to calculate the transaction similarity value. Among them, the transaction similarity value calculation formula between the ith historical transaction data and the current transaction data is: , where a is the commodity category ratio, Q is the current transaction price, and Qi is the transaction price of the i-th historical transaction; Obtain the calculated transaction similarity value between the historical transaction data and the current transaction data and compare it with the set transaction similarity threshold. If the transaction similarity value between the historical transaction data and the current transaction data is greater than or equal to the set transaction similarity threshold, the historical transaction data is set as a similar transaction. If the transaction similarity value between the historical transaction data and the current transaction data is less than the set transaction similarity threshold, the historical transaction data is not set as a similar transaction. The acquisition of post-transaction review data of customers with similar transactions and the importation of transaction data into the evaluation of transaction fulfillment authenticity to obtain the fulfillment real transaction includes the following specific steps: Obtain the evaluation information and return and change data of customers with similar transactions after the transaction, obtain the text similarity and evaluation time interval data of the text data of any two similar transaction evaluations, and substitute them into the performance anomaly value calculation formula to calculate the performance anomaly value, where the performance anomaly value calculation formula is: , exp() is the power of the natural constant e, zt is the average time between comments, ztx is the time between two comments, cd is the text similarity of text data, where L(0or1) means: if the transaction corresponding to the evaluation is cancelled or changed, L(0or1) takes 1, if the transaction corresponding to the evaluation is not cancelled or changed, L(0or1) takes 0; Obtain the performance abnormality value of any similar transaction and all other similar transactions, calculate the average value of the performance abnormality value and set it as the performance risk value of the corresponding similar transaction; Obtain the calculated performance risk value of the corresponding similar transaction and compare it with the set performance risk threshold. If the performance risk value of the corresponding similar transaction is less than or equal to the set performance risk threshold, it is set as a real performance transaction; if the performance risk value of the corresponding similar transaction is greater than the set performance risk threshold, it is not set as a real performance transaction; The process of importing the evaluated real transaction information and the transaction information data of this transaction into the transaction performance risk assessment strategy for transaction performance risk assessment includes the following specific contents: Obtaining the transaction score data, transaction similarity value and performance risk value data of the real transaction, and substituting them into the transaction performance assessment value calculation formula to calculate the transaction performance assessment value, wherein the transaction performance assessment value calculation formula is: , where R is the number of real fulfillment transactions, Ct is the transaction score data of the t-th real fulfillment transaction, Cm is the average transaction score, Tt is the transaction similarity value of the t-th real fulfillment transaction, Tm is the transaction similarity threshold, Sm is the fulfillment risk threshold, and St is the fulfillment risk value of the t-th real fulfillment transaction; The obtained transaction performance evaluation value is compared with the set transaction performance evaluation threshold. If the transaction performance evaluation value is greater than or equal to the set transaction performance evaluation threshold, the corresponding transaction will not be set as a risky transaction. If the transaction performance evaluation value is less than the set transaction performance evaluation threshold, the corresponding transaction will be set as a risky transaction.

2. A contract performance risk early warning method based on transaction data as claimed in claim 1, characterized in that: The transaction process early warning according to the transaction performance risk assessment results includes the following specific contents: Obtain the judgment result of risky transactions. If it is judged as a risky transaction, issue a transaction process warning to the transaction party and the management end.

3. A contract performance risk early warning method based on transaction data as claimed in claim 2, characterized in that: The specific contents of obtaining the historical transaction data of the customer on the platform and the transaction information data of the current transaction are: Acquire historical transaction data of transactions of the customer on the platform, wherein the historical transaction data includes transaction commodity type, transaction commodity quantity, transaction price and transaction commodity evaluation data of the transaction commodity, and store it in the first storage component; The transaction data of the transaction process is obtained, wherein the transaction data includes the type of the transaction commodity, the quantity of the transaction commodity and the transaction price data, and is stored in the second storage component.

4. A contract performance risk early warning system based on transaction data, which is implemented based on the contract performance risk early warning method based on transaction data as claimed in any one of claims 1 to 3, characterized in that: It specifically includes a data collection module, a similar transaction acquisition module, a fulfillment real transaction evaluation module, a fulfillment risk evaluation module and a transaction process early warning module; The data collection module is used to obtain the historical transaction data of the customer on the platform and the transaction information data of the current transaction; The similar transaction acquisition module is used to obtain the transaction history data of the customer on the platform and the transaction information data of the current transaction and import them into the similar transaction acquisition strategy to obtain similar transactions; The real transaction performance evaluation module is used to obtain the post-transaction review data of customers with similar transactions and the transaction data to evaluate the authenticity of the transaction performance, and obtain the real transaction performance; The performance risk assessment module is used to import the assessed performance real transaction information and the transaction information data of this transaction into the transaction performance risk assessment strategy to conduct transaction performance risk assessment; The transaction process early warning module is used to provide transaction process early warning based on the obtained transaction performance risk assessment results; It also includes a control module, which is used to control the operation of the data collection module, the similar transaction acquisition module, the performance real transaction evaluation module, the performance risk evaluation module and the transaction process early warning module.

5. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; It is characterized in that the processor executes the performance risk warning method based on transaction data as described in any one of claims 1 to 3 by calling the computer program stored in the memory.

6. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute a performance risk warning method based on transaction data as described in any one of claims 1 to 3.

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