Transaction flow data statistical analysis method, device, equipment, medium and program product

Abstract

The present disclosure provides a transaction diversion data statistical analysis method, which can be applied to the field of big data technology. The transaction diversion data statistical analysis method includes: after each diversion transaction is completed, obtaining the transaction element information of the diversion transaction; writing the transaction element information into a distributed message queue, and storing the transaction element information according to the category of diversion transactions; in response to the request of the diversion transaction reference party, obtaining the transaction element information of the diversion transaction category requested by the diversion transaction reference party from the distributed message queue; based on the transaction element information, performing transaction diversion analysis of the diversion transaction category. The present disclosure also provides a transaction diversion data statistical analysis device, equipment, storage medium and program product. The present disclosure simplifies the transaction diversion data statistical analysis process by writing the transaction element information into a distributed message queue, reduces invalid data, and improves the efficiency and accuracy of the transaction diversion data statistical analysis.

Description

Technical Field

[0001] The present disclosure relates to the field of big data, and specifically to a transaction flow data statistical analysis method, device, equipment, medium and program product. Background Art

[0002] At present, there are usually multiple APPs or websites in the same company system, and the APPs or websites in the same system can realize the consumption of the same product. Atomic transaction refers to a transaction function that can be reused and displayed in multiple APPs or websites. For example, the product purchase function can realize the display of product details and purchase and payment functions for the same product in the same store in two shopping APPs. In order to achieve the goal of one-time development and multiple operations, multiple APPs only provide an outer packaging shell, and the shell embeds references to atomic transactions. At the same time, the outer packaging shell and the atomic transaction provider have authorization authentication and mutual trust mechanisms, which are common in various shopping APPs or financial APPs. Traffic diversion transactions generally refer to mutual jumps or function references between different APPs or websites. When a user browses a page, he jumps to the APP through the jump interface. The user completes the transaction by purchasing and paying, which completes a traffic diversion transaction.

[0003] The same company system needs to perform the above transaction traffic statistics for multiple apps separately to analyze the traffic value. The traditional transaction traffic statistics analysis method generally only counts the number of clicks or impressions of the APP jump interface, which is actually an atomic transaction exposure statistics. It is based on the estimation of the historical model and uses the relationship between the exposure rate and the actual transaction volume to calculate the traffic transaction success rate and transaction volume. This statistical analysis method is easy to implement and simple to calculate, but it does not obtain the actual traffic transaction success rate and transaction volume. There is a deviation between the actual data and the accuracy of the final analysis results cannot be guaranteed. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a transaction flow data statistical analysis method, device, equipment, medium and program product.

[0005] According to a first aspect of the present disclosure, a transaction diversion data statistical analysis method is provided, comprising: after each diversion transaction is completed, obtaining transaction element information of the diversion transaction; writing the transaction element information into a distributed message queue, and storing the transaction element information according to the category of diversion transactions; in response to a request from a diversion transaction reference party, obtaining from the distributed message queue the transaction element information of the diversion transaction category requested by the diversion transaction reference party; and performing transaction diversion analysis of the diversion transaction category based on the transaction element information.

[0006] According to an embodiment of the present disclosure, the transaction element information includes transaction results, and the transaction diversion analysis of the diversion transaction category based on the transaction element information includes: calculating the diversion transaction success rate of the diversion transaction of the diversion transaction category based on the transaction results.

[0007] According to an embodiment of the present disclosure, the transaction element information also includes a transaction start time and a transaction end time, and the analysis of the transaction diversion effect of the diversion transaction category based on the transaction element information also includes: according to the transaction start time and the transaction end time, counting the transaction duration of successful diversion transactions in the diversion transaction category; according to the transaction duration, calculating the average transaction success time of the diversion transactions in the diversion transaction category.

[0008] According to an embodiment of the present disclosure, the transaction element information is the transaction element information of the traffic diversion transaction initiated by the traffic diversion transaction referee.

[0009] According to an embodiment of the present disclosure, the method further includes: writing the transaction element information into a database for backup.

[0010] The second aspect of the present disclosure provides a transaction diversion data statistical analysis device, including: an acquisition module, used to obtain transaction element information of the diversion transaction after each diversion transaction is completed; a writing module, used to write the transaction element information into a distributed message queue, and store the transaction element information according to the category of diversion transactions; a calling module, used to respond to the request of the diversion transaction reference party, and obtain the transaction element information of the diversion transaction category requested by the diversion transaction reference party from the distributed message queue; an analysis module, used to perform transaction diversion analysis of the diversion transaction category based on the transaction element information.

[0011] According to an embodiment of the present disclosure, the transaction element information includes transaction results, and the analysis module includes: a first calculation module, used to calculate the traffic diversion transaction success rate of the traffic diversion transaction of the traffic diversion transaction category according to the transaction results.

[0012] According to an embodiment of the present disclosure, the transaction element information also includes a transaction start time and a transaction end time, and the analysis module also includes: a first statistical module, used to count the transaction duration of successful traffic diversion transactions in the traffic diversion transaction category based on the transaction start time and the transaction end time; a second calculation module, used to calculate the average transaction success time of the traffic diversion transactions in the traffic diversion transaction category based on the transaction duration.

[0013] According to an embodiment of the present disclosure, the transaction element information analyzed by the analysis module is the transaction element information of the traffic diversion transaction initiated by the traffic diversion transaction referee.

[0014] According to an embodiment of the present disclosure, the device further includes: a backup module, configured to write the transaction element information into a database for backup.

[0015] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned transaction flow data statistical analysis method.

[0016] The fourth aspect of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned transaction flow data statistical analysis method.

[0017] The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above-mentioned transaction flow data statistical analysis method when executed by a processor.

[0018] At least one of the above technical solutions adopted in the embodiments of the present disclosure can achieve the following beneficial effects:

[0019] The present invention simplifies the data transmission process and the transaction diversion data statistical analysis process by obtaining the transaction element information of the diversion transaction after the diversion transaction is completed, and writes the transaction element information into a distributed message queue, thereby reducing invalid data in the diversion transaction statistical analysis and improving the efficiency and accuracy of the transaction diversion data analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0021] Figure 1 The application scenario diagram of the transaction flow data statistical analysis method according to the embodiment of the present disclosure is schematically shown. ;

[0022] Figure 2 A flowchart of a transaction flow data statistical analysis method according to an embodiment of the present disclosure is schematically shown;

[0023] Figure 3 The flowchart of steps S241-243 of the transaction flow data statistical analysis method according to an embodiment of the present disclosure is schematically shown.

[0024] Figure 4 The flowchart S250 of the transaction flow data statistical analysis method according to the embodiment of the present disclosure is schematically shown.

[0025] Figure 5The structural block diagram of the transaction flow data statistical analysis device according to an embodiment of the present disclosure is schematically shown.

[0026] Figure 6 The structural block diagram of the analysis module 540 of the transaction flow data statistical analysis device according to an embodiment of the present disclosure is schematically shown.

[0027] Figure 7 The structure block diagram of the transaction flow data statistical analysis device 500 according to the embodiment of the present disclosure is schematically shown; and

[0028] Figure 8 A block diagram of an electronic device suitable for implementing a transaction flow data statistical analysis method according to an embodiment of the present disclosure is schematically shown.

[0029] Description of reference numerals:

[0030] 101, 102, 103-terminal devices, 104, 105, 106-shell application servers, 107-atomic transaction application server, 108-distributed message queue, 109-database server. DETAILED DESCRIPTION

[0031] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0032] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0033] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0034] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0035] It should be noted that the present disclosure relates to the field of big data technology. In the technical solutions of the present disclosure, the acquisition, storage and application of user personal information involved all comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0036] The embodiments of the present disclosure provide a transaction diversion data statistical analysis method, device, equipment, medium and program product. The transaction diversion data statistical analysis method includes: after each diversion transaction is completed, obtaining the transaction element information of the diversion transaction; writing the transaction element information into a distributed message queue, and storing the transaction element information according to the category of diversion transactions; in response to the request of the diversion transaction reference party, obtaining the transaction element information of the diversion transaction category requested by the diversion transaction reference party from the distributed message queue; based on the transaction element information, performing transaction diversion analysis of the diversion transaction category. The present disclosure simplifies the data transmission process and the transaction diversion data statistical analysis process by obtaining the transaction element information of the diversion transaction after the diversion transaction is completed, and writing the transaction element information into a distributed message queue, and reduces the invalid data in the statistical analysis of the diversion transaction, thereby improving the efficiency and accuracy of the transaction diversion data analysis.

[0037] Figure 1 The following schematically shows an application scenario diagram of the transaction flow data statistical analysis method according to an embodiment of the present disclosure. It should be noted that: Figure 1 The examples shown are merely scenarios in which the embodiments of the present disclosure can be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0038] like Figure 1As shown, the application scenario 100 according to this embodiment shows the scenario of using the device to perform statistical analysis of transaction diversion data. The terminal devices 101, 102, and 103 are respectively installed with software APP-A, APP-B, and APP-C in the same system, which are respectively connected to the corresponding shell application servers 104, 105, and 106 through the network, and the shell application servers 104, 105, and 106 are connected to the atomic transaction application server 107 through the network. The atomic transaction application server 107 is connected to the distributed message queue 108 and the database server 109 through the network, and the network 110 provides the medium of the communication link. The network 110 can include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0039] The user can use the terminal devices 101, 102, 103 to interact with the shell application servers 104, 105, 106 through the network, and purchase goods on the software APP-A, APP-B, APP-C, etc. Various communication client applications can be installed on the terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0040] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0041] Shell application servers 104, 105, and 106 are shell application servers that provide corresponding transaction functions to software APP-A, APP-B, and APP-C, respectively. Atomic transaction application server 107 enables the shell application server to have corresponding transaction functions, and users conduct transactions in the APP on the terminal device, and feed back the necessary transaction element information (such as transaction source, transaction result, commodity information, transaction amount, transaction time, etc.) generated by the transaction completed in the APP to the atomic transaction application server 107.

[0042] It should be noted that the transaction diversion data statistical analysis method provided in the embodiment of the present disclosure can generally be executed by the atomic transaction application server 107. Accordingly, the transaction diversion data statistical analysis device provided in the embodiment of the present disclosure can generally be set in the atomic transaction application server 107. The transaction diversion data statistical analysis method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the atomic transaction application server 107 and can communicate with the terminal devices 101, 102, 103 and / or the atomic transaction application server 107. Correspondingly, the transaction diversion data statistical analysis device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the atomic transaction application server 107 and can communicate with the terminal devices 101, 102, 103 and / or the atomic transaction application server 107.

[0043] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0044] In one processing method for statistical analysis of transaction traffic data, the traffic transaction referee (i.e., the organization that manages the transaction business of APP-A, APP-B, and APP-C) calls the tracking interface on the traffic page to upload transaction element information (such as transaction source, transaction result, product information, transaction amount, transaction time, etc.) to the tracking server, which then collects statistics. This method is simple to implement, but it can only record the transaction traffic click volume and exposure rate, and cannot obtain the actual transaction success volume. The obtained data may contain a large number of invalid references, and it is impossible to conduct effective statistical analysis on the transaction traffic data.

[0045] In another processing method for statistical analysis of transaction traffic data, when the transaction is completed, the atomic transaction application server 107 transmits the transaction data of all APPs to the traffic transaction reference party by calling the data transmission interface of the traffic transaction reference party. The data obtained in this way is consistent with the actual situation, but there is a tight coupling between the traffic transaction reference party and the atomic transaction provider (used to provide transaction functions to the traffic transaction reference party). Every time an APP is added (that is, a new traffic transaction reference party is added), it is necessary to add a corresponding interface to perform data call, which is inconvenient, and each time the APP data managed by the traffic transaction reference party and the data of other unnecessary APPs are all transmitted to the traffic transaction reference party, there is data redundancy, which is not conducive to the statistical analysis of the transaction traffic data by the traffic transaction reference party, and the efficiency is low.

[0046] According to an embodiment of the present disclosure, a transaction flow data statistical analysis method is provided, which is used to statistically analyze transaction flow data to improve the statistical analysis efficiency, and to obtain more accurate transaction flow data to obtain more accurate analysis results.

[0047] The following will be based on Figure 1 The scene described by Figure 2 to Figure 4 The transaction flow data statistical analysis method of the disclosed embodiment is described in detail.

[0048] Figure 2 The flowchart of the transaction flow data statistical analysis method according to the embodiment of the present disclosure is schematically shown.

[0049] like Figure 2 As shown, the transaction flow data statistical analysis method of this embodiment includes operations S210 to S240.

[0050] In operation S210, after each traffic diversion transaction is completed, transaction element information of the traffic diversion transaction is obtained.

[0051] In the disclosed embodiment, the completion of the traffic flow transaction refers to the entire process from product browsing to order placement and payment, including traffic flow transactions with successful transactions and failed transactions.

[0052] In the disclosed embodiment, the transaction element information of the traffic diversion transaction includes the transaction source, transaction result, product information, transaction amount, transaction start time, transaction end time, etc. Among them, the transaction source is the identifier of the APP that completed the traffic diversion transaction, the transaction result includes transaction success and transaction failure, and the product information includes product name, product price, etc.

[0053] In operation S220, the transaction element information is written into the distributed message queue, and the transaction element information is classified and stored according to the flow-generating transaction category.

[0054] In the embodiment of the present disclosure, the transaction element information stored in the distributed message queue includes the transaction element information of the traffic diversion transactions initiated by all the traffic diversion transaction referencing parties. When storing, the transaction element information of the traffic diversion transactions is stored in the order in which the traffic diversion transactions are completed.

[0055] In the embodiment of the present disclosure, the transaction element information stored in the distributed message queue includes the transaction element information of the completed traffic diversion transaction, and the transaction element information of the traffic diversion transaction that is only browsed will not be stored in the distributed message queue, thereby reducing the data storage capacity of the distributed message queue, screening out invalid data of the traffic diversion transaction, improving the statistical efficiency of the transaction diversion data, and facilitating improving the accuracy of the next step of data analysis.

[0056] In operation S230, in response to a request from the traffic diversion transaction reference party, transaction element information of the traffic diversion transaction category requested by the traffic diversion transaction reference party is obtained from the distributed message queue;

[0057] When the traffic flow transaction reference party needs to conduct transaction traffic analysis on the traffic flow transaction on its APP, it will establish a connection with the distributed message queue and directly obtain the transaction element information of the traffic flow transaction initiated by the traffic flow transaction reference party from the distributed message queue. There is no need for the atomic transaction provider to establish a new data transmission interface with the traffic flow transaction reference party, which improves the data transmission efficiency and realizes the decoupling of the traffic flow transaction reference party and the atomic transaction provider.

[0058] In the disclosed embodiment, when a traffic diversion transaction is completed, the element information of the traffic diversion transaction includes the transaction source. Each traffic diversion transaction will have a corresponding transaction source, and each transaction source corresponds to a traffic diversion transaction reference party. When obtaining the transaction element information, the transaction element information of the transaction source corresponding to the traffic diversion transaction reference party is obtained by filtering according to the transaction source.

[0059] In the disclosed embodiment, the transaction element information is the transaction element information of the traffic diversion transaction initiated by the traffic diversion transaction reference party. After the traffic diversion transaction reference party issues a request, it establishes a connection with the distributed message queue, and obtains the transaction element information of the traffic diversion transaction that is only performed on the APP managed by the traffic diversion transaction application party according to the order in which the transaction element information enters and exits the distributed message queue. Therefore, the traffic diversion transaction reference party only obtains the data requested by itself, which reduces the amount of data transmission and improves the data transmission efficiency.

[0060] In the disclosed embodiment, the distributed message queue can establish connections with multiple traffic diversion transaction reference parties. When the traffic diversion transaction reference party needs to perform statistical analysis on its own data, it will establish a connection with the distributed message queue to perform data statistics and analysis. For each new traffic diversion transaction reference party, there is no need for the atomic transaction provider to establish a new interface to transmit data, thereby reducing the mutual dependence between the traffic diversion transaction reference party and the atomic transaction provider, and realizing the decoupling of the traffic diversion transaction reference party and the atomic transaction provider.

[0061] Figure 3 The flowchart of steps S241-243 of the transaction flow data statistical analysis method according to an embodiment of the present disclosure is schematically shown.

[0062] In operation S240, based on the transaction element information, a transaction flow analysis of the flow-inducing transaction category is performed.

[0063] In the embodiment of the present disclosure, in operation S241, the traffic diversion transaction success rate of the traffic diversion transaction in the traffic diversion transaction category is calculated according to the transaction results.

[0064] In the disclosed embodiment, the category of traffic diversion transaction is a product, a store or an APP of the traffic diversion transaction reference party. By counting the traffic diversion transaction category, the transaction results are the traffic diversion transactions of successful transactions and failed transactions, and by calculating the proportion of successful traffic diversion transactions to all traffic diversion transactions of the traffic diversion transaction category, the traffic diversion transaction success rate of the traffic diversion transaction of the traffic diversion transaction category is obtained. For example, there is a product on both APP-A and APP-B, and consumers jump to the APP-A and APP-B respectively through diversion, and complete the traffic diversion transaction by browsing the product details to payment. If the payment is successful, the uploaded transaction result is a successful transaction, and if the payment fails, the uploaded transaction result is a failed transaction; if the merchant of the product needs to obtain the traffic diversion transaction success rate of the product on APP-A and APP-B, the traffic diversion transaction reference party managing the APP-A and APP-B obtains the transaction element information of the traffic diversion transaction of the product on APP-A and APP-B respectively, and the obtained transaction element information can be statistically analyzed by the merchant of the product itself, or APP-A and APP-B can provide relevant data after analysis.

[0065] In the disclosed embodiment, the success rate of the traffic diversion transaction is compared with a preset success rate threshold to analyze whether the transaction diversion reaches the preset value of the traffic diversion transaction referee.

[0066] In the disclosed embodiment, the transaction element information also includes the transaction start time and the transaction end time. In operation S242, the transaction duration of successful traffic diversion transactions in the traffic diversion transaction category is counted according to the transaction start time and the transaction end time; in operation S243, the average transaction success time of the traffic diversion transactions in the traffic diversion transaction category is calculated according to the transaction duration. For example, in order to further analyze the transaction diversion effect of a certain commodity on APP-A and APP-B, the transaction duration of each successful traffic diversion transaction of the commodity can be obtained to calculate the average transaction success time of the traffic diversion transactions of the commodity on APP-A and APP-B respectively. By comparing the average transaction success time of the commodity on APP-A and APP-B, it can be determined whether the commodity is more in line with the consumption needs of users of APP-A or APP-B.

[0067] In the embodiment of the present disclosure, when performing the above operations S210 to S240, operation S250 is also performed.

[0068] Figure 4 The flowchart S250 of the transaction flow data statistical analysis method according to the embodiment of the present disclosure is schematically shown.

[0069] In operation S250, the transaction element information is written into the database for backup.

[0070] In the disclosed embodiment, if the atomic transaction provider needs to perform relevant analysis on the transaction drainage data, the transaction drainage data can be obtained through the distributed message queue, or the data written into the database can be statistically analyzed. The data is written into the database for the atomic transaction provider to perform statistical analysis on the transaction drainage data, and asynchronously written into the distributed message queue for the drainage transaction reference party to perform statistical analysis on the transaction drainage data, thereby realizing the decoupling of the atomic transaction provider and the drainage transaction reference party, and reducing the mutual dependence between the atomic transaction provider and the drainage transaction reference party.

[0071] Based on the above transaction flow data statistical analysis method, the present disclosure also provides a transaction flow data statistical analysis device. Figure 4 The device is described in detail.

[0072] Figure 5 The structural block diagram of the transaction flow data statistical analysis device according to an embodiment of the present disclosure is schematically shown.

[0073] like Figure 5 As shown, the transaction flow data statistical analysis device 500 of this embodiment includes an acquisition module 510, a writing module 520, a calling module 530 and an analysis module 540.

[0074] The acquisition module 510 is used to acquire the transaction element information of each traffic diversion transaction after the traffic diversion transaction is completed. In one embodiment, the acquisition module 510 can be used to perform the operation S210 described above, which will not be repeated here.

[0075] The writing module 520 is used to write the transaction element information into the distributed message queue, and store the transaction element information according to the category of the traffic diversion transaction. In one embodiment, the writing module 520 can be used to perform the operation S220 described above, which will not be repeated here.

[0076] The calling module 530 is used to obtain the transaction element information of the traffic diversion transaction category requested by the traffic diversion transaction reference party from the distributed message queue in response to the request of the traffic diversion transaction reference party. In one embodiment, the calling module 530 can be used to perform the operation S230 described above, which will not be repeated here.

[0077] The analysis module 540 is used to perform transaction flow analysis of the flow-inducing transaction category based on the transaction element information. In one embodiment, the calling module 540 can be used to perform the operation S240 described above, which will not be repeated here.

[0078] Figure 6 The structural block diagram of the analysis module 540 of the transaction flow data statistical analysis device according to an embodiment of the present disclosure is schematically shown.

[0079] According to an embodiment of the present disclosure, the analysis module 540 includes: a first calculation module 5401, which is used to calculate the traffic diversion transaction success rate of the traffic diversion transaction of the traffic diversion transaction category according to the transaction results.

[0080] According to an embodiment of the present disclosure, the analysis module 540 also includes: a first statistical module 5402, which is used to count the transaction duration of successful traffic-generating transactions in the traffic-generating transaction category based on the transaction start time and the transaction end time; and a second calculation module 5403, which is used to calculate the average transaction success time of the traffic-generating transactions in the traffic-generating transaction category based on the transaction duration.

[0081] Figure 7 The structural block diagram of the transaction flow data statistical analysis device 500 according to an embodiment of the present disclosure is schematically shown.

[0082] According to an embodiment of the present disclosure, the transaction flow data statistical analysis device 500 of this embodiment further includes: a backup module 550, which is used to write the transaction element information into the database for backup. In one embodiment, the backup module 550 can be used to perform the operation S250 described above, which will not be repeated here.

[0083] According to an embodiment of the present disclosure, any multiple modules among the acquisition module 510, the write module 520, the call module 530, the analysis module 540 and the backup module 550 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 510, the write module 520, the call module 530, the analysis module 540 and the backup module 550 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, at least one of the acquisition module 510, the writing module 520, the calling module 530, the analysis module 540 and the backup module 550 may be at least partially implemented as a computer program module, and when the computer program module is executed, a corresponding function may be performed.

[0084] According to an embodiment of the present disclosure, any multiple modules in the first calculation module 5401, the first statistical module 5402, and the second calculation module 5403 can be combined in one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first calculation module 5401, the first statistical module 5402, and the second calculation module 5403 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware or in any appropriate combination of any of them. Alternatively, at least one of the first calculation module 5401, the first statistical module 5402, and the second calculation module 5403 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

[0085] Figure 8 A block diagram of an electronic device suitable for implementing a transaction flow data statistical analysis method according to an embodiment of the present disclosure is schematically shown.

[0086] like Figure 7 As shown, the electronic device 800 according to an embodiment of the present disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 to a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include an onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0087] In RAM 803, various programs and data required for the operation of electronic device 800 are stored. Processor 801, ROM 802 and RAM 803 are connected to each other via bus 804. Processor 801 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 802 and / or RAM 803. It should be noted that the program can also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0088] According to an embodiment of the present disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to the bus 804. The electronic device 800 may further include one or more of the following components connected to the I / O interface 805: an input portion 806 including a keyboard, a mouse, etc.; an output portion 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 808 including a hard disk, etc.; and a communication portion 809 including a network interface card such as a LAN card, a modem, etc. The communication portion 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that a computer program read therefrom is installed into the storage portion 808 as needed.

[0089] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0090] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 802 and / or RAM 803 described above and / or one or more memories other than ROM 802 and RAM 803.

[0091] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the method provided by the embodiment of the present disclosure.

[0092] The above functions defined in the system / device of the embodiment of the present disclosure are executed when the computer program is executed by the processor 801. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0093] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 809, and / or installed from a removable medium 811. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0094] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the processor 801, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0095] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0096] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0097] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0098] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A transaction flow data statistical analysis method, comprising: After each traffic diversion transaction is completed, the transaction element information of the traffic diversion transaction is obtained. The transaction element information is the transaction element information of the traffic diversion transaction initiated by the traffic diversion transaction referee; Writing the transaction element information into a distributed message queue, and storing the transaction element information according to the category of the traffic diversion transaction; In response to a request from a traffic diversion transaction reference party, obtaining transaction element information of the traffic diversion transaction category requested by the traffic diversion transaction reference party from the distributed message queue; Based on the transaction element information, conduct transaction flow analysis of the flow-generating transaction category, The transaction element information includes a transaction result, and the transaction flow analysis of the flow-inducing transaction category based on the transaction element information includes: According to the transaction results, the traffic diversion transaction success rate of the traffic diversion transaction of the traffic diversion transaction category is calculated.

2. According to the transaction flow data statistical analysis method of claim 1, the transaction element information further includes transaction start time and transaction end time, and the analysis of the transaction flow effect of the flow-generating transaction category based on the transaction element information further includes: According to the transaction start time and transaction end time, the transaction duration of successful traffic diversion transactions in the traffic diversion transaction category is counted; According to the transaction duration, the average transaction success time of the traffic-generating transactions of the traffic-generating transaction category is calculated.

3. The transaction flow data statistical analysis method according to claim 1, further comprising: The transaction element information is written into a database for backup.

4. A transaction flow data statistical analysis device, comprising: An acquisition module, used to acquire transaction element information of each traffic diversion transaction after the transaction is completed; A writing module, used to write the transaction element information into a distributed message queue, and store the transaction element information according to the category of the traffic diversion transaction; A calling module, configured to obtain, in response to a request from a traffic diversion transaction reference party, transaction element information of a traffic diversion transaction category requested by the traffic diversion transaction reference party from the distributed message queue; An analysis module, for performing transaction flow analysis of the flow-inducing transaction category based on the transaction element information, The transaction element information analyzed by the analysis module is the transaction element information of the traffic diversion transaction initiated by the traffic diversion transaction referee; and The transaction element information includes transaction results, and the analysis module includes: The first calculation module is used to calculate the traffic diversion transaction success rate of the traffic diversion transaction of the traffic diversion transaction category according to the transaction result.

5. According to the transaction flow data statistical analysis device of claim 4, the transaction element information also includes the transaction start time and the transaction end time, and the analysis module also includes: A first statistics module is used to count the transaction duration of successful traffic diversion transactions in the traffic diversion transaction category according to the transaction start time and transaction end time; The second calculation module is used to calculate the average transaction success time of the traffic diversion transactions of the traffic diversion transaction category according to the transaction duration.

6. The transaction flow data statistical analysis device according to claim 4, further comprising: The backup module is used to write the transaction element information into a database for backup.

7. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors execute the method according to any one of claims 1 to 3.

8. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the method according to any one of claims 1 to 3.

9. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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