CN117011023A - Full link regression data management method, device, equipment and storage medium - Google Patents
Full link regression data management method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN117011023A CN117011023A CN202310985515.0A CN202310985515A CN117011023A CN 117011023 A CN117011023 A CN 117011023A CN 202310985515 A CN202310985515 A CN 202310985515A CN 117011023 A CN117011023 A CN 117011023A
- Authority
- CN
- China
- Prior art keywords
- data
- regression
- withdrawal
- node user
- link
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 238000013523 data management Methods 0.000 title claims description 20
- 238000012545 processing Methods 0.000 claims description 11
- 238000012546 transfer Methods 0.000 claims description 2
- 238000004891 communication Methods 0.000 abstract description 12
- 238000004458 analytical method Methods 0.000 abstract description 5
- 238000012423 maintenance Methods 0.000 abstract description 5
- 230000006870 function Effects 0.000 description 11
- 230000008569 process Effects 0.000 description 9
- 238000007726 management method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000005236 sound signal Effects 0.000 description 2
- 238000012384 transportation and delivery Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000010897 surface acoustic wave method Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/03—Credit; Loans; Processing thereof
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)
Abstract
The application discloses a method, a device, equipment and a storage medium for managing all-link regression data, which enable related link information of each piece of regression data to be traceable and know regression progress in real time by recording all-flow regression data from channel application and approval to repayment after loan and other key data, solve the technical problems that at present, the management of regression all-link data is usually carried out by adopting a table, the regression data is difficult to ensure that all business lines are closed by all links from application, approval to repayment and the like through manual maintenance and communication of the table, the effectiveness of all-link regression cannot be ensured, and meanwhile, effective disc copying and analysis cannot be carried out due to later problems.
Description
Technical Field
The present application relates to the technical field of financial science and technology, and in particular, to a method, an apparatus, a device, and a storage medium for managing full link regression data.
Background
Because banking scenes are numerous, and the business processes of each product are different. One change may affect other business lines and thus the overall process regression of each business line is performed at each large version. In principle, all business lines are required to carry out full-link regression from the flows of application, withdrawal, repayment and the like, so that stable operation of each business line after the version is on line is ensured.
At present, a form is generally adopted to manage regression all-link data, on one hand, the regression data is manually maintained and communicated through the form, the condition of link disconnection possibly occurs in multi-department circulation such as channel, credit, post-credit, external connection and the like, and the effectiveness of all-link regression cannot be ensured because all business lines are difficult to realize all-link closed loops from application, approval to withdrawal, repayment and the like; on the other hand, the test environment can be cleaned due to the information such as the internal application number, the bill number and the like of the data record, and when a problem occurs in the later period, the actual data of each node at the time of the full link regression can not be obtained, and the regression can not be effectively duplicated and analyzed.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for managing all-link regression data, which solve the technical problems that at present, a form is generally adopted to manage the regression all-link data, the regression data is difficult to ensure that all business lines are closed by all links from application, approval to withdrawal, repayment and the like through manual maintenance and communication of the form, the effectiveness of all-link regression cannot be ensured, and meanwhile, the effective duplication and analysis cannot be carried out due to the later occurrence of problems.
In view of this, a first aspect of the present application provides a method for full link regression data management, the method comprising:
s1, acquiring regression data uploaded by a first node user, wherein the regression data comprises an internal application form number and product information;
s2, sending the regression data to a second node user, so that the second node user uploads batch data and withdrawal data according to the regression data;
and S3, if the approval data is approved, sending the withdrawal data to a third node user, so that the third node user uploads the withdrawal data according to the withdrawal data.
Optionally, the method further comprises:
and if the approval data is not approved, sending the approval data to the first node user, and updating the state information corresponding to the regression data.
Optionally, the method further comprises:
generating a link information display page according to the regression data, the approval data, the withdrawal data and the repayment data, wherein the link information comprises the regression data, the approval data, the withdrawal data and the repayment data.
Optionally, the method further comprises:
receiving a first data query instruction sent by the second node user, wherein the first data query instruction comprises a first query condition;
and calling corresponding first link information according to the first query condition, wherein the first link information comprises the regression data.
Optionally, the method further comprises:
receiving a second data query instruction sent by the third node user, wherein the second data query instruction comprises a second query condition;
and calling corresponding second link information according to the second query condition, wherein the second link information comprises the regression data, the approval data and the withdrawal data.
Optionally, the uploading, by the second node user, the approval data and the withdrawal data according to the regression data specifically includes:
the second node user transfers all the withdrawal borrowing data according to the internal application form numbers in the regression data, and then the withdrawal borrowing data is reversely displayed to obtain corresponding withdrawal information;
and the second node user generates and uploads batch data and withdrawal data according to the repayment information.
Optionally, the uploading the payment data by the third node user according to the payment data specifically includes:
the third node user invokes the repayment information according to the approval data and the repayment data;
and the third node user generates and uploads repayment data according to the repayment information.
A second aspect of the present application provides a full link regression data management apparatus, the apparatus comprising:
the acquisition unit is used for acquiring regression data uploaded by the first node user, wherein the regression data comprises an internal application form number and product information;
the first processing unit is used for sending the regression data to a second node user, so that the second node user uploads batch data and withdrawal data according to the regression data;
and the second processing unit is used for sending the withdrawal data to a third node user if the approval data is approved, so that the third node user uploads the withdrawal data according to the withdrawal data.
A third aspect of the present application provides a full link regression data management apparatus, the apparatus comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the steps of the method of full link regression data management as described in the first aspect above according to the instructions in the program code.
A fourth aspect of the present application provides a computer readable storage medium storing program code for performing the steps of the full link regression data management method of the first aspect described above.
From the above technical solutions, the embodiment of the present application has the following advantages:
the application provides a method, a device, equipment and a storage medium for managing all-link regression data, which enable the related link information of each piece of regression data to be traceable and know the regression progress in real time by recording all-flow regression data from channel application and approval withdrawal to post-credit withdrawal and other key data, and solve the technical problems that at present, the management of the regression all-link data is usually carried out by adopting a table, the regression data is difficult to ensure that all business lines achieve all-link closed loops from application, approval to withdrawal, withdrawal and the like through manual maintenance and communication of the table, the validity of all-link regression cannot be ensured, and meanwhile, the effective disc duplication and analysis cannot be carried out due to later problems.
Drawings
FIG. 1 is a flow chart of a method for managing full link regression data according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a full link regression data management device according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a full link regression data management device according to an embodiment of the present application.
Detailed Description
In order to make the present application better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The application designs a method, a device, equipment and a storage medium for managing all-link regression data, which solve the technical problems that the management of the regression all-link data is generally carried out by adopting a form, the regression data is difficult to ensure all-link closed loops from application, approval to withdrawal, repayment and the like through manual maintenance and communication of the form, the effectiveness of all-link regression cannot be ensured, and meanwhile, effective duplication and analysis cannot be carried out due to later problems.
For ease of understanding, referring to fig. 1, fig. 1 is a flowchart of a method for managing full link regression data according to an embodiment of the present application, as shown in fig. 1, specifically:
s1, acquiring regression data uploaded by a first node user, wherein the regression data comprises an internal application form number and product information;
it should be noted that, the channel is used as the first node user to add a piece of regression data, after the success of the delivery, the internal application number and the product information are input, and then the regression data is submitted to the lending node.
S2, sending regression data to the second node user, so that the second node user uploads approval data and withdrawal data according to the regression data;
it should be noted that, as the second node user in the credit, approval and payment are performed after the second node user receives the item, so as to generate approval data and withdrawal data, specifically:
the second node user obtains corresponding repayment information by reflecting the repayment borrowing data after calling all the repayment borrowing data according to the internal application form number in the regression data;
and the second node user generates and uploads the batch data and the withdrawal data according to the repayment information.
It should be noted that, according to the internal application form number, all withdrawal borrows are inquired, and after the borrows are selected, information such as the corresponding repayment card number, payment mode, whether the borrows are trusted or not is displayed in a back-displaying mode. And submitting after the money is successfully withdrawn, and transferring the flow to a post-credit node.
S3, if the approval data is approved, sending withdrawal data to the third node user, so that the third node user uploads the withdrawal data according to the withdrawal data;
it should be noted that, if the approval data is that the approval passes, the third node user receives the withdrawal data after lending, and may upload corresponding withdrawal data according to the withdrawal data, specifically:
the third node user retrieves repayment information according to the approval data and the repayment data;
and the third node user generates and uploads repayment data according to the repayment information.
The check and confirmation of the accounting data such as the fund flow direction are performed. And (5) performing operations such as repayment, batch deduction and the like, and submitting the repayment to completion after confirming that the repayment flow is normal.
Further, the method further comprises the following steps:
if the approval data is that the approval is not passed, the approval data is sent to the first node user, and the state information corresponding to the return data is updated.
If the second node user does not pass the approval of the approval data generated by the regression data, the first node user needs to return a value to check or modify the approval data, and the state information corresponding to the regression data is synchronously updated.
Further, the method further comprises the following steps:
and generating a link information display page according to the regression data, the approval data, the withdrawal data and the repayment data, wherein the link information comprises the regression data, the approval data, the withdrawal data and the repayment data.
In order to facilitate the checking of the data link information, the link information display page can be generated according to the regression data, the approval data, the withdrawal data and the repayment data, and key data in the link process is checked, so that the data flow direction and the verification condition of all nodes of the full link are known.
The link information display page supports dimension searching and filtering of versions, internal application form numbers, client information, borrowing numbers and the like; the link information displays key information such as product information, state, submitters at each stage, and the like.
Further, the method further comprises the following steps:
receiving a first data query instruction sent by a second node user, wherein the first data query instruction comprises a first query condition;
and calling corresponding first link information according to the first query condition, wherein the first link information comprises regression data.
It should be noted that, in the node of the second node user, for the regression data that has not yet entered the next process node, the first link information that already includes the regression data may still be retrieved by sending the first data query command.
Further, the method further comprises the following steps:
receiving a second data query instruction sent by a third node user, wherein the second data query instruction comprises a second query condition;
and calling corresponding second link information according to the second query condition, wherein the second link information comprises regression data, approval data and withdrawal data.
It should be noted that, in the nodes of the third node user, the second link information including the regression data, the approval data and the withdrawal data may still be retrieved by sending the second data query command to the data that has not yet entered the next flow node.
In addition, the key data such as full link regression contract and account-out are subjected to mirror image storage, and the integrity and the historical traceability of regression test data within 3 months are ensured.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a full link regression data management device according to an embodiment of the present application, as shown in fig. 2, specifically:
an obtaining unit 201, configured to obtain regression data uploaded by the first node user, where the regression data includes an internal application form number and product information;
it should be noted that, the channel is used as the first node user to add a piece of regression data, after the success of the delivery, the internal application number and the product information are input, and then the regression data is submitted to the lending node.
A first processing unit 202, configured to send regression data to the second node user, so that the second node user uploads approval data and withdrawal data according to the regression data;
it should be noted that, as the second node user in the credit, approval and payment are performed after the second node user receives the item, so as to generate approval data and withdrawal data, specifically:
the second node user obtains corresponding repayment information by reflecting the repayment borrowing data after calling all the repayment borrowing data according to the internal application form number in the regression data;
and the second node user generates and uploads the batch data and the withdrawal data according to the repayment information.
It should be noted that, according to the internal application form number, all withdrawal borrows are inquired, and after the borrows are selected, information such as the corresponding repayment card number, payment mode, whether the borrows are trusted or not is displayed in a back-displaying mode. And submitting after the money is successfully withdrawn, and transferring the flow to a post-credit node.
And the second processing unit 203 is configured to send the withdrawal data to the third node user if the approval data is approved, so that the third node user uploads the withdrawal data according to the withdrawal data.
It should be noted that, if the approval data is that the approval passes, the third node user receives the withdrawal data after lending, and may upload corresponding withdrawal data according to the withdrawal data, specifically:
the third node user retrieves repayment information according to the approval data and the repayment data;
and the third node user generates and uploads repayment data according to the repayment information.
The check and confirmation of the accounting data such as the fund flow direction are performed. And (5) performing operations such as repayment, batch deduction and the like, and submitting the repayment to completion after confirming that the repayment flow is normal.
Further, the method further comprises the following steps:
and the third processing unit is used for sending the approval data to the first node user and updating the state information corresponding to the return data if the approval data is not passed.
If the second node user does not pass the approval of the approval data generated by the regression data, the first node user needs to return a value to check or modify the approval data, and the state information corresponding to the regression data is synchronously updated.
Further, the method further comprises the following steps:
the display unit is used for generating a link information display page according to the regression data, the approval data, the withdrawal data and the repayment data, wherein the link information comprises the regression data, the approval data, the withdrawal data and the repayment data.
In order to facilitate the checking of the data link information, the link information display page can be generated according to the regression data, the approval data, the withdrawal data and the repayment data, and key data in the link process is checked, so that the data flow direction and the verification condition of all nodes of the full link are known.
The link information display page supports dimension searching and filtering of versions, internal application form numbers, client information, borrowing numbers and the like; the link information displays key information such as product information, state, submitters at each stage, and the like.
Further, the method further comprises the following steps:
the first receiving unit is used for receiving a first data query instruction sent by the second node user, wherein the first data query instruction comprises a first query condition;
the first query unit is used for calling corresponding first link information according to the first query condition, and the first link information comprises regression data.
It should be noted that, in the node of the second node user, for the regression data that has not yet entered the next process node, the first link information that already includes the regression data may still be retrieved by sending the first data query command.
Further, the method further comprises the following steps:
the second receiving unit is used for receiving a second data query instruction sent by the third node user, and the second data query instruction comprises a second query condition;
the second query unit is used for retrieving corresponding second link information according to a second query condition, wherein the second link information comprises regression data, approval data and withdrawal data.
It should be noted that, in the nodes of the third node user, the second link information including the regression data, the approval data and the withdrawal data may still be retrieved by sending the second data query command to the data that has not yet entered the next flow node.
In addition, the key data such as full link regression contract and account-out are subjected to mirror image storage, and the integrity and the historical traceability of regression test data within 3 months are ensured.
The embodiment of the present application further provides another full link regression data management device, as shown in fig. 3, for convenience of explanation, only the portion related to the embodiment of the present application is shown, and specific technical details are not disclosed, please refer to the method portion of the embodiment of the present application. The terminal can be any terminal equipment including a mobile phone, a tablet personal computer, a personal digital assistant (English full name: personal DigitalAssistant, english abbreviation: PDA), a sales terminal (English full name: point of sales, english abbreviation: POS), a vehicle-mounted computer and the like, taking the mobile phone as an example of the terminal:
fig. 3 is a block diagram showing a part of a structure of a mobile phone related to a terminal provided by an embodiment of the present application. Referring to fig. 3, the mobile phone includes: radio Frequency (RF) circuit 1010, memory 1020, input unit 1030, display unit 1040, sensor 1050, audio circuit 1060, wireless fidelity (wireless fidelity, wiFi) module 1070, processor 1080, and power source 1090. Those skilled in the art will appreciate that the handset configuration shown in fig. 3 is not limiting of the handset and may include more or fewer components than shown, or may combine certain components, or may be arranged in a different arrangement of components.
The following describes the components of the mobile phone in detail with reference to fig. 3:
the RF circuit 1010 may be used for receiving and transmitting signals during a message or a call, and particularly, after receiving downlink information of a base station, the signal is processed by the processor 1080; in addition, the data of the design uplink is sent to the base station. Generally, RF circuitry 1010 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (English full name: lowNoiseAmplifier, english abbreviation: LNA), a duplexer, and the like. In addition, the RF circuitry 1010 may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for mobile communications (english: global System ofMobile communication, english: GSM), general packet radio service (english: generalPacket Radio Service, GPRS), code division multiple access (english: code Division Multiple Access, english: CDMA), wideband code division multiple access (english: wideband Code DivisionMultipleAccess, english: WCDMA), long term evolution (english: long TermEvolution, english: LTE), email, short message service (english: shortMessaging Service, SMS), and the like.
The memory 1020 may be used to store software programs and modules that the processor 1080 performs various functional applications and data processing of the handset by executing the software programs and modules stored in the memory 1020. The memory 1020 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, memory 1020 may include high-speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state memory device.
The input unit 1030 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the handset. In particular, the input unit 1030 may include a touch panel 1031 and other input devices 1032. The touch panel 1031, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on the touch panel 1031 or thereabout using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a predetermined program. Alternatively, the touch panel 1031 may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device and converts it into touch point coordinates, which are then sent to the processor 1080 and can receive commands from the processor 1080 and execute them. Further, the touch panel 1031 may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. The input unit 1030 may include other input devices 1032 in addition to the touch panel 1031. In particular, other input devices 1032 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a track ball, a mouse, a joystick, etc.
The display unit 1040 may be used to display information input by a user or information provided to the user and various menus of the mobile phone. The display unit 1040 may include a display panel 1041, and alternatively, the display panel 1041 may be configured in the form of a liquid crystal display (english full name: liquid Crystal Display, acronym: LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 1031 may overlay the display panel 1041, and when the touch panel 1031 detects a touch operation thereon or thereabout, the touch panel is transferred to the processor 1080 to determine a type of touch event, and then the processor 1080 provides a corresponding visual output on the display panel 1041 according to the type of touch event. Although in fig. 3, the touch panel 1031 and the display panel 1041 are two independent components for implementing the input and output functions of the mobile phone, in some embodiments, the touch panel 1031 and the display panel 1041 may be integrated to implement the input and output functions of the mobile phone.
The handset may also include at least one sensor 1050, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 1041 according to the brightness of ambient light, and the proximity sensor may turn off the display panel 1041 and/or the backlight when the mobile phone moves to the ear. As one of the motion sensors, the accelerometer sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and direction when stationary, and can be used for applications of recognizing the gesture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may also be configured with the handset are not described in detail herein.
Audio circuitry 1060, a speaker 1061, and a microphone 1062 may provide an audio interface between a user and a cell phone. Audio circuit 1060 may transmit the received electrical signal after audio data conversion to speaker 1061 for conversion by speaker 1061 into an audio signal output; on the other hand, microphone 1062 converts the collected sound signals into electrical signals, which are received by audio circuit 1060 and converted into audio data, which are processed by audio data output processor 1080 for transmission to, for example, another cell phone via RF circuit 1010 or for output to memory 1020 for further processing.
WiFi belongs to a short-distance wireless transmission technology, and a mobile phone can help a user to send and receive emails, browse webpages, access streaming media and the like through a WiFi module 1070, so that wireless broadband Internet access is provided for the user. Although fig. 3 shows a WiFi module 1070, it is understood that it does not belong to the necessary constitution of the handset, and can be omitted entirely as required within the scope of not changing the essence of the application.
Processor 1080 is the control center of the handset, connects the various parts of the entire handset using various interfaces and lines, and performs various functions and processes of the handset by running or executing software programs and/or modules stored in memory 1020, and invoking data stored in memory 1020, thereby performing overall monitoring of the handset. Optionally, processor 1080 may include one or more processing units; preferably, processor 1080 may integrate an application processor primarily handling operating systems, user interfaces, applications, etc., with a modem processor primarily handling wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 1080.
The handset further includes a power source 1090 (e.g., a battery) for powering the various components, which may preferably be logically connected to the processor 1080 by a power management system, such as to provide for managing charging, discharging, and power consumption by the power management system.
Although not shown, the mobile phone may further include a camera, a bluetooth module, etc., which will not be described herein.
In an embodiment of the present application, the processor 1080 included in the terminal further has the following functions:
s1, acquiring regression data uploaded by a first node user, wherein the regression data comprises an internal application form number and product information;
s2, sending regression data to the second node user, so that the second node user uploads approval data and withdrawal data according to the regression data;
and S3, if the approval data is approved, sending the withdrawal data to the third node user, so that the third node user uploads the withdrawal data according to the withdrawal data.
The embodiments of the present application further provide a computer readable storage medium storing program code for executing any one of the foregoing methods for managing full link regression data of the respective embodiments.
In the embodiment of the application, the method, the device, the equipment and the storage medium for managing the full-link regression data are provided, and the related link information of each piece of regression data is traceable, the regression progress can be known in real time, the effective full-link regression is realized, and the technical problems that the management of the regression full-link data is usually carried out by adopting a table at present, the regression data is difficult to ensure that all business lines achieve full-link closed loops from application, approval to withdrawal, repayment and the like through manual maintenance and communication of the table, the validity of the full-link regression cannot be ensured, and meanwhile, the effective duplication and analysis cannot be carried out due to later problems are solved.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (RandomAccess Memory, RAM), magnetic disk or optical disk, etc.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A method of full link regression data management, comprising:
s1, acquiring regression data uploaded by a first node user, wherein the regression data comprises an internal application form number and product information;
s2, sending the regression data to a second node user, so that the second node user uploads batch data and withdrawal data according to the regression data;
and S3, if the approval data is approved, sending the withdrawal data to a third node user, so that the third node user uploads the withdrawal data according to the withdrawal data.
2. The full link regression data management method of claim 1 further comprising:
and if the approval data is not approved, sending the approval data to the first node user, and updating the state information corresponding to the regression data.
3. The full link regression data management method of claim 1 further comprising:
generating a link information display page according to the regression data, the approval data, the withdrawal data and the repayment data, wherein the link information comprises the regression data, the approval data, the withdrawal data and the repayment data.
4. The full link regression data management method of claim 3, further comprising:
receiving a first data query instruction sent by the second node user, wherein the first data query instruction comprises a first query condition;
and calling corresponding first link information according to the first query condition, wherein the first link information comprises the regression data.
5. The full link regression data management method of claim 3, further comprising:
receiving a second data query instruction sent by the third node user, wherein the second data query instruction comprises a second query condition;
and calling corresponding second link information according to the second query condition, wherein the second link information comprises the regression data, the approval data and the withdrawal data.
6. The method for managing full link regression data according to claim 1, wherein the uploading of approval data and withdrawal data by the second node user according to the regression data specifically comprises:
the second node user transfers all the withdrawal borrowing data according to the internal application form numbers in the regression data, and then the withdrawal borrowing data is reversely displayed to obtain corresponding withdrawal information;
and the second node user generates and uploads batch data and withdrawal data according to the repayment information.
7. The method of claim 6, wherein the uploading the payment data by the third node user according to the payment data specifically comprises:
the third node user invokes the repayment information according to the approval data and the repayment data;
and the third node user generates and uploads repayment data according to the repayment information.
8. A full link regression data management apparatus, comprising:
the acquisition unit is used for acquiring regression data uploaded by the first node user, wherein the regression data comprises an internal application form number and product information;
the first processing unit is used for sending the regression data to a second node user, so that the second node user uploads batch data and withdrawal data according to the regression data;
and the second processing unit is used for sending the withdrawal data to a third node user if the approval data is approved, so that the third node user uploads the withdrawal data according to the withdrawal data.
9. A full link regression data management apparatus, the apparatus comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the full link regression data management method of any one of claims 1-7 according to instructions in the program code.
10. A computer readable storage medium for storing program code for performing the full link regression data management method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310985515.0A CN117011023A (en) | 2023-08-07 | 2023-08-07 | Full link regression data management method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310985515.0A CN117011023A (en) | 2023-08-07 | 2023-08-07 | Full link regression data management method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117011023A true CN117011023A (en) | 2023-11-07 |
Family
ID=88574056
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310985515.0A Pending CN117011023A (en) | 2023-08-07 | 2023-08-07 | Full link regression data management method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117011023A (en) |
-
2023
- 2023-08-07 CN CN202310985515.0A patent/CN117011023A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104516886A (en) | Method, mobile terminal and server for displaying data analysis result | |
| CN106534288A (en) | Data transmission method and mobile terminal | |
| CN116468382A (en) | RPA robot flow management method, device, equipment and storage medium | |
| CN117011023A (en) | Full link regression data management method, device, equipment and storage medium | |
| CN116804922A (en) | Service quality inspection management method, device, equipment and storage medium | |
| CN116303060A (en) | ESB call request processing method, device, system, equipment and storage medium | |
| CN116342243A (en) | Order data processing method, device, equipment and storage medium | |
| CN116382945A (en) | Card making processing method, system, equipment and computer readable storage medium | |
| CN116501413A (en) | Automatic generation interface calling method, device, equipment and storage medium | |
| CN116681438A (en) | Custom generation method, device, equipment and storage medium for collection interface | |
| CN106648972A (en) | Data backup method and mobile terminal | |
| CN116680185A (en) | Test automation execution optimization method, device, equipment and storage medium | |
| CN116401488A (en) | Service information release processing method, device, equipment and storage medium | |
| CN116069269A (en) | Custom receipt printing method, device, equipment and storage medium | |
| CN117539460A (en) | Data set editing processing method and system | |
| CN117251362A (en) | System interaction test method, device, equipment and storage medium | |
| CN117807132A (en) | Distributed database histogram creation method, device, equipment and storage medium | |
| CN116862473A (en) | Bank production application calling relation analysis method, device, equipment and storage medium | |
| CN116932625A (en) | Fuzzy data generation method, device, equipment and storage medium | |
| CN115965462A (en) | Transfer and refund risk prejudgment method, device and equipment and storage medium | |
| CN117743015A (en) | SQL fault positioning method, device, system and equipment | |
| CN116303086A (en) | End-to-end testing method, configuration method, device, equipment and storage medium | |
| CN116775158A (en) | Method, device, equipment and storage medium for processing configuration file | |
| CN116680188A (en) | Data processing method, device, equipment and computer readable storage medium | |
| CN115543805A (en) | Case test environment switching method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |