CN113807858B

CN113807858B - Data processing method and related equipment based on decision tree model

Info

Publication number: CN113807858B
Application number: CN202111115082.0A
Authority: CN
Inventors: 李佳颖; 汪凌峰
Original assignee: Sinosoft Co ltd
Current assignee: Sinosoft Co ltd
Priority date: 2021-09-23
Filing date: 2021-09-23
Publication date: 2024-04-26
Anticipated expiration: 2041-09-23
Also published as: CN113807858A

Abstract

The embodiment of the application is applied to the field of artificial intelligence, and discloses a data processing method and related equipment based on a decision tree model, wherein the method comprises the following steps: receiving a resource data transfer request carrying a task identifier of a target account, wherein the resource data transfer request is sent by the target account; acquiring first account information and first historical task information of a target account, and inputting the first account information and the first historical task information into a pre-trained target decision tree model; determining a first data volume corresponding to a first task indicated by the task identification; carrying out identity verification on the target account, and transferring the resource data of the first data quantity into the target account; determining first node and task abnormality information corresponding to the first task, determining second data volume according to the task abnormality information, and transferring resource data of the second data volume to the target account. By adopting the embodiment of the application, the loss of the resource data of the platform is reduced, and the security of the resource data transfer of the platform is improved. The present application relates to blockchain technology in which such data may be stored.

Description

Data processing method and related equipment based on decision tree model

Technical Field

The invention relates to the technical field of artificial intelligence, in particular to a data processing method based on a decision tree model and related equipment.

Background

With the rapid development of internet technology, a task platform appears, which refers to a platform capable of issuing a paid task provided by a product provider, wherein the paid task can be used for popularizing a specific product, selling a certain commodity and the like, and a user can finish the task after receiving the task on the task platform, so that resource data transferred by the task platform is obtained.

Currently, in order to attract more users, a task platform generally increases the speed of accounting and transferring resource data, for example, the task platform may transfer resource data corresponding to a task to a user after the user accepts and completes the task. However, in some scenarios, such as shopping scenarios, the task is selling a specific commodity, the user sells the commodity, the resource data transferred by the task platform is obtained, but the subsequent commodity is refunded. Therefore, if the product provider considers that the task is failed, the resource data corresponding to the task is not transferred to the task platform, but the task platform transfers the resource data of the task to the user, so that the task platform needs to transfer the resource data of the task to the user recover, the loss of the platform resource data is caused, and the task platform has larger transfer risk of the resource data and low safety.

Disclosure of Invention

The embodiment of the application provides a data processing method, device, equipment and readable medium based on a decision tree model, which are beneficial to reducing the loss of task platform resource data, can also reduce the transfer risk of the resource data and can improve the security of task platform resource transfer.

In a first aspect, an embodiment of the present application provides a data processing method based on a decision tree model, including:

Receiving a resource data transfer request sent by a target account, wherein the resource data transfer request carries a task identifier of the target account, and the task identifier is used for indicating a first task;

acquiring first account information and first historical task information of the target account, and inputting the first account information and the first historical task information into a pre-trained target decision tree model to obtain a risk assessment score of the target account;

under the condition that the risk assessment score is smaller than a risk assessment threshold, determining that a risk assessment result of the target account passes, and determining a first data volume corresponding to the first task;

Carrying out identity verification on the target account, and transferring the resource data of the first data amount to the target account when the identity verification result of the target account is passed;

And when the first task does not reach the task completion node, determining first node and task abnormality information corresponding to the first task, determining a second data amount according to the task abnormality information, and transferring resource data of the second data amount to the target account, wherein the second data amount is smaller than zero.

Further, before the first account information and the first historical task information are input into the pre-trained target decision tree model to obtain the risk assessment score of the target account, the method further includes:

constructing a training sample set, wherein the training sample set comprises a positive sample and a negative sample, and the positive sample and the negative sample are determined according to risk characteristics of second account information of a reference account in a plurality of accounts and risk characteristics of second historical task information;

Determining first proportion data of the number of reference accounts corresponding to the risk characteristics of the second account information to the number of the total accounts, and determining second proportion data of the number of reference accounts corresponding to the risk characteristics of the second historical task information to the number of the total accounts;

determining an information gain of a risk feature of the second account information according to the first proportion data, and determining an information gain of a risk feature of the second historical task information according to the second proportion data;

And inputting the information gain of the risk features of the second account information and the information gain of the risk features of the second historical task information into a preset decision tree model for training to obtain an initial decision tree model.

Further, the constructing a training sample set includes:

Acquiring second account information and second historical task information of a reference account in the plurality of accounts, and determining risk characteristics in the second account information and risk characteristics in the second historical task information according to a preset risk assessment item, wherein the reference account is any account in the plurality of accounts;

Determining that the second task executed by the reference account within the preset time reaches the proportion of the number of task completion nodes to the total number of executed tasks;

And if the proportion is larger than a preset proportion threshold value, taking the risk characteristics in the second account information and the risk characteristics in the second historical task information as positive samples, and if the proportion is smaller than the preset proportion threshold value, taking the risk characteristics in the second account information and the risk characteristics in the second historical task information as negative samples, and determining the positive samples and the negative samples as the training sample set.

Further, the training of inputting the information gain of the risk feature of the second account information and the information gain of the risk feature of the second historical task information into a preset decision tree model to obtain an initial decision tree model further includes:

obtaining a plurality of paths according to paths from a root node to each leaf node in the initial decision tree model;

The method comprises the steps of obtaining the number of negative samples and the total number of samples corresponding to a target path in the paths, wherein the target path is any path in the paths;

determining a risk assessment score corresponding to the target path according to the proportion of the number of negative samples corresponding to the target path to the total number of samples;

And labeling the risk assessment score corresponding to the target path at the leaf node of the target path to obtain the target decision tree model.

Further, the performing identity verification on the target account includes:

Determining whether the network quality of the terminal equipment of the target account reaches an identity verification condition, wherein the network quality refers to the signal strength of the terminal equipment for data transmission;

Under the condition that the network quality reaches an identity verification condition, carrying out video identity verification on the target account to obtain an identity verification result of the target account;

and under the condition that the network quality does not reach the authentication condition, sending a face authentication instruction to the terminal equipment, wherein the face authentication instruction carries verification information, and receiving an authentication result returned by the terminal equipment, and the verification information is verification data of a face image corresponding to the target account.

Further, the resource data transfer request also carries a second node corresponding to the first task; the determining the second data amount according to the task abnormality information includes:

the task abnormality information is indication information for indicating a task execution failure in the case where the first node is a node previous to the second node;

and acquiring a first calculation method corresponding to the task abnormality information, wherein the first calculation method is used for indicating that the opposite number of the first data volume is determined as the second data volume.

Further, the determining the second data amount according to the task anomaly information includes:

When the first node is the last node of the task completion node, the task abnormality information is indication information for indicating that the task is not completed according to a preset time;

acquiring a second calculation method corresponding to the task abnormality information, and determining the second data volume according to the second calculation method;

The determining the second data amount according to the second calculation method includes:

Determining a third data volume according to the first data volume and the first node, wherein the third data volume is the data volume of the first task reaching the first node, and the third data volume is smaller than the first data volume;

and determining a difference between the first data amount and the third data amount as the second data amount.

In a second aspect, an embodiment of the present application provides a data processing apparatus based on a decision tree model, including:

The receiving unit is used for receiving a resource data transfer request sent by a target account, wherein the resource data transfer request carries a task identifier of the target account, and the task identifier is used for indicating a first task;

the acquisition unit is used for acquiring first account information and first historical task information of the target account, and inputting the first account information and the first historical task information into a pre-trained target decision tree model to obtain a risk assessment score of the target account;

A determining unit, configured to determine that, when the risk assessment score is smaller than a risk assessment threshold, a risk assessment result of the target account passes, and determine a first data amount corresponding to the first task;

the verification unit is used for carrying out identity verification on the target account, and transferring the resource data of the first data volume to the target account when the identity verification result of the target account is passed;

the determining unit is further configured to determine first node and task abnormality information corresponding to the first task when it is determined that the first task does not reach a task completion node, determine a second data amount according to the task abnormality information, and transfer resource data of the second data amount to the target account, where the second data amount is a data amount smaller than zero.

In addition, in this aspect, other optional embodiments of the decision tree model-based data processing apparatus may refer to the relevant matters of the first aspect, which are not described in detail herein.

In a third aspect, embodiments of the present application provide a computer device comprising a memory and a processor, a transceiver; the processor is connected to the memory and the transceiver, respectively, wherein the memory stores computer program code, and the processor and the transceiver are configured to invoke the program code to perform the method provided by the first aspect and/or any possible implementation manner of the first aspect.

In a fourth aspect, embodiments provide a computer readable storage medium storing a computer program which, when executed by a computer device, implements a decision tree model based data processing method as disclosed in any one of the possible implementations of the first aspect.

In the embodiment of the application, by receiving a resource data transfer request carrying a task identifier sent by a target account, inputting acquired account information and historical task information of the target account into a pre-trained target decision tree model to obtain a risk assessment score of the target account, determining that a risk assessment result of the target account is passed under the condition that the risk assessment score is smaller than a risk assessment threshold value, performing risk assessment on the target account, determining a first data volume of a task executed by the target account, performing identity verification on the target account, transferring resource data of the first data volume to the target account under the condition that the identity verification is passed, determining current reached node and task abnormality information of the task under the condition that the task does not reach a task completion node, determining a second data volume according to the task abnormality information, transferring resource data of the second data volume to the target account under the condition that the second data volume is smaller than zero. In one aspect, risk assessment is performed on the target account through the target decision tree model, and under the condition that the risk assessment passes, the first resource data is transferred, so that the security of transferring the task platform resource data is improved. On the other hand, the nodes currently reached by the task are determined, the nodes are divided according to the completed nodes of the task, and negative resource data are determined according to the abnormal information of the task, so that the negative resource data are transferred into a target account, the loss of a task platform is reduced, and the transfer risk of the resource data is reduced.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic diagram of a data processing system based on a decision tree model according to an embodiment of the present application;

FIG. 2 is a schematic flow chart of a data processing method based on a decision tree model according to an embodiment of the present application;

FIG. 3 is another flow chart of a data processing method based on a decision tree model according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a data processing apparatus based on a decision tree model according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but 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.

A data processing method based on a decision tree model according to an embodiment of the present application is schematically described below with reference to fig. 1 to fig. 3.

The data processing method based on the decision tree model provided by the embodiment of the application can be applied to a platform, and the task platform can be borne in a data processing system or an application program based on the decision tree model. In some embodiments, the task platform refers to a platform with a paid task provided for a user, and the task platform can also provide a resource data transferring function, and after the user completes the task, the resource data corresponding to the completed task is transferred to the account of the user. The user is a user of the task platform, and the resource data of the task platform transferred into the account can be obtained after the paid task is received in the task platform and then the task is completed. In some embodiments, the task platform may establish a communication connection with a terminal device of the user, and receive a resource data transfer request initiated by the terminal device. In some embodiments, the task platform may also establish a communication connection with a terminal device and/or a server of the task provider. In some embodiments, the manner of Communication connection may include, but is not limited to, wireless Communication technology (WIRELESS FIDELITY, WIFI), bluetooth, near Field Communication (NFC), and the like.

Specifically, the task platform may receive a resource data transfer request sent by a user through a target account, where the resource data transfer request carries a task identifier of the target account, where the task identifier is used to instruct a task executed by the user of the target account, that is, transfer a task corresponding to the resource data; and the task platform acquires account information and historical task information of the target account, and inputs the acquired information into a pre-trained target decision tree model to obtain a risk assessment score of the target account. If the risk assessment fails, the task platform does not transfer the resource data to the target account, otherwise, if the risk assessment result is that the risk assessment score of the target account is smaller than the risk assessment threshold value, the risk assessment result of the target account is determined to pass, the data volume corresponding to the task is determined, the identity verification is carried out on the target account, and if the identity verification passes, the resource data of the data volume corresponding to the task is transferred to the target account. And finally, under the condition that the task does not reach a task completion node, the task platform determines the current node and task abnormality information of the task, determines another data volume according to the task abnormality information, wherein the data volume is smaller than zero and is used for settling the resource data of the multi-transfer target account, namely transferring the data volume into the target account for settlement.

The data processing method based on the decision tree model provided by the embodiment of the application relates to artificial intelligence, machine learning and other technologies, wherein: artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that simulates, extends, and extends human intelligence using a digital computer or a machine controlled by a digital computer, perceives the environment, obtains knowledge, and uses the knowledge to obtain optimal results. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions. Machine learning is a multidisciplinary cross-specialty covering probabilistic knowledge, statistical knowledge, approximate theoretical knowledge and complex algorithmic knowledge, uses a computer as a tool and aims at simulating human learning in real time, and performs knowledge structure division on existing content to effectively improve learning efficiency.

Based on the foregoing description, a data processing system based on a decision tree model according to an embodiment of the present application is schematically illustrated in the following with reference to fig. 1.

Referring to fig. 1, fig. 1 is a schematic structural diagram of a data processing system based on a decision tree model according to an embodiment of the present application. The data processing system based on the decision tree model comprises: a task platform 101, one or more terminal devices (terminal device 102 and terminal device 103), and one or more task providing devices (task providing device 104, task providing device 105, task providing device 106). Wherein one or more terminal devices may be directly or indirectly connected to the task platform 101 through a wired or wireless means. One or more task providing devices may also be directly or indirectly connected to the task platform 101 via wired or wireless means. Optionally, the decision tree model based data processing system may further comprise a training device 107, which may be used to obtain data in the task platform 101 and train the decision tree model based on the obtained data to obtain a target decision tree model. The task platform 101 and the training device 107 may be the same device or different devices, and in the case that the task platform 101 and the training device 107 are not the same device, the task platform 101 and the training device 107 may be directly or indirectly connected through a wired or wireless manner.

It should be noted that the number and the form of the devices shown in fig. 1 are used as examples, and are not limiting to the embodiments of the present application, in practical application, the data processing system based on the decision tree model may further include three or more terminal devices, or three or more task providing devices, that is, the task platform may be directly or indirectly connected to the three or more terminal devices through a wired or wireless manner, and may also be directly or indirectly connected to the three or more task providing devices through a wired or wireless manner. In the embodiment of the present application, two terminal devices (terminal device 102 and terminal device 103), one task platform 101, and three task providing devices (task providing device 104, task providing device 105, task providing device 106) are taken as examples, and the task platform 101 and training device 107 are explained for the same device.

The task platform 101 may provide paid tasks for each user, and the two different users view the paid tasks provided by the task platform 101 through the terminal devices and interact with the task platform 101, so that a plurality of paid tasks provided by the task platform 101 may be viewed, or the paid tasks provided by the task platform 101 may be received through the terminal devices, and after the user completes the tasks, the user may return to the task platform 101 through the terminal devices, so as to obtain resource data transferred by the task platform 101. The user registers an account in the task platform 101 through the terminal device, and the task platform 101 counts the task execution condition of the user and transfers the resource data to be executed through the account of the user.

Specifically, after the user completes the task, a resource data transfer request can be sent to the task platform 101 through the terminal device, the account of the user is a target account, the resource data transfer request carries a task identifier of the target account, the task identifier is used for indicating the task executed by the user, the task platform 101 further obtains account information and historical task information of the target account, and the account information and the historical task information are input into a pre-trained target decision tree model to obtain a risk assessment score of the target account of the user; under the condition that the risk assessment score is smaller than a risk assessment threshold, determining that a risk assessment result of a target account passes, and determining a first data volume corresponding to the task, namely transferring resource data to the target account under the condition that the risk assessment result passes, carrying out identity verification on the target account before transferring the resource data, and transferring the resource data of the first data volume to the target account under the condition that the identity verification result of the target account passes; if the task platform 101 determines that the task executed by the user does not reach the task completion node, determining the node and task abnormality information of the task, determining a second data volume according to the task abnormality information, wherein the second data volume is a negative data volume, and transferring the resource data of the second data volume to the target account, thereby avoiding the loss of the task platform.

The task platform 101, the terminal devices (terminal device 102 and terminal device 103), the task providing platform (task providing device 104, task providing device 105, task providing device 106) and the training device 107 may be smart phones, tablet computers, notebook computers, desktop computers, smart speakers, smart watches, etc.; the device 101 of the application platform and the device 102 of the authorization platform may be servers, for example, may be independent physical servers, may be a server cluster or a distributed system formed by a plurality of physical servers, and may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and an artificial intelligence platform.

Referring to fig. 2, fig. 2 is a schematic flow chart of a data processing method based on a decision tree model according to an embodiment of the present application. As shown in fig. 2, the data processing method based on the decision tree model may include:

201. and receiving a resource data transfer request sent by the target account.

In the embodiment of the application, in order to better enable the user to accept the paid task of the platform and complete the task, the task platform can receive the transfer request of the resource data initiated by the user after the user accepts the task and executes the task, and further transfer the resource data corresponding to the task received by the user to the account of the user. The target account is an account used by a user, the user is a user of the task platform, the task of the task platform is received, and the task is completed. The resource data transfer request can be initiated after the user accepts the task and performs the task execution, that is, when the target account reaches the execution node after the task accepts the node, the user can initiate the resource data transfer request to the task platform through the target account, the resource data transfer request carries the task identifier of the target account, the task is used for indicating a first task, the first task initiates the task corresponding to the transferred resource data for the target account, and the task accepted and being executed by the target account.

202. And acquiring first account information and first historical task information of the target account, and inputting the first account information and the first historical task information into a pre-trained target decision tree model to obtain a risk assessment score of the target account.

In one possible implementation, the account information is basic information of an account, and the first account information is account information of a target account, where the account information may include a name of a user, a company of the user, a job position of the user, a work place of the user, a type of task performed by the user, a recommender of the user, and the like. Wherein the first account information may be derived based on information entered by the user. The first historical task information is information and task conditions of tasks executed by the target account, and specifically may include information of each task executed by the account. The method specifically can comprise the time of each task received by the task, the time of initiating a resource data transfer request, the time of executing the task and the like; the method can also comprise a task completion situation, and specifically comprises the step of judging whether the task executed by the target account reaches a task completion node, wherein the number of the tasks reaching the task completion node is in proportion to the total number of the tasks, the time for reaching the task completion node is longer than the time for receiving the task, and the time for reaching the task completion node; the task information of the task completion node may be included, and specifically may include a task node where a task of the task completion node is not reached, a resource data transfer time corresponding to a task of the task completion node is not reached, an execution time corresponding to a task of the task completion node is not reached, and the like.

In one possible implementation manner, the first account information and the first historical task information of the target account are input into a pre-trained target decision tree model to obtain a risk assessment score of the target account. The decision tree is a classifier for training and predicting samples, and is a prediction model; he represents a mapping between object properties and object values. The decision tree comprises a plurality of nodes, the nodes are divided into root nodes and leaf nodes, the root nodes can correspond to at least two bifurcation paths, each path corresponds to one leaf node, and further the nodes can continue to divide downwards, so that the nodes cannot be divided any more, and a decision tree model is obtained. Each node in the decision tree represents an object and each bifurcation path represents a possible attribute value, while each leaf node corresponds to the value of the object represented by the path taken from the root node to that leaf node. The target decision tree model is pre-trained, and when the risk assessment score of the target account is actually required to be determined, the risk assessment score of the target account is determined only by directly using the target decision tree. Specifically, the first account information and the first historical task information of the target account are input into the pre-trained target decision tree model, so that the target decision tree model can determine a path from a root node to the leaf node in the target decision tree model according to the first account information and the first historical task information of the target account, and further determine a risk assessment score of the target account according to a score corresponding to the path determined in the target decision tree model.

Further, after the risk assessment score of the target account is obtained, judging the risk assessment score, if the risk assessment score is greater than or equal to a risk assessment threshold, determining that the risk of the target account is greater, and determining that the risk assessment result of the target account is not passed. Otherwise, if the risk assessment score is smaller than the risk assessment threshold, determining that the risk of the target account is smaller, and determining that the risk assessment result of the target account passes.

203. And under the condition that the risk assessment score is smaller than a risk assessment threshold, determining that the risk assessment result of the target account passes, and determining a first data volume corresponding to the first task.

In one possible implementation manner, the task platform stores a risk assessment threshold, compares the risk assessment threshold with the risk assessment score of the target account, and if the task platform determines that the risk assessment score is smaller than the risk assessment threshold, the task platform determines that the risk of the target account is smaller, that is, the risk assessment result is passed. Furthermore, the task platform can transfer the resource data to the target account, namely, determine a first data volume corresponding to the task identifier carried by the resource data transfer request initiated by the target account, where the first data volume is the data volume of the resource data of the first task. When each task is released, the task platform can mark the data volume corresponding to each task, and the data volume is determined according to the data volume provided by the task provider when the task provider provides the task for the task platform through the task providing device. Therefore, the task platform only needs to acquire the first data volume corresponding to the first task according to the task identifier.

In one possible implementation, if the task platform determines that the risk assessment score is greater than or equal to the risk assessment threshold, the task platform determines that the risk of the target account is greater, i.e., the risk assessment result is not passed. And the task platform does not need to acquire the first data volume corresponding to the first task and does not transfer the resource data to the target account. Optionally, the task platform may further send a prompt message that the risk assessment result does not pass to the target account, and prompt the user to complete the task, that is, increase the probability that the task reaches the task completion node to increase the risk assessment score, so as to strive for improving the risk assessment result of the target account in the subsequent risk assessment.

204. And carrying out identity verification on the target account, and transferring the resource data of the first data amount to the target account when the identity verification result of the target account is passed.

In one possible implementation, before transferring the resource data of the first data amount to the target account, authentication needs to be performed on the target account, and when the authentication result of the target account is that the target account passes, the resource data of the first data amount is transferred to the target account. The task platform may acquire network quality of a terminal device of a current target account, where the network quality refers to signal strength of the terminal device for data transmission, when the task platform initiates an authentication request for the target account, the terminal device carrying the target account may feed back the current network quality of the terminal device to the task platform, where the network quality does not meet the authentication condition may include that a network transmission rate is lower than a rate threshold, a network bandwidth is lower than a bandwidth threshold, a network packet loss rate is greater than a packet loss rate threshold, and a network response time period is greater than a duration threshold.

In one possible implementation manner, the terminal device may send, to the task platform, indication information of network quality, where the indication information is used to indicate whether the terminal device can perform the video identity authentication condition, and the indication information may also be parameters including a current network transmission rate, a network packet loss rate, a network response duration, and the like of the terminal device, where the task platform determines whether the current terminal device reaches the video identity authentication condition, and if the task platform determines that the current terminal device reaches the video identity authentication condition, sends a request for video identity authentication to the terminal device, so as to perform video authentication on a face of a user in the video.

In another possible implementation manner, when the task platform determines that the current terminal device does not reach the video identity verification condition, a face identity verification instruction may be sent to the terminal device, where the face identity verification instruction carries verification information, and the verification information is verification data of a face image corresponding to the target account. Specifically, the verification information may be verification data converted by the platform according to face image data of the user corresponding to the target account during registration. For example, after collecting one or more images of a user corresponding to a target account, the platform converts the one or more images into verification data, where the verification data may be positions where feature points identified in a face image are located, such as positions where eyes, nose, mouth, chin are located, and calculates euclidean distances, curvatures, angles, etc. between the feature points, and the positions of the feature points and association information between the feature points may be determined as the verification data corresponding to the face image.

Specifically, when receiving a face image of a target user, the terminal device converts the face image acquired during identity verification into feature data in the same manner, wherein the feature data can also comprise a plurality of feature points of the face of the user and associated information among the feature points, and further the terminal device can match the verification data with the feature data, if the matching degree is greater than a matching threshold value, the identity verification result of the user is determined to be passed, otherwise, the identity verification result of the user is determined to be failed. The matching degree can be the similarity between the verification data and the feature data, if the similarity is larger than a similarity threshold value, the feature data of the face image is determined to be matched with the verification data, and the authentication of the user is determined to pass. Otherwise, if the similarity between the verification data and the feature data is smaller than the similarity threshold, determining that the feature data of the face image is not matched with the verification data, and determining that the identity verification result of the user is not passed.

Further, after obtaining the authentication result of the target account, the terminal device may return the authentication result to the task platform. And the task platform can transfer the resource data of the first data amount into the target account under the condition that the authentication result of the target account is passed.

Optionally, the task platform does not transfer the resource data of the first data amount to the target account when determining that the authentication result of the target account is failed, and may send a prompt message for prompting the user to perform the second authentication to the terminal device corresponding to the target account, where the prompt message may also be used to prompt the user to keep the camera of the terminal device clean and clear, and so on.

205. And under the condition that the first task does not reach the task completion node, determining first node and task abnormality information corresponding to the first task, determining second data volume according to the task abnormality information, and transferring the resource data of the second data volume to the target account.

At present, after the resource data is transferred to the target account, if the first task is not completed, the task platform can determine the first node and task abnormality information corresponding to the first task, further determine the second data volume corresponding to the task abnormality information, wherein the second data volume is negative, and transfer the resource data of the second data volume to the target account. The first node is the node where the current task is located, and the task abnormality information is the reason that the current task is executed and ended, but the task completion node is not reached. The second data amount is determined based on the task anomaly information. And transferring the resource data corresponding to the second data volume smaller than zero to the target account to reduce that the first task is not completed because the user has obtained the resource data of the first data volume of the first task.

For example, taking a shopping scenario as an example, a user promotes a certain product to a consumer, the consumer purchases the product, but because the product can be returned within a certain time threshold, the consumer does not confirm the receipt and initiates a request for returning the refund, the task provider does not obtain a benefit based on the returned commodity, so that the resource data of the task is not transferred to the task platform, but the task platform transfers the resource data of the first task to the target account, and the resource data of the task platform is lost. Therefore, the task platform can determine the second data amount based on the task and transfer the resource data of the second data amount to the target account, so that the safety of the resource data of the task platform is ensured.

One task may be divided into a plurality of processing nodes, and a specific task node may be determined according to a task type provided by a task provider and a role type of the task provider. For example, in a sales scenario for a product, a task provider may be a brand party for a product, i.e., a role type may be a brand party, and a task may be selling the product provided by the brand party. The node of the task may include: the method comprises the steps of issuing a task, receiving the task by a user, selling a product, receiving the product by a consumer and confirming the completion of the task by a brand party. Still further exemplary, in the context of loan products, the task provider may be a loan base, i.e., the character type may be a loan base, and the task may be to issue a loan. The node of the task may include: the method comprises the steps of issuing a task, receiving the task by a user, determining a customer, paying money by a product provider, repaying money by the customer and confirming completion of the task by the product provider.

In one possible implementation manner, the task platform determines a current node of the first task, i.e. the first node, in a case that it determines that the first task does not reach a task completion node. When the target account initiates a resource data transfer request to the task platform, the resource data transfer request may carry a second node, where the second node may be a previous node of the first node, the task exception information may be used to indicate that the first task fails to execute, and further the task platform may obtain a first calculation method corresponding to the task exception information, and determine the second data amount by using the first calculation method. The first calculation method is used for indicating that the opposite number of the first data volume is determined to be the second data volume, namely the task execution fails, and returning all the resource data transferred in before the task platform.

In an exemplary scenario, the user corresponding to the target account is a host, the first task is selling a product, the host accepts the task, and the first task reaches a sold node (second node) in the live and shipped process, at which time the host may initiate a resource data transfer request, where the resource data transfer request may carry the sold node. After the risk assessment is passed, the task platform performs identity verification on the target account, and after the identity verification is passed, the task platform transfers the resource data corresponding to the first task, namely the resource data of the first data amount, to the target account. However, if the subsequent consumer initiates a refund, the task returns to the previous node, i.e. the first node, and the user accepts the task, and the task exception information is used to indicate that the user fails to execute the task. The task platform may acquire a first calculation method corresponding to task exception information when the task exception information indicates that the task execution fails, where the first node is a previous node of the second node, and the first calculation method is used to indicate that an opposite number of the first data volume is determined to be the second data volume. The task abnormality information may include a task type, an execution condition, and a failure reason of the task. The second data volume is negative data volume, which is equivalent to failure of the first task execution of the target account, and the full amount is returned to the resource data.

In another possible implementation manner, the first node may be a previous node of the task completion node, the task abnormality information may be a second calculation method for indicating that the task is not completed according to the preset time, and the task platform may acquire the task abnormality information, and determine the second data amount according to the second calculation method. The specific process of determining the second data amount according to the second calculation method may be: the task platform determines a third data volume according to the first data volume and the first node, and determines the third data volume as the second data volume according to the first data volume and the third data volume. The third data volume is the corresponding data volume reaching the first task node, the third data volume is smaller than the first data volume, and the second calculation method is to determine the difference between the first data volume and the third data volume as the second data volume.

In the scenario of the loan product, the user sells the loan product to the customer, and the customer pays in advance in the repayment period, that is, the customer does not pay according to the preset time, which is equivalent to that the task is not completed according to the preset time, and the current task node (that is, the first node) is the last node of the task completion node, that is, the customer pays, and the task abnormality information can be used for indicating that the task is not completed according to the preset time. The task platform may determine a third data amount according to the first data amount (i.e. the data amount obtained by the user who completes the task) and the first node (i.e. the customer repayment node), where the third data amount is a data amount determined by the task node according to a certain proportion in the third data amount, i.e. a data amount obtained by the user who executes the task, so that the third resource data is smaller than the first resource data.

Further, the task platform may determine a difference between the third amount of data and the first amount of data as the second amount of data. I.e. third data amount-first data amount = second data amount. Since the third data amount is smaller than the first data amount, the second data amount is a number smaller than zero. And transferring the second amount of data to the target account.

Optionally, if the balance in the target account still has the resource data which is not presented, the remaining resource data and the second data volume are settled, and finally the remaining resource data is obtained. If the target account also executes other tasks and can acquire the resource data of other tasks, the resource data of the second data amount can be flushed by using other resource data, and when the balance of the target account is positive, the target account can initiate a request for presentation.

Referring to fig. 3, fig. 3 is another flow chart of a data processing method based on a decision tree model according to an embodiment of the application. It should be noted that, in the present application, the same or similar parts between the embodiments may be referred to each other. In the embodiments of the present application, and the respective implementation/implementation methods in the embodiments, if there is no specific description and logic conflict, terms and/or descriptions between different embodiments, and between the respective implementation/implementation methods in the embodiments, may be consistent and may refer to each other, and technical features in the different embodiments, and the respective implementation/implementation methods in the embodiments, may be combined to form a new embodiment, implementation, or implementation method according to their inherent logic relationship. The above-described embodiments of the present application do not limit the scope of the present application. As shown in fig. 3, the decision tree model-based data processing method may include:

301. a training sample set is constructed, the training sample set comprising positive samples and negative samples.

In one possible implementation manner, before the target decision tree is applied, the task platform may construct a training sample set pair to construct a target decision tree model, and the training sample set used to construct the target decision tree model may be multiple accounts on the task platform, and specifically, the task platform may obtain second account information and second historical task information in a reference account in the multiple accounts, where the reference account is any account in the multiple accounts. The second account information and the second historical task information can include features for characterizing the reference account, wherein the features can be user features corresponding to the reference account or behavior features of the reference account. For the reference account, the second account information and the second historical task information of the reference account are used as data sets, the data sets are classified, and each reference data is divided into a positive sample or a negative sample to obtain a training sample set.

Optionally, after the second account information and the second historical task information of the reference account are acquired, the second account information and the second historical task information of the reference account may be preprocessed, where removing data that does not have any help to the data used to construct the target decision tree model may be included. The second account information includes, for example, a name of the reference account corresponding user, a company of the reference account corresponding user, a position of the reference account corresponding user, a work place of the reference account corresponding user, a type of task executed by the reference account corresponding user, a recommender of the reference account corresponding user, and the like. The name of the user corresponding to the reference account, the company of the user corresponding to the reference account, the work place of the user corresponding to the reference account, and the recommender of the user corresponding to the reference account can be deleted during preprocessing.

Specifically, risk characteristics in the second account information and risk characteristics in the second historical task information may be determined according to preset risk assessment items. The preset risk item is used for screening risk features from the second account information and the second history information, and can be set according to a specific application scene. Optionally, if one of the risk features in the second account information or the second history information is not a discrete feature, that is, cannot be used to construct the target decision tree, the preset risk item may further include a division threshold for the risk feature, so as to classify the risk feature, thereby better constructing the target decision tree model. For example, when a certain risk feature in the second historical task is a duration of executing the task, where the duration is a continuous value, the preset risk item may further include a duration threshold corresponding to the risk feature, where the duration threshold is used to divide the risk feature, and samples greater than or equal to the duration threshold are taken as one type, and samples less than the duration threshold are taken as another type. Further, the time length threshold may be two thresholds, and the time length of executing the task is divided into three types, which is not limited in the present application.

Further, the task platform can determine that the number of tasks of the second task executed by the reference account in the preset time reaches the task completion node, and the tasks account for the total number of tasks executed, if the ratio is greater than a preset ratio threshold, the risk features included in the second account information and the second historical task information of the reference account are taken as positive samples, otherwise, if the ratio is less than or equal to the preset ratio threshold, the risk features included in the second account information and the second historical task information of the reference account are taken as negative samples, and a training sample set is obtained.

302. And determining first proportion data of the number of the reference accounts corresponding to the risk characteristics of the second account information to the number of the total accounts, and determining second proportion data of the number of the reference accounts corresponding to the risk characteristics of the second historical task information to the number of the total accounts.

In one possible implementation, the task platform may construct the target decision tree model based on the positive and negative samples described above. Specifically, the task platform may determine the proportion data of the risk feature in the second account information and the risk feature in the second historical task information, and obtain the first proportion data and the second proportion data respectively. For example, one risk feature included in the second historical task information is a time length of executing the task, the time length of executing the task may be divided into two types, one type is greater than or equal to a time length threshold value, the other type is smaller than the time length threshold value, the proportion data of the number of the reference accounts corresponding to the two types in the risk feature to the total number of the reference accounts is counted respectively, and the second proportion data of the risk feature includes proportion data corresponding to each type in the risk feature of the time length of executing the task respectively.

303. And determining the information gain of the risk characteristic of the second account information according to the first proportion data, and determining the information gain of the risk characteristic of the second historical task information according to the second proportion data.

After the proportion data of all risk features are obtained, the information entropy of the risk features of the second account information can be determined according to the first proportion data, and then the information gain of the risk features of the second account information is determined according to the information entropy, and similarly, the information gain of the risk features of the second historical task information is determined according to the second proportion data, and then the decision tree model is trained according to the information gain. Specifically, the information entropy that can be calculated from the scale data can be as shown in formula 1:

wherein, ent (D) represents information entropy, D represents training sample set, K represents total number of categories, for example, the execution duration includes two categories, k=2, pk is the proportion occupied by the current category sample, that is, K represents the possible K values of the risk feature in the first proportion data and the second proportion data, and the proportion under the kth category.

After obtaining the information entropy of each risk feature, determining the information gain of each risk feature, wherein the information gain can be used for representing the influence of a certain risk feature on the classification result of the branch node of the risk feature obtained after the training sample set D is classified. Specifically, the information gain calculated according to the information entropy of a certain risk feature may be as shown in formula 2:

Where, ent (D) represents the entropy of the information, D represents the training sample set, K represents that the risk feature a may have K categories, and D _k represents the number of samples in the sample set that take the value a _k on the risk feature a.

And further, learning and training the decision tree model according to the information gain of each risk characteristic to construct a target decision tree model.

304. And inputting the information gain of the risk features of the second account information and the information gain of the risk features of the second historical task information into a preset decision tree model for training to obtain an initial decision tree model.

Since the larger the value of the information gain is, the larger the influence on the subsequent classification is, the calculated information gains are ranked in the order from large to small, and the risk characteristic with the largest information gain is taken as the root node of the target decision tree model.

Further, after the root node is selected, each branch of the risk feature of the root node is continuously partitioned. Similarly, dividing a first branch (namely a first category of the risk features) under the risk features according to another risk feature, calculating the information gain of each risk feature under the first branch, selecting the risk feature with the largest information gain as a leaf node of the first branch, and the like until the first branch cannot be divided any more, and obtaining an initial decision tree model through learning and training of a decision tree.

Further, according to paths from a root node to each leaf node in an initial decision tree model, obtaining a plurality of paths in the initial decision tree model, obtaining the number of negative samples of each path, determining a risk assessment score corresponding to the path according to the number of negative samples and the number of total samples, and labeling the risk assessment score of the path at the position of the leaf node at the lowest layer of the path to obtain a target decision tree model. The negative samples are classified based on the fact that the proportion of tasks executed by the reference account to the task completion node is smaller than or equal to a preset proportion threshold, so that the risk assessment score of the target decision tree model can be used for indicating the risk that the tasks executed by the user corresponding to the path cannot reach the task completion node, and the risk assessment is carried out on the target account.

Referring to fig. 4, fig. 4 is a schematic structural diagram of a data processing apparatus based on a decision tree model according to an embodiment of the present application. The decision tree model-based data processing apparatus 400 includes:

a receiving unit 401, configured to receive a resource data transfer request sent by a target account, where the resource data transfer request carries a task identifier of the target account, where the task identifier is used to indicate a first task;

an obtaining unit 402, configured to obtain first account information and first historical task information of the target account, and input the first account information and the first historical task information into a pre-trained target decision tree model to obtain a risk assessment score of the target account;

A determining unit 403, configured to determine that, when the risk assessment score is less than a risk assessment threshold, a risk assessment result of the target account passes, and determine a first data amount corresponding to the first task;

A verification unit 404, configured to perform identity verification on the target account, and transfer the resource data of the first data amount to the target account if the identity verification result of the target account is passed;

The determining unit 403 is configured to determine, when it is determined that the first task does not reach a task completion node, first node and task abnormality information corresponding to the first task, determine a second data amount according to the task abnormality information, and transfer resource data of the second data amount to the target account, where the second data amount is a data amount smaller than zero.

Further, the decision tree model-based data processing apparatus 400 further includes:

An input unit 405, configured to input the first account information and the first historical task information into a pre-trained target decision tree model, and before obtaining the risk assessment score of the target account, further include:

A building unit 406, configured to build a training sample set, where the training sample set includes a positive sample and a negative sample, where the positive sample and the negative sample are determined according to risk characteristics of second account information of a reference account of the plurality of accounts and risk characteristics of second historical task information;

The determining unit 403 is further configured to determine first proportion data of the number of reference accounts corresponding to the risk features of the second account information to the number of total accounts, and determine second proportion data of the number of reference accounts corresponding to the risk features of the second historical task information to the number of total accounts;

The determining unit 403 is further configured to determine an information gain of a risk feature of the second account information according to the first proportion data, and determine an information gain of a risk feature of the second historical task information according to the second proportion data;

The training unit 407 is configured to input the information gain of the risk feature of the second account information and the information gain of the risk feature of the second historical task information into a preset decision tree model for training, so as to obtain an initial decision tree model.

Further, the above construction unit 406 is specifically configured to:

The obtaining unit 402 is configured to obtain a plurality of paths according to paths from a root node to each leaf node in the initial decision tree model; the method comprises the steps of obtaining the number of negative samples and the total number of samples corresponding to a target path in the paths, wherein the target path is any path in the paths;

The determining unit 403 is further configured to determine a risk assessment score corresponding to the target path by using a ratio of the number of negative samples corresponding to the target path to the total number of samples;

And a labeling unit 408, configured to label the risk assessment score corresponding to the target path at a leaf node of the target path, so as to obtain the target decision tree model.

Further, the verification unit 404 is specifically configured to:

Further, the resource data transfer request also carries a second node corresponding to the first task; the determining unit 403 is specifically configured to:

Further, the determining unit 403 is specifically configured to:

The detailed descriptions of the receiving unit 401, the acquiring unit 402, the determining unit 403, the verifying unit 404, the input unit 405, the constructing unit 406, the training unit 407, and the labeling unit 408 may be directly obtained by referring to the related descriptions in the method embodiments shown in fig. 2 to 3, which are not repeated herein.

Referring to fig. 5, fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present application, and as shown in fig. 5, a computer device 500 according to an embodiment of the present application may include:

Processor 501, transceiver 502, and memory 505, and further, the above-described computer device 500 may further comprise: a user interface 504, and at least one communication bus 503. Wherein a communication bus 503 is used to enable connected communication between these components. The user interface 504 may include a Display screen (Display) and a Keyboard (Keyboard), and the memory 505 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 505 may also optionally be at least one storage device located remotely from the processor 501 and the transceiver 502. As shown in fig. 5, an operating system, a network communication module, a user interface module, and a device control application program may be included in the memory 505, which is one type of computer storage medium.

In the computer device 500 shown in FIG. 5, the transceiver 502 may provide network communication functions to enable communication between servers; while user interface 504 is primarily an interface for providing input to a user; and the processor 501 may be configured to invoke the device control application stored in the memory 505 to perform the following operations:

the transceiver 502 is configured to receive a resource data transfer request sent by a target account, where the resource data transfer request carries a task identifier of the target account, where the task identifier is used to indicate a first task;

The processor 501 is configured to obtain first account information and first historical task information of the target account, and input the first account information and the first historical task information into a pre-trained target decision tree model to obtain a risk assessment score of the target account;

The processor 501 is configured to determine that, when the risk assessment score is less than a risk assessment threshold, a risk assessment result of the target account passes, and determine a first data amount corresponding to the first task;

the processor 501 is configured to perform identity verification on the target account, and transfer the resource data of the first data amount to the target account if the identity verification result of the target account is passed;

The processor 501 is configured to determine, when it is determined that the first task does not reach a task completion node, first node and task abnormality information corresponding to the first task, determine a second data amount according to the task abnormality information, and transfer resource data of the second data amount to the target account, where the second data amount is a data amount smaller than zero.

In one possible implementation manner, before the inputting the first account information and the first historical task information into the pre-trained target decision tree model to obtain the risk assessment score of the target account, the processor 501 is further configured to:

In one possible implementation, the processor 501 constructs a training sample set, specifically for:

In a possible implementation manner, after the training is performed by inputting the information gain of the risk feature of the second account information and the information gain of the risk feature of the second historical task information into a preset decision tree model, the processor 501 is further configured to:

In one possible implementation, the processor 501 is configured to perform authentication on the target account, specifically configured to:

In a possible implementation manner, the resource data transfer request further carries a second node corresponding to the first task; the processor 501 is configured to determine, according to the task anomaly information, a second data volume, specifically configured to:

In a possible implementation manner, the processor 501 is configured to determine, according to the task anomaly information, a second data volume, specifically configured to:

It should be appreciated that in some possible embodiments, the processor 501 may be a central processing unit (central processing unit, CPU), and the processor 501 may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL processors, DSPs), application Specific Integrated Circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The memory 505 may include read only memory and random access memory and provide instructions and data to the processor. A portion of the memory 505 may also include non-volatile random access memory.

In a specific implementation, the computer device 500 may execute, through each functional module built in the computer device, an implementation manner provided by each step in fig. 2 and fig. 3, and specifically, the implementation manner provided by each step may be referred to, which is not described herein again.

Furthermore, it should be noted here that: the embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium stores a computer program executed by the aforementioned computer device, where the computer program includes program instructions, when executed by the aforementioned processor, can perform the description of any method in any of the corresponding embodiments of fig. 2 or fig. 3, and therefore, a detailed description will not be given here. In addition, the description of the beneficial effects of the same method is omitted. For technical details not disclosed in the embodiments of the computer-readable storage medium according to the present application, please refer to the description of the method embodiments of the present application.

Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by hardware associated with computer program instructions, and the above programs may be stored in a computer readable storage medium, and the programs may include processes in the embodiments of the methods when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), a random-access memory (random access memory, RAM), or the like.

It is emphasized that to further guarantee the privacy and security of the data, the data may also be stored in a blockchain node. The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The blockchain (Blockchain), essentially a de-centralized database, is a string of data blocks that are generated in association using cryptographic methods, each of which contains information from a batch of network transactions for verifying the validity (anti-counterfeit) of its information and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

The foregoing disclosure is illustrative of the present application and is not to be construed as limiting the scope of the application, which is defined by the appended claims.

Claims

1. A method for processing data based on a decision tree model, comprising:

Performing identity verification on the target account, and transferring the resource data of the first data amount to the target account when the identity verification result of the target account is passed;

Under the condition that the first task does not reach a task completion node, determining first node and task abnormality information corresponding to the first task, determining second data amount according to the task abnormality information, and transferring resource data of the second data amount to the target account, wherein the second data amount is smaller than zero; wherein,

The resource data transfer request also carries a second node corresponding to the first task; the determining the second data amount according to the task abnormality information includes:

the task abnormality information is indication information for indicating a task execution failure in the case that the first node is a previous node of the second node; acquiring a first calculation method corresponding to the task abnormality information, wherein the first calculation method is used for indicating that the opposite number of the first data volume is determined as the second data volume;

under the condition that the first node is the last node of the task completion node, the task abnormality information is indication information for indicating that the task is not completed according to preset time; acquiring a second calculation method corresponding to the task abnormality information, and determining the second data volume according to the second calculation method; wherein,

Said determining said second amount of data according to said second calculation method comprises:

Determining a third data volume according to the first data volume and the first node, wherein the third data volume is the data volume of the first task reaching the first node, and the third data volume is smaller than the first data volume; a difference between the first data amount and the third data amount is determined as the second data amount.

2. The method of claim 1, wherein the inputting the first account information and the first historical task information into a pre-trained target decision tree model, prior to deriving a risk assessment score for the target account, further comprises:

Constructing a training sample set, wherein the training sample set comprises a positive sample and a negative sample, and the positive sample and the negative sample are determined according to risk characteristics of second account information of a reference account and risk characteristics of second historical task information in a plurality of accounts;

determining first proportion data of the number of reference accounts corresponding to the risk features of the second account information to the number of the total accounts, and determining second proportion data of the number of reference accounts corresponding to the risk features of the second historical task information to the number of the total accounts;

Determining the information gain of the risk characteristic of the second account information according to the first proportion data, and determining the information gain of the risk characteristic of the second historical task information according to the second proportion data;

Inputting the information gain of the risk features of the second account information and the information gain of the risk features of the second historical task information into a preset decision tree model for training to obtain an initial decision tree model.

3. The method of claim 2, wherein the constructing a training sample set comprises:

Determining that the second task executed by the reference account in the preset time length reaches the proportion of the number of task completion nodes to the total number of executed tasks;

And if the proportion is larger than a preset proportion threshold, taking the risk features in the second account information and the risk features in the second historical task information as positive samples, and if the proportion is smaller than the preset proportion threshold, taking the risk features in the second account information and the risk features in the second historical task information as negative samples, and determining the positive samples and the negative samples as the training sample set.

4. The method according to claim 2, wherein inputting the information gain of the risk feature of the second account information and the information gain of the risk feature of the second historical task information into a preset decision tree model for training, and after obtaining an initial decision tree model, further comprises:

determining a risk assessment score corresponding to the target path by means of the proportion of the number of negative samples corresponding to the target path to the total number of samples;

and labeling the risk assessment score corresponding to the target path at a leaf node of the target path to obtain the target decision tree model.

5. The method of claim 1, wherein the authenticating the target account comprises:

6. A decision tree model-based data processing apparatus, comprising:

the determining unit is used for determining that the risk assessment result of the target account passes and determining a first data volume corresponding to the first task under the condition that the risk assessment score is smaller than a risk assessment threshold value;

The verification unit is used for carrying out identity verification on the target account, and transferring the resource data of the first data amount to the target account when the identity verification result of the target account is passed;

The determining unit is further configured to determine first node and task abnormality information corresponding to the first task, determine a second data amount according to the task abnormality information, and transfer resource data of the second data amount to the target account, where the second data amount is a data amount smaller than zero, if it is determined that the first task does not reach a task completion node; wherein,

7. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the method of any of claims 1-5.

8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, which is executed by a processor to implement the method of any of claims 1-5.