CN113821699A

CN113821699A - Method, apparatus, device and medium for processing data

Info

Publication number: CN113821699A
Application number: CN202110141883.8A
Authority: CN
Inventors: 莫增文
Original assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Wodong Tianjun Information Technology Co Ltd
Current assignee: Beijing Jingdong Century Trading Co Ltd; Beijing Wodong Tianjun Information Technology Co Ltd
Priority date: 2021-02-02
Filing date: 2021-02-02
Publication date: 2021-12-21

Abstract

The application discloses a method, a device, equipment and a medium for processing data, and relates to the field of computers, in particular to the field of artificial intelligence. The specific implementation scheme is as follows: acquiring input data; determining a data processing result according to the input data and a preset model set; in response to determining that the data processing result meets a preset verification condition, verifying the data processing result to obtain output data; and outputting the output data. This procedure can improve the data processing effect.

Description

Method, apparatus, device and medium for processing data

Technical Field

The present disclosure relates to the field of computers, and more particularly, to the field of artificial intelligence techniques, and more particularly, to a method, apparatus, device, and medium for processing data.

Background

At present, various models have been widely applied to various fields to solve corresponding technical problems, for example, in a financial wind control system, a user's credit can be graded using a wind control model.

In practice it has been found that using a single model to process data, the resulting accuracy of the output data is closely related to the model's own performance. If the model has some problems of unstable output, low accuracy and the like, the data processing effect is poor.

Disclosure of Invention

The present disclosure provides a method, apparatus, device, and medium for processing data.

According to an aspect of the present disclosure, there is provided a method for processing data, including: acquiring input data; determining a data processing result according to the input data and a preset model set; in response to determining that the data processing result meets a preset verification condition, verifying the data processing result to obtain output data; and outputting the output data.

According to another aspect of the present disclosure, there is provided an apparatus for processing data, including: a data acquisition unit configured to acquire input data; a data processing unit configured to determine a data processing result according to the input data and a preset model set; the data verification unit is configured to respond to the fact that the data processing result meets a preset verification condition, verify the data processing result and obtain output data; a data output unit configured to output the output data.

According to another aspect of the present disclosure, there is provided an electronic device for processing data, comprising: one or more computing units; a storage unit for storing one or more programs; when the one or more programs are executed by the one or more computing units, the one or more computing units are caused to implement the method for processing data as described in any one of the above.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method for processing data as any one of the above.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements a method for processing data as any one of the above.

According to the technology of the application, a method for processing data is provided, the data processing result can be determined by using at least one model in the model set and input data, and the reliability of the data processing result is improved. And further, the data processing result can be verified under the condition that the data processing result meets the verification condition to obtain output data, and the output data is output. Through the verification process, more accurate output data can be obtained, and therefore the data processing effect is improved.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

fig. 1 is an exemplary system architecture diagram according to a first embodiment of the present disclosure;

FIG. 2 is a schematic diagram of a method for processing data according to a second embodiment of the present disclosure;

FIG. 3 is a diagram of a data processing scenario in which embodiments of the present disclosure may be implemented;

FIG. 4 is a schematic diagram of a method for processing data according to a third embodiment of the present disclosure;

FIG. 5 is a schematic diagram of an apparatus for processing data according to a fourth embodiment of the present disclosure;

FIG. 6 is a block diagram of an electronic device for implementing a method for processing data of an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Fig. 1 is an exemplary system architecture diagram according to a first embodiment of the present disclosure, illustrating an exemplary system architecture 100 to which embodiments of the method for processing data or the apparatus for processing data of the present application may be applied.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, and 103 may be electronic devices such as a mobile phone, a computer, and a tablet, and input data in a specific application scenario may be acquired in the

terminal devices

101, 102, and 103, for example, user data for applying for loan input by a user in a financial wind control scenario, or user face data acquired in a face recognition scenario. In these examples, the user data and the user face data for applying for loan are input data in the embodiment of the present application.

The

terminal apparatuses

101, 102, and 103 may be hardware or software. When the

terminal devices

101, 102, 103 are hardware, they may be various electronic devices including, but not limited to, televisions, smart phones, tablet computers, e-book readers, car-mounted computers, laptop portable computers, desktop computers, and the like. When the

terminal apparatuses

101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.

The server 105 may be a server that provides various services, for example, input data transmitted by the

terminal devices

101, 102, and 103 may be acquired through the network 104, a model set may be stored in the server 105, and models in the model set may have a serial connection relationship or a parallel connection relationship. After the input data are acquired, the input data can be subjected to data processing through the models in the model set, and a data processing result is obtained. Further, if the data processing result meets the preset verification condition, it indicates that the data processing result has a certain deviation, and at this time, the data processing result may be verified to obtain output data within a reasonable range. The server 105 can transmit the resultant output data of a reasonable processing range to the

terminal apparatuses

101, 102, 103 via the network 104 to cause the

terminal apparatuses

101, 102, 103 to output the output data. For example, in a financial wind scenario where a user inputs user data for applying for a loan, the

terminal devices

101, 102, 103 may output a credit score corresponding to the user data. Optionally, an application result corresponding to the credit score may be output, and the application result may include credit granting or credit refusal.

The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server 105 is software, it may be implemented as multiple pieces of software or software modules (e.g., to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.

It should be noted that the method for processing data provided in the embodiment of the present application may be executed by the server 105, and may also be executed by the

terminal devices

101, 102, and 103. Accordingly, the apparatus for processing data may be provided in the server 105, or may be provided in the

terminal devices

101, 102, 103.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to fig. 2, fig. 2 is a schematic diagram of a method for processing data according to a second embodiment of the present disclosure, illustrating a flow 200 of one embodiment of a method for processing data according to the present application. The method for processing data of the embodiment comprises the following steps:

step 201, input data is acquired.

In this embodiment, the execution subject (such as the server 105 or the

terminal devices

101, 102, and 103) may obtain input data that needs to be processed in different application scenarios. The application scenario may include, but is not limited to, financial wind control, target recognition, image classification, and the like, which is not limited in this embodiment. Different data are required to be processed under different application scenarios. For example, a financial wind control scene needs to perform data processing on user identity data applying for loan, a target recognition scene needs to perform data processing on object or face data to be recognized, an image classification scene needs to perform data processing on image data, and the like. Further, the input data may be data stored in the execution subject in advance, or may be data acquired in real time.

Step 202, determining a data processing result according to the input data and a preset model set.

In this embodiment, the preset model set includes at least one model, and the at least one model can be combined in a certain combination manner to process data. The combination mode may be a serial combination, a parallel combination, or both a serial combination and a parallel combination, which is not limited in this embodiment. For the serial combination, the input data may be input into the first model to obtain the data output by the first model, and then the data output by the first model is input into the next model until the last model outputs the final data, which is the data processing result. For the parallel combination, the input data may be input into at least one parallel model, the data corresponding to the input data may be output from each model, and the data may be integrated to obtain the data processing result. In the case where the combination method is a serial combination and a parallel combination, the processing may be performed in a serial combination model according to the processing method corresponding to the serial combination, and the processing may be performed in a parallel combination model according to the processing method corresponding to the parallel combination.

Optionally, in the process of processing the input data by using each model in the model set, a logic calculation function may be introduced, and auxiliary data processing such as logic judgment, logic operation, and function may be set according to the requirements of the application scenario. Where the logical decision may include greater than, less than, or equal to such size decision logic, the logical operation may include and, or, not, the function may include a weighted average, a weighted sum, and the like. When the logical operation of the AND is adopted, if the data output by a certain model in the model set does not meet the required data condition, the threads of other models are terminated, and the consumption of extra resources can be reduced. When logical operation of OR is adopted, if data output by a certain model in the model set meets required data conditions, the thread of the other model is terminated. That is, the operation of the model is managed based on the logical operation state in the model set.

And step 203, responding to the data processing result meeting the preset verification condition, verifying the data processing result, and obtaining output data.

In this embodiment, the execution main body may preset a data range corresponding to the normal data processing result. After the data processing result is obtained, the data processing result may be compared with a preset data range, and if the comparison result indicates that the data in the data processing result exceeds the preset data range, it is determined that the data processing result meets a preset verification condition. Or, the preset verification condition may also be whether a verification instruction manually triggered by a worker based on the data processing result is received, and if the verification instruction is received, it is indicated that the data processing result meets the preset verification condition.

Further, the verification may include correcting and covering the data processing result. Wherein, the correction refers to that some adjustment is made on the basis of the data processing result, and the adjusted data processing result is used as output data; overwriting refers to discarding the data processing result and re-determining the output data according to the input data.

And step 204, outputting output data.

In this embodiment, the execution subject may directly output the output data, or may obtain a corresponding determination conclusion based on the output data in combination with an application scenario, and output the determination conclusion, which is not limited in this embodiment.

With continued reference to fig. 3, fig. 3 is a data processing scenario diagram illustrating one application scenario of a method for processing data according to the present application in which embodiments of the present disclosure may be implemented. In the application scenario of fig. 3, the above-described method for processing data may be applied in the scenario of financial wind control. As shown in fig. 3, first, input data, that is, user data 301 is acquired. The user data 301 may be data of the user who needs to apply for a loan, such as data of the user's age, city, income record, credit record, etc. that needs to apply for a loan. Further, the model combiner 302 includes the above-mentioned model set, and the model set may include at least an artificial intelligence model for credit scoring. And inputting the filtered user data 301 into an artificial intelligence model to obtain data output by the model. In addition, other non-artificial intelligence models for scoring may also be included in the set of models, and user data 301 may be input into these other models to obtain data output by the models. And integrating the output data to obtain a data processing result. At this time, it may be determined whether the data processing result satisfies the check condition based on the model insurance module 303. For example, the data processing result indicates that the score of the user a is 30, the model insurance module 303 may calculate a difference between the score of the user a, which is 30, and a preset score, and if the difference exceeds a certain threshold (e.g., 20), check the data processing result to obtain a final scoring result 304.

The method for processing data provided by the above embodiment of the present application may determine the data processing result by using at least one model in the model set and the input data, thereby improving the reliability of the data processing result. And further, the data processing result can be verified under the condition that the data processing result meets the verification condition to obtain output data, and the output data is output. Through the verification process, more accurate output data can be obtained, and therefore the data processing effect is improved.

With continued reference to fig. 4, fig. 4 is a schematic diagram of a method for processing data according to a third embodiment of the present disclosure, illustrating a flow 400 of another embodiment of a method for processing data according to the present application. As shown in fig. 4, the method for processing data of the present embodiment may include the steps of:

step 401, input data is acquired.

In this embodiment, please refer to the detailed description of step 201 for the detailed description of step 401, which is not repeated herein.

At step 402, at least one data filter condition is obtained.

In this embodiment, the data filtering condition refers to a data determination rule obtained by combining based on conventional threshold determination, basic logic operation, and/or custom rule determination, and retains data that meets the data determination rule, and discards data that does not meet the data determination rule. Wherein, each data filtering condition can be a screening condition with different granularity.

And 403, sequentially filtering the input data by using at least one data filtering condition according to a preset filtering sequence to obtain filtered input data.

In this embodiment, the preset filtering order is a preset order using the data filtering conditions, and when data filtering is performed, at least one data filtering condition may be sequentially used for filtering according to the filtering order. Optionally, the preset filtering order may be a filtering granularity order from coarse to fine, and during data filtering, coarse-granularity data filtering is performed first, and then fine-granularity data filtering is performed. Each data filtering condition can be a code segment which is independent of each other, before each data filtering condition is accessed, the data formats of the data filtering conditions can be unified, and then each data filtering condition with the unified formats is introduced. Alternatively, a linked list structure may be used to introduce the respective data filter conditions.

In some optional implementations of this embodiment, the following steps may also be performed: counting the information of the data filtered by each data filtering condition; and outputting the information obtained by statistics.

In this implementation, in the data filtering process, the information of the data filtered by each data filtering condition can be counted by using the filtering counter and the performance counter in the filtering component, and the information obtained by counting is output, so that the execution main body can adjust the data filtering condition according to the information. The filtering counter is used for counting the total data quantity, the data interception quantity and the like of each data filtering condition, and the performance counter is used for counting the filtering time consumption corresponding to each data filtering condition. For example, if a certain data filtering condition can filter one hundred percent of data, after outputting information, the execution subject may delete the filtering condition; or, if the filtering granularity of a certain data filtering condition is relatively coarse, after the information is output, the execution main body may pre-position the data filtering condition, execute the data filtering condition first, and then execute other data filtering conditions.

Step 404, determining a data processing result according to the filtered input data and the model set.

In this embodiment, please refer to the detailed description of step 202 for the detailed description of step 404, and the input data in step 202 may be replaced by the filtered input data, which is not described herein again.

In some optional implementation manners of this embodiment, determining a data processing result according to the input data and a preset model set includes: for each model in the model set, converting the input data into a data format corresponding to the model to obtain model input data; determining a model processing result corresponding to the model according to the model input data and the model; carrying out format conversion on each model processing result to obtain each model processing result conforming to a target format; and integrating the processing results of the models conforming to the target format to obtain a data processing result.

In this implementation manner, the model set may include at least one model, and for each model, the input data may be converted into a data format corresponding to the model through an input mapping layer corresponding to the model set, so as to obtain model input data of each model. And determining the model processing result of each model according to each model and the model input data of the model. And carrying out format conversion on the model processing results of the models through an output mapping layer corresponding to the model set to obtain the model processing results conforming to the target format, and integrating to obtain the data processing results. The integration may include logic calculations, averaging calculations, weighted score calculations, and the like. The process can realize the unification of the input and output formats of each model in the model set, and is convenient for accessing various different models.

Step 405, determining a verification model according to the execution logic of the models in the model set.

In this embodiment, the verification model may have the same execution logic as the models in the model set, for example, the models in the model set may be used for risk scoring for the user, and then the verification model may also be used for risk scoring for the user. Or, the calibration model may determine a corresponding correction logic operation manner according to the execution logic of the models in the model set, as the execution logic of the calibration model, which is not limited in this embodiment.

In some optional implementations of this embodiment, determining the verification model according to the execution logic of the models in the model set includes: in response to determining that the black box model is included in the set of models, a previous version model of the black box model is determined to be the verification model.

In this embodiment, if the model set includes the black box model, the previous version model of the black box model may be determined as the verification model, so that when the version of the black box model is updated, the black box model in the historical version is used to improve the stability of the output data.

Step 406, in response to determining that the data processing result meets a preset verification condition, determining output data based on the verification model and the data processing result; or determining output data according to the data output by the verification model.

In this embodiment, if the data in the data processing result satisfies the first data range, the data processing result may be corrected by using the verification model to obtain output data; if the data in the data processing result meets the second data range, the input data or the filtered input data can be input into the verification model to obtain the data output by the verification model, and the data output by the verification model is used as the output data. Optionally, the first data range is a data range with a smaller deviation value from the standard data range, and the second data range is a data range with a larger deviation value from the standard data range. Optionally, in the data verification process, the data verification record of this time may be recorded, so that the worker optimizes the verification model based on the data verification record.

In some optional implementations of this embodiment, determining the output data according to the data output by the verification model includes: determining data output by the verification model based on the verification model and the input data; and determining output data according to the data output by the verification model.

In this implementation manner, if the verification model is the previous version of the black box model, the input data or the filtered input data may be input into the previous version of the black box model, and the data output by the previous version of the black box model may be used as the output data.

Step 407, output data.

In this embodiment, please refer to the detailed description of step 204 for the detailed description of step 407, which is not repeated herein.

As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the flow 400 of the method for processing data in this embodiment may also sequentially filter input data according to at least one data filtering condition and a corresponding filtering order, so as to filter out data that is not needed to be used in the current application scenario, and improve data validity in the data processing process. And the information of the filtered data obtained by statistics can be output for the user to adjust the data filtering condition. In addition, when data verification is carried out, a data processing result adjusted based on the verification model can be selected according to the requirements of an actual application scene, the corrected data is used as output data, and the data output by the verification model can also be directly used as the output data, so that various different verification requirements are met. And for the condition that the model set comprises the black box model, the previous version model of the black box model can be determined as the verification model, the output result of the previous version model can be used for maintaining stability when the version of the black box model is updated, and the output data is more stable and reliable. And the input and the output of each model in the model set can be subjected to format conversion, so that the model set can be adapted to various different models, and the expandability is stronger.

With further reference to fig. 5, fig. 5 is a schematic diagram of an apparatus for processing data according to a fourth embodiment of the present disclosure, which provides an embodiment of an apparatus for processing data, the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be applied to various terminal devices or servers.

As shown in fig. 5, the apparatus 500 for processing data of the present embodiment includes: a data acquisition unit 501, a data processing unit 502, a data verification unit 503, and a data output unit 504.

A data acquisition unit 501 configured to acquire input data.

A data processing unit 502 configured to determine a data processing result according to the input data and a preset model set.

And a data verification unit 503 configured to verify the data processing result in response to determining that the data processing result satisfies a preset verification condition, resulting in output data.

A data output unit 504 configured to output the output data.

In some optional implementations of this embodiment, the data processing unit 502 is further configured to: acquiring at least one data filtering condition; sequentially filtering input data by using at least one data filtering condition according to a preset filtering sequence to obtain filtered input data; and determining a data processing result according to the filtered input data and the model set.

In some optional implementations of this embodiment, the apparatus further includes: an information statistical unit configured to count information of the data filtered by each data filtering condition; an information output unit configured to output the statistically derived information.

In some optional implementations of this embodiment, the method further includes: and the model determining unit is configured to determine the verification model according to the execution logic of the models in the model set.

In some optional implementations of this embodiment, the data checking unit 503 is further configured to: determining output data based on the verification model and the data processing result; or determining output data according to the data output by the verification model.

In some optional implementations of this embodiment, the model determining unit is further configured to: in response to determining that the black box model is included in the set of models, a previous version model of the black box model is determined to be the verification model.

In some optional implementations of this embodiment, the data checking unit 503 is further configured to: determining data output by the verification model based on the verification model and the input data; and determining output data according to the data output by the verification model.

In some optional implementations of this embodiment, the data processing unit 502 is further configured to: for each model in the model set, converting the input data into a data format corresponding to the model to obtain model input data; determining a model processing result corresponding to the model according to the model input data and the model; carrying out format conversion on each model processing result to obtain each model processing result conforming to a target format; and integrating the processing results of the models conforming to the target format to obtain a data processing result.

It should be understood that the units 501 to 504, which are described in the apparatus 500 for processing data, correspond to the respective steps in the method described with reference to fig. 2, respectively. Thus, the operations and features described above for the method for processing data are equally applicable to the apparatus 500 and the units included therein and will not be described again here.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

Fig. 6 shows a block diagram of an electronic device 600 for implementing a method for processing data of an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the respective methods and processes described above, such as a method for processing data. For example, in some embodiments, the method for processing data may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the method for processing data described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured by any other suitable means (e.g. by means of firmware) to perform the method for processing data.

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method for processing data, comprising:

acquiring input data;

determining a data processing result according to the input data and a preset model set;

in response to determining that the data processing result meets a preset verification condition, verifying the data processing result to obtain output data;

and outputting the output data.

2. The method of claim 1, wherein determining a data processing result from the input data and a preset set of models comprises:

acquiring at least one data filtering condition;

sequentially filtering the input data by using the at least one data filtering condition according to a preset filtering sequence to obtain filtered input data;

and determining a data processing result according to the filtered input data and the model set.

3. The method of claim 2, wherein the method further comprises:

counting the information of the data filtered by each data filtering condition;

and outputting the information obtained by statistics.

4. The method of claim 1, wherein the method further comprises:

and determining a verification model according to the execution logic of the models in the model set.

5. The method of claim 4, wherein said verifying said data processing results to obtain output data comprises:

determining the output data based on the verification model and the data processing result; or

And determining the output data according to the data output by the verification model.

6. The method of claim 4, wherein determining a verification model from execution logic of the models in the set of models comprises:

in response to determining that a black-box model is included in the set of models, determining a last version model of the black-box model as the verification model.

7. The method of claim 6, wherein said verifying said data processing results to obtain output data comprises:

determining data output by the verification model based on the verification model and the input data;

8. The method of claim 1, wherein determining a data processing result from the input data and a preset set of models comprises:

for each model in the model set, converting the input data into a data format corresponding to the model to obtain model input data;

determining a model processing result corresponding to the model according to the model input data and the model;

carrying out format conversion on each model processing result to obtain each model processing result conforming to a target format;

and integrating the processing results of the models conforming to the target format to obtain the data processing result.

9. An apparatus for processing data, comprising:

a data acquisition unit configured to acquire input data;

a data processing unit configured to determine a data processing result according to the input data and a preset model set;

the data verification unit is configured to respond to the fact that the data processing result meets a preset verification condition, verify the data processing result and obtain output data;

a data output unit configured to output the output data.

10. The apparatus of claim 8, wherein the data processing unit is further configured to:

acquiring at least one data filtering condition;

11. The apparatus of claim 10, wherein the apparatus further comprises:

an information statistical unit configured to count information of the data filtered by each data filtering condition;

an information output unit configured to output the statistically derived information.

12. The apparatus of claim 9, wherein the apparatus further comprises:

and the model determining unit is configured to determine the verification model according to the execution logic of the models in the model set.

13. The apparatus of claim 12, wherein the data verification unit is further configured to:

14. An electronic device that performs a method for processing data, comprising:

at least one computing unit; and

a storage unit communicatively coupled to the at least one computing unit; wherein the content of the first and second substances,

the storage unit stores instructions executable by the at least one computing unit to enable the at least one computing unit to perform the method of any one of claims 1-8.

15. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-8.

16. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-8.