Method and system for identifying a human or machine

1. A method, comprising:

in response to a human or machine identification request sent by a client, sending information pertaining to a first operation configured to identify human or machine to the client to be displayed;

receiving a second operation that is submitted by the client, the second operation being triggered in response to an interactive operation performed on an operation track associated with the first operation; and

identifying a triggering entity for the second operation as either a human or a machine based on a behavior characteristic model, wherein the behavior characteristic model includes a classifier for human operations and machine operations, and the classifier is trained based on operation samples, and wherein the identifying of the triggering entity for the second operation as either a human or a machine based on the behavior characteristic model includes:

extracting to-be-identified behavioral characteristic data of the second operation; and

in response to a determination that the to-be-identified behavioral characteristic data conforms to a human operation, determining that the triggering entity of the second operation is a human; or in response to a determination that the to-be-identified behavioral characteristic data conforms to a machine operation, determining that the triggering entity of the second operation is a machine; and

in response to the determination that the to-be-identified behavioral characteristic data of the second operation conforms to the human operation, responding to an access request sent by the human, or in response to the determination that the to-be-identified behavioral characteristic data conforms to a machine operation, rejecting an access request sent by the machine.

2. The method as described in claim 1 , wherein behavioral characteristic data of the operation samples includes operative slide time length.

3. The method as described in claim 1 , wherein behavioral characteristic data of the operation samples includes operative slide time length, the operative slide time length being obtained by determining a total time length of valid operations of the operation samples.

4. The method as described in claim 1 , wherein behavioral characteristic data of the behavior characteristic model includes at least one of: operative slide time length of at least one segment, center line-deviation frequency of at least one segment, and average deviation distance of at least one segment.

5. The method as described in claim 1 , wherein:

behavioral characteristic data of the behavior characteristic model includes at least one of: operative slide time length of at least one segment, center line-deviation frequency of at least one segment, and average deviation distance of at least one segment;

the operative slide time length of the at least one segment is obtained based at least in part on:

dividing a total operation time of the operation samples into a plurality of equal segments; and

calculating a time length of at least one operation segment;

the center line-deviation frequency of the at least one segment is obtained based at least in part on:

dividing the total operation time of the operation samples into a plurality of equal segments; and

calculating a number of deviations from a center axis for at least one operation segment; and

the average deviation distance of the at least one segment is obtained based on:

dividing the total operation time of at least one of the operation samples into a plurality of equal segments; and

calculating a distance of deviations from the center axis for the at least one operation segment.

6. The method as described in claim 1 , wherein behavioral characteristic data of the operation samples comprises: an operation triggering initial position and an operation triggering terminal position.

7. A method, comprising:

in response to a human or machine identification request sent by a client, sending information pertaining to a first operation configured to identify human or machine to the client to be displayed; receiving a second operation that is submitted by the client, the second operation being triggered in response to an interactive operation performed on an operation track associated with the first operation; and

identifying a triggering entity for the second operation as either a human or a machine based on a behavior characteristic model, wherein the behavior characteristic model includes a classifier for human operations and machine operations, and the classifier is trained based on operation samples; and

in response to a determination that to-be-identified behavioral characteristic data of the second operation conforms to a human operation, responding to an access request sent by a human, or in response to a determination that the to-be-identified behavioral characteristic data conforms to a machine operation, rejecting an access request sent by a machine; wherein:

behavioral characteristic data of the operation samples comprises: operation triggering initial position and operation triggering terminal position; and

the operation triggering initial position and the operation triggering terminal position are obtained based on:

determining coordinates in response to an operation sample beginning to undergo triggering as the operation triggering initial position; or

determining coordinates in response to the operation sample ceasing to undergo triggering as the operation triggering terminal position.

8. A device, comprising:

one or more processors; and

one or more memories coupled with the one or more processors, wherein the one or more memories are configured to provide the one or more processors with instructions which when executed cause the one or more processors to:

in response to a human or machine identification request sent by a client, send information pertaining to a first operation configured to identify human or machine to the client to be displayed;

receive a second operation that is submitted by the client, the second operation being triggered in response to an interactive operation performed on an operation track associated with the first operation; and

identify a triggering entity for the second operation as either a human or a machine based on a behavior characteristic model, wherein the behavior characteristic model includes a classifier for human operations and machine operations, and the classifier is trained based on operation samples, and wherein to identify a triggering entity for the second operation as either a human or a machine based on the behavior characteristic model includes to:

extract to-be-identified behavioral characteristic data of the second operation; and

in response to the to-be-identified behavioral characteristic data conforming to a human operation, determine that the triggering entity of the second operation is human, or in response to the to-be-identified behavioral characteristic data conforming to a machine operation, determine that the triggering entity of the second operation is a machine; and

in response to a determination that the to-be-identified behavioral characteristic data of the second operation conforms to a human operation, respond to an access request sent by a corresponding human, or in response to the determination that the to-be-identified behavioral characteristic data conforms to the machine operation, reject an access request sent by a corresponding machine.

9. The device as described in claim 8 , wherein behavioral characteristic data of the operation samples includes operative slide time length.

10. The device as described in claim 8 , wherein behavioral characteristic data of the operation samples includes operative slide time length, the operative slide time length being obtained by determining a total time length of valid operations of the operation samples.

11. The device as described in claim 8 , wherein behavioral characteristic data of the behavior characteristic model includes at least one of: operative slide time length of at least one segment, center line-deviation frequency of at least one segment, and average deviation distance of at least one segment.

12. The device as described in claim 8 , wherein:

behavioral characteristic data of the behavior characteristic model includes at least one of: operative slide time length of at least one segment, center line-deviation frequency of at least one segment, and average deviation distance of at least one segment;

the operative slide time length of the at least one segment is obtained based at least in part on:

dividing a total operation time of the operation samples into a plurality of equal segments; and

calculating a time length of at least one operation segment;

the center line-deviation frequency of the at least one segment is obtained based at least in part on:

dividing the total operation time of the operation samples into a plurality of equal segments; and

calculating a number of deviations from a center axis for at least one operation segment; and

the average deviation distance of the at least one segment is obtained based on:

dividing the total operation time of at least one of the operation samples into a plurality of equal segments; and

calculating a distance of deviations from the center axis for the at least one operation segment.

13. The device as described in claim 8 , wherein behavioral characteristic data of the operation samples comprises: an operation triggering initial position and an operation triggering terminal position.

14. A computer program product being embodied in a tangible non-transitory computer readable storage medium and comprising computer instructions for:

in response to a human or machine identification request sent by a client, sending information pertaining to a first operation configured to identify human or machine to the client to be displayed;

receiving a second operation that is submitted by the client, the second operation being triggered in response to an interactive operation performed on an operation track associated with the first operation;

identifying a triggering entity for the second operation as either a human or a machine based on a behavior characteristic model, wherein the behavior characteristic model includes a classifier for human operations and machine operations, and the classifier is trained based on operation samples, and wherein the identifying of the triggering entity for the second operation as either a human or a machine based on the behavior characteristic model includes:

extracting to-be-identified behavioral characteristic data of the second operation; and

in response to a determination that the to-be-identified behavioral characteristic data conforms to a human operation, determining that the triggering entity of the second operation is a human; or in response to a determination that the to-be-identified behavioral characteristic data conforms to a machine operation, determining that the triggering entity of the second operation is a machine; and

in response to the determination that the to-be-identified behavioral characteristic data of the second operation conforms to a human operation, responding to an access request sent by the human; or in response to a determination that the to-be-identified behavioral characteristic data conforms to a machine operation, rejecting an access request sent by the machine.