Information processing method and device, and method and device for displaying dynamic information

1. A computer-implemented method, comprising:

collecting a plurality of types of user data;

storing the plurality of types of user data into a plurality of data queues, wherein each data queue of the plurality of data queues has a predetermined maximum length and stores one type of user data of the plurality of types of user data, and wherein the plurality of types of user data stored in the plurality of data queues is used to update data in a cache;

determining that a current time is a scheduled time for updating the cache, wherein the cache is updated periodically;

in response to determining that a current time is a scheduled time for updating the cache, determining a weighting coefficient and a current length of user data for each data queue of the plurality of data queues;

selecting a priority data queue from the plurality of data queues based on the weighting coefficient and the current length of user data corresponding to each data queue of the plurality of data queues; and

writing one or more pieces of user data stored in the priority data queue to the cache.

2. The computer-implemented method of claim 1 , wherein the weighting coefficient is determined based on a popularity value of a corresponding type of user data, and the popularity value is determined based on an amount of the corresponding type of user data generated in a predetermined period of time.

3. The computer-implemented method of claim 1 , wherein the weighting coefficient is determined based on a manual intervention value of a corresponding type of user data, the manual intervention value is set by a back end administrator, and the manual intervention value is determined based on an information value of the corresponding type of user data.

4. The computer-implemented method of claim 1 , wherein the plurality of types of user data includes front-end user data and back-end user data, wherein the front-end user data includes at least one of browsing data, social media interaction data, or search data, and the back-end user data includes transaction data.

5. The computer-implemented method of claim 1 , wherein selection of the priority data queue from the plurality of data queues is based on:

multiplying the weighting coefficient and the current length of user data for each of the plurality of data queues; and

selecting, as the priority data queue, a data queue that has a largest multiplication value.

6. The computer-implemented method of claim 1 , wherein a predetermined maximum length of a particular data queue is larger if a corresponding type of user data associated with the particular data queue has a higher information value.

7. A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform operations comprising:

collecting a plurality of types of user data;

storing the plurality of types of user data into a plurality of data queues, wherein each data queue of the plurality of data queues has a predetermined maximum length and stores one type of user data of the plurality of types of user data, and wherein the plurality of types of user data stored in the plurality of data queues is used to update data in a cache;

determining that a current time is a scheduled time for updating the cache, wherein the cache is updated periodically;

in response to determining that a current time is a scheduled time for updating the cache, determining a weighting coefficient and a current length of user data for each data queue of the plurality of data queues;

selecting a priority data queue from the plurality of data queues based on the weighting coefficient and the current length of user data corresponding to each data queue of the plurality of data queues; and

writing one or more pieces of user data stored in the priority data queue to the cache.

8. The non-transitory, computer-readable medium of claim 7 , wherein the weighting coefficient is determined based on a popularity value of a corresponding type of user data, and the popularity value is determined based on an amount of the corresponding type of user data generated in a predetermined period of time.

9. The non-transitory, computer-readable medium of claim 7 , wherein the weighting coefficient is determined based on a manual intervention value of a corresponding type of user data, the manual intervention value is set by a back end administrator, and the manual intervention value is determined based on an information value of the corresponding type of user data.

10. The non-transitory, computer-readable medium of claim 7 , wherein the plurality of types of user data includes front-end user data and back-end user data, wherein the front-end user data includes at least one of browsing data, social media interaction data, or search data, and the back-end user data includes transaction data.

11. The non-transitory, computer-readable medium of claim 7 , wherein selection of the priority data queue from the plurality of data queues is based on:

multiplying the weighting coefficient and the current length of user data for each of the plurality of data queues; and

selecting, as the priority data queue, a data queue that has a largest multiplication value.

12. The non-transitory, computer-readable medium of claim 7 , wherein a predetermined maximum length of a particular data queue is larger if a corresponding type of user data associated with the particular data queue has a higher information value.

13. A computer-implemented system, comprising:

one or more computers; and

one or more computer memory devices interoperably coupled with the one or more computers and having tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more computers, perform one or more operations comprising:

collecting a plurality of types of user data;

storing the plurality of types of user data into a plurality of data queues, wherein each data queue of the plurality of data queues has a predetermined maximum length and stores one type of user data of the plurality of types of user data, and wherein the plurality of types of user data stored in the plurality of data queues is used to update data in a cache;

determining that a current time is a scheduled time for updating the cache, wherein the cache is updated periodically;

in response to determining that a current time is a scheduled time for updating the cache, determining a weighting coefficient and a current length of user data for each data queue of the plurality of data queues;

selecting a priority data queue from the plurality of data queues based on the weighting coefficient and the current length of user data corresponding to each data queue of the plurality of data queues; and

writing one or more pieces of user data stored in the priority data queue to the cache.

14. The computer-implemented system of claim 13 , wherein the weighting coefficient is determined based on a popularity value of a corresponding type of user data, and the popularity value is determined based on an amount of the corresponding type of user data generated in a predetermined period of time.

15. The computer-implemented system of claim 13 , wherein the plurality of types of user data includes front-end user data and back-end user data, wherein the front-end user data includes at least one of browsing data, social media interaction data, or search data, and the back-end user data includes transaction data.

16. The computer-implemented system of claim 13 , wherein selection of the priority data queue from the plurality of data queues is based on:

multiplying the weighting coefficient and the current length of user data for each of the plurality of data queues; and

selecting, as the priority data queue, a data queue that has a largest multiplication value.

17. The computer-implemented system of claim 13 , wherein a predetermined maximum length of a particular data queue is larger if a corresponding type of user data associated with the particular data queue has a higher information value.

18. The computer-implemented system of claim 13 , wherein the weighting coefficient is determined based on a manual intervention value of a corresponding type of user data, the manual intervention value is set by a back end administrator, and the manual intervention value is determined based on an information value of the corresponding type of user data.