CN106970832B - Information processing method and device and computer equipment - Google Patents
Information processing method and device and computer equipment Download PDFInfo
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
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Abstract
The invention relates to an information processing method, an information processing device and information processing equipment, wherein the information processing method comprises the following steps: acquiring an event occurring within a preset time period and the time of the event; detecting whether the size of the occupied space in the first cache reaches a preset space threshold value; if so, shifting the earliest added event in the first cache out of the first cache; adding the events into the first cache according to the time sequence of the events, and updating the occurrence times corresponding to the events added into the first cache; calculating the occurrence frequency of the event according to the occurrence frequency corresponding to the event stored in the first cache; and determining the event with the occurrence frequency larger than a preset threshold as the hot event. The information processing method, the device and the equipment can detect the hot event from the events occurring in the preset time period.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to an information processing method and apparatus, and a computer device.
Background
A hot event refers to an event that occurs very frequently over a period of time. The occurrence of the hot event generally causes wide attention and strong reverberation, and when the hot event occurs, if the information of the hot event can be known as early as possible, the method can well promote the processing of some major emergencies, public opinion monitoring and the like.
Because the number of events occurring in a unit time is massive, most of the events have a small number of occurrences, and only a small number of events have a high occurrence frequency, if all events occurring in the unit time are to be recorded and counted, then the occurrences of all events are sorted, and hot events in the events are counted, so that the requirements on the memory capacity and the processing speed of a processor are high.
Disclosure of Invention
The embodiment of the invention provides an information processing method, an information processing device and computer equipment.
An information processing method comprising:
acquiring an event occurring within a preset time period and the time of the event;
detecting whether the size of the occupied space in the first cache reaches a preset space threshold value;
if so, shifting the earliest added event in the first cache out of the first cache;
adding the events into the first cache according to the time sequence of the events, and updating the occurrence times corresponding to the events added into the first cache;
calculating the occurrence frequency of the event according to the occurrence frequency corresponding to the event stored in the first cache;
and determining the event with the occurrence frequency larger than a preset threshold as the hot event.
A hotspot event detection device, comprising:
the event acquisition module is used for acquiring events occurring in a preset time period and the time of the events;
the storage space detection module is used for detecting whether the size of the occupied space in the first cache reaches a preset space threshold value;
an event moving-out module, configured to move out an event added earliest in the first cache from the first cache if the size of the occupied space in the first cache reaches a preset space threshold;
the event storage module is used for adding the events into a first cache according to the time sequence of the events, and updating the occurrence times corresponding to the events added into the first cache;
the occurrence frequency calculation module is used for calculating the occurrence frequency of the event according to the occurrence frequency corresponding to the event stored in the first cache;
and the hot event determining module is used for determining the event with the occurrence frequency greater than the preset threshold as the hot event.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps when executing the program of:
acquiring an event occurring within a preset time period and the time of the event;
detecting whether the size of the occupied space in the first cache reaches a preset space threshold value;
if so, shifting the earliest added event in the first cache out of the first cache;
adding the events into the first cache according to the time sequence of the events, and updating the occurrence times corresponding to the events added into the first cache;
calculating the occurrence frequency of the event according to the occurrence frequency corresponding to the event stored in the first cache;
and determining the event with the occurrence frequency larger than a preset threshold as the hot event.
Drawings
FIG. 1 is a flow diagram of a method of information processing in one embodiment;
FIG. 2 is a diagram illustrating the structure of a ConcurrentLinkedHashMap in one embodiment;
FIG. 3 is a flowchart illustrating how to calculate the frequency of occurrence of an event according to the number of occurrences of the event stored in the first cache in one embodiment;
FIG. 4 is a flow diagram of calculating an average number of occurrences of an event within a sampling window in one embodiment
FIG. 5 is a schematic diagram of the structure of a sliding window arrangement in one embodiment;
FIG. 6 is a block diagram of an information processing apparatus according to an embodiment;
FIG. 7 is a block diagram of a frequency calculation module in one embodiment;
FIG. 8 is a block diagram of an average number of occurrences calculation module in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It will be understood that, as used herein, the terms "first," "second," and the like may be used herein to describe various elements, but these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first client may be referred to as a second client, and similarly, a second client may be referred to as a first client, without departing from the scope of the present invention. Both the first client and the second client are clients, but they are not the same client.
FIG. 1 is a flow diagram of a method for information processing in one embodiment. As shown in fig. 1, the method specifically includes the following steps:
In one embodiment, the length of the preset time period may be set manually, so that the number of events occurring in the preset time period is not too much or too little. The event occurring within the preset time period may be a request event of the server, for example, a certain download interface receives a download request of the server; or an exception event thrown by the system recorded in an exception log. In different scenes, the detected data are different, but as long as the data in the scene can be abstracted into events, the hot events occurring in the scene can be found. Multiple time points may exist in a preset time period, the same event may occur multiple times at the same time point, and multiple events may also occur at the same time point.
and 106, if the size of the occupied space in the first cache reaches a preset space threshold, moving the event added in the first cache at the earliest out of the first cache.
In one embodiment, the first cache has a preset space threshold, when the size of the occupied space in the first cache reaches the preset space threshold, it indicates that the first cache is full, and if part of the events stored in the first cache are not cleared, the events cannot be continuously added into the first cache. Therefore, when the first cache is full, one of the events needs to be moved out of the first cache each time before the event is added to the first cache. Specifically, the events may be stored using a cache based on an LRU algorithm that utilizes a locality principle, where events that are added earliest to the first cache are least likely to occur again and events that are added most recently are more likely to occur again, where the event that is added earliest is not the event that occurs the earliest, but is the event that is added first to the first cache, but is the event that is not updated or operated the longest in the first cache, and thus when the first cache is full, the event that is added earliest may be moved out of the first cache.
In one embodiment, to facilitate access and modification of events stored in the first cache, a ConcurrentLinkedHashMap is employed to implement LRU policy-based caching. The overall framework of the ConcurrentLinkedHashMap is shown in fig. 2, and is essentially that the segments of the ordinary concurrenthashmap are maintained by an additional doubly linked list, the position of the corresponding node is changed for each read and write, and when a node is accessed, the node is moved to the head of the queue. Each element has a weight, which is accumulated each time an element is added. When the accumulated weight reaches the preset capacity of the doubly linked list, the least operational element, i.e., the element at the end of the queue, is removed. This ensures that the head of the table is the most recently used node and the tail of the table is the least recently used node.
And 108, adding the events into the first cache according to the time sequence of the events, and updating the occurrence times corresponding to the events added into the first cache.
In one embodiment, when an event is added to the first cache, whether the event exists in the first cache is detected, and if the event exists, the position of the event is accessed and the occurrence frequency of the event is updated. If the event does not exist, whether the occupied space in the first cache reaches a preset space threshold value needs to be detected, if the occupied space does not reach the preset space threshold value, the event can be directly stored into the first cache, the occurrence frequency of the event is updated, and if the cache space is full, the event needs to be stored into the first cache after the event which is added in the first cache is removed, and the occurrence frequency of the event is updated.
And step 110, calculating the occurrence frequency of the event according to the occurrence frequency corresponding to the event stored in the first cache.
In an embodiment, when an event is added to the first cache, it is detected whether the event already exists in the first cache, if the event already exists in the first cache, the occurrence frequency corresponding to the event in the first cache is updated, and if the event does not exist in the first cache, the occurrence frequency of the event is initialized. After all events are added into the first cache, the occurrence frequency of the stored events can be obtained according to the occurrence frequency of the events stored in the first cache and the length of the preset time period. The occurrence frequency of the events is the occurrence frequency of the events in unit time, the length of the unit time needs to be adjusted manually, if the set length of the unit time is too small, the event occurrence frequency obtained through statistics is generally higher, the difference between the occurrence frequencies of different events is increased, and the requirement on the system performance is higher; if the set unit time length is too large, the event occurrence frequency is low, the occurrence frequencies of different events are close to each other, burrs are brought, and the statistical accuracy is reduced.
And step 112, determining the event with the occurrence frequency larger than the preset threshold as the hot event.
In one embodiment, the events stored in the first cache are the most frequently occurring events, and the less frequently occurring events are not stored in the first cache. If the occurrence frequency of only part of the events stored in the first cache is greater than the preset threshold, it indicates that the occurrence frequency of the events which are not stored in the first cache is not greater than the preset threshold. The first events occurring within the preset time period may be considered to be all stored in the first cache. However, if statistics shows that the occurrence frequencies of the events stored in the first cache are all greater than the preset threshold, the set unit time length may be too small, at this time, the preset threshold needs to be adjusted according to the unit time length, and if the set unit time length is appropriate, part of the events whose occurrence frequencies are greater than the preset threshold are not stored in the first cache. A second cache may be provided for holding the most frequently occurring ones of the events not added to the first cache. If the statistics shows that the occurrence frequency of the events stored in the first cache is greater than the preset threshold, whether the events with the occurrence frequency greater than the preset threshold exist in the second cache is searched, and the events with the occurrence frequency greater than the preset threshold in the second cache can also be determined as the first events. The method of adding events to the second cache may be the same as the method of adding events to the first cache.
According to the information processing method, only the preset number of events with the largest occurrence frequency are stored in the first cache, and the events with lower occurrence frequency in the preset time period can be cleared, so that the data volume is kept in a bearable range, the pressure on the processor is greatly reduced, the speed of detecting the first events is greatly improved, and the first events can be quickly detected from the mass events in the preset time period.
As shown in fig. 3, in an embodiment, calculating the occurrence frequency of an event according to the occurrence frequency corresponding to the event stored in the first cache specifically includes the following steps:
In one embodiment, it is determined whether the event is a hot event or not according to the occurrence frequency of the event in a single certain time period, where a large error may exist, so in this embodiment, a sampling window is set, where the sampling window includes a plurality of consecutive time periods, and an end point of each time period coincides with a start point of a next time period, that is, a large time period composed of a plurality of consecutive time periods is obtained. The length of each time segment is the same. The events and the occurrence frequency of the events stored in the first buffer in each different time period in the sampling window may be different, so the occurrence frequency of the events stored in the first buffer in each time period in the sampling window also needs to be acquired.
At step 304, the average number of occurrences of the event within the sampling window is calculated.
In an embodiment, since the occurrence frequency of the event stored in the first buffer of each time period in the sampling window is obtained in advance, the occurrence frequency of the event in an average time period in the sampling window may be directly calculated according to the occurrence frequency of the event stored in the first buffer of each time period in each time period.
In another embodiment, for non-stationary data, it may be considered to be near stationary over a reasonably small interval, and data closer to the two ends of the interval may contribute less to smoothing than data closer to the middle, so errors may also be reduced by locally weighted averaging. And carrying out weighted average on the occurrence times of the events in all the time periods in the acquired sampling window, wherein the time periods at the two ends of the sampling window have smaller weight of the occurrence times of the events stored in the first cache, and the time periods in the middle of the sampling window have larger weight of the occurrence times of the events stored in the first cache, so that the average occurrence times of the events in the sampling window are acquired.
And step 306, calculating the occurrence frequency of the event according to the average occurrence times of the event.
In one embodiment, the frequency of occurrence of an event is obtained by dividing the number of times the event occurs in a time period by the length of the time period, and since the average number of times the event occurs in a plurality of consecutive time periods is used, the error can be reduced to some extent.
As shown in fig. 4, in an embodiment, the calculating the average occurrence number of events in the sampling window specifically includes the following steps:
In one embodiment, non-stationary data is considered to be near stationary across the appropriate cells, so that errors or noise-induced data non-stationary can be reduced by some local averaging. For a sampling window comprising n continuous time periods, the length of each time period is equal, events and the occurrence times thereof occurring in the time period are stored in a first cache in each time period, a sliding window comprising k (k < n) continuous time periods can be set for local averaging, and from the start time period of the sampling window, the sliding window is moved backwards one by one, so that n-k +1 sliding window sequences can be obtained.
And step 404, calculating the average occurrence frequency of the events in the sliding window, and calculating the average occurrence frequency of the events in the sampling window according to the average occurrence frequency of the events in the sliding window.
In one embodiment, assume that the sequence of occurrences of an event in the sampling window is x, and the number of occurrences in the nth time period is xnThe average occurrence number of times of the time within the sliding window is calculated, i.e. the sliding average sequence can be expressed as:
n-k +1 is the length of the running average sequence. Since the average occurrence frequency of the events in a time period needs to be obtained, the average occurrence frequency of the events in the sampling window can be obtained by performing arithmetic averaging on the sequence according to the obtained moving average sequence
For example, it may be assumed that the sampling window comprises 5 consecutive time periods of length 1 second, and the number of occurrences of an event in these 5 time periods is a, b, c, d, e, respectively. The length of the sliding window is 3 consecutive time segments, then by sliding one by one, 3 sliding window sequences are obtained: (a, b, c), (b, c, d), (c, d, e). According to the formula, the average occurrence frequency of the events with reduced errors can be obtained by obtaining the sliding average value of the occurrence frequency of the events.
Specifically, as shown in fig. 5, the sliding window is configured to include a plurality of consecutive time periods, each of the time periods is connected end to form a circle, the sampling window rotates in one direction, the event is added to the first buffer of the time period in real time, and when the sampling window rotates to the next time period, the data stored in the first buffer of the time period is cleared first, and then the event is added thereto. The sliding window is embedded on the sampling window, and the sliding window sequence is continuously updated along with the rotation of the sampling window. The sliding average value of the event occurrence times can be calculated according to the sliding window sequence.
As shown in fig. 6, in one embodiment, there is provided an information processing apparatus including:
the event obtaining module 602 is configured to obtain an event occurring within a preset time period and a time when the event occurs.
The storage space detection module 604 is configured to detect whether the size of the occupied space in the first cache reaches a preset space threshold.
An event moving-out module 606, configured to move the earliest added event in the first cache out of the first cache if the size of the occupied space in the first cache reaches a preset space threshold.
The event storage module 608 is configured to add the events to the first cache according to the time sequence of the events, and update the occurrence frequency corresponding to the events added to the first cache.
The occurrence frequency calculating module 610 is configured to calculate an occurrence frequency of an event according to the occurrence frequency corresponding to the event stored in the first cache.
The hot event determining module 612 is configured to determine an event with an occurrence frequency greater than a preset threshold as a hot event.
As shown in fig. 7, in one embodiment, the occurrence frequency calculation module 610 includes:
a sampling window obtaining module 702, configured to obtain a sampling window including a first preset number of consecutive time segments.
And an average occurrence count calculation module 704, configured to calculate an occurrence frequency of the event according to the average occurrence count of the event.
And a frequency calculating module 706, configured to calculate an occurrence frequency of the event according to the average occurrence number of the event.
As shown in fig. 8, in one embodiment, the average number of occurrences calculation module 704 includes:
a sliding window obtaining module 802, configured to slide a sliding window including a second preset number of consecutive time periods one by one from the sampling window, and obtain the occurrence frequency of the event stored in the first cache in each time period in the sliding window.
And a sliding average calculation module 804, configured to calculate an average occurrence number of events in the sliding window, and calculate an average occurrence number of time in the sampling window according to the average occurrence number of time in the sliding window.
In one embodiment, the event storage module 608 is further configured to add a preset number of events, which occur the most frequently among the events removed from the first cache, to the second cache.
The occurrence frequency calculation module 610 is further configured to calculate an occurrence frequency of the events stored in the second cache according to the occurrence frequency of the events stored in the second cache.
The hot event determining module 612 is further configured to determine an event that the occurrence frequency stored in the second cache is greater than a preset threshold as a hot event.
The embodiment of the invention also provides computer equipment. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the program:
acquiring events occurring in a preset time period and the time of the events;
detecting whether the size of the occupied space in the first cache reaches a preset space threshold value;
if so, shifting the earliest added event in the first cache out of the first cache;
adding the events into a first cache according to the time sequence of the events, and updating the occurrence times corresponding to the events added into the first cache;
calculating the occurrence frequency of the events according to the occurrence times corresponding to the events stored in the first cache;
and determining the event with the occurrence frequency larger than a preset threshold as the hot event.
According to the computer equipment, the events which do not occur for the longest time are moved out of the cache, only the events which occur recently are stored, the influence of most of the events which occupy the acquired events and have low occurrence frequency on the detection of the hot events can be eliminated, the requirement of the number of the events on the performance of the computer equipment can be reduced, and the detection speed of the hot events is accelerated.
In one embodiment, the processor in the computer device executes the program to implement the following steps: calculating the occurrence frequency of the event according to the occurrence frequency corresponding to the event stored in the first cache, wherein the method comprises the following steps:
acquiring a sampling window comprising a first preset number of continuous time periods, wherein the time ending point of each time period in the sampling window is overlapped with the time starting point of the next time period, and acquiring the occurrence frequency of events stored in a first cache in each time period;
calculating the average occurrence frequency of events in a sampling window;
and calculating the occurrence frequency of the events according to the average occurrence times of the events.
In one embodiment, the processor in the computer device executes the program to implement the following steps: calculating an average number of occurrences of events within a sampling window, comprising:
sliding a sliding window comprising a second preset number of continuous time periods one by one from the sampling window to obtain the occurrence frequency of the events stored in the first cache in each time period in the sliding window;
and calculating the average occurrence frequency of the events in the sliding window, and calculating the average occurrence frequency of the events in the sampling window according to the average occurrence frequency of the events in the sliding window.
In one embodiment, the processor in the computer device executes the program to further implement the following steps:
adding a preset number of events with the maximum occurrence frequency in the events shifted out of the first cache into a second cache;
calculating the occurrence frequency of the events stored in the second cache according to the occurrence frequency of the events stored in the second cache;
and determining the events with the frequency of occurrence greater than a preset threshold value stored in the second cache as first events.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), or the like.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (8)
1. An information processing method comprising:
acquiring an event occurring within a preset time period and the time of the event;
detecting whether the size of the occupied space in the first cache reaches a preset space threshold value;
if so, shifting the earliest added event in the first cache out of the first cache;
adding the events into the first cache according to the time sequence of the events, and updating the occurrence times corresponding to the events added into the first cache;
calculating the occurrence frequency of the event according to the occurrence frequency corresponding to the event stored in the first cache;
determining the event with the occurrence frequency larger than a preset threshold value as a hot event;
adding a preset number of events with the maximum occurrence frequency in the events shifted out of the first cache into a second cache;
calculating the occurrence frequency of the events stored in the second cache according to the occurrence frequency of the events stored in the second cache;
and determining the events with the occurrence frequency larger than a preset threshold value stored in the second cache as first events.
2. The method according to claim 1, wherein the calculating the occurrence frequency of the event according to the occurrence frequency corresponding to the event stored in the first cache comprises:
acquiring a sampling window comprising a first preset number of continuous time periods, wherein the time ending point of each time period in the sampling window is overlapped with the time starting point of the next time period, and acquiring the occurrence frequency of events stored in a first cache in each time period;
calculating the average occurrence number of the events in the sampling window;
and calculating the occurrence frequency of the event according to the average occurrence frequency of the event.
3. The method of claim 2, wherein said calculating an average number of occurrences of said event within said sampling window comprises:
sliding a sliding window comprising a second preset number of continuous time periods one by one from the sampling window to obtain the occurrence frequency of the events stored in the first cache in each time period in the sliding window; and calculating the average occurrence frequency of the events in the sliding window, and calculating the average occurrence frequency of the events in the sampling window according to the average occurrence frequency of the events in the sliding window.
4. An information processing apparatus characterized in that the apparatus comprises:
the event acquisition module is used for acquiring events occurring in a preset time period and the time of the events;
the storage space detection module is used for detecting whether the size of the occupied space in the first cache reaches a preset space threshold value;
an event moving-out module, configured to move out an event added earliest in the first cache from the first cache if the size of the occupied space in the first cache reaches a preset space threshold;
the event storage module is used for adding the events into a first cache according to the time sequence of the events, and updating the occurrence times corresponding to the events added into the first cache;
the occurrence frequency calculation module is used for calculating the occurrence frequency of the event according to the occurrence frequency corresponding to the event stored in the first cache;
the hot event determining module is used for determining the event with the occurrence frequency larger than a preset threshold as the hot event;
the event storage module is further configured to add a preset number of events, which occur most frequently among the events removed from the first cache, to a second cache;
the occurrence frequency calculation module is further configured to calculate the occurrence frequency of the events stored in the second cache according to the occurrence frequency of the events stored in the second cache;
and the first event determining module is used for determining the events with the occurrence frequency larger than a preset threshold value, which are stored in the second cache, as the first events.
5. The apparatus of claim 4, wherein the occurrence frequency calculation module comprises:
the sampling window acquisition module is used for acquiring a sampling window comprising a first preset number of continuous time periods, wherein the time ending point of each time period in the sampling window is overlapped with the time starting point of the next time period, and the occurrence frequency of events stored in each first cache of each time period is acquired;
the average occurrence frequency calculation module is used for calculating the average occurrence frequency of the events in the sampling window;
and the frequency calculation module is used for calculating the occurrence frequency of the event according to the average occurrence frequency of the event.
6. The apparatus of claim 5, wherein the average number of occurrences calculating module comprises:
a sliding window obtaining module, configured to slide sliding windows including a second preset number of consecutive time periods one by one from the sampling window, and obtain the occurrence frequency of events stored in the first cache in each time period in the sliding window;
and the sliding average value calculating module is used for calculating the average occurrence frequency of the events in the sliding window and calculating the average occurrence frequency of the events in the sampling window according to the average occurrence frequency of the events in the sliding window.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps when executing the program of:
acquiring an event occurring within a preset time period and the time of the event;
detecting whether the size of the occupied space in the first cache reaches a preset space threshold value;
if so, shifting the earliest added event in the first cache out of the first cache;
adding the events into the first cache according to the time sequence of the events, and updating the occurrence times corresponding to the events added into the first cache;
calculating the occurrence frequency of the event according to the occurrence frequency corresponding to the event stored in the first cache;
determining the event with the occurrence frequency larger than a preset threshold value as a hot event;
adding a preset number of events with the maximum occurrence frequency in the events shifted out of the first cache into a second cache;
calculating the occurrence frequency of the events stored in the second cache according to the occurrence frequency of the events stored in the second cache;
and determining the events with the occurrence frequency larger than a preset threshold value stored in the second cache as first events.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 3.
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| CN112076464B (en) * | 2020-09-04 | 2022-06-21 | 腾讯科技（深圳）有限公司 | Data request processing method and device, computer equipment and storage medium |
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| CN104102725A (en) * | 2014-07-22 | 2014-10-15 | 浪潮(北京)电子信息产业有限公司 | Method and system for dynamically creating duplicates of hotspot data files in HDFS (Hadoop distributed file system) |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN100568957C (en) * | 2007-10-17 | 2009-12-09 | 华中科技大学 | A kind of video frequency request program caching method based on the p2p technology |
| CN101424550B (en) * | 2008-09-03 | 2010-06-23 | 中国海洋石油总公司 | Instrument meter freezing fault rapid detecting method |
| CN104102725A (en) * | 2014-07-22 | 2014-10-15 | 浪潮(北京)电子信息产业有限公司 | Method and system for dynamically creating duplicates of hotspot data files in HDFS (Hadoop distributed file system) |
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