CN113676531B - E-commerce flow peak clipping method and device, electronic equipment and readable storage medium - Google Patents
E-commerce flow peak clipping method and device, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The disclosure provides an e-commerce flow peak clipping method, an e-commerce flow peak clipping device, electronic equipment and a readable storage medium, and relates to the technical field of Internet, in particular to the technical field of e-commerce flow control. The specific implementation scheme is as follows: acquiring access frequency of users in a history period in advance to acquire access frequency distribution of different users in a preset time, and calculating an abnormal access threshold according to the access frequency distribution; based on the access request of the user, judging whether the access frequency of the user in the preset time reaches an abnormal access threshold value or not: if yes, the commodity data requested by the user are stored in a cache, and the commodity data are directly called from the cache and sent to the user when the user accesses the commodity data next time; if not, commodity data is requested from the server and sent to the user. The method realizes effective peak clipping for the access flow of the e-commerce, can maintain the stability of the service even in the face of large-batch access under the mechanism, and can also maintain the recommendation experience of the user.
Description
Technical Field
The disclosure relates to the technical field of internet, in particular to the technical field of e-commerce flow control.
Background
The traditional peak clipping method at present mainly comprises the following steps: (1) According to the method, through offline mining of anti-cheating flow, the user behaviors in the past period are subjected to offline data mining analysis, so that the cheating users in the past day are obtained, then accumulated, and are shielded when the cheating users revisit, on one hand, the method has one-day delay, the new cheating users in the day cannot be shielded, and meanwhile, the cheating identification is easy to cause larger accidental injury, for example, some users put mobile phones in pockets and unlock screens, so that the access frequency is too high, and the users are misjudged as the cheating users by a system; (2) According to the method, the frequency of a user accessing a recommended service in a certain time is analyzed, the frequency distribution of the user accessing is analyzed through offline, the maximum access frequency value of the user in a time interval is set in combination with statistics and business general knowledge, when the user exceeds the maximum access frequency value, the user accessing is refused or the user is required to log in to limit the user accessing the service, and the like.
Disclosure of Invention
The disclosure provides an e-commerce traffic peak clipping method, an e-commerce traffic peak clipping device, electronic equipment and a readable storage medium.
According to a first aspect of the present disclosure, there is provided an e-commerce traffic peak clipping method, including:
acquiring access frequency of users in a history period in advance to acquire access frequency distribution of different users in a preset time, and calculating an abnormal access threshold according to the access frequency distribution;
based on the access request of the user, judging whether the access frequency of the user in the preset time reaches an abnormal access threshold value or not:
if yes, the commodity data requested by the user are stored in a cache, and the commodity data are directly called from the cache and sent to the user when the user accesses the commodity data next time;
if not, commodity data is requested from the server and sent to the user.
According to a second aspect of the present disclosure, there is provided an e-commerce traffic peak clipping device, comprising:
the acquisition module is used for acquiring the access frequency of the user in a history period to acquire the access frequency distribution of different users in a preset time;
the first calculation module is used for calculating an abnormal access threshold according to the access frequency distribution;
the judging module is used for judging whether the access frequency of the user in the preset time reaches an abnormal access threshold value or not based on the access request of the user;
The buffer memory module is used for storing commodity data requested by a user when the judging module judges that the access frequency reaches the abnormal access threshold value;
and the recommending module is used for calling commodity data from the caching module and sending the commodity data to the user when the judging module judges that the access frequency reaches the abnormal access threshold value, or requesting commodity data from the server and sending the commodity data to the user when the judging module judges that the access frequency does not reach the abnormal access threshold value.
According to a third aspect of the present disclosure, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method.
According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the above-described method.
According to a fifth aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the above method.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a flow diagram of an electronic commerce traffic clipping method provided in accordance with the present disclosure;
FIG. 2 is a schematic diagram of the architecture of an electronic commerce traffic clipping device provided in accordance with the present disclosure;
FIG. 3 is a block diagram of an electronic device for implementing an e-commerce traffic clipping method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one 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.
Example 1
An embodiment of the present invention provides a method for peak clipping of e-commerce traffic, as shown in fig. 1, including:
step S101, acquiring access frequency of users in a history period in advance to acquire access frequency distribution of different users in a preset time, and calculating an abnormal access threshold according to the access frequency distribution;
step S102, based on the access request of the user, judging whether the access frequency of the user in the preset time reaches an abnormal access threshold value or not:
if yes, the commodity data requested by the user are stored in a cache, and the commodity data are directly called from the cache and sent to the user when the user accesses the commodity data next time;
if not, commodity data is requested from the server and sent to the user.
Specifically, the technical solution of this embodiment may be divided into two parts: and analyzing the historical access frequency of the user, and establishing a caching mechanism according to an analysis result. Firstly, user access frequency analysis is carried out, user access frequency in a past history period is collected, one history period can be one month or one quarter or one year, access frequency of users to different interfaces of recommended services in the past year is collected according to actual needs, for example, because the current sources of different flow ports have larger difference in recommended service access frequency, when user recommended service access frequency analysis is carried out, the recommended scene and the flow sources are respectively analyzed, so that different cache mechanisms (caches) can be built for different flow ports later, access frequency distribution of users in preset time is obtained through analyzing the user access frequency of each flow port, for example, one access frequency distribution is obtained according to the access frequency of different users in 2s, finally, an abnormal access threshold is calculated according to the access frequency in 2s, for example, the abnormal access threshold is 3, and the user is judged to be an abnormal access user when the access frequency in 2s reaches 3 times.
Further, in the recommendation architecture, when a user requests a recommendation service, the recommendation architecture may go through a recall stage, a coarse ranking stage and a fine ranking stage, and if the user frequently requests a service, a large stress is caused to the server in a short time, and even the server is down. Therefore, after corresponding abnormal access thresholds are obtained through analysis aiming at different flow ports, a cache mechanism is built according to the abnormal access thresholds, user id, recommended scene id and flow port source id are used as user identification tags, and aiming at user flows of different scenes and sources, the abnormal access thresholds are triggered for the first time when a user accesses a service, commodity data are recommended by a server to carry out a recall ordering process, then the fine-ordering commodity data returned by the server and the user identification tags are stored in a cache, and when the user accesses the recommended service again, the fine-ordering commodity data are directly taken out from the cache and pushed to the user without recommending the commodity data through the server again, so that the pressure of the server can be effectively relieved.
By means of the technical scheme, compared with the traditional anti-cheating method, only cheating users in the past day can be mined for shielding, the method can timely identify real-time abnormal access users, measures are immediately taken for the abnormal access users to conduct flow peak clipping, if the access frequency of the users continuously reaches the abnormal access threshold, the system continuously adopts the cache to conduct commodity data recommendation, and the commodity data recommendation is not conducted through the server until the access frequency of the users is normal (namely, the access frequency of the users is smaller than the abnormal access threshold). In addition, the traditional online rule intervention method refuses the user access after the user access exceeds the maximum access frequency, but the peak clipping method of the invention does not completely prevent the abnormal user access, but adopts a cache to recommend services to the abnormal user access, which can lead to better user experience, especially for non-malicious abnormal access users, for example, some users put the mobile phone in a pocket without the screen, so that the mobile phone can access the services continuously, if the traditional online rule intervention rule is adopted, the user access is refused after the maximum access value is exceeded, and when the user normally uses the mobile phone access, the user is found to be forbidden by the system, which can lead to very bad user experience. The peak clipping method of the invention can not cause overlarge access flow of the server and cause accidental injury to users. Compared with the online rule intervention method and the offline anti-cheating method in the prior art, the system stability can be improved by 3 times compared with the online rule intervention method and the offline anti-cheating method in the prior art by fusing the caching mechanism into the recommended system architecture, the effective peak clipping is realized for the access flow of the electronic commerce, the stability of the service can be maintained even in case of mass access under the mechanism, and the recommended experience of the user can be maintained.
As an alternative implementation, the cache in this embodiment preferably employs Redis (Remote Dictionary Server, i.e., remote dictionary service), which is a key-value storage system. Like Memcached, it supports relatively more stored value types, including string, list, set, zset (sorted set-ordered set), and hash (hash type). These data types all support push/pop, add/remove, and pick intersection union and difference and richer operations, and these operations are all atomic. On this basis, redis supports a variety of different ways of ordering. Redis supports master-slave synchronization, which can be used to synchronize data from a master server to any number of slave servers, which can be master servers associated with other slave servers, and which can be helpful for scalability of read operations and data redundancy.
As an optional implementation manner, after step S102 determines whether the access frequency of the user within the preset time reaches the abnormal access threshold, the method further includes: under the condition that the buffer memory is used for sending commodity data to the user, in response to the fact that the access frequency of the user at the preset time is smaller than the abnormal access threshold value, the attenuation deleting algorithm is started to delete the commodity data in the buffer memory. The attenuation deletion algorithm includes the following calculation formula:
y＝n-e xt
Wherein n is the number of commodity data normally displayed, x is the super parameter, t is the time interval between the current access and the last access of the user, and the unit is seconds.
Specifically, after the access frequency of the user reaches the abnormal access threshold, the server is not used to recommend commodity data to the user, but the cache is used to recommend commodity data to the user. Meanwhile, the system also judges whether the current access frequency reaches an abnormal access threshold value or not in each access process of the user, and if the current access frequency of the user is still greater than or equal to the abnormal access threshold value, the system judges that the user is still an abnormal access user, so that commodity data is still only called from a cache and pushed to the user.
Further, in order to care for the user experience quality of the platform and simultaneously reduce the storage of the Redis service, an algorithm for attenuating and deleting commodity data stored in the Redis service is designed. When the system judges that the access times of the second level of the user is not greater than the abnormal access threshold, namely the user becomes a non-abnormal access user, an attenuation deleting algorithm is started, and the algorithm for deleting commodity data of the user in Redis service along with time is carried out, so that the diversity of the commodity data and the experience of the user are maintained, corresponding commodity data are deleted randomly from a commodity list of the Redis along with time change until the number of the commodity list reserved in the Redis is not in the number of the displayed commodity supporting a recommended scene, at the moment, all commodity data of the user in the Redis are deleted until the next time the user triggers an abnormal user access mechanism again, and the fine-arranged commodity data are saved in the Redis again.
For example, if the abnormal access threshold is 3 times, and when the access frequency of a user is greater than 3 times within 2s due to the error point, the system judges that the user is an abnormal access user, and the fine-ordering commodity data requested from the server is stored in the Redis service together with the user identification tag. The number of commodities of the server fine-ranking module in the traditional recommendation architecture is far smaller than the number of commodities recalled by the recall and coarse-ranking module, fine-ranking commodity data of the server are stored in Redis when a user triggers an abnormal access threshold, and 130% of commodity data except the fine-ranking commodity data are reserved for subsequent commodity data attenuation deletion to ensure user experience for better user experience. For example, the server may select 13 candidate commodity data from the coarse commodity data after coarse commodity data and fine commodity data are processed, and the 13 candidate commodity data and the 10 fine commodity data are retained in the Redis, that is, 23 commodity data are stored in total in the Redis. After the user starts normal access, 10 fine-ranked commodity data need to be replaced from 13 candidate commodity data every time, otherwise, after the user normally accesses, only recommending the 10 fine-ranked commodity data to the user still causes poor user experience, further, the replaced fine-ranked commodity data is deleted from Redis until 23 commodity data in the Redis are deleted to be less than 10, and when the replaced fine-ranked commodity data is not enough to be recommended to the user again, the Redis deletes all commodity data of the user. Through the technical scheme, on one hand, the requirements of users for recommending commodity diversification can be met, and on the other hand, the occupied storage resources of Redis can be reduced.
As an alternative embodiment, the process of saving commodity data requested by a user in the cache includes: and forming a user identification tag according to the user identification tag, the recommended scene identification tag and the traffic port source identification tag contained in the access request, simultaneously storing commodity data requested by the user and the user identification tag into a cache, setting a valid period for the user identification tag, and calling the commodity data according to the user identification tag corresponding to the user when accessing next time.
Specifically, because the recommended service scenes of the e-commerce are more, the access frequency of users is different due to different recommended scenes, and meanwhile, because the current traffic source ports are more diversified, the access frequency of recommended services triggered by the pc, the wise and the applet is correspondingly different due to different channels. In the current internet products, a user may log in different application scenes, but access frequencies in user second levels of different scenes are greatly different, and meanwhile, in the analysis of the user history frequency, abnormal access threshold extraction is performed for different application scenes and traffic sources. Therefore, when designing the keys of the redis, the user ID, the flow source ID and the recommendation scene ID are spliced together to be used as a character string of the user, namely, the character string is used as a user identification tag, meanwhile, in order to optimize the storage of the redis, the user identification tag is converted into an md5 value, meanwhile, a commodity list precisely arranged by the user is used as a value, the corresponding key and the value of the user are reserved in the redis, so that the user directly inquires the redis service according to the key of the user when accessing the redis next time, if the corresponding key can be inquired in the redis service, the commodity list to be displayed is returned according to the ordering size directly, the server does not need to request one recommendation service, and if the key is not in the redis service, the recommendation service is requested to the server, further, the access pressure of the recommendation service is greatly reduced, and the stability of the recommendation service is further improved.
As an alternative embodiment, calculating the abnormal access threshold from the access frequency distribution includes: and detecting abnormal users according to the access frequency of the users in 1s, the access frequency in 2s and the access frequency in 3s through an unsupervised clustering algorithm to obtain the access frequency distribution of the users in 2 s.
Specifically, in order to enable the recommendation service to be stabilized without wasting more resources, analysis is performed for the access frequency of users of different scenes within a second level. Because the normal access frequency of the e-commerce user is one second access, but sometimes the user can access for a plurality of times in 1 second due to the false point, the normal access frequency in 1s acquired at the later stage can be too high, so that the abnormal access threshold is set too high, and the peak clipping effect is not achieved or is poor, therefore, in order to better acquire the access frequency in the normal user second level, the access frequency in 2 seconds of the user is counted, so that the abnormal access threshold acquired according to the access frequency distribution in 2 seconds is more reasonable, the system can reasonably utilize a caching mechanism according to the abnormal access threshold, prevent the peak clipping effect from being not achieved due to the fact that the abnormal access threshold is set too high, and on the other hand, avoid the fact that the commodity data of the user occupy the cache resource too much due to the fact that the abnormal access threshold is set too low, so that the storage space of the cache is optimally utilized on the premise of guaranteeing the peak clipping effect. In addition, in order to obtain the access frequency of the normal access user in the second level, we detect the abnormal value of the access frequency of the user in different scenes under different flow sources in the past year through an ifrost algorithm (unsupervised clustering algorithm) so as to obtain the abnormal access threshold.
In order to enable the ifrost algorithm to accurately identify the access frequency of the normal user within 2 seconds, the user access frequency within 1 second nearby 2 seconds is collected, so that the abnormality detection model can accurately judge that the access frequency of the normal user within 2 seconds is obtained, therefore, the collected access frequency of the user within 1 second, the collected access frequency of the user within 2 seconds and the collected access frequency of the user within 3 seconds are detected by the ifrost algorithm, the range of the access frequency of the normal access user within 2 seconds is analyzed, and the access distribution of the abnormal user within 2 seconds is obtained. For example, when it is analyzed that the access frequency of the normal access user is between 0 and 2.2 seconds, the access frequency of the abnormal access user should be a section greater than 2.2, and a value may be selected from the section greater than 2.2 as the abnormal access threshold.
Further, under normal conditions, we can select the minimum value in the access frequency interval within 2 seconds of the abnormal user as the abnormal access threshold, but in order to make the model have certain robustness, we select 10% of the access frequency interval within 2 seconds of the abnormal access user as the abnormal access threshold, that is, select 10% of the data from the interval greater than 2.2 to calculate the abnormal access threshold. It should be noted that, in the above technical solution, determining the abnormal access frequency interval and selecting 10% of data are only an example of the present invention, and not limiting the present invention, and the specific adopted values also need to be determined according to factors such as actual application scenario and flow source.
As an optional implementation manner, after step S102 determines whether the access frequency of the user within the preset time reaches the abnormal access threshold, the method further includes: and in response to the fact that the access frequency of the user in the preset time is continuously n times greater than the abnormal access threshold, commodity data requested by the user are stored in a cache, wherein n is a positive integer.
Specifically, in the present embodiment, it is considered that if each user saves commodity data of the user to the dis at the time of triggering the abnormal access threshold for the first time, this causes the storage amount of the dis to be very large. Therefore, in this embodiment, a counter is set, where the counter is used to count the number of times that the user triggers the abnormal access threshold, and when the count of the current counter indicates that the user triggers the abnormal access threshold n times continuously, commodity data requested by the user is saved in the dis. Note that, the value of n in this embodiment also needs to be determined according to the actual application scenario.
Example two
The invention also provides an e-commerce flow peak clipping device, as shown in fig. 2, comprising:
the acquisition module 201 is configured to acquire access frequency of users in a history period to obtain access frequency distribution of different users in a preset time;
A first calculation module 202, configured to calculate an abnormal access threshold according to the access frequency distribution;
the judging module 203 is configured to judge, based on an access request of a user, whether an access frequency of the user in a preset time reaches an abnormal access threshold;
the buffer module 204 is configured to store commodity data requested by a user when the judging module 203 judges that the access frequency reaches the abnormal access threshold;
and the recommendation module 205 is configured to retrieve commodity data from the cache module and send the commodity data to the user when the judgment module 203 judges that the access frequency reaches the abnormal access threshold, or request the commodity data from the server and send the commodity data to the user when the judgment module judges that the access frequency does not reach the abnormal access threshold.
Specifically, the technical scheme of the embodiment includes analysis of historical access frequency of the user and establishment of a caching mechanism according to analysis results. Firstly, user access frequency analysis is performed, an acquisition module 201 acquires the user access frequency in a past history period, which may be one month, one quarter or one year, according to actual needs, for example, acquires the access frequency of a user to different interfaces of a recommended service in the past year, because the current sources of different flow ports have larger differences in the recommended service access frequency, when the user recommended service access frequency analysis is performed, the recommended scene and the flow sources are used for respectively analyzing, so that different cache mechanisms can be built for different flow ports later, the access frequency distribution of the user in a preset time is acquired by analyzing the user access frequency of each flow port, for example, one access frequency distribution is acquired according to the access frequency of different users in 2s, and finally, a first calculation module 202 calculates an abnormal access threshold value according to the access frequency in 2s, for example, the abnormal access threshold value is 3, and the user is judged to be an abnormal access user when the access frequency in 2s reaches 3 s.
Further, in the recommendation architecture, when a user requests a recommendation service, the recommendation architecture may go through a recall stage, a coarse ranking stage and a fine ranking stage, and if the user frequently requests a service, a large stress is caused to the server in a short time, and even the server is down. Therefore, after the corresponding abnormal access threshold is obtained by analyzing different flow ports, a cache mechanism is built according to the abnormal access threshold, the user id, the recommended scene id and the flow port source id are used as user identification tags, and for the user flow of different scenes and sources, the abnormal access threshold is triggered for the first time when the user accesses the service, the recall ordering process is performed by recommending commodity data through the server, then the fine-ordering commodity data returned by the server and the user identification tags are stored in the cache module 204, and when the user accesses the recommended service again, the fine-ordering commodity data is directly taken out from the cache module 204 and pushed to the user, and the commodity data is not recommended through the server again, so that the server pressure can be effectively relieved. The cache module 204 in this embodiment preferably employs Redis.
Through the technical scheme, compared with the traditional anti-cheating method, only the cheating users in the past day can be mined for shielding, the method can timely identify real-time abnormal access users, measures are immediately taken for the abnormal access users for flow peak clipping, if the access frequency of the users continuously reaches the abnormal access threshold, the recommendation module 205 continuously adopts the cache module 204 for commodity data recommendation, and the recommendation module 205 can not recommend commodity data through a server until the access frequency of the users is normal (namely, less than the abnormal access threshold). In addition, the traditional online rule intervention method refuses the user access after the user access exceeds the maximum access frequency, but the peak clipping device of the invention does not completely prevent the abnormal user access, but adopts a cache to recommend services to the abnormal user access, which can lead to better user experience, especially for non-malicious abnormal access users, for example, some users put the mobile phone in a pocket without the screen, so that the mobile phone can access the services continuously, if the traditional online rule intervention is adopted, the user access is refused after the maximum access value is exceeded, and when the user normally uses the mobile phone access, the user is found to be forbidden by the system, which can lead to very bad user experience. By using the peak clipping device, the access flow of the server is effectively clipped on the premise of not damaging the access experience of the user.
As an alternative embodiment, as shown in fig. 2, further includes:
the second calculation module 206 is configured to, in a case where the commodity data is sent to the user using the buffer module 204, initiate an attenuation deletion algorithm to delete the commodity data in the buffer module 204 in response to the access frequency of the user at the preset time being less than the abnormal access threshold. The attenuation deletion algorithm set in the second calculation module 206 includes the following calculation formula:
y＝n-e xt
wherein n is the number of commodity data normally displayed, x is the super parameter, t is the time interval between the current access and the last access of the user, and the unit is seconds.
Specifically, after the determining module 203 determines that the access frequency of the user reaches the abnormal access threshold, the recommending module 205 does not use the server to recommend the commodity data to the user, but uses the commodity data in the caching module 204 to recommend the commodity data to the user. Meanwhile, the determining module 203 may determine whether the current access frequency reaches the abnormal access threshold during each access process of the user, and if it is determined that the current access frequency of the user is still greater than or equal to the abnormal access threshold, it is determined that the user is still an abnormal access user, so that the recommending module 205 still only retrieves the commodity data from the cache and pushes the commodity data to the user.
Further, to take care of the user experience quality of the platform while reducing the storage of the Redis service, we have designed an attenuation deletion algorithm for commodity data stored in the Redis service in the second calculation module 206. When the judging module 203 judges that the number of accesses of the second level of the user is not greater than the abnormal access threshold, that is, the user becomes a non-abnormal access user, the second calculating module 206 starts a decay deleting algorithm, and performs an algorithm of deleting commodity data of the user in the Redis service over time, which is to maintain diversity of the commodity data and experience of the user, and randomly delete corresponding commodity data from the commodity list of the Redis over time until the number of the commodity list reserved in the Redis is not in the number of the revealed commodities supporting the recommended scene, at this time, delete all commodity data of the user in the Redis until the next time when the user triggers the abnormal user access mechanism again, and save the fine-arranged commodity data in the Redis again.
For example, if the abnormal access threshold set in the determining module 203 is 3 times, and when the access frequency of a user is greater than 3 times within 2s due to a false point, the determining module 203 determines that the user is an abnormal access user, the recommending module 205 stores the fine-grained commodity data requested from the server together with the user identification tag into the Redis service. The number of commodities of the server fine-ranking module in the traditional recommendation architecture is far smaller than the number of commodities recalled by the recall and coarse-ranking module, fine-ranking commodity data of the server are stored in Redis when a user triggers an abnormal access threshold, and 130% of commodity data except the fine-ranking commodity data are reserved for subsequent commodity data attenuation deletion to ensure user experience for better user experience. For example, the server may select 13 candidate commodity data from the coarse commodity data after coarse commodity data and fine commodity data are processed, and the 13 candidate commodity data and the 10 fine commodity data are retained in the Redis, that is, 23 commodity data are stored in total in the Redis. After the user starts normal access, 10 fine-ranked commodity data need to be replaced from 13 candidate commodity data every time, otherwise, after the user normally accesses, only recommending the 10 fine-ranked commodity data to the user still causes poor user experience, further, the replaced fine-ranked commodity data is deleted from Redis until 23 commodity data in the Redis are deleted to be less than 10, and when the replaced fine-ranked commodity data is not enough to be recommended to the user again, the Redis deletes all commodity data of the user. Through the technical scheme, on one hand, the requirements of users for recommending commodity diversification can be met, and on the other hand, the occupied storage resources of Redis can be reduced.
As an alternative embodiment, as shown in fig. 2, the e-commerce traffic peak clipping device further includes:
the tag generation module 207 is configured to form a tag with a user identification tag according to the user identification tag, the recommendation scene identification tag, and the traffic port source identification tag included in the access request, store the commodity data requested by the user and the user identification tag in the cache at the same time, set a valid period for the user identification tag, and call the commodity data according to the user identification tag corresponding to the user when accessing next time.
Specifically, because the recommended service scenes of the e-commerce are more, the access frequency of users is different due to different recommended scenes, and meanwhile, because the current traffic source ports are more diversified, the access frequency of recommended services triggered by the pc, the wise and the applet is correspondingly different due to different channels. In the current internet products, a user may log in different application scenes, but access frequencies in user second levels of different scenes are greatly different, and meanwhile, in the analysis of the user history frequency, abnormal access threshold extraction is performed for different application scenes and traffic sources. Therefore, when designing the key of the Redis, the tag generation module 207 splices the user id+the traffic source id+the recommended scene ID together as a character string of a user, namely as a user identification tag, and in order to more optimize the storage of the Redis, converts the user identification tag into an md5 value, and simultaneously takes the commodity list precisely arranged by the user as a value, and then retains the key corresponding to the user and the value in the Redis, and sets a valid period for the key, and the key is deleted from the Redis beyond the valid period, so that the long-term occupation of the storage freedom of the Redis is avoided.
Further, when the user accesses next time, inquiring the redis service according to the key of the user, if the corresponding key can be inquired in the redis service, returning the commodity list to be displayed according to the ordering size, the server does not need to be requested for the recommended service once, if the key is not in the redis service, the recommended service is requested for the server, so that the access pressure of the recommended service is greatly reduced, and the stability of the recommended service is further improved.
As an optional implementation manner, the acquisition module 201 detects abnormal users by using an ifrost algorithm (unsupervised clustering algorithm) according to the access frequency of the user in 1s, the access frequency in 2s and the access frequency in 3s, so as to obtain the access frequency distribution of the user in 2s, and further obtain the abnormal access threshold according to the access frequency distribution. It should be noted that, the ifrost algorithm is the same as the usage method in the first embodiment, so that the description is omitted in this embodiment.
As an alternative embodiment, as shown in fig. 2, further includes:
the counting module 208 is configured to count the access frequency of the user, and store commodity data requested by the user in the cache module 3 in response to the access frequency of the user in a preset time being greater than the abnormal access threshold value for n consecutive times, where n is a positive integer.
Specifically, in the present embodiment, it is considered that if each user saves commodity data of the user to the dis at the time of triggering the abnormal access threshold for the first time, this causes the storage amount of the dis to be very large. Therefore, in this embodiment, a counter is set, where the counter is used to count the number of times that the user triggers the abnormal access threshold, and when the count of the current counter indicates that the user triggers the abnormal access threshold n times continuously, commodity data requested by the user is saved in the dis. Note that, the value of n in this embodiment also needs to be determined according to the actual application scenario.
Embodiments of the present invention also provide an electronic device, a computer-readable storage medium, and a computer program product.
FIG. 3 illustrates a schematic block diagram of an example electronic device 300 that may be used to implement embodiments 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 telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 3, the apparatus 300 includes a computing unit 301 that may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 302 or a computer program loaded from a storage unit 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 may also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other by a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Various components in device 300 are connected to I/O interface 305, including: an input unit 306 such as a keyboard, a mouse, etc.; an output unit 307 such as various types of displays, speakers, and the like; a storage unit 308 such as a magnetic disk, an optical disk, or the like; and a communication unit 309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 301 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 301 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the respective methods and processes described above, such as the e-commerce traffic clipping method. For example, in some embodiments, the e-commerce traffic clipping method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 300 via the ROM 302 and/or the communication unit 309. When the computer program is loaded into RAM 303 and executed by computing unit 301, one or more steps of the e-commerce traffic clipping method described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the e-commerce traffic clipping method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On 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, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code 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 code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. 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. The 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 pointing device (e.g., a mouse or 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 may 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 input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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 a client and a server. The client and server are typically 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. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. An e-commerce traffic peak clipping method comprising:
acquiring access frequency of users in a history period in advance to acquire access frequency distribution of different users in a preset time, and calculating an abnormal access threshold according to the access frequency distribution;
based on the access request of the user, judging whether the access frequency of the user in the preset time reaches the abnormal access threshold value or not:
if yes, the commodity data requested by the user are stored in a cache, and the commodity data are directly called from the cache and sent to the user when the user accesses the commodity data next time;
Under the condition that the buffer memory is used for sending the commodity data to a user, responding to the condition that the access frequency of the user at the preset time is smaller than the abnormal access threshold value, starting an attenuation deleting algorithm to delete the commodity data in the buffer memory;
the attenuation deletion algorithm comprises the following calculation formula:
wherein y is the number of the attenuated commodity data, n is the number of the normal commodity data, x is the super parameter, t is the time interval between the current access and the last access of the user, and the unit is seconds;
if not, the commodity data is requested to the server and sent to the user.
2. The method for peak clipping of e-commerce traffic of claim 1, wherein the storing the commodity data requested by the user in the cache comprises: and forming a user identification tag according to a user identification tag, a recommended scene identification tag and a flow port source identification tag contained in the access request, simultaneously storing commodity data requested by a user and the user identification tag into the cache, setting a valid period for the user identification tag, and calling the commodity data according to the user identification tag corresponding to the user when accessing next time.
3. The method of claim 1, wherein the preset time is 2s, and the calculating an abnormal access threshold according to the access frequency distribution comprises: and detecting abnormal users according to the access frequency of the users in 1s, the access frequency in 2s and the access frequency in 3s through an unsupervised clustering algorithm to obtain the access frequency distribution of the users in 2 s.
4. The method for peak clipping of e-commerce traffic according to claim 1, wherein the determining whether the access frequency of the user within the preset time reaches the abnormal access threshold further comprises: and in response to the fact that the access frequency of the user in the preset time is continuously n times greater than the abnormal access threshold, storing the commodity data requested by the user into the cache, wherein n is a positive integer.
5. An e-commerce traffic peak clipping device comprising:
the acquisition module is used for acquiring the access frequency of the user in a history period to acquire the access frequency distribution of different users in a preset time;
the first calculation module is used for calculating an abnormal access threshold according to the access frequency distribution;
the judging module is used for judging whether the access frequency of the user in the preset time reaches the abnormal access threshold value or not based on the access request of the user;
The buffer memory module is used for storing commodity data requested by a user when the judging module judges that the access frequency reaches the abnormal access threshold value;
the second calculation module is used for responding to the fact that the access frequency of the user at the preset time is smaller than the abnormal access threshold value under the condition that the caching module is used for sending the commodity data to the user, and starting an attenuation deletion algorithm to delete the commodity data in the caching module;
the attenuation deletion algorithm set in the second calculation module includes the following calculation formula:
wherein y is the number of the attenuated commodity data, n is the number of the normal commodity data, x is the super parameter, t is the time interval between the current access and the last access of the user, and the unit is seconds;
and the recommending module is used for calling the commodity data from the caching module and sending the commodity data to a user when the judging module judges that the access frequency reaches the abnormal access threshold, or requesting the commodity data from a server and sending the commodity data to the user when the judging module judges that the access frequency does not reach the abnormal access threshold.
6. The e-commerce traffic peak clipping device of claim 5, further comprising:
The label generating module is used for forming a label with user identification according to the user identification mark, the recommended scene identification mark and the flow port source identification mark contained in the access request, simultaneously storing the commodity data requested by the user and the user identification label into the cache, setting a valid period for the user identification label, and calling the commodity data according to the user identification label corresponding to the user in the next access.
7. The e-commerce traffic peak clipping device according to claim 5, wherein the preset time is 2s, and the acquisition module detects abnormal users by using an unsupervised clustering algorithm from the access frequency of the users within 1s, the access frequency within 2s and the access frequency within 3s to obtain the access frequency distribution of the users within 2 s.
8. The e-commerce traffic peak clipping device of claim 5, further comprising:
the counting module is used for counting the access frequency of the user, and responding to the fact that the access frequency of the user in the preset time is continuously n times larger than the abnormal access threshold value, the commodity data requested by the user are stored in the caching module, and n is a positive integer.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the e-commerce traffic clipping method of any one of claims 1-4.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the e-commerce traffic clipping method according to any one of claims 1-4.
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