CN108492150B - Method and system for determining entity heat degree - Google Patents
Method and system for determining entity heat degree Download PDFInfo
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- CN108492150B CN108492150B CN201810322486.9A CN201810322486A CN108492150B CN 108492150 B CN108492150 B CN 108492150B CN 201810322486 A CN201810322486 A CN 201810322486A CN 108492150 B CN108492150 B CN 108492150B
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Abstract
The invention discloses a method and a system for determining entity heat degree, which relate to the field of electronic information, and the method comprises the following steps: real-time statistics results of all entity heat indexes contained in log data information related to the entities are counted in real time; when a preset time period is reached, acquiring a real-time statistical result of each entity heat index counted in the time period; and determining the real-time heat value of the entity in the time period according to the real-time statistical result of each entity heat index counted in the time period and a preset heat calculation rule. According to the method, the real-time heat value of the entity can be calculated through actual offline transactions according to the characteristics of the O2O marketing scene, and the aim of periodic calculation is fulfilled by utilizing a real-time calculation technology, so that the online transaction and the offline transaction and the visit amount are combined to calculate the real heat value, and the result is more correct and has timeliness.
Description
Technical Field
The invention relates to the field of electronic information, in particular to a method and a system for determining entity heat.
Background
In an O2O (online to offline) marketing scenario, marketing popularity data is generally acquired in order to explore and improve the marketing effect of the marketing scenario. In the prior art, generally, the online visit amount of each store is calculated according to the offline counted user visit amount of each store, so that the popularity of the calculated stores is determined according to the online visit amount, and the stores are sorted according to the popularity.
However, in the process of implementing the present invention, the inventor finds that at least the following problems exist in the prior art: firstly, the method comprises the following steps: in the prior art, the heat data is not calculated by actual offline transaction according to the characteristics of an O2O marketing scene, and the heat data obtained only based on the online data is inaccurate and cannot completely reflect the real heat. Secondly, a real-time computing technology is not adopted, and heat data are computed only through basic index data of off-line statistics on the previous day, so that the timeliness of the final heat data is also lagged, and the requirement of a user on real-time heat during online consumption decision making cannot be met.
Disclosure of Invention
In view of the above, the present invention has been made to provide a method and system for determining the degree of heating of an entity that overcomes or at least partially solves the above problems.
According to an aspect of the present invention, there is provided a method for determining a heat degree of an entity, including: real-time statistics results of all entity heat indexes contained in log data information related to the entities are counted in real time; when a preset time period is reached, acquiring a real-time statistical result of each entity heat index counted in the time period; and determining the real-time heat value of the entity in the time period according to the real-time statistical result of each entity heat index counted in the time period and a preset heat calculation rule.
Optionally, the log data information is streaming data information, and the step of performing real-time statistics on real-time statistics results of each entity heat index included in the log data information related to the entity in real time specifically includes:
and carrying out real-time statistics on real-time statistical results of various entity heat indexes contained in log data information related to the entities through a streaming computing framework.
Optionally, the preset heat degree calculation rule includes: calculating the real-time heat value of the entity by combining the static attribute information of the entity;
wherein the static attribute information includes: business turn information, geographic information, and/or historical transaction information.
Optionally, the real-time statistics includes real-time statistics results of each entity heat index included in log data information related to the entity; the step of obtaining the real-time statistical result of each entity heat index counted in the current time period when the preset time period is reached specifically includes:
counting real-time counting results of each entity heat index contained in log data information related to an entity in real time through a main thread, and sending the real-time counting results of each entity heat index counted in the current time period to each sub-thread synchronous with the main thread when a preset time period is reached so that each sub-thread can obtain the real-time counting results of each entity heat index counted in the current time period;
the step of determining the real-time heat value of the entity in the time period according to the real-time statistical result of each entity heat index counted in the time period and the preset heat calculation rule specifically includes: each sub-thread determines a real-time heat value of the entity in the current time period according to the real-time statistical result of each entity heat index counted in the current time period and a preset heat calculation rule, and reports the real-time heat value to the main thread;
wherein the sub-thread is a plurality of parallel running sub-threads.
Optionally, wherein the method further comprises: when the next time period is reached, the main thread judges whether a sub-thread with abnormal processing progress exists or not;
if so, sending a stop message to the sub-thread with the abnormal processing progress so as to enable the sub-thread with the abnormal processing progress to stop the processing of the current time period and start the processing of the next time period.
Optionally, the step of performing real-time statistics on real-time statistics results of each entity heat index included in log data information related to the entity through the main thread specifically includes: persistently storing the real-time statistical result into a preset storage device;
the method further comprises: and when the main thread is abnormal, recovering the counted real-time counting result through the preset storage equipment.
Optionally, when the main thread further includes a plurality of main threads running in parallel, and the preset storage device further includes a plurality of data sub-buckets corresponding to the respective main threads, the step of recovering the counted real-time statistical result through the preset storage device specifically includes:
and determining a data sub-bucket corresponding to the abnormal main thread according to a preset thread sub-bucket mapping relation, and recovering the counted real-time counting result according to the data sub-bucket corresponding to the abnormal main thread.
Optionally, the step of performing real-time statistics on real-time statistics results of the individual entity heat indicators included in the log data information related to the entity further includes:
and judging whether a preset time period is reached in real time, if so, executing the step of acquiring the real-time statistical result of each entity heat index counted in the time period and the subsequent steps.
Optionally, before the step of performing real-time statistics on real-time statistics results of the individual entity heat indicators included in the log data information related to the entity in real time, the method further includes:
acquiring log data information related to the type of entity in real time according to the type of the entity; wherein the log data information comprises: a ticket audit log, an access log, and/or a transaction log.
Optionally, the step of performing real-time statistics on real-time statistics results of each entity heat index included in log data information related to the entity specifically includes:
determining an entity heat index corresponding to the type of the entity according to the type of the entity;
extracting entity heat indexes corresponding to the type of entities from the log data information in real time, and counting the extracted entity heat indexes respectively;
wherein the types of the entities include: store type, and/or electronic coupon type; the entity heat index includes: a transaction amount index, a coupon reimbursement index, and/or a coupon pickup index.
According to another aspect of the present invention, there is provided a system for determining a heat degree of an entity, including:
the statistical module is suitable for carrying out real-time statistics on real-time statistical results of various entity heat indexes contained in log data information related to the entities; the first acquisition module is suitable for acquiring real-time statistical results of various entity heat indexes counted in the current time period when the preset time period is reached; and the determining module is suitable for determining the real-time heat value of the entity in the time period according to the real-time statistical result of each entity heat index counted in the time period and a preset heat calculation rule.
Optionally, the log data information is streaming data information, and the statistics module is specifically adapted to:
and carrying out real-time statistics on real-time statistical results of various entity heat indexes contained in log data information related to the entities through a streaming computing framework.
Optionally, the preset heat degree calculation rule includes: calculating the real-time heat value of the entity by combining the static attribute information of the entity;
wherein the static attribute information includes: business turn information, geographic information, and/or historical transaction information.
Optionally, the statistical module and the first obtaining module are specifically adapted to:
counting real-time counting results of each entity heat index contained in log data information related to an entity in real time through a main thread, and sending the real-time counting results of each entity heat index counted in the current time period to each sub-thread synchronous with the main thread when a preset time period is reached so that each sub-thread can obtain the real-time counting results of each entity heat index counted in the current time period;
the step of determining the real-time heat value of the entity in the time period according to the real-time statistical result of each entity heat index counted in the time period and the preset heat calculation rule specifically includes: each sub-thread determines a real-time heat value of the entity in the current time period according to the real-time statistical result of each entity heat index counted in the current time period and a preset heat calculation rule, and reports the real-time heat value to the main thread;
wherein the sub-thread is a plurality of parallel running sub-threads.
Optionally, wherein the first obtaining module is further adapted to: when the next time period is reached, the main thread judges whether a sub-thread with abnormal processing progress exists or not;
if so, sending a stop message to the sub-thread with the abnormal processing progress so as to enable the sub-thread with the abnormal processing progress to stop the processing of the current time period and start the processing of the next time period.
Optionally, wherein the statistics module is specifically adapted to: persistently storing the real-time statistical result into a preset storage device;
the system further comprises: and when the main thread is abnormal, recovering the counted real-time counting result through the preset storage equipment.
Optionally, when the main thread further includes a plurality of main threads running in parallel, and the preset storage device further includes a plurality of data sub-buckets corresponding to the respective main threads, the statistics module is specifically adapted to:
and determining a data sub-bucket corresponding to the abnormal main thread according to a preset thread sub-bucket mapping relation, and recovering the counted real-time counting result according to the data sub-bucket corresponding to the abnormal main thread.
Optionally, wherein the first obtaining module is further adapted to:
and judging whether a preset time period is reached in real time, if so, executing the step of acquiring the real-time statistical result of each entity heat index counted in the time period and the subsequent steps.
Optionally, wherein the system further comprises a second obtaining module:
the method comprises the steps of obtaining log data information related to entities of the type in real time according to the types of the entities; wherein the log data information comprises: a ticket audit log, an access log, and/or a transaction log.
Optionally, wherein the statistics module is specifically adapted to:
determining an entity heat index corresponding to the type of the entity according to the type of the entity;
extracting entity heat indexes corresponding to the type of entities from the log data information in real time, and counting the extracted entity heat indexes respectively;
wherein the types of the entities include: store type, and/or electronic coupon type; the entity heat index includes: a transaction amount index, a coupon reimbursement index, and/or a coupon pickup index.
According to still another aspect of the present invention, there is provided an electronic apparatus including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the entity heat determination method.
According to still another aspect of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform operations corresponding to the method for determining a heat degree of an entity as described above.
According to the method and the system for determining the entity heat degree, provided by the invention, the real-time statistical result of each entity heat degree index contained in the log data information related to the entity is counted in real time, the real-time statistical result of each entity heat degree index counted in the current time period is obtained when the preset time period is reached, and then the real-time heat degree value of the entity in the current time period is determined according to the real-time statistical result of each entity heat degree index counted in the current time period and the preset heat degree calculation rule. According to the method, the real-time heat value of the entity can be calculated through actual offline transaction according to the characteristics of the O2O marketing scene, and the aim of periodic calculation is fulfilled by utilizing a real-time calculation technology, so that the online transaction and the offline transaction and the visit amount are combined to calculate the real heat value, the result is more correct, and the timeliness is better. In addition, the scheme provided by the invention is based on the real-time behavior of the subscriber line, accumulates the data of transaction, verification and cancellation and the like in a period, and calculates the heat result according to the statistical data. Therefore, the technical scheme provided by the invention ensures the timeliness of the heat data by utilizing the real-time calculation technology on one hand, breaks through the limitation of periodic statistics and calculation on the other hand, and reconstructs the marketing activity effect of the big data technology of people, goods and places by combining the real-time calculation with the periodic calculation to calculate the entity heat value which is more accordant with the O2O marketing scene.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating a method for determining a heat degree of an entity according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a method for determining entity heat according to a second embodiment of the present invention;
fig. 3 is a schematic flowchart illustrating a method for determining entity heat according to a second embodiment of the present invention;
FIG. 4 is a block diagram illustrating a system for determining entity heat according to a third embodiment of the present invention;
fig. 5 shows a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Example one
Fig. 1 shows a flowchart of a method for determining entity heat according to an embodiment of the present invention. As shown in fig. 1, the method includes:
step S110: and carrying out real-time statistics on real-time statistics results of all entity heat indexes contained in the log data information related to the entities.
An entity may refer to various entities that may calculate popularity, such as stores and/or electronic coupons, or other types of entities. The entity heat index refers to various indexes which can be used for calculating or reflecting the entity heat size, and the entity heat index comprises but is not limited to at least one of the following: a transaction amount index, a coupon reimbursement index, and/or a coupon pickup index. Specifically, log data information related to the type of entity can be acquired in real time according to the type of the entity; and then acquiring real-time statistical results of the entity heat indexes contained in the log information. Wherein, the log data information includes but is not limited to at least one of the following: a ticket audit log, an access log, and/or a transaction log. It is worth emphasizing that for different entity types, the heat indexes of the entities corresponding to the entity types can be correspondingly obtained; therefore, the heat index of the entity under the service scene can be correspondingly acquired aiming at different service scenes. Specifically, for the store type, entity heat indexes such as a transaction amount index, a preferential amount index and a transaction number index corresponding to the store type can be obtained and used for calculating the real-time heat of the store type; aiming at the type of the electronic ticket, the corresponding ticket verification and cancellation index, the ticket verification and cancellation amount index, the ticket receiving quantity index, the ticket verification and cancellation stroke number index and the like can be obtained and used for calculating the real-time heat index of the type of the ticket.
Step S120: and when a preset time period is reached, acquiring a real-time statistical result of each entity heat index counted in the time period.
The size of the preset time period may be specifically set by a person in the art according to an actual situation, for example, may be 15 minutes, 10 minutes, or half an hour, and the specific value may be set by a person in the art according to an actual service requirement: when the time period is set to be smaller, the real-time performance of the statistical result can be better; when the time period is set to be larger, the consumption of computing resources can be reduced, and the system performance is improved. By implementing this step, real-time and periodic can be combined, so that the heat value of each entity can be calculated more accurately.
Step S130: and determining the real-time heat value of the entity in the time period according to the real-time statistical result of each entity heat index counted in the time period and a preset heat calculation rule.
The preset heat calculation rule may refer to various rules for calculating the real-time heat value of the entity, and for example, the preset heat calculation rule may be a weighted calculation rule, where the real-time heat value of the entity is calculated by setting weight values of various entity heat indicators and then according to the weight values of the various entity heat indicators. The preset heat calculation rule may be other calculation rules besides the above weighted calculation rule, and in short, the present invention does not limit the specific rule of calculating the real-time heat value of the entity.
According to the method provided by the embodiment, the real-time statistical results of the entity heat indexes contained in the log data information related to the entity are counted in real time, the real-time statistical results of the entity heat indexes counted in the current time period are obtained when the preset time period is reached, and then the real-time heat value of the entity in the current time period is determined according to the real-time statistical results of the entity heat indexes counted in the current time period and the preset heat calculation rule. According to the method, the real-time entity heat value can be calculated through actual offline transactions according to the characteristics of the O2O marketing scene, and the aim of periodic calculation is fulfilled by utilizing a real-time calculation technology, so that the real-time entity heat value calculated by combining online transactions and offline transactions and visit volumes is more correct in result and has timeliness.
Fig. 2 shows a flowchart of a method for determining entity heat according to a second embodiment of the present invention. In addition, fig. 3 is a schematic flowchart illustrating a method for determining entity heat according to a second embodiment of the present invention. In order to more clearly and clearly illustrate the technical solution provided by the present invention, a complete flow of the technical solution provided by the present invention will be described with reference to fig. 3. As shown in fig. 3, the data source of the technical solution provided by the present invention includes all real-time data related to O2O scenario, such as transaction log, verification and cancellation log, access log, etc., and the bottom layer uses a streaming computing framework to collect the logs, and then analyzes the structured information required by the heat computation in the off-line real-time data analyzing step. The information includes, for example, entity types and individual entity heat indicators. The entity types comprise store types, electronic ticket types and the like; each entity heat index comprises a transaction index, a discount index and the like. Specifically, when the entity is a shop, the entity popularity index corresponding to the entity may include a transaction amount index, a preferential amount index, a transaction number index, and the like; when the entity is an electronic ticket, the entity heat index corresponding to the entity can comprise a ticket verification and cancellation index, a ticket verification and cancellation amount index, a ticket getting index, a ticket verification and cancellation stroke number index and the like. After the information needed by the structured heat calculation is analyzed, data of different data sources are summarized and aggregated, and the heat indexes (money amount, stroke number and access amount) are accumulated according to different dimensions of entities, wherein the steps are streaming calculation.
Further, as shown in fig. 3, after the accumulation of the heat index is completed, the subsequent calculation process is not started immediately, but the current calculation time point is determined at the interval of the accumulation logic, and if the time does not reach the specified window time (e.g. 15 minutes), no new logic is processed, and the index accumulation is continued; and calculating the accumulated entity heat indexes and some auxiliary information based on a designed algorithm until the time reaches the window time to obtain a real-time heat value of each entity, and finally outputting the calculated real-time heat value of each entity. As shown in fig. 3, static attribute information of an entity may also be incorporated when calculating the heat of the entity. The static attribute information may include, but is not limited to, at least one of the following: business sector information, store territory information, and/or historical transaction information.
The details of the technical solution provided by the present invention will be described by the following steps S210 to S240: as shown in fig. 2, the method includes:
step S210, according to the type of the entity, acquiring the log data information related to the type of the entity in real time.
Wherein the type of entity includes, but is not limited to, at least one of: a store type and/or an electronic coupon type. The log data includes, but is not limited to, at least one of: a ticket audit log, an access log, and/or a transaction log. Specifically, for the shop, the log data information related thereto may be an access log, a transaction log, or the like; for the ticket, the log data information related to the ticket may include a ticket verification log, a ticket retrieval log, and the like, which are not described herein.
Step S220, real-time statistics of the heat index of each entity included in the log data information related to the entity is performed in real time.
The entity heat index refers to various indexes that can be used to calculate or reflect the entity heat magnitude, and the entity heat index includes, but is not limited to, at least one of the following: a transaction amount index, a coupon reimbursement index, and/or a coupon pickup index. Specifically, first, according to the type of the entity, an entity heat index corresponding to the type of the entity is determined. For example, for a store type, the entity popularity indicators corresponding to the store type may include an entity popularity indicator such as a transaction amount indicator, a preferential amount indicator, and a transaction number indicator, which may be used to calculate a real-time popularity of the store type; for the electronic ticket type, the entity heat index which can be corresponding to the electronic ticket type can include a ticket verification and cancellation index, a ticket verification and cancellation amount index, a ticket getting index, a ticket verification and cancellation stroke number index and the like, which can be used for calculating the entity heat index of the real-time heat size of the ticket type. After the entity heat indexes corresponding to the entities are determined, the entity heat indexes corresponding to the entities of the type are extracted from the log data information in real time, and statistics is carried out on the extracted entity heat indexes respectively.
Further, when the log data information is streaming data information, a real-time statistical result of each entity heat index contained in the log data information related to the entity can be counted in real time through a streaming calculation framework. In particular, in a big data environment, the streaming data is a novel data type, which is a data type for real-time data processing. Streaming data can arrive quickly and continuously in a highly concurrent manner and is typically discarded after processing is complete. The streaming computation refers to a mode for processing streaming data in real time, and the streaming computation is characterized by continuity, no boundary and instantaneity and is suitable for processing scenes of high-speed concurrent and large-scale data, such as continuous real-time logs and real-time messages. The streaming computing framework refers to an engineering infrastructure implemented based on streaming computing theory, and may include, but is not limited to, at least one of the following: storm, spark streaming, blink.
In order to periodically obtain the real-time statistical result of each entity heat index, a time period may be preset, the size of the time period may be specifically set by a person skilled in the art according to an actual situation, for example, the time period may be 15 minutes, 10 minutes, or half an hour, and a specific numerical value may be set by a person skilled in the art according to an actual business requirement: when the time period is set to be smaller, the real-time performance of the statistical result can be better; when the time period is set to be larger, the consumption of computing resources can be reduced, and the system performance is improved. Then, it is determined whether the predetermined time period has been reached, and if yes, step S230 and the following steps are executed. By presetting a time period and executing the step of acquiring the real-time statistical result of each entity heat index counted in the time period and the subsequent steps when the preset period is reached, the real-time statistical result of each entity heat index contained in the log data information related to the entity can be counted in real time and the statistical result can be acquired periodically, the statistical result of each entity heat index acquired in real time offline and the statistical result acquired periodically online are combined, the characteristics of the O2O scene are further met, and the calculated real-time heat value of the entity is more accurate.
Step S230, when the preset time period is reached, obtaining a real-time statistical result of each entity heat index counted in the time period.
In order to ensure the real-time performance of the heat index statistics of each entity, a sub-thread synchronous with the main thread can be arranged. The number of the sub-threads can be determined according to the size of the data volume processed by the main thread in real time, and can be one or more. Specifically, when a preset time period arrives, a real-time statistical result of each entity heat index contained in log data information related to an entity can be counted in real time through a main thread, and when the preset time period arrives, the real-time statistical result of each entity heat index counted in the current time period is sent to a sub-thread synchronized with the main thread, so that the sub-thread can obtain the real-time statistical result of each entity heat index counted in the current time period. For example, when the preset time period is 15 minutes, the real-time statistical result of each entity heat index counted in the 15 minutes is sent to the sub-thread synchronized with the main thread every 15 minutes, so that the sub-thread obtains the real-time statistical result of each entity heat index counted in the 15 minutes. Further, when the sub-thread is a plurality of sub-threads running in parallel, the real-time statistical result of each entity heat index counted in the current time period can be distributed to each sub-thread according to a preset distribution rule. For load balancing, the preset distribution rule may be an average distribution rule, so that the real-time statistical result of each entity heat index counted in the current time period may be averagely distributed to each sub-thread, and of course, the preset distribution rule may be other distribution rules set by those skilled in the art besides the above average distribution rule. When the statistic of the sub-thread is particularly large, in order to prevent the sub-thread from not counting up when the next period arrives or prevent the sub-thread from other faults, when the next period arrives, the main thread judges whether the sub-thread with abnormal processing progress exists or not; thereby realizing the monitoring of the sub-threads. And if the sub-thread with abnormal processing progress exists, sending a stop message to the sub-thread with abnormal processing progress so as to stop the processing of the current time period by the sub-thread with abnormal processing progress and start the processing of the next time period. By monitoring the processing progress of the sub-thread and whether the sub-thread is abnormal or not, the sub-thread can be ensured to restart the calculation when each period comes, and therefore the real-time performance of the heat index statistics of each entity is ensured. It should be noted that, since the streaming computing framework is not good at handling periodic computation, such as how to quickly schedule a full amount of thermal computation tasks of an entity to a sub-process for computation after reaching a time window, how to return data to the sub-process after computation is completed, how to stop computation if time is out, and the like, all of the problems need to be implemented again on the streaming computing framework.
Further, after each piece of streaming data from the data source is processed, the processing is already finished at the level of the streaming computing framework, but the periodic computing needs to accumulate data and temporarily store the entity information to be computed before reaching the time window, which are all imperceptible to the streaming computing framework. When the system is abnormal, the recovery mechanism of the streaming computing framework can not be utilized. If some processing threads in the computing topology are abnormally quitted and then automatically restarted by the streaming computing framework, data before quitting cannot be obtained, and therefore the statistical index and the heat computing result are inaccurate. In order to solve the above problem, the real-time statistical results of the entity heat indexes included in the main thread statistics may be persistently stored in a preset storage device, so that when the main thread is abnormal, for example, when a certain node of the streaming framework fails or when the operating system is abnormal, the counted real-time statistical results may be recovered through the preset storage device. The preset storage device may be a hbase storage device, a hard disk storage device, or the like. Further, when the main thread includes a plurality of main threads running in parallel, the preset storage device further includes a plurality of data sub-buckets corresponding to the respective main threads, so that when the main thread is abnormal, the data sub-buckets corresponding to the abnormal main threads can be determined according to a preset thread sub-bucket mapping relationship, and the counted real-time statistical result is recovered according to the data sub-buckets corresponding to the abnormal main threads. The mapping relationship of the thread sub-buckets may be determined according to the number of main threads and the number of data sub-buckets, for example, a total of 100 main threads may be determined, and the real-time statistical results of the entity heat indicators included in the 100 main threads statistics may be persistently stored in 1000 data sub-buckets corresponding to the 100 main threads respectively. In particular, real-time statistics of the various entity heat metrics contained in the main-thread statistics may be persisted to data buckets based on the identity of the various entities (e.g., store IDs). When determining the mapping relationship of the thread sub-buckets, numbering each main thread and each data sub-bucket, and then determining the corresponding relationship between the number of each main thread and the number of each data sub-bucket. For example, the number 1 main thread may correspond to the number 1-10 data sub-buckets, the number 2 main thread may correspond to the number 11-20 data sub-buckets, and the correspondence between the numbers of the other main threads and the numbers of the data sub-buckets may be analogized, and therefore, the description is omitted here. Therefore, when one main thread fails, the serial number of the main thread can be determined, so that the serial number of the data sub-bucket corresponding to the main thread can be determined according to the mapping relation of the thread sub-buckets, and the counted real-time statistical result of the failed main thread can be recovered according to the backup data stored in the serial number sub-bucket. By arranging a plurality of data sub-buckets corresponding to the main threads respectively, when the main threads break down, the counted real-time counting results can be recovered accurately and quickly according to the data in the data sub-buckets corresponding to the main threads, so that the possibility of repeated calculation or loss of the real-time counting results of the entity heat indexes contained in the main thread counting is reduced, and the accuracy and the calculation efficiency are improved.
Step S240, determining a real-time heat value of the entity in the current time period according to the real-time statistical result of each entity heat index counted in the current time period and a preset heat calculation rule.
Wherein, the preset heat calculation rule may include: and calculating the real-time heat value of the entity by combining the static attribute information of the entity. The static attribute information may refer to various attribute information that is not obtained in real time and is related to the real-time heat value of the entity. The static attribute information may include, but is not limited to, at least one of the following: business turn information, geographic information, and/or historical transaction information. For example, for the type of the store, the static attribute information may include the type of the store, the location of the store, the historical transaction amount of the store, the business circle of the store, and the like, which are not described in detail herein. Specifically, the sub-thread may determine the real-time heat value of the entity in the current time period according to the real-time statistical result of each entity heat index counted in the current time period and the preset heat calculation rule, and report the real-time heat value to the main thread. The preset heat calculation rule may refer to various rules for calculating the real-time heat value of the entity, and for example, the preset heat calculation rule may be a weighted calculation rule, where the real-time heat value of the entity is calculated by setting weight values of various entity heat indicators and then according to the weight values of the various entity heat indicators. The preset heat calculation rule may be other calculation rules besides the above weighted calculation rule, and in short, the present invention does not limit the specific rule of calculating the real-time heat value of the entity. After determining the real-time heat value of the entity in the current time period, the sub-thread may store the real-time heat value data in a preset storage device and return the real-time heat value data to the main thread, and the main thread may further process the real-time heat value data, for example, may perform TOP100 calculation on the real-time heat value data.
According to the method for determining the entity heat degree provided by the second embodiment, the log data information related to the entity of the type is obtained in real time according to the type of the entity, the real-time statistical result of each entity heat degree index included in the log data information related to the entity is counted in real time, the real-time statistical result of each entity heat degree index counted in the current time period is obtained every time when the preset time period is reached, and the real-time heat degree value of the entity in the current time period is determined according to the real-time statistical result of each entity heat degree index counted in the current time period and the preset heat degree calculation rule. According to the method, the real-time entity heat value can be calculated through actual offline transactions according to the characteristics of the O2O marketing scene, and the aim of periodic calculation is fulfilled by utilizing a real-time calculation technology, so that the real-time entity heat value calculated by combining online transactions and offline transactions and visit volumes is more correct in result and has timeliness. In addition, the scheme uses a real-time calculation technology, all index statistics and heat calculation are performed immediately when an external event or a fixed time point occurs, data delay caused by offline basic index data statistics is avoided, and the real-time data condition under an O2O scene is reflected. In addition, the scheme utilizes the data of O2O offline transaction and consumption, reflects the real data condition in the O2O scene, and ensures the accuracy of the heat data instead of only using partial online data simulation.
EXAMPLE III
Fig. 4 is a schematic structural diagram illustrating a system for determining entity heat according to a third embodiment of the present invention, where the system includes:
a statistic module 42 adapted to count real-time statistics results of each entity heat index included in log data information related to the entity in real time;
the first obtaining module 43 is adapted to obtain a real-time statistical result of each entity heat index counted in the current time period when the preset time period is reached;
the determining module 44 is adapted to determine the real-time heat value of the entity in the current time period according to the real-time statistical result of each entity heat index counted in the current time period and a preset heat calculation rule.
Optionally, the log data information is streaming data information, and the statistics module 42 is specifically adapted to:
and carrying out real-time statistics on real-time statistical results of various entity heat indexes contained in log data information related to the entities through a streaming computing framework.
Optionally, the preset heat degree calculation rule includes: calculating the real-time heat value of the entity by combining the static attribute information of the entity;
wherein the static attribute information includes: business turn information, geographic information, and/or historical transaction information.
Optionally, the statistical module 42 and the first obtaining module 43 are specifically adapted to:
counting real-time counting results of each entity heat index contained in log data information related to an entity in real time through a main thread, and sending the real-time counting results of each entity heat index counted in the current time period to each sub-thread synchronous with the main thread when a preset time period is reached so that each sub-thread can obtain the real-time counting results of each entity heat index counted in the current time period;
the step of determining the real-time heat value of the entity in the time period according to the real-time statistical result of each entity heat index counted in the time period and the preset heat calculation rule specifically includes: each sub-thread determines a real-time heat value of the entity in the current time period according to the real-time statistical result of each entity heat index counted in the current time period and a preset heat calculation rule, and reports the real-time heat value to the main thread;
wherein the sub-thread is a plurality of parallel running sub-threads.
Optionally, wherein the first obtaining module 43 is further adapted to: when the next time period is reached, the main thread judges whether a sub-thread with abnormal processing progress exists or not;
if so, sending a stop message to the sub-thread with the abnormal processing progress so as to enable the sub-thread with the abnormal processing progress to stop the processing of the current time period and start the processing of the next time period.
Optionally, wherein the statistics module 42 is specifically adapted to: persistently storing the real-time statistical result into a preset storage device;
the system further comprises: and when the main thread is abnormal, recovering the counted real-time counting result through the preset storage equipment.
Optionally, when the main thread further includes a plurality of main threads running in parallel, and the preset storage device further includes a plurality of data sub-buckets corresponding to the respective main threads, the statistics module 42 is specifically adapted to:
and determining a data sub-bucket corresponding to the abnormal main thread according to a preset thread sub-bucket mapping relation, and recovering the counted real-time counting result according to the data sub-bucket corresponding to the abnormal main thread.
Optionally, wherein the first obtaining module 43 is further adapted to:
and judging whether a preset time period is reached in real time, if so, executing the step of acquiring the real-time statistical result of each entity heat index counted in the time period and the subsequent steps.
Optionally, wherein the system further comprises a second obtaining module 41:
the method comprises the steps of obtaining log data information related to entities of the type in real time according to the types of the entities; wherein the log data information comprises: a ticket audit log, an access log, and/or a transaction log.
Optionally, wherein the statistics module 42 is specifically adapted to:
determining an entity heat index corresponding to the type of the entity according to the type of the entity;
extracting entity heat indexes corresponding to the type of entities from the log data information in real time, and counting the extracted entity heat indexes respectively;
wherein the types of the entities include: store type, and/or electronic coupon type; the entity heat index includes: a transaction amount index, a coupon reimbursement index, and/or a coupon pickup index.
The specific structure and operation principle of each module described above may refer to the description of the corresponding part in the method embodiment, and are not described herein again.
Example four
An embodiment of the present application provides a non-volatile computer storage medium, where the computer storage medium stores at least one executable instruction, and the computer executable instruction may execute the method for determining the entity heat in any method embodiment.
The executable instructions may be specifically configured to cause the processor to:
real-time statistics results of all entity heat indexes contained in log data information related to the entities are counted in real time;
when a preset time period is reached, acquiring a real-time statistical result of each entity heat index counted in the time period;
and determining the real-time heat value of the entity in the time period according to the real-time statistical result of each entity heat index counted in the time period and a preset heat calculation rule.
EXAMPLE five
Fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the electronic device.
As shown in fig. 5, the electronic device may include: a processor (processor)502, a Communications Interface 506, a memory 504, and a communication bus 508.
Wherein:
the processor 502, communication interface 506, and memory 504 communicate with each other via a communication bus 508.
A communication interface 506 for communicating with network elements of other devices, such as clients or other servers.
The processor 502 is configured to execute the program 510, and may specifically execute relevant steps in the embodiment of the method for determining the entity heat.
In particular, program 510 may include program code that includes computer operating instructions.
The processor 502 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the invention. The electronic device comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
The memory 504 is used for storing the program 510. Memory 504 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 510 may specifically be used to cause the processor 502 to perform the following operations:
real-time statistics results of all entity heat indexes contained in log data information related to the entities are counted in real time;
when a preset time period is reached, acquiring a real-time statistical result of each entity heat index counted in the time period;
and determining the real-time heat value of the entity in the time period according to the real-time statistical result of each entity heat index counted in the time period and a preset heat calculation rule.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of the entity heat determination system according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (18)
1. A method for determining the heat of an entity comprises the following steps:
real-time statistics results of all entity heat indexes contained in log data information related to the entities are counted in real time through a streaming computation framework; wherein the log data information is streaming data information;
when a preset time period is reached, acquiring a real-time statistical result of each entity heat index counted in the time period; the method comprises the steps that real-time statistical results of all entity heat indexes contained in log data information related to an entity are counted in real time through a main thread, and when a preset time period is reached, the real-time statistical results of all the entity heat indexes counted in the current time period are sent to all sub-threads synchronous with the main thread, so that all the sub-threads can obtain the real-time statistical results of all the entity heat indexes counted in the current time period; the number of the sub-threads is determined according to the data size processed by the main thread in real time;
determining real-time heat value of the entity in the time period according to the real-time statistical result of each entity heat index counted in the time period and a preset heat calculation rule; each sub-thread determines a real-time heat value of an entity in the current time period according to the real-time statistical result of each entity heat index counted in the current time period and a preset heat calculation rule, and reports the real-time heat value to the main thread; wherein the sub-thread is a plurality of parallel running sub-threads.
2. The method of claim 1, wherein the preset heat calculation rule comprises: calculating the real-time heat value of the entity by combining the static attribute information of the entity;
wherein the static attribute information includes: business turn information, geographic information, and/or historical transaction information.
3. The method of claim 1, wherein the method further comprises: when the next time period is reached, the main thread judges whether a sub-thread with abnormal processing progress exists or not;
if so, sending a stop message to the sub-thread with the abnormal processing progress so as to enable the sub-thread with the abnormal processing progress to stop the processing of the current time period and start the processing of the next time period.
4. The method according to claim 1, wherein the step of performing real-time statistics on real-time statistics results of each entity heat index included in log data information related to the entity by the main thread specifically includes: persistently storing the real-time statistical result into a preset storage device;
the method further comprises: and when the main thread is abnormal, recovering the counted real-time counting result through the preset storage equipment.
5. The method according to claim 4, wherein when the main thread further includes a plurality of main threads running in parallel, and the preset storage device further includes a plurality of data buckets corresponding to the respective main threads, the step of recovering the counted real-time statistical result through the preset storage device specifically includes:
and determining a data sub-bucket corresponding to the abnormal main thread according to a preset thread sub-bucket mapping relation, and recovering the counted real-time counting result according to the data sub-bucket corresponding to the abnormal main thread.
6. The method according to any one of claims 1-5, wherein the step of performing real-time statistics on the real-time statistics of the individual entity heat indicators included in the log data information associated with the entity further comprises:
and judging whether a preset time period is reached in real time, if so, executing the step of acquiring the real-time statistical result of each entity heat index counted in the time period and the subsequent steps.
7. The method according to any one of claims 1 to 5, wherein the step of performing real-time statistics on the real-time statistics of the individual entity heat index included in the log data information related to the entity further comprises:
acquiring log data information related to the type of entity in real time according to the type of the entity; wherein the log data information comprises: a ticket audit log, an access log, and/or a transaction log.
8. The method according to any one of claims 1 to 5, wherein the step of performing real-time statistics on the real-time statistics result of each entity heat index included in the log data information related to the entity specifically comprises:
determining an entity heat index corresponding to the type of the entity according to the type of the entity;
extracting entity heat indexes corresponding to the type of entities from the log data information in real time, and counting the extracted entity heat indexes respectively;
wherein the types of the entities include: store type, and/or electronic coupon type; the entity heat index includes: a transaction amount index, a coupon reimbursement index, and/or a coupon pickup index.
9. A system for determining a heat of an entity, comprising:
the statistical module is suitable for performing real-time statistics on real-time statistical results of various entity heat indexes contained in log data information related to the entities through a stream type computing frame; wherein the log data information is streaming data information;
the first acquisition module is suitable for acquiring real-time statistical results of various entity heat indexes counted in the current time period when the preset time period is reached; the method comprises the steps that real-time statistical results of all entity heat indexes contained in log data information related to an entity are counted in real time through a main thread, and when a preset time period is reached, the real-time statistical results of all the entity heat indexes counted in the current time period are sent to all sub-threads synchronous with the main thread, so that all the sub-threads can obtain the real-time statistical results of all the entity heat indexes counted in the current time period; the number of the sub-threads is determined according to the data size processed by the main thread in real time;
the determining module is suitable for determining the real-time heat value of the entity in the current time period according to the real-time statistical result of each entity heat index counted in the current time period and a preset heat calculation rule; each sub-thread determines a real-time heat value of an entity in the current time period according to the real-time statistical result of each entity heat index counted in the current time period and a preset heat calculation rule, and reports the real-time heat value to the main thread; wherein the sub-thread is a plurality of parallel running sub-threads.
10. The system of claim 9, wherein the preset heat calculation rule comprises: calculating the real-time heat value of the entity by combining the static attribute information of the entity;
wherein the static attribute information includes: business turn information, geographic information, and/or historical transaction information.
11. The system of claim 9, wherein the first acquisition module is further adapted to: when the next time period is reached, the main thread judges whether a sub-thread with abnormal processing progress exists or not;
if so, sending a stop message to the sub-thread with the abnormal processing progress so as to enable the sub-thread with the abnormal processing progress to stop the processing of the current time period and start the processing of the next time period.
12. The system according to claim 9, wherein the statistics module is specifically adapted to: persistently storing the real-time statistical result into a preset storage device;
the system further comprises: and when the main thread is abnormal, recovering the counted real-time counting result through the preset storage equipment.
13. The system according to claim 12, wherein when the main thread further includes a plurality of main threads running in parallel, and the preset storage device further includes a plurality of data buckets corresponding to the respective main threads, the statistics module is specifically adapted to:
and determining a data sub-bucket corresponding to the abnormal main thread according to a preset thread sub-bucket mapping relation, and recovering the counted real-time counting result according to the data sub-bucket corresponding to the abnormal main thread.
14. The system of any of claims 9-13, wherein the first acquisition module is further adapted to:
and judging whether a preset time period is reached in real time, if so, executing the step of acquiring the real-time statistical result of each entity heat index counted in the time period and the subsequent steps.
15. The system of any of claims 9-13, wherein the system further comprises a second acquisition module:
the method comprises the steps of obtaining log data information related to entities of the type in real time according to the types of the entities; wherein the log data information comprises: a ticket audit log, an access log, and/or a transaction log.
16. The system according to any of claims 9-13, wherein the statistics module is specifically adapted to:
determining an entity heat index corresponding to the type of the entity according to the type of the entity;
extracting entity heat indexes corresponding to the type of entities from the log data information in real time, and counting the extracted entity heat indexes respectively;
wherein the types of the entities include: store type, and/or electronic coupon type; the entity heat index includes: a transaction amount index, a coupon reimbursement index, and/or a coupon pickup index.
17. An electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the entity heat determination method according to any one of claims 1-8.
18. A computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to perform operations corresponding to the method for determining the heat of an entity according to any one of claims 1 to 8.
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| CN111339073A (en) * | 2020-02-24 | 2020-06-26 | 天津满运软件科技有限公司 | Real-time data processing method and device, electronic equipment and readable storage medium |
| CN112035559B (en) * | 2020-11-02 | 2021-02-02 | 浙江口碑网络技术有限公司 | Thermodynamic diagram display method, server and system |
| CN112559592A (en) * | 2020-12-11 | 2021-03-26 | 深圳前海微众银行股份有限公司 | Real-time data processing method, device and equipment |
| WO2022204845A1 (en) * | 2021-03-29 | 2022-10-06 | 深圳市欢太科技有限公司 | Method and apparatus for generating entity popularity, and storage medium and electronic device |
| CN117237059A (en) * | 2023-11-09 | 2023-12-15 | 深圳美云集网络科技有限责任公司 | Commodity recommendation method and terminal |
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