CN113361891A - Data acquisition method and device - Google Patents
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Abstract
The disclosure relates to a data acquisition method and device. The data acquisition method comprises the following steps: acquiring a data evaluation index of a current service, wherein the data evaluation index comprises a first data evaluation index in a first preset time period and a second data evaluation index in a second preset time period; obtaining a first relational expression based on the first data evaluation index and parameters of at least one preset stage in a first preset time period, and obtaining a second relational expression based on the second data evaluation index and parameters of at least one preset stage in a second preset time period, wherein the preset stage is a stage in the flow funnel corresponding to the data evaluation index, and the parameters are determined based on the data evaluation index; and obtaining the contribution rate of each stage in at least one predetermined stage based on the first relational expression and the second relational expression.
Description
Technical Field
The present disclosure relates to the field of service evaluation, and in particular, to a data acquisition method and apparatus.
Background
At present, a descriptive statistical method is generally adopted to determine which stage in the traffic funnel causes the data evaluation index of the traffic to change, for example, taking the consumption index of the advertisement traffic as an example, it is generally assumed that which stage in the traffic funnel causes the consumption index of the advertisement by observing the Page View (PV) amount change curve graph of each stage (eg, targeting, recall, coarse ranking, fine ranking, bidding, winning) in the traffic funnel and the sequential change of the PV amount change curve peak value. The method can display the accumulated PV quantity, the pass rate of the unit, the industry pass rate and the like of each stage of the flow funnel in the bidding process, can check the self competitiveness and compare the industry mean value, but the change curve graph (also called as a trend graph) of each stage only displays the absolute value and the conversion rate trend change of each stage of the unit, and cannot quantify the contribution of each stage to the final change of the data evaluation index. For example, if the consumption of an ad group drops by 80% compared to the day before, it is not known by the trend graph of the phases how much each phase of the traffic funnel contributes to the drop in consumption by a percentage, respectively.
Disclosure of Invention
The present disclosure provides a data acquisition method and apparatus, which at least solve the problem that the magnitude of the influence of the related art on the final change of a data evaluation index at each stage of a flow funnel cannot be quantified.
According to a first aspect of the embodiments of the present disclosure, there is provided a data acquisition method, including: acquiring a data evaluation index of a current service, wherein the data evaluation index comprises a first data evaluation index in a first preset time period and a second data evaluation index in a second preset time period; obtaining a first relational expression based on the first data evaluation index and parameters of at least one preset stage in a first preset time period, and obtaining a second relational expression based on the second data evaluation index and parameters of at least one preset stage in a second preset time period, wherein the preset stage is a stage in the flow funnel corresponding to the data evaluation index, and the parameters are determined based on the data evaluation index; and obtaining the contribution rate of each stage in at least one predetermined stage based on the first relational expression and the second relational expression.
Optionally, obtaining the contribution rate of each of the at least one predetermined stage based on the first relation and the second relation includes: performing division processing on the first relational expression and the second relational expression; carrying out logarithmic transformation processing on the result after the division processing to obtain a logarithmic expression; acquiring the value of each addend in the logarithmic expression; and obtaining the contribution rate of each stage in at least one preset stage based on the value of each addend and an index ratio, wherein the index ratio is obtained through a first data evaluation index and a second data evaluation index.
Optionally, the left side of the equal sign of the first relation is a first data evaluation index, and the right side of the equal sign of the first relation is an equation determined based on the parameter of the at least one predetermined stage in the first predetermined time period and the first data evaluation index.
Optionally, the equation is (parameter 1 × (parameter 2/parameter 1) × … × (parameter n/parameter n-1) × (first data evaluation index/parameter n)), where the parameter n is a parameter of a predetermined phase n of the at least one predetermined phase for a first predetermined period of time, and n is a positive integer.
Optionally, the left side of the equal sign of the second relation is a second data evaluation index, and the right side of the equal sign of the second relation is an equation determined based on the parameter of the at least one predetermined stage in the second predetermined period of time and the second data evaluation index.
Optionally, the equation is (parameter 1 × (parameter 2/parameter 1) × … × (parameter n/parameter n-1) × (second data evaluation index/parameter n)), where parameter n is a parameter of a predetermined phase n of the at least one predetermined phase for a second predetermined time period, and n is a positive integer.
Optionally, after obtaining the contribution rate of each of the at least one predetermined stage based on the first relation and the second relation, the method further includes: determining a stage causing the data evaluation index to change based on the contribution rate; and obtaining a processing indication aiming at the current service based on the determined stage and the contribution rate corresponding to the determined stage.
Optionally, the first relation is an identity and the second relation is an identity.
According to a second aspect of the embodiments of the present disclosure, there is provided a data acquisition apparatus including: the data evaluation index acquisition unit is configured to acquire a data evaluation index of the current service, wherein the data evaluation index comprises a first data evaluation index in a first predetermined time period and a second data evaluation index in a second predetermined time period; the relational expression obtaining unit is configured to obtain a first relational expression based on the first data evaluation index and parameters of at least one predetermined stage in a first predetermined time period, and obtain a second relational expression based on the second data evaluation index and parameters of at least one predetermined stage in a second predetermined time period, wherein the predetermined stage is a stage in the flow funnel corresponding to the data evaluation index, and the parameters are determined based on the data evaluation index; and the contribution rate acquisition unit is configured to obtain the contribution rate of each stage in at least one predetermined stage based on the first relational expression and the second relational expression.
Optionally, the contribution rate obtaining unit is further configured to perform division processing on the first relational expression and the second relational expression; carrying out logarithmic transformation processing on the result after the division processing to obtain a logarithmic expression; acquiring the value of each addend in the logarithmic expression; and obtaining the contribution rate of each stage in at least one preset stage based on the value of each addend and an index ratio, wherein the index ratio is obtained through a first data evaluation index and a second data evaluation index.
Optionally, the left side of the equal sign of the first relation is a first data evaluation index, and the right side of the equal sign of the first relation is an equation determined based on the parameter of the at least one predetermined stage in the first predetermined time period and the first data evaluation index.
Optionally, the equation is (parameter 1 × (parameter 2/parameter 1) × … (parameter n/parameter n-1) × (first data evaluation index/parameter n)), where the parameter n is a parameter of the predetermined phase n of the at least one predetermined phase within the first predetermined time period, and n is a positive integer.
Optionally, the left side of the equal sign of the second relation is a second data evaluation index, and the right side of the equal sign of the second relation is an equation determined based on the parameter of the at least one predetermined stage in the second predetermined period of time and the second data evaluation index.
Optionally, the equation is (parameter 1 × (parameter 2/parameter 1) × … (parameter n/parameter n-1) × (second data evaluation index/parameter n)), where the parameter n is a parameter of the predetermined phase n of the at least one predetermined phase for a second predetermined time period, and n is a positive integer.
Optionally, the contribution rate obtaining unit is further configured to determine, after obtaining the contribution rate of each stage in the at least one predetermined stage based on the first relational expression and the second relational expression, a stage causing the data evaluation index to change based on the contribution rate; and obtaining a processing indication aiming at the current service based on the determined stage and the contribution rate corresponding to the determined stage.
Optionally, the first relation is an identity and the second relation is an identity.
According to a fifth aspect of embodiments of the present disclosure, there is provided an electronic apparatus including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the data acquisition method according to the present disclosure.
According to a sixth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium, wherein instructions, when executed by at least one processor, cause the at least one processor to perform a data acquisition method as described above according to the present disclosure.
According to a seventh aspect of embodiments of the present disclosure, there is provided a computer program product comprising computer instructions which, when executed by a processor, implement a data acquisition method according to the present disclosure.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:
according to the data acquisition method and the data acquisition device, the relational expression corresponding to the two periods of time is established through the data evaluation indexes in the two periods of time and the parameters corresponding to the concerned stages in the corresponding time, the contribution rate of each concerned stage to the change of the data evaluation indexes is obtained based on the two established relational expressions, and the influence of each stage of the flow funnel on the change of the data evaluation indexes is quantized. Therefore, the method and the device solve the problem that the influence of the final change of the data evaluation index caused by each stage of the flow funnel cannot be quantified in the related art.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure and are not to be construed as limiting the disclosure.
Fig. 1 is a schematic diagram illustrating an implementation scenario of a data acquisition method according to an exemplary embodiment of the present disclosure;
FIG. 2 is a flow chart illustrating a method of data acquisition according to an exemplary embodiment;
FIG. 3 is a schematic diagram illustrating consumption metric change, according to an exemplary embodiment;
FIG. 4 is a block diagram illustrating a data acquisition device according to an exemplary embodiment;
fig. 5 is a block diagram of an electronic device in accordance with an embodiment of the disclosure.
Detailed Description
In order to make the technical solutions of the present disclosure better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. The embodiments described in the following examples do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
In this case, the expression "at least one of the items" in the present disclosure means a case where three types of parallel expressions "any one of the items", "a combination of any plural ones of the items", and "the entirety of the items" are included. For example, "include at least one of a and B" includes the following three cases in parallel: (1) comprises A; (2) comprises B; (3) including a and B. For another example, "at least one of the first step and the second step is performed", which means that the following three cases are juxtaposed: (1) executing the step one; (2) executing the step two; (3) and executing the step one and the step two.
The disclosure provides a data acquisition method, which can quantify the final change rate of data evaluation indexes caused by each stage of a flow funnel, and the size of the influence. The method can be applied to advertisement services and any services in which the flow funnel is arranged in time sequence. When applied to an advertisement service, the data evaluation index may include, but is not limited to: a consumption index, an exposure index, a behavior number index, a conversion number index, and the like. The following description will take the consumption index of the advertisement service as an example.
Fig. 1 is a schematic diagram illustrating an implementation scenario of a data acquisition method according to an exemplary embodiment of the present disclosure, as shown in fig. 1, the implementation scenario includes a server 100, a user terminal 110, and a user terminal 120, where the number of the user terminals is not limited to 2, and includes not limited to a mobile phone, a personal computer, and the like, the user terminal may install an application program that can browse an advertisement, the server may be one server, or several servers form a server cluster, or may be a cloud computing platform or a virtualization center.
The server 100 receives the phase that the evaluation sent by the user terminal 110, 120 results in a change of consumption index (i.e. data evaluation index) of a certain advertisement, the traffic funnel corresponding to the advertisement is also determined, and the phase of interest in the traffic funnel can be determined based on the actual situation. At this time, the server 100 may receive the consumption index of the user terminals 110 and 120 within two days before and after the transmission (for convenience of explanation, the first day represents the previous day, and the second day represents the subsequent day), and may also receive the parameters of the phases of interest transmitted by the user terminals 110 and 120 within the first day and the second day, for example, the parameters may be the number of advertisement requests of the respective phases at this time. The server 100 obtains a first relational expression based on the consumption index of the first day and the number of advertisement requests of the concerned stage in the first day, obtains a second relational expression based on the consumption index of the second day and the number of advertisement requests of the concerned stage in the second day, and then obtains a contribution rate of each stage in the concerned stage based on the determined first relational expression and the determined second relational expression, wherein the contribution rate is used for indicating the degree of influence of the current stage on the change of the data evaluation index, so that the influence of each stage of the traffic funnel on the rise or fall of the consumption index is quantized, and the stage causing the change of the consumption index can be determined based on the magnitude of the contribution rate.
Hereinafter, a data acquisition method and apparatus according to an exemplary embodiment of the present disclosure will be described in detail with reference to fig. 2 to 5.
FIG. 2 is a flow chart illustrating a data acquisition method according to an exemplary embodiment, as shown in FIG. 2, the data acquisition method including the steps of:
in step S201, a data evaluation index of a current service is obtained, where the data evaluation index includes a first data evaluation index in a first predetermined time period and a second data evaluation index in a second predetermined time period. The current traffic may be an advertisement traffic or any traffic with a corresponding traffic funnel arranged in a time sequence. When the current service is an advertisement service, the data evaluation index may include, but is not limited to: a consumption index, an exposure index, a behavior number index, a conversion number index, and the like.
For convenience of explanation, the consumption index of the advertisement service may be acquired from the user terminal on two days before and after the advertisement service (for convenience of explanation, the first day represents the previous day, and the second day represents the subsequent day), and the consumption index of the advertisement service on the second day (2020.12.27) may be reduced to about 30% of the consumption index on the first day (2020.12.26), as shown in fig. 3.
Returning to fig. 2, in step S202, a first relational expression is obtained based on the first data evaluation index and the parameter of the at least one predetermined stage in the first predetermined time period, and a second relational expression is obtained based on the second data evaluation index and the parameter of the at least one predetermined stage in the second predetermined time period, wherein the predetermined stage is a stage in the flow funnel corresponding to the data evaluation index, and the parameter is determined based on the data evaluation index. The first relation may be an identity, and the second relation may also be an identity. The at least one predetermined phase may be a phase of interest in the traffic funnels determined based on actual conditions, and generally, the traffic funnels corresponding to the services in one scene are unique. The parameters used above are determined based on data evaluation indexes, that is, the indexes to be evaluated are known, and parameters capable of representing the indexes are adopted.
According to an exemplary embodiment of the present disclosure, the left side of the equal sign of the first relational expression may be a first data evaluation index, and the right side of the equal sign of the first relational expression may be an expression determined based on the parameter of the at least one predetermined stage for the first predetermined period of time and the first data evaluation index. For example, the above equation may be (parameter 1 × (parameter 2/parameter 1) × … × (parameter n/parameter n-1) × (first data evaluation index/parameter n)), where the parameter n is a parameter of a predetermined phase n of the at least one predetermined phase for a first predetermined period of time, and n is a positive integer. Through the embodiment, the first relational expression can be established simply and quickly.
For example, the consumption index of the advertisement service is still taken as an example for explanation, and the concerned stage is recall, refined ranking and winning according to the actual requirement. The first relation may then be the following identity split based on the phase of interest:
total _ cost1 ═ number of large disk requests 1 × (number of recall requests 1/number of large disk requests 1) × (number of precise requests 1/number of recall requests 1) × (number of winning requests 1/number of precise requests 1) × (total _ cost 1/number of winning requests 1) (1)
Wherein, total _ cost1 is a consumption index value of the first day, the number of requests for large disks 1 is the number of requests for advertisements of the whole advertisement platform (i.e. large disks) of the first day, the number of recall requests 1 is the number of requests for advertisements entering the recall phase of the first day, the number of requests for precise ranking 1 is the number of requests for advertisements entering the precise ranking phase of the first day, and the number of requests for winning 1 is the number of requests for advertisements entering the winning phase of the first day. Generally, the request refers to a request for placing an advertisement, which needs to be initiated to the system in the advertisement placing process.
According to an exemplary embodiment of the present disclosure, the left side of the equal sign of the second relation may be a second data evaluation index, and the right side of the equal sign of the second relation may be an equation determined based on the parameter of the at least one predetermined stage in the second predetermined period of time and the second data evaluation index. For example, the above equation may be (parameter 1 × (parameter 2/parameter 1) × … × (parameter n/parameter n-1) × (second data evaluation index/parameter n)), where parameter n is a parameter of a predetermined phase n of the at least one predetermined phase for a second predetermined period of time, and n is a positive integer. Through the embodiment, the first relational expression can be established simply and quickly.
For example, the consumption index of the advertisement service is still taken as an example for explanation, and the concerned stage is recall, refined ranking and winning according to the actual requirement. The second relation may then be the following identity split based on the phase of interest:
total _ cost2 ═ number of large disk requests 2 × (number of recall requests 2/number of large disk requests 2) × (number of precise requests 2/number of recall requests 2) × (number of winning requests 2/number of precise requests 2) × (total _ cost 2/number of winning requests 2) (2)
Wherein, total _ cost2 is the consumption index value of the next day, the number of large disk requests 2 is the number of advertisement requests of the whole advertisement platform of the next day, the number of recall requests 2 is the number of advertisement requests entering the recall phase of the next day, the number of fine ranking requests 2 is the number of advertisement requests entering the fine ranking phase of the next day, and the number of winning requests 2 is the number of advertisement requests entering the winning phase of the next day.
Returning to fig. 2, in step S203, the contribution rate of each of the at least one predetermined stage is obtained based on the first relational expression and the second relational expression. The contribution rate is used to indicate the influence degree of the current stage on the change of the data evaluation index, for example, when the contribution rate of the recall stage is 20%, the change of the data evaluation index is 20% caused by the recall stage.
According to an exemplary embodiment of the present disclosure, obtaining a contribution rate of each of at least one predetermined phase based on a first relation and a second relation includes: performing division processing on the first relational expression and the second relational expression; carrying out logarithmic transformation processing on the result after the division processing to obtain a logarithmic expression; acquiring the value of each addend in the logarithmic expression; and obtaining the contribution rate of each stage in at least one preset stage based on the value of each addend and an index ratio, wherein the index ratio is obtained through a first data evaluation index and a second data evaluation index. By the embodiment, the relational expression between each stage and the data evaluation index can be converted into the additive relation, so that the influence of each stage on the change of the data evaluation index can be obtained more conveniently.
For example, still taking the consumption index of the advertisement service as an example for explanation, at this time, the above formula (1) and formula (2) may be divided to obtain the following formula:
total _ cost1/total _ cost2 ═ number of large disk requests 1/number of large disk requests 2 × [ (number of recall requests 1/number of large disk requests 1)/(number of recall requests 2/number of large disk requests 2) ] × [ (number of requests for top ranking 1/number of recall requests 1)/(number of requests for top ranking 2/number of recall requests 2) ] × [ (number of requests for top ranking 1/number of requests for top ranking 1)/(number of requests for top ranking 2/number of requests for top ranking 2) ] × [ (total _ cost 1/number of requests for top ranking 1)/(total _ cost 2/number of requests for top ranking 2) ] (3)
Then, taking logarithm of both sides of the above formula (3) to obtain the following formula:
log (total _ cost1/total _ cost2) ═ log [ number of large disk requests 1/number of large disk requests 2] + log [ (number of recall requests 1/number of large disk requests 1)/(number of recall requests 2/number of large disk requests 2) ] + log [ (number of requests for precise rearrangement 1/number of recall requests 1)/(number of requests for precise rearrangement 2/number of recall 2) ] + log [ (number of winning requests 1/number of requests for precise rearrangement 1)/(number of winning requests 2/number of requests for precise rearrangement 2) ] + log [ (total _ cost 1/number of winning requests 1)/(total _ cost 2/number of winning requests 2) ] (4)
Based on the above formula (4), the contribution rate of each stage of the flow funnel can be quantified, which is specifically shown in the following table:
TABLE 1 contribution rate of each stage
The percentages of each addend are obtained by dividing the logarithm of the addend by the logarithm of the consumption ratio.
It should be noted that the contribution rate may have different results according to the stage of interest, for example, when the stage of interest is orientation, recall pre-cut, recall, rough ranking, fine ranking, bid bidding, win, the logarithmic expression listed in the above formula (4) is replaced by the following formula:
log (total _ cost1/total _ cost2) ═ log [ number of large disk requests 1/number of large disk requests 2] + log [ (number of directed 1/number of large disk requests 1)/(number of directed 2/number of large disk requests 2) ] + log [ (number of recall pre-truncated requests 1/number of directed requests 1)/(number of recall pre-truncated requests 2/number of directed requests 2) ] + log [ (number of recall requests 1/number of recall pre-truncated requests 1)/(number of recall requests 2/number of recall pre-truncated requests 2) ] + log [ (number of coarse line requests 1/number of recall requests 1)/(number of coarse line requests 2/number of recall requests 2) ] + log [ (number of bid requests 1/number of coarse line requests 1)/(number of fine line requests 2/number of coarse line requests 2) ] + log [ (number of bid requests 1/number of fine line requests 1)/(number of fine line requests 2/number of fine line requests 2) ] + [ (number of bid requests 1/number of winning wins 1 ] + 2/number of fine line requests 2) ] + [ (number of winning-out requests 1 ] + at Number of requests 1/number of bid requests 1)/(number of winning requests 2/number of bid requests 2) ] + log [ (total _ cost 1/number of winning requests 1)/(total _ cost 2/number of winning requests 2) ] (5)
The phases concerned above are not limited to the phases listed in the present disclosure, and any phases may be determined as necessary.
According to an exemplary embodiment of the present disclosure, after obtaining the contribution rate of each of the at least one predetermined phase based on the first relational expression and the second relational expression, the method further includes: determining a stage causing the data evaluation index to change based on the contribution rate; and obtaining a processing indication aiming at the current service based on the determined stage and the contribution rate corresponding to the determined stage. By the embodiment, the stage causing the data evaluation index to change can be determined based on the acquired contribution rate, and a corresponding adjustment suggestion is given to avoid the data evaluation index from changing.
For example, when the stage of interest is recall, essence, win: in the reason for the consumed amount of the advertisement service on the next day (2020.12.27), the recall request count/large disk request count contributes 58.92% to the consumed amount, and the fine line request count/recall request count contributes 29.30% to the consumed amount, so it can be assumed that there may be a factor causing the consumed amount in the stages before the recall and the recall, and the concerned stages can be increased to locate the problem, for example, finer-grained splitting can be performed to obtain the corresponding relational expression, and the above equation (5) is a more fine-grained split relational expression.
As another example, when the stage of interest is orientation, recall pre-truncation, recall, rough ranking, fine ranking, bid, win: the advertisement service shown in fig. 3 recalls the number of pre-truncated requests/the number of targeted requests contributing 48.72% to the consumed amount and the number of targeted requests/the number of large disk requests contributing 16.36% to the consumed amount among the reasons for the consumed amount on the next day (2020.12.27), and thus, it can be presumed that the client has a behavior of modifying the targeting and then, can inform the client that such a behavior is highly likely to cause the consumed amount, does not suggest modifying the targeting, and can consider to change back the original targeting.
The method can quantify changes of data evaluation indexes (such as consumption, exposure, behavior number, conversion number and the like) of the service to each stage in the traffic funnel, for example, under the condition that the advertisement consumption is lost, the method can obtain the contribution rate of the concerned stage (such as recall, fine discharge and winning) in the traffic funnel to the lost consumption of the advertisement, the positive and negative values of the contribution rate can reflect the positive and negative influences of the stage on the final lost consumption of the advertisement, the absolute value of the contribution rate reflects the relative size of the contribution rate, the funnel stage with problems can be quickly locked, the diagnosis and investigation efficiency is improved, namely, a client is helped to optimize the analysis and attribution of the consumed lost quantity of the advertisement, and attribution quantification is realized.
It should be noted that the above-mentioned services are not limited to advertisement services, and any services arranged in a time sequence in the stages in the traffic funnel are applicable, and the method of the present disclosure is also applicable to different scenes in the same service, as long as the corresponding data evaluation index and the concerned stage are determined according to the specific scene.
FIG. 4 is a block diagram illustrating a data acquisition device according to an example embodiment. Referring to fig. 4, the apparatus includes a data evaluation index acquisition unit 40, a relational expression acquisition unit 42, and a contribution rate acquisition unit 46.
A data evaluation index obtaining unit 40 configured to obtain a data evaluation index of a current service, wherein the data evaluation index includes a first data evaluation index in a first predetermined time period and a second data evaluation index in a second predetermined time period; a relation obtaining unit 42 configured to obtain a first relation based on the first data evaluation index and a parameter of at least one predetermined stage in a first predetermined time period, and obtain a second relation based on the second data evaluation index and a parameter of at least one predetermined stage in a second predetermined time period, wherein the predetermined stage is a stage in the flow funnel corresponding to the data evaluation index, and the parameter is determined based on the data evaluation index; a contribution rate obtaining unit 46 configured to obtain a contribution rate of each of the at least one predetermined phase based on the first relational expression and the second relational expression.
Optionally, the contribution rate obtaining unit 46 is further configured to perform a division process on the first relational expression and the second relational expression; carrying out logarithmic transformation processing on the result after the division processing to obtain a logarithmic expression; acquiring the value of each addend in the logarithmic expression; and obtaining the contribution rate of each stage in at least one preset stage based on the value of each addend and an index ratio, wherein the index ratio is obtained through a first data evaluation index and a second data evaluation index.
Optionally, the left side of the equal sign of the first relation is a first data evaluation index, and the right side of the equal sign of the first relation is an equation determined based on the parameter of the at least one predetermined stage in the first predetermined time period and the first data evaluation index.
Optionally, the equation is (parameter 1 × (parameter 2/parameter 1) × … × (parameter n/parameter n-1) × (first data evaluation index/parameter n)), where the parameter n is a parameter of a predetermined phase n of the at least one predetermined phase for a first predetermined period of time, and n is a positive integer.
Optionally, the left side of the equal sign of the second relation is a second data evaluation index, and the right side of the equal sign of the second relation is an equation determined based on the parameter of the at least one predetermined stage in the second predetermined period of time and the second data evaluation index.
Optionally, the equation is (parameter 1 × (parameter 2/parameter 1) × … × (parameter n/parameter n-1) × (second data evaluation index/parameter n)), where parameter n is a parameter of a predetermined phase n of the at least one predetermined phase for a second predetermined time period, and n is a positive integer.
Optionally, the contribution rate obtaining unit 46 is further configured to, after obtaining the contribution rate of each stage in the at least one predetermined stage based on the first relational expression and the second relational expression, determine a stage causing the data evaluation index to change based on the contribution rate; and obtaining a processing indication aiming at the current service based on the determined stage and the contribution rate corresponding to the determined stage.
Optionally, the first relation is an identity and the second relation is an identity.
According to an embodiment of the present disclosure, an electronic device is provided, and fig. 5 is a block diagram of an electronic device according to an embodiment of the present disclosure. Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and does not constitute a limitation on the electronic devices to which the disclosed aspects apply, as a particular electronic device may include more or less components than those shown, or combine certain components, or have a different arrangement of components.
In an exemplary embodiment, an electronic device is provided. The electronic device includes at least one memory having a set of computer-executable instructions stored therein that, when executed by the at least one processor, perform a data acquisition method in accordance with an embodiment of the present disclosure.
By way of example, the electronic device may be a PC computer, tablet device, personal digital assistant, smartphone, or other device capable of executing the set of instructions described above. The electronic device 1000 need not be a single electronic device, but can be any collection of devices or circuits that can execute the above instructions (or sets of instructions) individually or in combination. The electronic device may also be part of an integrated control system or system manager, or may be configured as a portable electronic device that interfaces with local or remote (e.g., via wireless transmission).
In an electronic device, a processor may include a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a programmable logic device, a special-purpose processor system, a microcontroller, or a microprocessor. By way of example, and not limitation, processors may also include analog processors, digital processors, microprocessors, multi-core processors, processor arrays, network processors, and the like.
The processor may execute instructions or code stored in the memory, which may also store data. The instructions and data may also be transmitted or received over a network via a network interface device, which may employ any known transmission protocol.
The memory may be integral to the processor, e.g., RAM or flash memory disposed within an integrated circuit microprocessor or the like. Further, the memory may comprise a stand-alone device, such as an external disk drive, storage array, or any other storage device usable by a database system. The memory and the processor may be operatively coupled or may communicate with each other, such as through an I/O port, a network connection, etc., so that the processor can read files stored in the memory.
In addition, the electronic device may also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, mouse, touch input device, etc.). All components of the electronic device may be connected to each other via a bus and/or a network.
According to an embodiment of the present disclosure, there may also be provided a computer-readable storage medium, wherein when executed by at least one processor, instructions in the computer-readable storage medium cause the at least one processor to perform the data acquisition method of the embodiment of the present disclosure. The computer-readable storage medium herein may be ROM, Random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk and optical data storage, and any other means configured to store the computer program and any associated data, data files and data structures in a non-transitory manner and to provide the computer program and any associated data, data files and data structures to a processor or computer so that the processor or computer can execute the computer program. The computer program in the computer-readable storage medium described above can be run in an environment deployed in a computer apparatus, such as a client, a host, a proxy device, a server, and the like, and further, in one example, the computer program and any associated data, data files, and data structures are distributed across a networked computer system such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed fashion by one or more processors or computers.
According to an embodiment of the present disclosure, there is provided a computer program product including computer instructions that, when executed by a processor, implement the data acquisition method of the embodiment of the present disclosure.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), random access programmable read-only ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method of data acquisition, comprising:
acquiring a data evaluation index of a current service, wherein the data evaluation index comprises a first data evaluation index in a first preset time period and a second data evaluation index in a second preset time period;
obtaining a first relational expression based on the first data evaluation index and parameters of at least one predetermined stage in the first predetermined time period, and obtaining a second relational expression based on the second data evaluation index and parameters of the at least one predetermined stage in the second predetermined time period, wherein the predetermined stage is a stage in a flow funnel corresponding to the data evaluation index, and the parameters are determined based on the data evaluation index;
and obtaining the contribution rate of each stage in the at least one predetermined stage based on the first relational expression and the second relational expression.
2. The data acquisition method as claimed in claim 1, wherein said deriving a contribution rate for each of said at least one predetermined phase based on said first relation and said second relation comprises:
performing division processing on the first relational expression and the second relational expression;
carrying out logarithmic transformation processing on the result after the division processing to obtain a logarithmic expression;
obtaining the value of each addend in the logarithmic expression;
and obtaining the contribution rate of each stage in the at least one predetermined stage based on the value of each addend and an index ratio, wherein the index ratio is obtained through the first data evaluation index and the second data evaluation index.
3. The data acquisition method according to claim 1, wherein a left side of a constant sign of the first relational expression is the first data evaluation index, and a right side of the constant sign of the first relational expression is an expression determined based on the parameter of the at least one predetermined stage in the first predetermined period of time and the first data evaluation index.
4. The data acquisition method according to claim 3, wherein the equation is (parameter 1 × (parameter 2/parameter 1) × … × (parameter n/parameter n-1) × (the first data evaluation index/parameter n)), where the parameter n is a parameter of a predetermined stage n of the at least one predetermined stage for the first predetermined period of time, and n is a positive integer.
5. The data acquisition method according to claim 1, wherein a left side of a constant sign of the second relational expression is the second data evaluation index, and a right side of the constant sign of the second relational expression is an expression determined based on the parameter of the at least one predetermined stage in the second predetermined period of time and the second data evaluation index.
6. The data acquisition method according to claim 5, wherein the equation is (parameter 1 × (parameter 2/parameter 1) × … × (parameter n/parameter n-1) × (the second data evaluation index/parameter n)), where the parameter n is a parameter of a predetermined stage n of the at least one predetermined stage for the second predetermined period of time, n being a positive integer.
7. A data acquisition apparatus, comprising:
the data evaluation index acquisition unit is configured to acquire a data evaluation index of the current service, wherein the data evaluation index comprises a first data evaluation index in a first predetermined time period and a second data evaluation index in a second predetermined time period;
a relational expression obtaining unit configured to obtain a first relational expression based on the first data evaluation index and a parameter of at least one predetermined stage in the first predetermined time period, and obtain a second relational expression based on the second data evaluation index and a parameter of the at least one predetermined stage in the second predetermined time period, wherein the predetermined stage is a stage in a flow funnel corresponding to the data evaluation index, and the parameter is determined based on the data evaluation index;
a contribution rate obtaining unit configured to obtain a contribution rate of each of the at least one predetermined stage based on the first relational expression and the second relational expression.
8. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the data acquisition method of any one of claims 1 to 6.
9. A computer-readable storage medium, wherein instructions in the computer-readable storage medium, when executed by at least one processor, cause the at least one processor to perform the data acquisition method of any one of claims 1 to 6.
10. A computer program product comprising computer instructions, characterized in that the computer instructions, when executed by a processor, implement the data acquisition method according to any one of claims 1 to 6.
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