CN106940701B - Index expectation dynamic updating method, device and system - Google Patents
Index expectation dynamic updating method, device and system Download PDFInfo
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Abstract
The embodiment of the application provides an index expectation dynamic updating method, an index expectation dynamic updating device and an index expectation dynamic updating system, and relates to the technical field of real-time computing. The method comprises the following steps: scanning data according to an initial theoretical expected value expected by the index expectation to obtain a result value; determining whether the result value and a theoretical expected value, a baseline expected value and a fluctuation threshold value expected by the index meet a first preset condition; when a first preset condition is met, determining whether the baseline expected value and/or the fluctuation threshold value need to be updated according to a second preset condition; and if the baseline expected value needs to be updated, updating the baseline expected value to the result value, and calculating and updating the fluctuation threshold value based on the updated baseline expected value. The embodiment of the application can reduce human intervention to a great extent, reduce excessive dependence of the system on personnel familiar with business or data, improve the judgment accuracy and improve the working efficiency of the system.
Description
Technical Field
The present application relates to the field of real-time computing technologies, and in particular, to a method and a device and a system for dynamically updating an expected index.
Background
With the rapid development of information technology, information is growing explosively, and people have higher and higher requirements on how to acquire information more efficiently. For example, in an application of personalized information recommendation, when a seller in an e-commerce website recommends commodity information to a user, if the seller can recommend commodity information more suitable for the customer according to the needs of the user, the chance of the seller in making a deal will be greatly improved; but it will also be an enhancement to the user experience for the user. Based on this demand, a technique of anticipating a desire to screen commodity information according to a set index has been developed. However, the most important link in this technology is how to set the index expectation.
One solution in the prior art is to manually set a fixed index expectation by a person familiar with the business or data, and to screen the data information by scanning and comparing the data information with the fixed index expectation through a computer program. It is clear that the drawback of this solution is that it is too dependent on the personnel familiar with the traffic or data and that it is also inefficient.
Another solution in the prior art is to scan and compare data information with a set index expected expectation through a computer program, then perform discussion and analysis together by a plurality of persons familiar with business or data, and then re-determine whether the set index expected expectation is abnormal. The disadvantage of this scheme is also obvious, namely, since the data information is generated first, and then the manual judgment is performed according to the data information to judge whether the index expectation set previously is reasonable, the post inference is easy to be caused, thereby causing the misjudgment.
From the above, the prior art schemes all have obvious disadvantages, too much human intervention is needed, and the efficiency is low.
Disclosure of Invention
In view of the above problems, embodiments of the present application are proposed to provide an index expected dynamic update method and a corresponding index expected dynamic update apparatus and system that overcome or at least partially solve the above problems.
In order to solve the above problem, the present application discloses a method for index expectation dynamic update, including:
scanning data according to an initial theoretical expected value expected by the index expectation to obtain a result value;
determining whether the result value and a theoretical expected value, a baseline expected value and a fluctuation threshold value expected by the index meet a first preset condition;
when a first preset condition is met, determining whether a theoretical expected value, a baseline expected value and a fluctuation threshold value of the index expectation meet a second preset condition;
and when a second preset condition is met, updating the baseline expected value to the result value, and calculating and updating the fluctuation threshold value based on the updated baseline expected value.
The application also discloses an index expectation dynamic updating device, which comprises:
the acquisition module is used for scanning data according to an initial theoretical expected value expected by the index expectation to obtain a result value;
the first determination module is used for determining whether the result value and a theoretical expected value, a baseline expected value and a fluctuation threshold value of the index expected value meet first preset conditions;
a second determination module, configured to determine whether a theoretical expected value, a baseline expected value, and a fluctuation threshold value of the index expectation satisfy a second preset condition when the first determination module determines that the first preset condition is satisfied;
and the updating module is used for updating the baseline expected value to the result value when the second determining module determines that a second preset condition is met, and calculating and updating the fluctuation threshold value based on the updated baseline expected value.
The application also discloses a system comprising the index expectation and expectation dynamic updating device.
The embodiment of the application has the following advantages:
according to the method and the device, for the index expectation of the real-time computing system for screening the data information, a theoretical expectation value is set for the corresponding index expectation value, the data information is scanned based on the theoretical expectation value to obtain a result value, and then judgment is carried out based on the set judgment condition, so that the baseline expectation value and the fluctuation threshold value of the index expectation value are adaptively corrected and adjusted, and the index expectation value is dynamically updated. Therefore, the dynamic structure expected by the index can be simply realized through system scanning and automatic judgment processing, the manual intervention can be reduced to a great extent, the excessive dependence of the system on personnel familiar with business or data is reduced, the judgment accuracy is improved, and the working efficiency of the system is improved. In addition, the embodiment of the invention can be conveniently applied to various models adopting general index expectation and similar services, is beneficial to the expansion of the index expectation, and is more suitable for the actual situation that the whole data changes and fluctuates continuously.
Drawings
FIG. 1 is a flow chart of steps of an embodiment of a method for index expected dynamic update of the present application;
FIG. 2 is a flow chart of steps of an embodiment of a method for index expected dynamic update of the present application;
FIG. 3 is a block diagram illustrating an embodiment of an apparatus for index expected dynamic update of the present application;
FIG. 4 is a block diagram illustrating an embodiment of an apparatus for index expected dynamic update of the present application;
FIG. 5 is a block diagram illustrating an embodiment of a system for index expected dynamic update according to the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, the present application is described in further detail with reference to the accompanying drawings and the detailed description.
For the purpose of more conveniently describing the embodiment of the application, relevant terms related to the embodiment of the application are introduced by taking an application system developed by java as an example.
Indexes are as follows: i.e. a statistical indicator. Generally, each index has a corresponding name, a specific value, and the specific value can be evaluated by expectation (baseline). The index expected expectations may specifically include: theoretical expected value, baseline expected value, fluctuation threshold value.
Theoretical expected value (idealp) is one of important constituents expected by the index, and represents that the index expects theoretically optimal results. Actual data may not reach this theoretical expected value (idealp), but there will always be a biased theoretical value, as can be seen by name. In one index expectation, the corresponding theoretical expected value (idealp) is fixed once initially.
The baseline expected value (basep) is one of important constituents expected to be expected by the index and represents the result expected to be achieved by the index. The baseline expected value (basep) is typically dynamically changing and is also one of the important aspects of indicating that dynamic updates are expected.
And the fluctuation threshold (th) is one of important constituents expected by the index, and represents the degree of online fluctuation of the expected baseline expected value (basep) of the index. The fluctuation threshold (th) is typically dynamically changing and is one of the important aspects of indicating that dynamic updates are expected.
Result value (resultp): the method is obtained after scanning data according to an initial theoretical expected value (ideal) expected by an index, and is also one of important bases of dynamic updating of a baseline expected value (basep).
Algorithm Evaluation platform (AEC, Algorithm Evaluation Carrier): the method can be an application system developed by java and can be used for evaluating related data in an algorithm.
One of the core ideas of the embodiment of the application is that for the index expectation of a real-time computing system for screening data information, a theoretical expected value is set for the index expectation, the data information is scanned based on the theoretical expected value to obtain a result value, and then the result value is judged based on a set judgment condition, so that the baseline expected value and the fluctuation threshold value of the index expectation are adaptively corrected and adjusted, and the index expectation is dynamically updated. Therefore, the dynamic structure expected by the index can be simply realized through system scanning and automatic judgment processing, the manual intervention can be reduced to a great extent, the excessive dependence of the system on personnel familiar with business or data is reduced, the judgment accuracy is improved, and the working efficiency of the system is improved. In addition, the embodiment of the invention can be conveniently applied to various models adopting general index expectation and similar services, is beneficial to the expansion of the index expectation, and is more suitable for the actual situation that the whole data changes and fluctuates continuously.
Example one
Referring to fig. 1, a flowchart illustrating steps of an embodiment of a method for index expectation dynamic update according to the present application is shown, which may specifically include the following steps:
Preferably, in another preferred embodiment of the present application, step 110 may further include:
and step 110A, inputting all index expectation expectations of the scanned data information according to the requirements of the business mode. Usually in practical traffic pattern applications, the number of such indicators is a dozen or more.
At step 110B, a theoretical expected value (idealp) corresponding to all index expectations is initiated. It is understood that, in practical applications, the theoretical expected value (idealp) can be set differently according to different traffic patterns. For example, the theoretically expected value (idealp) may be generally initially set to 0 or 1.
in an embodiment of the present application, the first preset condition includes any one of the following conditions:
a condition 11, where the result value (resultp) is greater than or equal to the baseline expected value (basep) minus the fluctuation threshold (th), and less than or equal to the baseline expected value (basep) plus the fluctuation threshold (th) (i.e. it can be simplified as follows: basep-th < (resultp + th); it is understood that the condition 11(basep-th < basep + th) is that the result value (resultp) is within an interval of the baseline expected value (basep) fluctuating above and below the threshold value (th), which can be considered as a normal case.
Conditional 12, said result value (resultp) being greater than said baseline expected value (basep) plus said fluctuation threshold (th), and less than said theoretical expected value (idealp) (i.e. can be simplified as follows: basep + th < resultp < idealp); it is understood that the condition 12(basep + th < resultp < idealp) is that when idealp tends to 1, the result value (resultp) is within the interval from the baseline expected value (basep) to the upward fluctuation threshold (th) to the theoretical expected value (idealp).
Conditional 13, the result value (resultp) is greater than or equal to the theoretical expected value (idealp) and less than the baseline expected value (basep) minus the fluctuation threshold (th) (i.e., simplified as the equation, idealp < (resultp < basep-th)); it is understood that the condition 13(ideal < ═ result p < basep-th) is that when ideal tends to 0, the resulting value (result p) is within the interval from the theoretical expected value (ideal) to the baseline expected value (basep) with the threshold (th) fluctuating downwards.
It is to be understood that, in step 120, by determining whether or not the relationship between the result value (resultp) and the theoretical expected value (idealp) expected by the indicator, the baseline expected value (basep), and the fluctuation threshold value (th) satisfies any of the above conditions 11, 12, and 13, it is determined that the relationship is satisfied as long as any condition is satisfied, and the process proceeds to step 130.
Preferably, in another preferred embodiment of the present application, before the step 120, the method further includes:
step 120A, determine if there is already a baseline expected value (basep): if not, then initiate baseline expected value (basep), if already, then proceed to step 120B;
in practical applications, the baseline expected value (basep) may collect a relatively reasonable result value (resultp) through the traffic data base. The initial setting of the baseline expectation (basep) is more complex and more dependent on the experience of the person familiar with the traffic or data than the theoretical expectation (idealp) and the fluctuation threshold (th). For example, vendor data, often have a baseline expectation (basep) of 0.01% for an index expectation of vendor inconsistency.
Preferably, in another preferred embodiment of the present application, before the step 120, the method further includes:
step 120B, determining whether a fluctuation threshold (th) already exists: if not, the fluctuation threshold (th) is initiated, and if so, step 120 is continued.
In practical applications, the initial setting of the fluctuation threshold (th) may refer to a theoretically expected value (ideal), that is, the fluctuation threshold (th) may also be set differently according to different traffic patterns. For example, vendor data, the fluctuation threshold (th) may typically be initially set to 0.001% for a vendor inconsistency index expectation.
In an embodiment of the present application, the second preset condition includes any one of the following conditions:
conditional 21, said result value (resultp) being greater than said theoretical expected value (idealp) and less than said baseline expected value (basep) (i.e. it can be simplified as the formula: idealp < resultp < basep); it is understood that the condition 21(ideal < result < basep) is that the result value (result) is within the interval from the theoretical expected value (ideal) to the baseline expected value (basep) in the case where ideal tends to 0.
Conditional 22, said result value (resultp) being greater than said baseline expected value (basep) and less than said theoretical expected value (idealp) (i.e. can be reduced to the formula: basep < resultp < idealp); it is understood that the condition 22(basep < resultp < idealp) is that the result value (resultp) is within the interval from the baseline expected value (basep) to the theoretical expected value (idealp) in the case where idealp tends to 1.
It is to be understood that, in step 130, by determining whether or not the relationship between the theoretical expected value (idealp) expected by the index, the baseline expected value (basep), and the fluctuation threshold value (th) satisfies any of the above-described conditions 21 and 22, the relationship is determined to be satisfied as long as any condition is satisfied, and the process proceeds to step 140.
Preferably, in another preferred embodiment of the present application, the step 130 further includes:
in step 131, when it is determined that the result value (resultp) and the theoretical expected value (idealp), the baseline expected value (basep) and the fluctuation threshold (th) expected by the indicator do not satisfy the first preset condition, the baseline expected value (basep) may be manually reset.
Preferably, in another preferred embodiment of the present application, the step 140 further includes:
a substep 141 of updating said baseline expected value (basep) to said result value (resultp);
a substep 142 of calculating a difference between the updated baseline expected value (basep) and each historical baseline expected value (old _ basep) including the baseline expected value before updating, and updating the fluctuation threshold (th) to a maximum value of absolute values of the difference.
According to the method and the device, for the index expectation of the real-time computing system for screening the data information, a theoretical expectation value is set for the corresponding index expectation value, the data information is scanned based on the theoretical expectation value to obtain a result value, and then judgment is carried out based on set judgment conditions, so that the baseline expectation value and the fluctuation threshold value of the index expectation value are adaptively corrected and adjusted, and the index expectation value is dynamically updated. Therefore, the dynamic structure expected by the index can be simply realized through system scanning and automatic judgment processing, the manual intervention can be reduced to a great extent, the excessive dependence of the system on personnel familiar with business or data is reduced, the judgment accuracy is improved, and the working efficiency of the system is improved. In addition, the embodiment of the invention can be conveniently applied to various models adopting general index expectation and similar services, is beneficial to the expansion of the index expectation, and is more suitable for the actual situation that the whole data changes and fluctuates continuously.
Example two
Recommending a batch of commodities to a user in combination with an actual service, namely a B page of an E-commerce website A, wherein the service requirement is that adult private data cannot exist; and (4) the commodity is sold in a hot mode as little as possible. Referring to fig. 2, a flowchart illustrating steps of a preferred embodiment of a method for index expected dynamic update of the present application is shown, which may specifically include the following steps:
the theoretically expected value idealp2 is 20% transmittance, accounting for < 10%. The penetration rate is a single occurrence rate of the hot product, and a specific value thereof is related to how many display pit positions are, for example, 5 display pit positions, and it is generally reasonable to have 1 product, that is, the penetration rate > is 20%. The occupancy ratio is a ratio of the hot commodity to the whole, and it is reasonable to set the occupancy ratio to < 10% based on an empirical value when the penetration ratio > is 20%. It is understood that if 2 goods appear in 5 display pit positions, the transmittance > is 40%, and the corresponding percentage < 20% may be reasonable. Therefore, the two numerical values of the percentage of penetration and the percentage of occupation have certain correlation, whether the numerical value of the percentage of penetration meets the condition or not is judged firstly when the specific judgment is carried out, and whether the numerical value of the percentage of occupation meets the condition or not is further judged under the condition that the numerical value of the percentage of penetration meets the condition or not.
It should be noted that, for different indexes, the corresponding theoretical expected value idealp is also different. For example, the index 1 is the inspection of the commodity of the adult category, and the index expectation 1 corresponds to the theoretical expected value idealp1 and only needs to be embodied by the content of the commodity of the adult category; index 2 is a hot commodity single penetration rate check, and index expectation 2 corresponds to a theoretical expected value idealp2 and needs to be represented by two values of penetration rate and ratio. It can be understood that for other indexes, due to different specific contents of the indexes, the indexes expect that the corresponding theoretically expected value idealp may also need to be represented by more numerical values. The relevance of these more values depends on the specific criteria.
it should be noted that when data scanning is expected using a plurality of indexes, a preferable mode is parallel scanning, and each index is expected to produce a corresponding result value, and the result expected by each index and the subsequent judgment processing are independent. Obviously, such a process is the most efficient.
in step 260, it is determined whether the result value resultp1 and ideal p1, basep1, and th1 of the index expectation 1 satisfy any one of the conditions 11, 12, and 13 in the first preset condition:
in this example, the index expects 1: idealp1 (0%) <result 1 (0.01%) < basep-th (0.02% -0.001% > -0.019), apparently satisfying condition 13.
It is determined whether the result value resultp2 and ideal p2, base p2, and th2 of the index expectation 2 are any one of the conditions 11, 12, and 13 in the first preset conditions:
in this example, the index expects 2: resultp2 (15% in percentage), idealp2 (10%), basep2 (12% in percentage), and th2 ═ 2%, it is clear that any of condition 11(basep-th ═ resultp ═ basep + th), condition 12(basep + th < resultp < idealp), and condition 13(idealp ═ resultp < basep-th) is not satisfied.
Thus, in step 260, index expects a 1 scan to pass, while index expects a 2 scan to fail.
In step 270, for the index expected expectation 1, it is further determined whether the ideal 1, the base 1, and the th1 satisfy any one of the conditions 21 and 22 in the second preset condition:
in this example, the index expects 1: ideal p1 (0%) < result tp1 (0.01%) < basep1 (0.02%), it is clear that condition 21 is satisfied;
It should be noted that in step 260, it has been determined that the index expected 2 scan failed, for which case the baseline expected value basep2 may be changed to 15% by manual intervention if it is only 15% confirmed by algorithmic analysis and cannot be improved in a short time. By the time the algorithm is scanned for the second and subsequent times, if some scan after a period of time finds result 2 to be below 15%, e.g., result 2 has a percentage of 12%, then the index is readjusted to expect the expected baseline expected value basep2 to be 12%, and so on.
The embodiment of the application has the following advantages:
the method comprises the steps of setting a theoretical expected value for an index expected expectation of a real-time computing system for screening data information, scanning the data information based on the theoretical expected value to obtain a result value, and judging based on a set judgment condition, so that a baseline expected value and a fluctuation threshold value of the index expected value are adaptively corrected and adjusted, and the index expected value is dynamically updated. Therefore, the dynamic structure expected by the index can be simply realized through system scanning and automatic judgment processing, the manual intervention can be reduced to a great extent, the excessive dependence of the system on personnel familiar with business or data is reduced, the judgment accuracy is improved, and the working efficiency of the system is improved. In addition, the embodiment of the invention can be conveniently applied to various models adopting general index expectation and similar services, is beneficial to the expansion of the index expectation, and is more suitable for the actual situation that the whole data changes and fluctuates continuously.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the embodiments. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred and that no particular act is required of the embodiments of the application.
EXAMPLE III
Referring to fig. 3, a block diagram of an embodiment of an index expected dynamic update apparatus according to the present application is shown, which may specifically include the following modules:
an obtaining module 310, configured to scan data according to an initial theoretical expected value (idealp) expected by the indicator, and obtain a result value (resultp); the result value (resultp) may be obtained by using a different program, for example, by processing the existing data through a separate script task, and then synchronizing to the index expected to participate in the scanning. And a part of the data can be obtained by real-time scanning on an algorithm evaluation platform AEC. In short, the obtaining manner of the result value (resultp) can support various types, and the embodiment of the present invention is not limited.
A first determining module 320, configured to determine whether the result value (resultp) and the theoretical expected value (idealp), the baseline expected value (basep), and the fluctuation threshold (th) of the index expectation satisfaction first preset conditions;
a second determining module 330, configured to determine whether the theoretical expected value (idealp), the baseline expected value (basep), and the fluctuation threshold (th) of the index expectation satisfy a second preset condition when the first determining module 320 determines that the first preset condition is satisfied;
an updating module 340, configured to update the baseline expected value (basep) to the result value (resultp) when the second determining module 330 determines that the second preset condition is met, and calculate and update the fluctuation threshold (th) based on the updated baseline expected value (basep).
In another preferred embodiment of the present application, the apparatus comprises:
and the input module is used for inputting all index expectation expectations of the scanned data information according to the needs of the business mode. Usually, in actual business model application, the number of the indexes is ten or more, and then the expected number of the indexes corresponding to each index is also ten or more.
And the initial module is used for initializing theoretical expected values (idealp) corresponding to all index expectation expectations. It is understood that, in practical applications, the theoretical expected value (idealp) can be set differently according to different traffic patterns. For example, the theoretically expected value (idealp) may be generally initially set to 0 or 1.
In another preferred embodiment of the present application, the apparatus 300 comprises:
and the setting module is used for resetting the baseline expected value (basep) manually when the determining module determines that the result value (resultp) and the theoretical expected value (idealp), the baseline expected value (basep) and the fluctuation threshold value (th) which are expected by the index do not meet the first preset condition.
In another preferred embodiment of the present application, the first determining module 320 is further configured to determine whether a baseline expected value (basep): if not, then through the initial module initial baseline expected value (basep), if already existing, then further determine if a fluctuation threshold (th) already exists: if not, the fluctuation threshold (th) is initially set by the initial module, and if so, whether the result value (resultp) and the theoretical expected value (idealp) expected by the index, the baseline expected value (basep) and the fluctuation threshold (th) meet the first preset condition is continuously determined.
In an embodiment of the present application, the first preset condition includes any one of the following conditions:
a condition 11, where the result value (resultp) is greater than or equal to the baseline expected value (basep) minus the fluctuation threshold (th), and less than or equal to the baseline expected value (basep) plus the fluctuation threshold (th) (i.e. it can be simplified as follows: basep-th < (resultp + th); it is understood that the condition 11(basep-th < basep + th) is that the result value (resultp) is within an interval of the baseline expected value (basep) fluctuating above and below the threshold value (th), which can be considered as a normal case.
Conditional 12, said result value (resultp) being greater than said baseline expected value (basep) plus said fluctuation threshold (th), and less than said theoretical expected value (idealp) (i.e. can be simplified as follows: basep + th < resultp < idealp); it is understood that the condition 12(basep + th < resultp < idealp) is that when idealp tends to 1, the result value (resultp) is within the interval from the baseline expected value (basep) to the upward fluctuation threshold (th) to the theoretical expected value (idealp).
Conditional 13, the result value (resultp) is greater than or equal to the theoretical expected value (idealp) and less than the baseline expected value (basep) minus the fluctuation threshold (th) (i.e., simplified as the equation, idealp < (resultp < basep-th)); it is understood that the condition 13(ideal < ═ result p < basep-th) is that when ideal tends to 0, the resulting value (result p) is within the interval from the theoretical expected value (ideal) to the baseline expected value (basep) with the threshold (th) fluctuating downwards.
It is understood that the first determining module 320 determines whether the relationship between the result value (resultp) and the theoretical expected value (idealp) expected by the indicator, the baseline expected value (basep) and the fluctuation threshold value (th) satisfies any one of the above conditions 11, 12 and 13 by judging, and if any one of the conditions is satisfied, the relationship is determined to be satisfied, and then the second determining submodule performs processing.
In an embodiment of the present application, the second preset condition includes any one of the following conditions:
conditional 21, said result value (resultp) being greater than said theoretical expected value (idealp) and less than said baseline expected value (basep) (i.e. it can be simplified as the formula: idealp < resultp < basep); it is understood that the condition 21(ideal < result < basep) is that the result value (result) is within the interval from the theoretical expected value (ideal) to the baseline expected value (basep) in the case where ideal tends to 0.
Conditional 22, said result value (resultp) being greater than said baseline expected value (basep) and less than said theoretical expected value (idealp) (i.e. can be reduced to the formula: basep < resultp < idealp); it is understood that the condition 22(basep < resultp < idealp) is that the result value (resultp) is within the interval from the baseline expected value (basep) to the theoretical expected value (idealp) in the case where idealp tends to 1.
It is understood that the second determination module 330 determines whether the relationship between the theoretical expected value (ideal) expected by the index, the baseline expected value (basep), and the fluctuation threshold (th) satisfies any of the above conditions 21 and 22 by judging, and if so, the relationship is determined to be satisfied, and then processed by the update module 340.
In another preferred embodiment of the present application, the update module 340 includes:
a rewriting submodule for updating the baseline expected value (basep) to the result value (resultp);
and the calculation submodule is used for calculating the difference value between the updated baseline expected value (basep) and each historical baseline expected value (basep) including the baseline expected value (basep) before updating, providing the maximum value of the absolute value in the difference value as the updated value of the fluctuation threshold value (th) to the rewriting submodule, and updating the fluctuation threshold value (th) to the maximum value of the absolute value in the difference value by the rewriting submodule.
According to the method and the device, for the index expectation of the real-time computing system for screening the data information, a theoretical expectation value is set for the corresponding index expectation value, the data information is scanned based on the theoretical expectation value to obtain a result value, and then judgment is carried out based on set judgment conditions, so that the baseline expectation value and the fluctuation threshold value of the index expectation value are adaptively corrected and adjusted, and the index expectation value is dynamically updated. Therefore, the dynamic structure expected by the index can be simply realized through system scanning and automatic judgment processing, the manual intervention can be reduced to a great extent, the excessive dependence of the system on personnel familiar with business or data is reduced, the judgment accuracy is improved, and the working efficiency of the system is improved. In addition, the embodiment of the invention can be conveniently applied to various models adopting general index expectation and similar services, is beneficial to the expansion of the index expectation, and is more suitable for the actual situation that the whole data changes and fluctuates continuously.
Example four
Practical applications of the embodiments of the present application will be described with reference to another practical service. Recommending different types of commodities to the user in a D page of the E-commerce website, wherein the service requirement is the proportion of the similar commodities; and checking the diversity commodities of the seller. Referring to fig. 4, a block diagram of an embodiment of an apparatus for index expected dynamic update of the present application is shown, which may include:
the entry module 401, according to the needs of the service mode, enters two indexes of the scanned data information, that is: index 1, proportion of similar commodities; and index 2, seller diversity commodity inspection. The expectation corresponding to index 1 is index expectation 1, and the expectation corresponding to index 2 is index expectation 2;
the initial module 402 respectively initializes theoretical expected values idealp1 and idealp2 corresponding to the index expected 1 and the index expected 2, namely: theoretically expected values idealp1 ═ 0%, idealp2 ═ 1%;
the obtaining module 410 is configured to use the index expected expectation 1 and the index expected expectation 2 to scan data for the first time, and obtain result values of resultp1 and resultp 2; wherein, resultp1 is 0.0001%, and resultp2 is 0.97%. The result value (resultp) may be obtained by using a different program, for example, by processing the existing data through a separate script task, and then synchronizing to the index expected to participate in the scanning. And a part of the data can be obtained by real-time scanning on an algorithm evaluation platform AEC. In short, the obtaining manner of the result value (resultp) can support various types, and the embodiment of the present invention is not limited.
A first determining module 420, configured to determine whether the result value (resultp) and the theoretical expected value (idealp), the baseline expected value (basep), and the fluctuation threshold (th) of the index expectation satisfaction first preset conditions;
in another preferred embodiment of the present application, the first determining module 420 determines that there have been baseline expected values basep1 and basep2 for index expected 1 and index expected 2; in this example, basep1 ═ 0.0002%, basep2 ═ 0.95%; it is also determined that there has been a fluctuation threshold th 1-0.00009% and th 2-0.05%. If it is determined that there is no baseline expected value (basep), an initial baseline expected value (basep) is also required to be initialized by the initialization module 402; if the fluctuation threshold (th) is not determined to exist, the initial module 402 is required to pass through the initial fluctuation threshold (th).
The first determination module 420 determines whether the result value resultp1 and ideal p1, basep1, and th1 of the index expectation 1 are any one of the conditions 11(basep-th < resultp + th), 12(basep + th < resultp < idelp), and 13(ideal < resultp < basep-th) among the first preset conditions: in this example, the index expects 1: idealp1 (0%) <result 1 (0.0001%) < basep-th (0.0002% -0.00009% > -0.00011%), it is clear that condition 13 is satisfied.
The first determination module 420 determines whether the result value resultp2 and ideal p2, basep2, and th2 of the index expectation 2 are any one of the conditions 11(basep-th < resultp + th), 12(basep + th < resultp < idelp), and 13(ideal < resultp < basep-th) among the first preset conditions: in this example, the index expects 2: basep2-th2 (0.95% -0.05% ═ 0.9%) < ═ resultp2 (0.97%) < basep2+ th2 (0.95% + 0.05% > -1%), apparently satisfying condition 11.
In this way, the first determination module 420 determines that both index expected 1 and index expected 2 scans pass.
It is understood that the first determining module 420 determines whether the relationship between the result value (resultp) and the theoretical expected value (idealp) expected by the indicator, the baseline expected value (basep) and the fluctuation threshold value (th) satisfies any one of the above conditions 11, 12 and 13 by judging, and if any one of the conditions is satisfied, the relationship is determined to be satisfied, and then the second determining submodule 422 processes the relationship.
For the index expectation 1, it is further determined by the second determination module 430 whether the idealp1, basep1, and th1 are any one of the conditions 21(idealp < resultp < basep) and 22(basep < resultp < idealp) in the second preset conditions: in this example, the index expects 1: ideal 1 (0%) < result 1 (0.0001%) < basep1 (0.0002%), it is clear that condition 21 is satisfied;
for the index expectation 2, it is further determined by the second determination module 430 whether the idealp2, basep2, and th2 are any one of the conditions 21(idealp < resultp < basep) and 22(basep < resultp < idealp) in the second preset conditions: in this example, the index expects 2: basep2 (0.95%) < resultp2 (0.97%) < ideal ip 2 (1%), it is clear that condition 22 is satisfied;
it is to be understood that the second determination module 430 determines whether the relationship between the theoretical expected value (ideal) expected by the index, the baseline expected value (basep), and the fluctuation threshold (th) satisfies any of the above conditions 21 and 22 by judging, and if so, is determined to be satisfied, and is further processed by the update module 440.
An updating module 440, configured to update the baseline expected value (basep) to the result value (resultp) when the second determining module 430 determines the updating, and calculate and update the fluctuation threshold (th) based on the updated baseline expected value (basep).
In another preferred embodiment of the present application, the update module 440 includes:
a rewrite submodule 441 for updating the baseline expected value basep1 to a result value resultp1 of 0.0001%; the baseline expected value basep2 is updated to result value resultp2, which is 0.97%;
a calculating submodule 442, configured to calculate a difference between the updated baseline expected value (basep) and each of the historical baseline expected values (basep) including the baseline expected value (basep) before updating, provide a maximum value of an absolute value of the difference to the rewriting submodule 441 as an updated value of the fluctuation threshold (th), and update the fluctuation threshold (th) to the maximum value of the absolute value of the difference by the rewriting submodule 441. The specific calculation of the updated fluctuation thresholds th1 and th2 is not described in detail herein.
Furthermore, in another preferred embodiment of the present application, the apparatus comprises:
a setting module 450, configured to reset the baseline expected value (basep) manually when the first determining module 420 determines that the result value (resultp) and the theoretical expected value (idealp), the baseline expected value (basep) and the fluctuation threshold (th) expected by the indicator do not satisfy the first preset condition.
The embodiment of the application has the following advantages:
the method comprises the steps of setting a theoretical expected value for an index expected expectation of a real-time computing system for screening data information, scanning the data information based on the theoretical expected value to obtain a result value, and judging based on a set judgment condition, so that a baseline expected value and a fluctuation threshold value of the index expected value are adaptively corrected and adjusted, and the index expected value is dynamically updated. Therefore, the dynamic structure expected by the index can be simply realized through system scanning and automatic judgment processing, the manual intervention can be reduced to a great extent, the excessive dependence of the system on personnel familiar with business or data is reduced, the judgment accuracy is improved, and the working efficiency of the system is improved. In addition, the embodiment of the invention can be conveniently applied to various models adopting general index expectation and similar services, is beneficial to the expansion of the index expectation, and is more suitable for the actual situation that the whole data changes and fluctuates continuously.
EXAMPLE five
Referring to fig. 5, a block diagram of an embodiment of a system for index expected dynamic update 500 of the present application is shown, wherein the system may comprise:
an obtaining module 501, configured to scan data according to an initial theoretical expected value (idealp) expected by an index to obtain a result value (resultp); the result value (result) may be obtained by using different programs, for example, by implementing a computation script in Structured Query Language (SQL), running on a data computing system 510, such as an aerial ladder, for collecting a scan result value (result tp) of data information, and then synchronizing to the index expected to participate in the scan.
In another preferred embodiment of the present application, a part of the result value (resultp) may also be obtained by real-time scanning on the AEC 520 of the application system developed by java, for example, the algorithm evaluation platform AEC 520. In short, the obtaining manner of the result value (resultp) can support various types, and the embodiment of the present invention is not limited.
A first determining module 502, configured to determine whether the result value (resultp) and the theoretical expected value (idealp), the baseline expected value (basep), and the fluctuation threshold (th) of the index expectation satisfaction first preset conditions;
a second determining module 503, configured to determine whether the theoretical expected value (idealp), the baseline expected value (basep), and the fluctuation threshold (th) of the index expected to meet a second preset condition when the first determining module 502 determines that the first preset condition is met;
an updating module 504, configured to update the baseline expected value (basep) to the result value (resultp) when the second determining module 503 determines that a second preset condition is met, and calculate and update the fluctuation threshold (th) based on the updated baseline expected value (basep).
It should be noted that the index expectation and expectation dynamic updating system 500 according to the embodiment of the present application may be applied to the algorithm evaluation platform AEC 520; or applied to both the data computing system 510 and the algorithm evaluation platform AEC 520. The embodiment of the present application does not limit this.
The embodiment of the application has the following advantages:
the method comprises the steps of setting a theoretical expected value for an index expected expectation of a real-time computing system for screening data information, scanning the data information based on the theoretical expected value to obtain a result value, and judging based on a set judgment condition, so that a baseline expected value and a fluctuation threshold value of the index expected value are adaptively corrected and adjusted, and the index expected value is dynamically updated. Therefore, the dynamic structure expected by the index can be simply realized through system scanning and automatic judgment processing, the manual intervention can be reduced to a great extent, the excessive dependence of the system on personnel familiar with business or data is reduced, the judgment accuracy is improved, and the working efficiency of the system is improved. In addition, the embodiment of the invention can be conveniently applied to various models adopting general index expectation and similar services, is beneficial to the expansion of the index expectation, and is more suitable for the actual situation that the whole data changes and fluctuates continuously.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one of skill in the art, embodiments of the present application may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD @ ROM, optical storage, and the like) having computer-usable program code embodied therein.
In a typical configuration, the computer device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium. Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD @ ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (fransitory media), such as modulated data signals and carrier waves.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the true scope of the embodiments of the application.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. The term "comprising" is used to specify the presence of stated elements, but not necessarily the presence of stated elements, unless otherwise specified.
The method for dynamically updating the index expectation and the device and the system for dynamically updating the index expectation provided by the application are described in detail above, and specific examples are applied in the text to explain the principles and embodiments of the application, and the description of the above embodiments is only used to help understand the method and the core ideas of the application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and as described above, the content of the present specification should not be construed as a limitation to the present application.
Claims (13)
1. A method for index expected dynamic update, comprising:
scanning data according to an initial theoretical expected value expected by the index expectation to obtain a result value;
determining whether the result value and a theoretical expected value, a baseline expected value and a fluctuation threshold value expected by the index meet a first preset condition;
when a first preset condition is met, determining whether a theoretical expected value, a baseline expected value and a fluctuation threshold value of the index expectation meet a second preset condition;
when a second preset condition is met, updating the baseline expected value to the result value, and calculating and updating the fluctuation threshold value based on the updated baseline expected value; wherein the indicator is expected to be used for screening commodity information; wherein the index in the index expectation is an index of commodity information.
2. The method of claim 1, further comprising:
and resetting the baseline expected value manually when the theoretical expected value of the result value and the index expected value, the baseline expected value and the fluctuation threshold value are determined not to meet the first preset condition.
3. The method according to claim 2, wherein the first preset condition comprises any one of the following conditions:
the result value is greater than or equal to the baseline expected value minus the fluctuation threshold value, and less than or equal to the baseline expected value plus the fluctuation threshold value;
the result value is greater than the baseline expected value plus the fluctuation threshold and less than the theoretical expected value;
the result value is greater than or equal to a theoretical expected value and less than the baseline expected value minus the fluctuation threshold.
4. The method according to claim 1, wherein the second preset condition comprises any one of the following conditions:
the result value is greater than the theoretical expected value and less than the baseline expected value;
the result value is greater than the baseline expected value and less than the theoretical expected value.
5. The method of claim 1, wherein the step of calculating an updated fluctuation threshold based on the updated baseline expected value comprises:
calculating a difference between the updated baseline expected value and each historical baseline expected value including a baseline expected value before updating;
and updating the fluctuation threshold value to be the maximum value of the absolute value in the difference value.
6. The method of claim 1, wherein the baseline expected value is initially set based on the result value.
7. An apparatus for index expected dynamic update, comprising:
the acquisition module is used for scanning data according to an initial theoretical expected value expected by the index expectation to obtain a result value;
the first determination module is used for determining whether the result value and a theoretical expected value, a baseline expected value and a fluctuation threshold value of the index expected value meet first preset conditions;
a second determination module, configured to determine whether a theoretical expected value, a baseline expected value, and a fluctuation threshold value of the index expectation satisfy a second preset condition when the first determination module determines that the first preset condition is satisfied;
an updating module, configured to update the baseline expected value to the result value when the second determining module determines that a second preset condition is met, and calculate and update the fluctuation threshold value based on the updated baseline expected value; wherein the indicator is expected to be used for screening commodity information; wherein the index in the index expectation is an index of commodity information.
8. The apparatus of claim 7, further comprising:
and the setting module is used for resetting the baseline expected value manually when the first determining module determines that the result value and the theoretical expected value expected by the index, the baseline expected value and the fluctuation threshold value do not meet the first preset condition.
9. The apparatus of claim 8, wherein the first preset condition comprises any one of the following conditions:
the result value is greater than or equal to the baseline expected value minus the fluctuation threshold value, and less than or equal to the baseline expected value plus the fluctuation threshold value;
the result value is greater than the baseline expected value plus the fluctuation threshold and less than the theoretical expected value;
the result value is greater than or equal to a theoretical expected value and less than the baseline expected value minus the fluctuation threshold.
10. The apparatus of claim 7, wherein the second preset condition comprises any one of the following conditions:
the result value is greater than the theoretical expected value and less than the baseline expected value;
the result value is greater than the baseline expected value and less than the theoretical expected value.
11. The apparatus of claim 7, wherein the update module comprises:
a rewrite submodule for updating the baseline expected value to the result value;
and the calculation submodule is used for calculating the difference between the updated baseline expected value and each historical baseline expected value including the baseline expected value before updating, and the maximum value of the absolute value in the difference is used as the updated value of the fluctuation threshold value to be provided to the rewriting submodule for updating.
12. The apparatus of claim 7, wherein the baseline expected value is initially set based on the result value.
13. A system for dynamic update of an index expectation, comprising an apparatus according to one of claims 7-12.
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