Method and device for automatically adjusting card extraction probability and medium

Abstract

The invention discloses a method and device for automatically adjusting the card extraction probability and a medium. The method comprises the steps that page view data of a website are monitored in real time, and the page view data comprise website traffic and user behaviors; performing normalization processing on the page view data to obtain normalized page view data; calculating an adjustment factor of a preset reference probability according to the normalized page view data; adjusting the reference probability according to the adjustment factor, and determining a final card extraction probability; and carrying out random lottery drawing according to the final card drawing probability to obtain a lottery drawing result.

Description

Technical Field

[0001] The present invention relates to the technical field of card drawing probability adjustment, and more particularly to a method, device and medium for automatically adjusting the card drawing probability. Background Art

[0002] Instructors on the training platform participate in a card draw event every quarter. Instructors create posters on the platform and share them with students. Students then take an exam linked to the poster. Passing the exam has a chance of earning the instructor a card. Instructors who collect eight cards complete the card draw event and receive a reward. However, the system invites users to take the exam to draw cards, which are then given to the corresponding instructor. However, the system sometimes has a high user count and sometimes a low user count, so the probability remains constant. When the system is low, it is difficult for instructors to collect cards. Furthermore, since the number of users cannot be determined, administrators who monitor the number of users in real time and modify the probability are time-consuming. Summary of the Invention

[0003] In view of the deficiencies of the prior art, the present invention provides a method, device and medium for automatically adjusting the probability of drawing a card.

[0004] According to one aspect of the present invention, a method for automatically adjusting the probability of drawing a card is provided, comprising:

[0005] Real-time monitoring of website traffic data, including website traffic and user behavior;

[0006] Normalizing the pageview data to obtain normalized pageview data;

[0007] Calculating an adjustment factor for a pre-set baseline probability based on normalized visit volume data;

[0008] Adjust the baseline probability based on the adjustment factor to determine the final card drawing probability;

[0009] A random draw is conducted based on the final card drawing probability to obtain the draw results.

[0010] Optionally, the normalization expression is:

[0011] normalized_visits=(X-X_min) / (X_max-X_min)

[0012] Where normalized_visits is the normalized visit data; X is the visit data to be normalized; X_max is the maximum value of the visit data; and X_min is the minimum value of the visit data.

[0013] Optionally, calculating an adjustment factor of a preset baseline probability based on the normalized pageview data includes:

[0014] Determine the adjustment function of the adjustment factor based on business needs;

[0015] An adjustment factor for the baseline probability is calculated based on the adjustment function and the normalized visit volume data.

[0016] Optionally, the adjustment function includes a nonlinear function, a linear function and a machine learning model function, wherein the nonlinear function includes an exponential function and a logarithmic function.

[0017] Optionally, when the adjustment function is an exponential function, the calculation expression of the adjustment factor is:

[0018] adjustment_factor=exp(normalized_visits)

[0019] Where, exp() represents the exponential function; normalized_visits is the normalized visit data;

[0020] When the adjustment function is a logarithmic function, the calculation expression of the adjustment factor is:

[0021] adjustment_factor=log(normalized_visits+1)

[0022] Where log() represents the logarithmic function.

[0023] Optionally, a random draw is performed based on the final card drawing probability to obtain the draw results, including:

[0024] Determine whether the final card drawing probability meets the threshold range in the pre-set probability calculation model, and if so, determine the final card drawing probability as the final card drawing probability; otherwise, adjust the final card drawing probability according to the preset threshold;

[0025] A random draw is conducted based on the final card drawing probability to obtain the draw results.

[0026] According to another aspect of the present invention, there is provided a device for automatically adjusting the probability of drawing a card, comprising:

[0027] The monitoring module is used to monitor the website's visit data in real time, where the visit data includes website traffic and user behavior;

[0028] Normalization module, used to normalize the pageview data to obtain normalized pageview data;

[0029] A calculation module, configured to calculate an adjustment factor of a preset baseline probability based on normalized pageview data;

[0030] The adjustment module is used to adjust the baseline probability according to the adjustment factor to determine the final card drawing probability;

[0031] The lottery module is used to conduct random lottery according to the final card drawing probability and obtain the lottery results.

[0032] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, and the computer program is used to execute the method according to any one of the above aspects of the present invention.

[0033] According to another aspect of the present invention, an electronic device is provided, comprising: a processor; a memory for storing instructions executable by the processor; and the processor for reading the executable instructions from the memory and executing the instructions to implement the method described in any one of the above aspects of the present invention.

[0034] Therefore, the present invention uses a logarithmic function as the calculation formula for the adjustment factor. This function adjusts the probability by multiplying the normalized number of visits by the base probability of the lottery draw. This allows it to handle systems with varying visit volumes. Furthermore, the core technology of this algorithm is dynamic and adaptive, adjusting the probability of a draw in real time based on actual visit volume, increasing user engagement and interest. Furthermore, the logarithmic function ensures that the probability of a draw will not be excessively high, even with extremely high visit volumes, thus maintaining fairness. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] A more complete understanding of exemplary embodiments of the present invention may be obtained by referring to the following drawings:

[0036] Figure 1 1 is a flow chart of a method for automatically adjusting the probability of drawing a card provided by an exemplary embodiment of the present invention;

[0037] Figure 2 2 is a schematic structural diagram of a device for automatically adjusting the probability of drawing a card provided by an exemplary embodiment of the present invention;

[0038] Figure 3 This is a structure of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0039] Below, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein.

[0040] It should be noted that the relative arrangement of components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.

[0041] Those skilled in the art will understand that the terms "first" and "second" in the embodiments of the present invention are only used to distinguish different steps, devices or modules, and neither represent any specific technical meaning nor indicate the necessary logical order between them.

[0042] It should also be understood that, in the embodiments of the present invention, “a plurality of” may refer to two or more than two, and “at least one” may refer to one, two or more than two.

[0043] It should also be understood that any component, data or structure mentioned in the embodiments of the present invention can generally be understood as one or more, unless explicitly limited or otherwise indicated in the context.

[0044] In addition, the term "and / or" in this invention merely describes an association relationship between related objects, indicating that three possible relationships exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the character " / " in this invention generally indicates that the related objects are in an "or" relationship.

[0045] It should also be understood that the description of the various embodiments of the present invention focuses on the differences between the various embodiments, and the same or similar aspects thereof can be referenced with each other. For the sake of brevity, they will not be described one by one.

[0046] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0047] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0048] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0049] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0050] Embodiments of the present invention can be applied to electronic devices such as terminal devices, computer systems, and servers, and can operate in conjunction with numerous other general-purpose or specialized computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with terminal devices, computer systems, servers, and other electronic devices include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the above.

[0051] Electronic devices such as terminal devices, computer systems, and servers can be described in the general context of computer system-executable instructions (such as program modules) executed by a computer system. Generally, program modules can include routines, programs, object programs, components, logic, data structures, etc., which perform specific tasks or implement specific abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, where tasks are performed by remote processing devices linked via a communication network. In a distributed cloud computing environment, program modules can be located on local or remote computing system storage media, including storage devices.

[0052] Exemplary Methods

[0053] Figure 1 This is a flow chart of a method for automatically adjusting the probability of drawing a card provided by an exemplary embodiment of the present invention. This embodiment can be applied to electronic devices, such as Figure 1 As shown, the method 100 for automatically adjusting the probability of drawing a card includes the following steps:

[0054] Step 101: monitor website visit data in real time, where the visit data includes website traffic and user behavior;

[0055] Step 102: normalize the pageview data to obtain normalized pageview data.

[0056] Step 103, calculating an adjustment factor of a preset baseline probability based on the normalized pageview data;

[0057] Step 104, adjusting the baseline probability according to the adjustment factor to determine the final card drawing probability;

[0058] Step 105: Perform a random draw based on the final card drawing probability to obtain the draw result.

[0059] Specifically, the present invention uses server logs to monitor website traffic and user behavior in real time, and uses a probability calculation model to calculate the probability of drawing a card in real time based on the current user visits. The specific implementation steps are as follows:

[0060] 1. Use webhooks, APIs, or server logs to monitor website traffic and user behavior in real time, especially focusing on the number of times users participate in sweepstakes.

[0061] 2. Normalize the visit data: Since visit data can range widely, normalization can convert the data to a value between 0 and 1 for easier calculation. The normalization formula is: X_normalized = (X - X_min) / (X_max - X_min).

[0062] 3. Set a set of lottery benchmark probabilities, such as [0.01, 0.02, 0.03, …, 0.9, 1]. This is the initial probability setting without considering factors such as the real-time website traffic. It is a fixed probability set that provides a benchmark for calculating the final card drawing probability.

[0063] 4. Set a threshold range for the probability calculation model to ensure that the probability does not fall below a minimum value to ensure user experience, and does not rise above a maximum value to avoid excessive resource consumption (the threshold range refers to a range of winning probabilities, for example, the minimum winning probability is 1% and the maximum is 99%. There is no fixed value, but it is a value set manually based on the specific transaction).

[0064] Specifically, the probability calculation model primarily defines the scope of the adjustment factor: the probability calculation model sets a threshold range, determining the upper and lower limits of the final card draw probability, such as a minimum of 1% and a maximum of 99%. When calculating the final card draw probability, the adjustment factor adjusts the baseline probability, but this adjusted probability must fall within the threshold range set by the probability calculation model. For example, if the calculated adjustment factor causes the final card draw probability to exceed 99%, it will be capped at 99% according to the settings of the probability calculation model; if it is below 1%, it will be capped at 1%.

[0065] 5. Calculate the adjustment factor: The adjustment factor is a function that generates a value based on the normalized visit data. This value is used to adjust the baseline probability of the lottery draw. This function can be linear, nonlinear, or based on a machine learning model. The choice of function should be based on the actual data characteristics and business requirements.

[0066] Assume that the normalized visit data is normalized_visits, and its value range is between 0 and 1.

[0067] The adjustment factor can be calculated using a nonlinear function, such as an exponential function or a logarithmic function, depending on the requirements and application scenario.

[0068] Exponential function method: Use an exponential function to calculate the adjustment factor. The exponential function can make the adjustment factor corresponding to higher visit volume data increase faster.

[0069] Adjustment factor = exp(normalized_visits)

[0070] Logarithmic function method: Use the logarithmic function to calculate the adjustment factor. The logarithmic function can make the adjustment factor corresponding to lower visit volume data increase faster.

[0071] Adjustment factor = log(normalized_visits+1)

[0072] In these formulas, exp() represents the exponential function, log() represents the logarithmic function, and normalized_visits is the normalized visit data.

[0073] The calculated adjustment factor can be used to adjust the base probability of the draw. For example, if the base probability is 0.5, the adjustment factor is multiplied by the base probability to get the final draw probability.

[0074] Final card draw probability = adjustment factor * base probability

[0075] 6. Adjust the base probability of the lottery based on the visit data and the adjustment factor. The formula is: P = min(1, base_probability * adjustment_factor).

[0076] 7. Use the adjusted final card drawing probability to conduct a random draw and return the result to the user.

[0077] Therefore, the present invention uses a logarithmic function as the calculation formula for the adjustment factor. This function adjusts the probability by multiplying the normalized number of visits by the base probability of the lottery draw. This allows it to handle systems with varying visit volumes. Furthermore, the core technology of this algorithm is dynamic and adaptive, adjusting the probability of a draw in real time based on actual visit volume, increasing user engagement and interest. Furthermore, the logarithmic function ensures that the probability of a draw will not be excessively high, even with extremely high visit volumes, thus maintaining fairness.

[0078] Exemplary devices

[0079] Figure 2 FIG is a schematic diagram of a structure of a device for automatically adjusting the probability of drawing a card provided by an exemplary embodiment of the present invention. Figure 2 As shown, the apparatus 200 includes:

[0080] Monitoring module 210, for real-time monitoring of website traffic data, where the traffic data includes website traffic and user behavior;

[0081] Normalization module 220, used to perform normalization processing on the pageview data to obtain normalized pageview data;

[0082] The calculation module 230 is used to calculate the adjustment factor of the preset baseline probability based on the normalized pageview data;

[0083] An adjustment module 240 is used to adjust the baseline probability according to the adjustment factor to determine the final card drawing probability;

[0084] The lottery module 250 is used to conduct a random lottery according to the final card drawing probability and obtain the lottery result.

[0085] Optionally, the normalization expression is:

[0086] normalized_visits=(X-X_min) / (X_max-X_min)

[0087] Where normalized_visits is the normalized visit data; X is the visit data to be normalized; X_max is the maximum value of the visit data; and X_min is the minimum value of the visit data.

[0088] Optionally, the calculation module 230 includes:

[0089] A determination submodule is used to determine an adjustment function of the adjustment factor according to business requirements;

[0090] The calculation submodule is used to calculate the adjustment factor of the baseline probability based on the adjustment function and the normalized pageview data.

[0091] Optionally, the adjustment function includes a nonlinear function, a linear function and a machine learning model function, wherein the nonlinear function includes an exponential function and a logarithmic function.

[0092] Optionally, when the adjustment function is an exponential function, the calculation expression of the adjustment factor is:

[0093] adjustment_factor=exp(normalized_visits)

[0094] Where, exp() represents the exponential function; normalized_visits is the normalized visit data;

[0095] When the adjustment function is a logarithmic function, the calculation expression of the adjustment factor is:

[0096] adjustment_factor=log(normalized_visits+1)

[0097] Where log() represents the logarithmic function.

[0098] Optionally, the lottery module 250 includes:

[0099] A judgment submodule is used to judge whether the final card drawing probability meets the threshold range in the pre-set probability calculation model, and if so, determine the final card drawing probability as the final card drawing probability; otherwise, adjust the final card drawing probability according to the preset threshold;

[0100] The lottery submodule is used to conduct random lottery according to the final card drawing probability and obtain the lottery results.

[0101] Exemplary electronic devices

[0102] Figure 3 This is the structure of an electronic device provided by an exemplary embodiment of the present invention. Figure 3 As shown, the electronic device 30 includes one or more processors 31 and a memory 32 .

[0103] The processor 31 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions.

[0104] The memory 32 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), a hard disk, a flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 31 may execute the program instructions to implement the methods of the software programs of the various embodiments of the present invention described above and / or other desired functions. In one example, the electronic device may further include: an input device 33 and an output device 34, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0105] In addition, the input device 33 may also include, for example, a keyboard, a mouse, and the like.

[0106] The output device 34 can output various information to the outside. The output device 34 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto.

[0107] Of course, to simplify, Figure 3 Only some of the components related to the present invention in the electronic device are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, the electronic device may further include any other appropriate components according to specific application scenarios.

[0108] Exemplary computer program products and computer-readable storage media

[0109] In addition to the above-mentioned methods and devices, an embodiment of the present invention may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to perform the steps of the method according to various embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0110] The computer program product may be written in any combination of one or more programming languages to implement the operations of embodiments of the present invention, including object-oriented programming languages such as Java, C++, and conventional procedural programming languages such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0111] In addition, an embodiment of the present invention may also be a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, enables the processor to execute the steps of the method for information mining of historical change records according to various embodiments of the present invention described in the above "Exemplary Method" section of this specification.

[0112] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to a system, system or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0113] The basic principles of the present invention have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in the present invention are merely illustrative and non-limiting, and should not be construed as necessarily possessed by each embodiment of the present invention. Furthermore, the specific details disclosed above are provided for illustrative purposes and to facilitate understanding, and are not intended to be limiting. These details do not necessarily limit the present invention to being implemented using these specific details.

[0114] Each embodiment in this specification is described in a progressive manner, with each embodiment focusing on its differences from the other embodiments. References to the same or similar parts between the various embodiments are sufficient. For system embodiments, since they largely correspond to method embodiments, their description is relatively simple. For relevant parts, references to the description of the method embodiments are sufficient.

[0115] The block diagrams of the devices, systems, equipment, and systems involved in the present invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, systems, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "including," "comprising," "having," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0116] The method and system of the present invention may be implemented in many ways. For example, the method and system of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above sequence of steps for the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above, unless otherwise specified. In addition, in some embodiments, the present invention may also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Thus, the present invention also covers recording media that store programs for executing the method according to the present invention.

[0117] It should also be noted that, in the system, device and method of the present invention, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. The above description of the disclosed aspects is provided to enable any technician in this field to make or use the present invention. Various modifications to these aspects will be very obvious to those skilled in the art, and the general principles defined here can be applied to other aspects without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the aspects shown here, but according to the widest scope consistent with the principles disclosed here and novel features.

[0118] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present invention to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for automatically adjusting the probability of drawing a card, characterized in that: include: Real-time monitoring of website traffic data, wherein the traffic data includes website traffic and user behavior; Normalizing the pageview data to obtain normalized pageview data; Calculating an adjustment factor of a preset baseline probability based on the normalized pageview data; Adjust the baseline probability according to the adjustment factor to determine the final card drawing probability; A random draw is performed according to the final card drawing probability to obtain a draw result.

2. The method according to claim 1, characterized in that The expression of the normalization process is: normalized_visits=(X-X_min) / (X_max-X_min) Where normalized_visits is the normalized visit data; X is the visit data to be normalized; X_max is the maximum value of the visit data; and X_min is the minimum value of the visit data.

3. The method according to claim 1, characterized in that Calculating an adjustment factor of a preset baseline probability according to the normalized pageview data includes: Determine the adjustment function of the adjustment factor based on business needs; An adjustment factor of the baseline probability is calculated based on the adjustment function and the normalized visit volume data.

4. The method according to claim 3, characterized in that The adjustment function includes a nonlinear function, a linear function and a machine learning model function, wherein the nonlinear function includes an exponential function and a logarithmic function.

5. The method according to claim 4, characterized in that When the adjustment function is an exponential function, the calculation expression of the adjustment factor is: adjustment_factor=exp(normalized_visits) Where, exp() represents the exponential function; normalized_visits is the normalized visit data; When the adjustment function is a logarithmic function, the calculation expression of the adjustment factor is: adjustment_factor=log(normalized_visits+1) Where log() represents the logarithmic function.

6. The method according to claim 1, characterized in that A random draw is performed according to the final card drawing probability to obtain a draw result, including: Determine whether the final card drawing probability satisfies a threshold range in a pre-set probability calculation model, and if so, determine the final card drawing probability as the final card drawing probability; otherwise, adjust the final card drawing probability according to the preset threshold; A random draw is performed according to the final card drawing probability to obtain a draw result.

7. A device for automatically adjusting the probability of drawing a card, characterized in that: include: A monitoring module, configured to monitor website traffic data in real time, wherein the traffic data includes website traffic and user behavior; A normalization module, configured to perform normalization processing on the pageview data to obtain normalized pageview data; A calculation module, configured to calculate an adjustment factor of a preset baseline probability based on the normalized pageview data; An adjustment module, configured to adjust the baseline probability according to the adjustment factor to determine a final card drawing probability; The lottery module is used to conduct a random lottery according to the final card drawing probability to obtain the lottery result.

8. The device according to claim 7, characterized in that The expression of the normalization process is: normalized_visits=(X-X_min) / (X_max-X_min) Where normalized_visits is the normalized visit data; X is the visit data to be normalized; X_max is the maximum value of the visit data; and X_min is the minimum value of the visit data.

9. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and the computer program is used to execute the method according to any one of claims 1 to 6.

10. An electronic device, characterized in that: The electronic device comprises: processor; a memory for storing instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the method according to any one of claims 1 to 6.

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