Identification value adjustment parameter analysis method and device, equipment and medium

Abstract

The invention relates to the technical field of data processing and the field of financial science and technology, and discloses an identification value adjustment parameter analysis method, which comprises the following steps of: judging whether current adjustment is first-time adjustment or not by obtaining a preset total identification value, a total adjustment frequency, a first-time identification value adjustment proportion and a non-first-time identification value adjustment proportion; determining a target value according to the total identification value and the first adjustment proportion during first adjustment; when the adjustment is not performed for the first time, calculating an average value according to the residual identification value and the residual adjustment times, determining a minimum value and a maximum value of an adjustment range based on the average value and a non-first-time identification value adjustment proportion, and randomly determining a target value in the range; and after each adjustment, outputting the currently adjusted parameters. According to the method, the adjustment parameters are output in real time after the identification values are adjusted, so that a user can clearly master the adjustment process each time, the flexibility and transparency of adjustment are enhanced, misunderstanding and operation complexity caused by information asymmetry are effectively reduced, and the user experience and the operability of the system are improved.

Description

Technical Field

[0001] The present invention relates to the fields of data processing technology and financial technology, and in particular to an identification value adjustment parameter analysis method, device, equipment and storage medium. Background Art

[0002] With the rapid development of the Internet and financial technology, online financial transactions and service models are becoming increasingly diverse, and collective purchasing models such as group buying are gradually being applied to financial products. For example, financial institutions have launched financial management or insurance products such as "group purchase discounts" or "collective investment". However, the current financial group buying model still has many problems in actual application, resulting in poor user experience, low operational efficiency, and even affecting user trust.

[0003] First, insufficient price transparency is a common problem in financial group purchases. The price of financial products fluctuates greatly, especially in group purchases or discount transactions. The price calculation rules are complicated and users cannot clearly understand the specific price and discount of each transaction. This lack of information transparency can easily confuse users, causing them to doubt product prices and affect transaction decisions.

[0004] Secondly, information asymmetry is particularly evident in financial group purchases. When users participate in group purchases, they usually cannot understand the progress of the group purchase and the actual effect of their own contributions in real time. For example, in some financial discount plans, users may not know how their purchase behavior affects the discount level of the entire group purchase. This information asymmetry not only affects the user's enthusiasm for participation, but also may increase the operator's explanation costs.

[0005] In addition, insufficient process visualization is also an important shortcoming in financial group buying. Existing financial group buying models usually fail to provide real-time transaction progress display, and users cannot see the impact of their own or others' operations on the overall progress, especially in multiple transactions or collective investment products, where users cannot track the progress of each operation in real time. Summary of the invention

[0006] The main purpose of the present invention is to provide a method, device, equipment and storage medium for analyzing identification value adjustment parameters, aiming to solve the technical problem in the prior art that the transparency of identification value adjustment is insufficient, the user cannot obtain the specific parameters of each adjustment and the remaining situation after adjustment in real time, resulting in the non-visualization of the adjustment process.

[0007] To achieve the above object, the present invention provides a method for analyzing identification value adjustment parameters, comprising:

[0008] Obtain the preset total identification value, total adjustment times, first identification value adjustment ratio, and non-first identification value adjustment ratio;

[0009] When an identification value adjustment request is detected, determining whether the current identification value adjustment is the first identification value adjustment;

[0010] If the current identification value adjustment is the first identification value adjustment, determine the target value of the first adjustment according to the total identification value and the first identification value adjustment ratio, and update the current remaining remaining identification value and the remaining number of adjustments;

[0011] Output the first adjustment parameters of the current identification value adjustment, wherein the first adjustment parameters include the target value of the first adjustment, the identification value before adjustment, the first adjustment sequence number, the remaining identification value and the remaining number of adjustments;

[0012] If the current identification value adjustment is not the first identification value adjustment, then the average value of the current identification value adjustment is determined according to the current remaining remaining identification value and the remaining number of adjustments;

[0013] Determine a minimum value and a maximum value of an estimated adjustment range of the current identification value adjustment based on the average value and the non-first identification value adjustment ratio;

[0014] Randomly determine a target value for adjusting the current identification value between the minimum value and the maximum value, and determine a non-first adjustment parameter for adjusting the current identification value based on the target value for adjusting the current identification value;

[0015] Update the current remaining remaining identification value and remaining adjustment times, and output the non-first adjustment parameters for the current identification value adjustment, wherein the non-first adjustment parameters include the target value for the current identification value adjustment, the identification value before adjustment, the adjustment times sequence number, the remaining identification value, the remaining adjustment times and the estimated adjustment range.

[0016] Furthermore, in order to achieve the above object, the present invention provides an identification value adjustment parameter analysis device, comprising:

[0017] A data acquisition module is used to obtain a preset total identification value, a total number of adjustments, a first identification value adjustment ratio, and a non-first identification value adjustment ratio;

[0018] A request detection module, used to determine whether the current identification value adjustment is the first identification value adjustment when an identification value adjustment request is detected;

[0019] A first adjustment analysis module, for determining a target value for the first adjustment according to the total identification value and the first identification value adjustment ratio if the current identification value adjustment is the first identification value adjustment, and updating the current remaining remaining identification value and the remaining number of adjustments;

[0020] The first adjustment parameter output module is used to output the first adjustment parameters of the current identification value adjustment, wherein the first adjustment parameters include the target value of the first adjustment, the identification value before adjustment, the first adjustment sequence number, the remaining identification value and the remaining number of adjustments;

[0021] A non-first adjustment analysis module, for determining an average value of the current identification value adjustment according to the currently remaining remaining identification value and the remaining number of adjustments if the current identification value adjustment is not the first identification value adjustment;

[0022] An adjustment range analysis module, used to determine a minimum value and a maximum value of an estimated adjustment range of a current identification value adjustment based on the average value and the non-first identification value adjustment ratio;

[0023] A random adjustment value generation module, used for randomly determining a target value for adjusting the current identification value between the minimum value and the maximum value, and determining a non-first adjustment parameter for adjusting the current identification value based on the target value for adjusting the current identification value;

[0024] The non-first adjustment parameter output module is used to update the current remaining remaining identification value and the remaining adjustment times, and output the non-first adjustment parameters for the current identification value adjustment. The non-first adjustment parameters include the target value for the current identification value adjustment, the identification value before adjustment, the adjustment times sequence number, the remaining identification value, the remaining adjustment times and the estimated adjustment range.

[0025] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer device, which includes a memory, a processor, and an identification value adjustment parameter analysis program stored in the memory and executable on the processor, and the identification value adjustment parameter analysis program, when executed by the processor, implements the steps of the identification value adjustment parameter analysis method as described above.

[0026] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which is stored an identification value adjustment parameter analysis program, which, when executed by a processor, implements the steps of the identification value adjustment parameter analysis method as described above.

[0027] Beneficial effects: The present invention relates to the fields of data processing technology and financial technology, and discloses a method for analyzing identification value adjustment parameters. By obtaining the preset total identification value, total adjustment times, first identification value adjustment ratio and non-first identification value adjustment ratio, it is determined whether the current adjustment is the first adjustment; when adjusting for the first time, the target value is determined according to the total identification value and the first adjustment ratio, and the remaining identification value and the number of adjustments are updated; when adjusting for the non-first time, the average value is calculated according to the remaining identification value and the remaining number of adjustments, and the minimum and maximum values ​​of the adjustment range are determined based on the average value and the non-first identification value adjustment ratio, and the target value is randomly determined within this range; after each adjustment, the parameters of the current adjustment are output, including information such as the target value, the remaining identification value, and the number of adjustments. The present invention outputs the adjustment parameters in real time after adjusting the identification value, so that the user can clearly grasp the process of each adjustment, enhance the flexibility and transparency of the adjustment, effectively reduce the misunderstanding and operation complexity caused by information asymmetry, and improve the user experience and the operability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0029] Figure 1 A schematic diagram of an application environment of a method for analyzing identification value adjustment parameters in an embodiment of the present invention;

[0030] Figure 2 A schematic diagram of a flow chart of an embodiment of a method for analyzing identification value adjustment parameters of the present invention;

[0031] Figure 3 A schematic diagram of functional modules of a preferred embodiment of the identification value adjustment parameter analysis device of the present invention;

[0032] Figure 4 A schematic diagram of the structure of a computer device in one embodiment of the present invention;

[0033] Figure 5 FIG. 4 is another schematic diagram of the structure of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0034] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0035] The identification value adjustment parameter analysis method provided by the embodiment of the present invention can be applied in the following aspects: Figure 1In an application environment, the user terminal communicates with the server terminal through a network. The server terminal can obtain the preset total identification value, total adjustment times, first identification value adjustment ratio and non-first identification value adjustment ratio through the user terminal to determine whether the current adjustment is the first adjustment; when adjusting for the first time, the target value is determined according to the total identification value and the first adjustment ratio, and the remaining identification value and the number of adjustments are updated; when adjusting for the non-first time, the average value is calculated according to the remaining identification value and the remaining adjustment times, and the minimum and maximum values ​​of the adjustment range are determined based on the average value and the non-first identification value adjustment ratio, and the target value is randomly determined within this range; after each adjustment, the current adjustment parameters are output, including information such as the target value, the remaining identification value, and the number of adjustments. The present invention outputs the adjustment parameters in real time after adjusting the identification value, so that the user can clearly grasp the process of each adjustment, enhance the flexibility and transparency of the adjustment, effectively reduce the misunderstanding and operation complexity caused by information asymmetry, and improve the user experience and the operability of the system. Among them, the user terminal can be but not limited to various personal computers, laptops, smart phones, tablet computers and portable wearable devices. The server terminal can be implemented with an independent server or a server cluster composed of multiple servers. The present invention is described in detail below through specific embodiments.

[0036] See also Figure 2 , Figure 2 This is a flow chart of an embodiment of the identification value adjustment parameter analysis method provided by the present invention. It should be noted that although the logical sequence is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order than that here.

[0037] like Figure 2 As shown, the identification value adjustment parameter analysis method proposed by the present invention includes the following steps:

[0038] S10, obtaining a preset total identification value, a total number of adjustments, a first identification value adjustment ratio and a non-first identification value adjustment ratio;

[0039] In this embodiment, the total identification value refers to the initial price that needs to be gradually reduced during the entire adjustment process. This value is usually the original price or initial list price of the product. For example, in a group purchase scenario of financial products, the total identification value represents the initial price of the group purchase product, that is, the target amount that the user hopes to reduce through multiple rounds of bargaining. The system can obtain the total identification value through user input, database reading, or preset product prices. For example, the initial group purchase price of a financial product is 1,000 yuan, and the system inputs 1,000 yuan as the total identification value into the adjustment algorithm.

[0040] The total number of adjustments refers to the number of bargaining attempts the system allows users to make. The identification value (i.e., price) is gradually reduced through multiple adjustments. The total number of adjustments can be set according to the marketing strategy of the financial product. The system can automatically determine the total number of adjustments from the product settings or marketing rules. For example, if a financial product allows 10 bargaining attempts, the total number of adjustments is set to 10. The system can also adjust the total number of adjustments in real time according to the user's participation, such as dynamically increasing or decreasing the number of bargaining attempts.

[0041] The first identification value adjustment ratio is used to determine the ratio of the amount of the first bargaining. The first adjustment is generally a larger part to guide users to actively participate in group buying bargaining. The system can set the first adjustment ratio through marketing rules. For example, setting it to 30% means that the first bargaining can reduce the total identification value by 30%. For example, when the user bargains for the first time, according to the initial identification value of 1,000 yuan and the first adjustment ratio of 30%, the system calculates that the amount of the user's first bargaining is 300 yuan.

[0042] The non-first time identification value adjustment ratio is used to control the amount of each non-first bargaining. Usually, this ratio is small to ensure that the price adjustment can be refined after multiple bargaining. The system can dynamically adjust the non-first time identification value adjustment ratio based on the user's historical participation behavior, the number of remaining adjustments, or the marketing strategy. For example, when the remaining amount is 700 yuan, the system calculates the next bargaining amount by setting each adjustment to 10%.

[0043] Example: A bank launched a group purchase activity for financial products, where users can reduce the product price through multiple rounds of bargaining. The system first obtains the preset total identification value, total number of adjustments, first identification value adjustment ratio, and non-first identification value adjustment ratio.

[0044] Total identification value: The system obtains the initial price of the financial product as 1,000 yuan, which is the total identification value of this group purchase.

[0045] Total number of adjustments: The system allows users to bargain 10 times for this group purchase, and the total number of adjustments is 10 times.

[0046] First identification value adjustment ratio: The system sets the first bargaining adjustment ratio to 30%, which is used to calculate the amount that the user can deduct when bargaining for the first time.

[0047] Adjustment ratio of non-first identification value: The system sets the adjustment ratio of non-first identification value to 10%, which is used for calculation of each subsequent bargaining.

[0048] When the user bargains for the first time, the system calculates that the user can deduct 300 yuan according to the 30% first adjustment ratio, and the remaining identification value is 700 yuan. In each subsequent bargaining, the system will continue to adjust according to the 10% non-first adjustment ratio and the remaining identification value until all adjustments are completed or the user completes the purchase.

[0049] By obtaining the total identification value, the number of adjustments, and the adjustment ratio, the system can automatically calculate the adjustment amount each time the price is bargained, and update the remaining identification value and the number of adjustments to the user in real time. This not only improves the transparency of the bargaining process, but also enhances the user's sense of participation. By dynamically adjusting the ratio, it avoids user loss due to a single bargaining range that is too large or too small. In addition, by flexibly setting the number of adjustments and the ratio, it can better guide users to gradually complete group purchases and improve the success rate of financial product sales.

[0050] S20, when an identification value adjustment request is detected, determining whether the current identification value adjustment is the first identification value adjustment;

[0051] In this embodiment, the identification value adjustment request refers to an operation behavior triggered by the user or detected by the system, indicating that the system needs to make an identification value adjustment. After receiving the request, the system will start a series of adjustment operations. This request is usually generated by user operation (such as clicking a button) or automatic system triggering (such as a scheduled task). The system detects adjustment requests by monitoring user operation events or scheduled tasks. When a user clicks the "bargaining" button on the financial group buying platform, the system will immediately receive an adjustment request signal. The adjustment request can be sent to the background processing module through the front-end interface or API call.

[0052] The system needs to determine whether this adjustment is the first adjustment. The first adjustment usually has different rules or processing methods, so after detecting an adjustment request, the system must query the historical adjustment record or the current operation status to determine the current adjustment round. The system can determine whether an adjustment operation has been performed by querying the user's historical adjustment record or looking up the participation record of the group purchase activity in the database. If no adjustment record is found in the historical adjustment record, the current adjustment is marked as the first adjustment; if there is an adjustment record, it is marked as not the first adjustment. An adjustment record table can be maintained in the database for each user and group purchase activity to record the specific information of each adjustment. Querying this table can quickly determine whether it is the first adjustment.

[0053] Example: In a bank's financial group purchase activity, users can obtain more favorable prices for financial products through multiple rounds of bargaining. When a user clicks the "bargain" button for the first time, the system will detect the adjustment request and determine whether the user has participated in the bargaining activity by querying the database.

[0054] Assuming that this is the first time for a user to participate in a group purchase, the system does not find any bargaining records of the user in the database, and the system immediately determines that it is the first adjustment. At this time, the system calculates the user's first bargaining amount according to the first adjustment rules (such as a 30% first adjustment ratio) and saves the relevant information of this adjustment to the database. After that, when the user bargains again, the system will query the records and determine that it is not the first adjustment, and use different adjustment rules.

[0055] By detecting the identification value adjustment request and judging whether it is the first adjustment, the system can more flexibly handle the difference between the first adjustment and subsequent adjustments. The special rules for the first adjustment can better motivate users to actively participate in group buying, while the subsequent judgment logic ensures the continuity and correctness of the adjustment.

[0056] S30, if the current identification value adjustment is the first identification value adjustment, determine the target value of the first adjustment according to the total identification value and the first identification value adjustment ratio, and update the current remaining remaining identification value and the remaining number of adjustments;

[0057] In this embodiment, the target value for the first adjustment is calculated based on the preset total identification value and the first identification value adjustment ratio. This value determines the amount to be subtracted from the total identification value during the first adjustment. The first adjustment generally accounts for a larger proportion to stimulate the user's enthusiasm for participation. After the system receives the first adjustment request, it calculates the target value according to the formula "target value for the first adjustment = total identification value × first identification value adjustment ratio". For example, assuming that the total identification value is 1,000 yuan and the first adjustment ratio is 30%, the target value for the first adjustment is 1,000 × 30% = 300 yuan. After the calculation is completed, the system uses this value as the target value for this adjustment.

[0058] After calculating the target value for the first adjustment, the system needs to update the current remaining identification value. The remaining identification value is the value obtained by subtracting the target value for this adjustment from the total identification value, indicating the remaining amount that needs to be adjusted. Remaining identification value = total identification value - target value for the first adjustment. For example, if the target value for the first adjustment is 300 yuan, then the remaining identification value = 1000 yuan - 300 yuan = 700 yuan. The system stores the calculated remaining identification value in the database or cache to ensure that it can be used again during the next adjustment.

[0059] After each adjustment of the identification value, the system needs to update the remaining number of adjustments. The number of adjustments is used to control the number of adjustments that the user can make. After the first adjustment, the number of adjustments will be reduced by one. The remaining number of adjustments = the total number of adjustments - 1. For example, the total number of adjustments is 10 times. After the first adjustment, the system updates the remaining number of adjustments to 10-1 = 9 times. The system records the current remaining number of adjustments and uses it in the subsequent adjustment process.

[0060] Example description: In a group-buying activity for financial products launched by a bank, users can reduce the price of the product by bargaining multiple times. The system detects the user's first adjustment request and calculates the target value of the user's first bargaining to be 300 yuan based on the total price of the group-buying product of 1,000 yuan and the preset 30% first adjustment ratio. The system then updates the remaining identification value to 700 yuan and reduces the number of remaining adjustments from 10 to 9. In this way, users can see their bargaining amount in real time and clearly understand the remaining price and the number of available adjustments after each adjustment. This not only enhances the user's sense of participation, but also improves the system's responsiveness and transparency, effectively promoting the progress of group-buying activities.

[0061] By determining the target value of the first adjustment according to the first identification value adjustment ratio and updating the remaining identification value and the number of adjustments, the system can quickly and accurately perform the first adjustment operation. The first adjustment provides users with a clear discount strength, allowing users to see the adjustment effect more intuitively, and enhancing user participation. At the same time, the real-time updated identification value and adjustment number ensure the continuity and controllability of subsequent adjustments, reduce user confusion, and improve the transparency of operations and user experience.

[0062] S40, outputting the first adjustment parameters for adjusting the current identification value, wherein the first adjustment parameters include the target value for the first adjustment, the identification value before adjustment, the first adjustment sequence number, the remaining identification value, and the remaining number of adjustments;

[0063] In this embodiment, outputting the first adjustment parameters of the current identification value adjustment means that after the first identification value adjustment is completed, the relevant adjustment information is passed to the user or other parts of the system. These parameters are used to feedback the adjustment results to the user, including the identification value before the adjustment, the identification value updated after the adjustment, the sequence number of this adjustment, etc. The system can output these parameters through an API interface, a user interface, or a log file. The user interface can display detailed adjustment information, such as the adjusted identification value and the remaining number of times. The system can also pass these parameters to the background database or cache for subsequent adjustment calls.

[0064] The target value of the first adjustment is the core result of the first identification value adjustment, which indicates the amount of money reduced after the first adjustment. The system directly outputs the pre-calculated target value of the first adjustment to the user interface or records it in the background database. For example, in a bargaining activity, the target value of the first adjustment is 300 yuan, and the system displays the amount immediately after the user completes the first bargaining.

[0065] The identification value before adjustment refers to the identification value before this adjustment, that is, the original identification value before bargaining. The system reads the initial identification value from the storage and outputs it as the value before adjustment. Usually this value is equal to the initial identification value or the remaining identification value after the last adjustment. For example, before the user bargains for the first time, the total identification value is 1,000 yuan, and the system outputs 1,000 yuan as the identification value before adjustment.

[0066] The first adjustment number is used to mark this as the user's first adjustment, so as to distinguish subsequent adjustment operations. When the system makes the first adjustment, the adjustment number is set to 1 and outputs it together with other parameters. For example, when making the first adjustment, the system displays the adjustment number as 1, and each subsequent adjustment number will increase.

[0067] The remaining indicator value is the current indicator value calculated by the system after the first adjustment. The remaining indicator value is used to indicate the amount that needs to be adjusted after the first adjustment. The remaining indicator value is obtained by subtracting the target value of the first adjustment from the total indicator value. The system will output it after calculation as a reference for the user's subsequent adjustments. For example, if the target value of the first adjustment is 300 yuan, the total indicator value is 1,000 yuan, and the remaining indicator value is 700 yuan.

[0068] The remaining number of adjustments refers to the number of adjustments that the user can continue to make after this adjustment. This value will decrease after each adjustment until the set total number of adjustments is reached. The system updates the remaining number of adjustments in real time by subtracting the number of adjustments that have been performed from the total number of adjustments. After the first adjustment, the remaining number is the total number of adjustments minus 1. For example, if the total number of adjustments is 10, the remaining number of adjustments after the first adjustment is 9.

[0069] Example description: In a group purchase activity for financial products launched by a bank, users can reduce product prices through bargaining activities. When the user makes the first adjustment, the system reduces the total identification value of 1,000 yuan by 300 yuan based on the first adjustment ratio of 30%, and displays the target value of the first adjustment (300 yuan), the identification value before adjustment (1,000 yuan), the remaining identification value (700 yuan), and the remaining number of adjustments (9 times) on the user interface. In this way, users can clearly see the results of the first adjustment and the possibility of subsequent adjustments, which enhances the user's sense of control over the adjustment process. At the same time, the system saves these parameters in the background to facilitate subsequent adjustment operations or analysis.

[0070] By outputting the target value of the first adjustment, the identification value before adjustment, the adjustment sequence number, the remaining identification value, and the remaining number of adjustments, users can grasp the bargaining progress in real time. This not only improves the user's sense of participation, but also enhances the transparency of the adjustment process. For the system, outputting these parameters helps to record each step of the adjustment, providing a basis for subsequent adjustment decisions and data analysis, avoiding user confusion, and improving the operating experience and system efficiency.

[0071] S50, if the current identification value adjustment is not the first identification value adjustment, determining the average value of the current identification value adjustment according to the remaining remaining identification values ​​and the remaining number of adjustments;

[0072] In this embodiment, the average value of the current identification value adjustment is a reference value calculated by the "current remaining identification value" and the "remaining number of adjustments", which is mainly used to determine a preliminary benchmark value. However, this average value is not the final value of each actual adjustment, but is provided to the system for calculating the adjustment range or other rule adjustments.

[0073] In order to prevent each adjustment from being too linear and causing all identification values ​​to be cut off in the end, the system will introduce an adjustment coefficient or random factor, use the calculated average value as a benchmark, and combine these coefficients to generate the actual adjustment range and final adjustment value.

[0074] The adjustment coefficient needs to be dynamically adjusted according to the current remaining identification value and the number of adjustments to ensure that each adjustment does not cause the remaining identification value to be over-deducted. Therefore, the range of the adjustment coefficient should be between 0 and 1, and it can even be dynamically adjusted according to the remaining number of times to make it gradually decrease. The system can use an adjustment coefficient range of 0.5 to 1 to ensure that each adjustment target value does not exceed the reasonable range of the remaining identification value. For example, if the current average value is 70 yuan, the adjustment coefficient is between 0.5 and 1, and the actual adjustment value will fluctuate between 35 yuan and 70 yuan. When the number of remaining adjustments is small, the system will gradually reduce the range of the adjustment coefficient to avoid deducting all the remaining identification values ​​at once in the later adjustments.

[0075] The dynamic mechanism of the adjustment coefficient ensures that each adjustment is reduced according to the remaining identification value and the number of adjustments, so as to avoid the identification value becoming negative or exceeding each adjustment. As the number of remaining adjustments decreases, the system will gradually reduce the adjustment coefficient, and ultimately ensure that the identification value is reasonably bargained to a certain range, rather than being completely cut off. The system can dynamically adjust the coefficient according to the remaining identification value and the number of adjustments. For example, in the first few adjustments, the adjustment coefficient can be set to 0.8 to 1. When the number of remaining adjustments is small, the coefficient will gradually decrease to between 0.5 and 0.8 to ensure that the adjustment amount is within a reasonable range. For example, the system will ensure that if the current remaining identification value is 500 yuan and the number of remaining adjustments is 5 times, the adjustment coefficient may be between 0.5 and 1 to avoid adjusting the target value too large each time.

[0076] Example: In a bank's financial product group purchase activity, users can lower the product price by bargaining multiple times. Assume that after the user's first adjustment, the remaining identification value is 700 yuan, the remaining number of adjustments is 9, and the system calculates the average value to be 77.78 yuan. In order to prevent the price from being cut excessively, the system sets the adjustment coefficient between 0.7 and 1, and the adjusted target value will be between 54.45 yuan and 77.78 yuan. Next, the user bargains, and the actual adjustment amount is 60 yuan, and the remaining identification value is 640 yuan. As the user continues to bargain, when the remaining number of adjustments is reduced to 3 times, the system will dynamically reduce the adjustment coefficient range, for example, to between 0.5 and 0.7. At this time, the amount of each adjustment will be closer to a part of the remaining identification value, ensuring that there will be no excessive deductions at the end of the entire bargaining process.

[0077] By introducing the adjustment coefficient and combining it with the average value as a reference, the system can avoid the situation where the price is completely cut due to the linear decrease of the identification value. The adjustment coefficient ensures the flexibility of each bargaining, and the decreasing strategy makes each adjustment more scientific and reasonable, thereby increasing the enthusiasm of user participation, improving the user experience, and ensuring that the identification value is gradually reduced within a reasonable range.

[0078] S60, determining a minimum value and a maximum value of an estimated adjustment range of a current identification value adjustment based on the average value and the non-first identification value adjustment ratio;

[0079] In this embodiment, the average value of the current identification value adjustment is a basic reference value used to help determine the reasonable range of this adjustment. The average value is calculated by dividing the current remaining identification value by the remaining number of adjustments, indicating the theoretical basis for the change in the identification value during a non-first adjustment. The system first calculates a benchmark adjustment reference value based on the formula average value = current remaining identification value / remaining number of adjustments. For example, if the remaining identification value is 600 yuan and the remaining number of adjustments is 8, the average value is 600 yuan / 8 = 75 yuan.

[0080] The adjustment ratio of the non-first identification value determines the amplitude of each non-first adjustment and is used to set the upper and lower limits of the adjustment. By applying the adjustment ratio, the system can increase or decrease the floating amplitude of each adjustment, making the adjustment process more flexible. The system will adjust the range of each adjustment up and down based on the average value according to the set non-first identification value adjustment ratio. For example, if the non-first identification value adjustment ratio is 10%, the upper and lower floating range is ±10%. Taking the average value of 75 yuan as an example, if the adjustment ratio is 10%, the minimum adjustment value is 75 yuan - 75 yuan × 10% = 67.5 yuan, and the maximum adjustment value is 75 yuan + 75 yuan × 10% = 82.5 yuan.

[0081] The minimum and maximum values ​​of the estimated adjustment range are intervals calculated by the system, which are used to determine the actual adjustment range during the non-first adjustment process. Within this interval, the system can finally determine the specific adjustment value based on random or other algorithms. The system calculates the upper and lower limits of the estimated adjustment range based on the average value and the non-first adjustment ratio. The minimum value is the average value minus the floating range of the adjustment ratio, and the maximum value is the average value plus the floating range of the adjustment ratio. For example, if the average value is 75 yuan and the non-first adjustment ratio is 10%, the estimated adjustment range is 67.5 yuan to 82.5 yuan.

[0082] By determining the minimum and maximum values ​​of the estimated adjustment range based on the average value and the non-first identification value adjustment ratio, the system can flexibly manage the magnitude of each non-first adjustment. This not only ensures the flexibility and uncertainty of the adjustment process, but also avoids the situation where the identification value is over-adjusted or under-adjusted. In addition, by introducing upper and lower limits, the system can provide users with a smoother adjustment experience and enhance user participation.

[0083] S70, randomly determining a target value for adjusting the current identification value between the minimum value and the maximum value, and determining a non-first adjustment parameter for adjusting the current identification value based on the target value for adjusting the current identification value;

[0084] In this embodiment, after calculating the minimum and maximum values ​​for non-first adjustments, the system needs to randomly determine the target value for the current identification value adjustment within the range. The target value is used to determine the size of the identification value to be reduced each time when the non-first adjustment is actually performed. The system calls a random number generation function to generate a random number within the estimated adjustment range (minimum to maximum value) as the target value for this adjustment. For example, if the minimum value is 67.5 yuan and the maximum value is 82.5 yuan, the system will randomly select a value between 67.5 yuan and 82.5 yuan, such as 75 yuan, as the target value for the current adjustment.

[0085] After randomly generating the target value, the system needs to integrate the target value with other relevant information to generate the complete parameters for this non-first adjustment. These parameters include the target value, the identification value before adjustment, the adjustment number, the remaining identification value, and the remaining adjustment number. The system determines the non-first adjustment parameters through the following steps:

[0086] Record the identification value before adjustment: Read the current remaining identification value and record the value before the adjustment operation.

[0087] Record the current adjustment serial number: record the serial number of this non-first adjustment based on the number of adjustments.

[0088] Update the remaining identification value: subtract the randomly generated target value from the current identification value to obtain a new remaining identification value.

[0089] Update remaining adjustment times: reduce the remaining adjustment times by 1 and record after updating.

[0090] For example, assuming that the current remaining identification value is 600 yuan and the randomly generated target value is 75 yuan, the remaining identification value after adjustment is 600 yuan - 75 yuan = 525 yuan, and the remaining adjustment times are reduced from 8 times to 7 times.

[0091] By randomly determining the target value between the minimum and maximum values, the system introduces a certain degree of randomness for each non-first adjustment, making each adjustment result uncertain and flexible. Based on the randomly generated target value, the system can also dynamically update the remaining identification value and the number of adjustments to ensure a smooth and reasonable adjustment process. Users can not only clearly understand the details of each adjustment, but also experience the flexibility brought by different adjustments, which improves the overall interactive experience.

[0092] S80, update the current remaining remaining identification value and remaining adjustment times, and output the non-first adjustment parameters for the current identification value adjustment, wherein the non-first adjustment parameters include the target value for the current identification value adjustment, the identification value before adjustment, the adjustment times sequence number, the remaining identification value, the remaining adjustment times and the estimated adjustment range.

[0093] In this embodiment, the remaining identification value refers to the identification value that has not been adjusted and is recorded by the system after each adjustment operation. After each identification value is adjusted, the system will update and subtract the value adjusted this time according to the target value currently adjusted to obtain a new remaining identification value. The system updates the remaining identification value by the formula: remaining identification value = identification value before adjustment - target value currently adjusted. For example, if the identification value before adjustment is 525 yuan and the target value currently adjusted is 75 yuan, the remaining identification value is updated to 525 yuan - 75 yuan = 450 yuan. This value will be stored in the database or cache after each adjustment to ensure that the next adjustment can continue to be executed correctly.

[0094] After each adjustment, the system needs to update the number of remaining adjustments. The number of remaining adjustments indicates the number of adjustments that the user can continue to make. After each adjustment is completed, this value will be reduced by one. The number of remaining adjustments is calculated by: total number of adjustments - number of completed adjustments. For example, if the user initially has 10 adjustment opportunities and has currently adjusted 3 times, the number of remaining adjustments is 10-3=7. The system will update this value after each adjustment to ensure that the correct number of remaining adjustments is used for subsequent adjustments.

[0095] The non-first adjustment parameters contain all data related to this adjustment, and are used to provide users or systems with detailed information about the current adjustment. These parameters include the target value, the identification value before adjustment, the adjustment sequence number, the remaining identification value, the remaining number of adjustments, and the estimated adjustment range. The target value for the current identification value adjustment: The target value determined by the system within the adjustment range based on the previously generated random number, such as 75 yuan. For example, it includes:

[0096] Identification value before adjustment: The remaining identification value before the current adjustment, for example, 525 yuan.

[0097] Adjustment number: This is the current adjustment number, for example, this is the 4th adjustment.

[0098] Remaining identification value: the updated identification value, for example, 450 yuan.

[0099] Remaining number of adjustments: the remaining number of adjustments after update, for example, 7 adjustment opportunities remaining.

[0100] Estimated adjustment range: the range of minimum and maximum values ​​based on the average value and the adjustment ratio of non-first-identification values, for example, RMB 67.5 to RMB 82.5.

[0101] The system will record these adjustment parameters in the database, log file, or return them to the user interface through the API so that users can clearly view the status after each adjustment.

[0102] By updating the current remaining identification value and the number of adjustments, the system can provide real-time feedback on the status after each adjustment, ensuring that the adjustment process is transparent and controllable. At the same time, outputting non-first adjustment parameters provides users with detailed adjustment information, allowing users to clearly understand the results of each adjustment, the remaining adjustment opportunities and the adjustment range, increasing the user's sense of participation and experience.

[0103] The present invention relates to the fields of data processing technology and financial technology, and discloses a method for analyzing identification value adjustment parameters, by obtaining a preset total identification value, a total adjustment number of times, a first identification value adjustment ratio and a non-first identification value adjustment ratio, to determine whether the current adjustment is the first adjustment; when adjusting for the first time, a target value is determined according to the total identification value and the first adjustment ratio, and the remaining identification value and the number of adjustments are updated; when adjusting for the non-first time, an average value is calculated according to the remaining identification value and the remaining number of adjustments, and the minimum and maximum values ​​of the adjustment range are determined based on the average value and the non-first identification value adjustment ratio, and the target value is randomly determined within this range; after each adjustment, the parameters of the current adjustment are output, including information such as the target value, the remaining identification value, and the number of adjustments. The present invention outputs the adjustment parameters in real time after adjusting the identification value, so that the user can clearly grasp the process of each adjustment, enhance the flexibility and transparency of the adjustment, effectively reduce the misunderstanding and operational complexity caused by information asymmetry, and improve the user experience and the operability of the system.

[0104] In one embodiment, the above S20 includes:

[0105] S201, when an identification value adjustment request is detected, query the historical adjustment record to determine whether there is a historical adjustment operation;

[0106] S202, if there is no historical adjustment operation in the historical adjustment record, marking the current identification value adjustment as the first identification value adjustment;

[0107] S203, if there is a historical adjustment operation in the historical adjustment record, marking the current identification value adjustment as a non-first identification value adjustment;

[0108] S204: After determining and marking the current identification value adjustment, update the historical adjustment record.

[0109] In this embodiment, when the user initiates an adjustment operation, the system detects an identification value adjustment request. This request is used to start the system to determine whether the identification value adjustment is required for the first time or not. The system detects the adjustment request by monitoring the user operation, such as clicking the "bargain" button or the timed adjustment operation automatically triggered by the system. For example, when the user clicks the button to bargain, the system detects the user's request and proceeds to the next step of judgment.

[0110] After receiving an adjustment request, the system needs to check whether there has been an adjustment operation, so it will query the user's historical adjustment records. If there has been no adjustment operation before, the current operation will be regarded as the first identification value adjustment. The system queries the database or cache to check whether there is a historical adjustment record related to the current user or product. The historical adjustment record saves all the user's previous bargaining operations. For example, the system queries the database to see if the user has bargained before. If there is no record, it means this is the first adjustment.

[0111] Based on the query results, the system determines whether this adjustment is the first or non-first identification value adjustment. If the query finds that the user has not made any adjustment operations, it is marked as the first adjustment, otherwise it is a non-first adjustment. If the query result is empty (no historical records), the system will mark the current adjustment as the first identification value adjustment. If the system finds that the user has made historical adjustment operations, it will be marked as a non-first identification value adjustment. For example, when a user participates in a group purchase activity for the first time, the system will find that there is no historical adjustment record, so this operation will be marked as the first adjustment.

[0112] Regardless of whether the current adjustment is the first or not, the system needs to update the user's historical adjustment record after the judgment is completed. This is to ensure that the system can correctly judge whether the user has made an adjustment the next time. The system will store the operation information of this adjustment in the historical adjustment record and update the database or cache. After each adjustment is completed, the system will add a new record to indicate the time of the adjustment, the number of adjustments, the adjustment value and other information. For example, after the user completes a bargaining, the system will store the results of this adjustment (such as the adjusted identification value, adjustment time, and number of operations) in the historical record.

[0113] By detecting the identification value adjustment request and querying the historical adjustment records, the system can correctly determine whether the user has made adjustments, and flexibly handle different situations of first adjustment and non-first adjustment according to the results. The system automatically updates the historical records to ensure the continuity of each adjustment, reduce the risk of misjudgment and operation interruption, and improve the stability and reliability of the user experience.

[0114] In one embodiment, the above S60 includes:

[0115] S601, using the average value minus the product of the average value and the adjustment ratio of the non-first identification value, to generate a minimum value, where the minimum value is not less than zero;

[0116] S602, using the average value plus the product of the average value and the adjustment ratio of the non-first identification value to generate a maximum value, where the maximum value is not greater than the remaining identification value currently remaining;

[0117] S603: Construct an estimated adjustment range for adjusting the current identification value based on the minimum value and the maximum value.

[0118] In this embodiment, during the identification value adjustment process, the system first needs to determine the lower limit of the reasonable range of this adjustment, that is, the minimum value, through the average value. The minimum value is calculated by subtracting a floating value determined by the non-first identification value adjustment ratio from the average value, that is, the product of the average value and the non-first identification value adjustment ratio is subtracted from the average value. This floating value ensures that the adjustment process will not decrease linearly and has a certain volatility. Formula: Minimum value = average value - (average value × non-first identification value adjustment ratio), ensuring that the fluctuation range of the minimum value will not deviate too much from the average value. For example, if the average value is 75 yuan and the non-first identification value adjustment ratio is 10%, the minimum value is 75-(75×0.10)=67.5 yuan. At the same time, the system must ensure that the minimum value is not less than 0 to avoid the adjusted value becoming negative or too small, and to ensure the rationality of the adjustment.

[0119] The maximum value is the upper limit of the estimated adjustment range, which is calculated by adding the average value to the floating value generated by the non-first identification value adjustment ratio, that is, the average value plus the product of the average value and the non-first identification value adjustment ratio. The maximum value represents the maximum adjustment value that the user may obtain in this adjustment. Formula: Maximum value = average value + (average value × non-first identification value adjustment ratio), ensuring that the maximum value is within a reasonable floating range and does not affect the remaining identification value too much. For example, if the average value is 75 yuan and the non-first identification value adjustment ratio is 10%, the maximum value is 75 + (75 × 0.10) = 82.5 yuan. In addition, the system must ensure that the maximum value does not exceed the current remaining identification value to ensure that the user cannot adjust the amount exceeding the remaining identification value. For example, if the remaining identification value is 70 yuan, the maximum value is limited to no more than 70 yuan.

[0120] The minimum and maximum values ​​calculated by the system constitute the estimated adjustment range for this non-first adjustment. Within this range, the system will randomly generate the actual adjustment target value. The system will use the previously calculated minimum and maximum values ​​as the upper and lower limits of the adjustment range to ensure flexibility and uncertainty for each adjustment. At the same time, the system will dynamically adjust the minimum and maximum values ​​to cope with changes in the user's remaining identification value and the number of adjustments. For example, the currently calculated minimum value is 67.5 yuan and the maximum value is 82.5 yuan. The system will use this range to generate the adjustment target value.

[0121] This embodiment generates the minimum and maximum values ​​of the estimated adjustment range based on the average value and the non-first identification value adjustment ratio, so that the system ensures the rationality, flexibility and uncertainty of each non-first adjustment. The upper and lower limits of the estimated adjustment range ensure that users cannot adjust the amount exceeding the remaining identification value, avoiding the system from exceeding the tolerance range. At the same time, this mechanism increases the diversity of the user adjustment experience, making the adjustment process more attractive and participatory.

[0122] In one embodiment, the above S70 includes:

[0123] S701, calling a random number generation module, and setting a random number generation range based on the minimum and maximum values ​​of the estimated adjustment range;

[0124] S702, generating a random number by the random number generation module, and verifying whether the random number is within the random number generation range;

[0125] S703, using the verified random number as the target value for adjusting the current identification value;

[0126] S704: Use the current remaining remaining identification value to subtract the target value to determine the adjusted remaining identification value, and update the remaining adjustment times.

[0127] In this embodiment, the system generates a random number within the estimated adjustment range by calling the random number generation module, which is used as the target value for this identification value adjustment. The random number generation module ensures that the adjustment result is uncertain, while ensuring that the random number falls within the range set by the system. The system first sets the random number generation range to the minimum and maximum values ​​calculated in the previous step. For example, the minimum value is 67.5 yuan and the maximum value is 82.5 yuan. The random number generation module is called to generate a random number within this range. For example, the system calls the random number module to generate a value of 75 yuan, which is between 67.5 yuan and 82.5 yuan.

[0128] After generating a random number, the system needs to verify whether the random number is indeed within the range of the minimum and maximum values. If the random number is not within the set range, the system will regenerate a random number to ensure the accuracy of the adjustment. The system uses simple logical judgment: if the minimum value ≤ random number ≤ maximum value, the verification is passed. If the generated random number is not within the range, the system will re-call the random number generation module until the generated value is within the range. For example, the value 75 yuan generated by the random number generation module passes the verification because it meets the conditions: 67.5 yuan ≤ 75 yuan ≤ 82.5 yuan.

[0129] When the random number is verified to be within the adjustment range, the system uses it as the target value for the current adjustment and uses it for the actual identification value adjustment operation. The system records this qualified random number (e.g., 75 yuan) as the target value for this adjustment, and subsequent adjustment calculations are based on this value. For example, 75 yuan is recorded as the target value for the current adjustment, which is used for the subsequent identification value deduction and adjustment number update.

[0130] The system uses the current remaining identification value minus the verified target value to update the new remaining identification value. At the same time, the remaining number of adjustments will also decrease by 1, indicating that the user's adjustment has been completed. Remaining identification value = current remaining identification value - target value. For example, if the current remaining identification value is 600 yuan and the target value is 75 yuan, the adjusted remaining identification value is 600 yuan - 75 yuan = 525 yuan. Remaining number of adjustments = remaining number of adjustments - 1. For example, the user originally had 8 adjustment opportunities, and now the remaining number of adjustments after the adjustment is 8 times - 1 = 7 times. The system stores the updated remaining identification value and remaining number of adjustments in the database or memory to ensure that these new values ​​are used in the next round of adjustments.

[0131] This embodiment calls the random number generation module and generates the target value based on the minimum and maximum values ​​of the estimated adjustment range. The system provides users with uncertainty and flexibility for each non-first adjustment, which improves the diversity and participation of the adjustment process. The random number generation and verification process ensures the fairness of each adjustment. At the same time, the remaining identification value and the number of adjustments are updated so that the system can dynamically track the results of each adjustment, which enhances the transparency and control of the adjustment.

[0132] In one embodiment, the above S10 includes:

[0133] S101, obtaining a target task, and extracting a preset total identification value from the target task;

[0134] S102, generating a total number of adjustments based on the complexity of the target task;

[0135] S103, determining the type of the target task, obtaining multiple historical adjustment tasks of the same type as the target task, analyzing the first identification value adjustment ratios of the multiple historical adjustment tasks and corresponding feedback data, and determining the first identification value adjustment ratio of the target task according to the analysis results;

[0136] S104: Generate a non-first identification value adjustment ratio based on the total identification value, the total number of adjustments, and the first identification value adjustment ratio.

[0137] In this embodiment, the target task refers to the specific task or operation object associated with the system when processing the identification value adjustment. It is the core basis for the system adjustment operation and represents a complete identification value adjustment process that the user needs to perform. The target task can set different adjustment strategies based on the task type, complexity and other specific parameters. The target task defines the core data and parameters in the adjustment process, including:

[0138] Total identification value: the total amount of initial identification values ​​of the target task.

[0139] Total number of adjustments: how many times the identification value adjustment needs to be performed.

[0140] First adjustment ratio and non-first adjustment ratio: the ratio of each adjustment to the total identification value.

[0141] The type of target task determines how the system calculates the adjustment parameters. For example:

[0142] Group purchase task: If the target task is to adjust the price in a group purchase activity, the system will set the total identification value and the number of adjustments based on the total price of the goods.

[0143] Bidding task: If it is a bidding task, the target task may be the bid adjustment in a bidding project, and the system needs to set the adjustment parameters according to the bidding rules.

[0144] Financial data processing: In the financial field, the target task may be the interest rate adjustment or pricing process of a financial product. The system will set adjustment parameters based on the product type and market feedback.

[0145] The target task is usually obtained by the system from the background database, configuration file or user input. For example, for a group purchase task, the system will query the background database and obtain the total price of the product as the total identification value. For a bidding task, the system may automatically generate the number of adjustments and the adjustment ratio according to the bidding process.

[0146] The system first needs to extract the total identification value from the target task. The total identification value refers to the total amount of identifications initially allocated during the identification value adjustment process. This value is usually set based on the attributes, scale or importance of the target task as the starting benchmark for all adjustment operations. The system extracts the preset total identification value from the target task's configuration file or database. This value is automatically generated by the system based on the task type or manually set by the administrator. For example, the preset total identification value in a financial product group purchase activity may be 1,000 yuan.

[0147] The total number of adjustments is generated based on the complexity of the target task, indicating the maximum number of times the identification value can be adjusted. The more complex the task, the more total adjustments may be required to ensure a more detailed adjustment process. The system sets the complexity based on different attributes of the task (such as scale, user engagement, product complexity, etc.) and generates the total number of adjustments through pre-defined rules or algorithms. For example, for tasks with higher complexity, the system may set a larger number of adjustments, such as 15 times; while for simple tasks, only 5 adjustments may be required.

[0148] The task type determines how the system handles the parameters related to the identification value adjustment. The task type includes the nature and function of the task, such as whether it is a group purchase task, a bidding task, or another type of identification value adjustment task. The task type helps the system to classify the data when analyzing historical data. The system first obtains the type of the target task from the task database or configuration, and uses this as a basis to retrieve historical adjustment tasks. For example, if the current task is a group purchase task, the system will look for the same historical records as the group purchase task. For example, the system determines that the task is a "group purchase activity", so the historical adjustment tasks are limited to group purchase activities of the same type.

[0149] The system needs to obtain multiple historical adjustment tasks of the same type as the current task, and analyze the changes in the first identification value adjustment ratio in these historical tasks. By analyzing the feedback data of these historical tasks, the system can infer a first adjustment ratio that is more suitable for the current task. The system will retrieve historical tasks of the same type as the current task from the database, extract the first identification value adjustment ratio of these tasks, and the corresponding feedback data, such as user satisfaction, adjustment effect, etc. For example, the system retrieves multiple historical group purchase activities, analyzes their first adjustment ratio (such as the first adjustment accounts for 30% of the total identification value) and user participation feedback to determine the first adjustment ratio of the current task.

[0150] The non-first identification value adjustment ratio is the ratio used to adjust the remaining identification values ​​after the first adjustment. The system determines the non-first adjustment ratio by combining the total identification value, the total number of adjustments, and the first adjustment ratio to ensure that the remaining adjustment process can gradually reduce the identification value and the magnitude of each adjustment is appropriate. The system calculates the non-first adjustment ratio based on the relationship between the total identification value, the total number of adjustments, and the first adjustment ratio. For example, the system may set the adjustment magnitude for each non-first adjustment based on a first adjustment ratio of 30%, a remaining identification value of 70%, and a number of adjustments of 7 times. For example, the system calculates a non-first adjustment ratio of 10%, so that the remaining identification value will be gradually reduced in 7 adjustments.

[0151] This embodiment extracts the preset total identification value from the target task, and combines the task complexity and the feedback data of the historical adjustment task, so that the system can reasonably determine the parameters for each adjustment. This adjustment mechanism based on historical data and task complexity not only improves the accuracy of the adjustment, but also automatically optimizes the adjustment process according to different task types, making each adjustment process more scientific and efficient.

[0152] In one embodiment, in the above S80, outputting the non-first adjustment parameter of the current identification value adjustment includes:

[0153] S801, obtaining adjustment parameters for each identification value adjustment before the current identification value is adjusted, where the adjustment parameters include first adjustment parameters and / or non-first adjustment parameters;

[0154] S802, integrating the non-first adjustment parameters of the current identification value adjustment and the adjustment parameters of each previous identification value adjustment to generate a complete adjustment record sequence;

[0155] S803, based on the complete adjustment record sequence, displaying the target value of each identification value adjustment and the change trend of the remaining identification value on a graphical interface.

[0156] In this embodiment, before the system performs the current identification value adjustment, it needs to query and obtain the detailed parameters of all previous adjustments of the task. These parameters include the first adjustment parameters and all previous non-first adjustment parameters, so as to integrate all adjustment records. The system extracts the historical data of each previous identification value adjustment by querying the database, memory or log file. The first adjustment parameters refer to the relevant parameters when the user adjusts the identification value for the first time, and the non-first adjustment parameters refer to the data of each adjustment made by the user thereafter. For example, if the user has made three adjustments, the system will extract all information of the first adjustment (first adjustment parameters) and the next two adjustments (non-first adjustment parameters).

[0157] After the current adjustment is completed, the system integrates the current adjustment parameters with the previous adjustment parameters to generate a complete adjustment record sequence. This record sequence contains information about the user's first adjustment, non-first adjustment, and latest adjustment. The system integrates the current adjustment non-first adjustment parameters (such as target value, pre-adjustment identification value, remaining identification value, etc.) with the previously obtained adjustment parameters to form a complete adjustment history record. For example, the current non-first adjustment target value is 50 yuan, and the remaining identification value is 450 yuan. The system will integrate this information with the values ​​of each previous adjustment to form a complete adjustment record sequence.

[0158] The complete adjustment record sequence contains detailed information about all adjustments, recording the target value of each adjustment, the identification value before and after the adjustment, the number of remaining adjustments, etc. in chronological order. This sequence is used to display and track the adjustment history. The system integrates the parameters of each adjustment in chronological order to form a complete adjustment record. These records may include: the identification value before the adjustment, the adjustment target value, the remaining identification value, the number of adjustments, etc. For example, a complete adjustment record may be as follows:

[0159] First adjustment: The target value is 100 yuan, and the remaining mark value is 900 yuan;

[0160] Second adjustment: The target value is 50 yuan, and the remaining identification value is 850 yuan;

[0161] The third adjustment: the target value is 50 yuan, and the remaining identification value is 800 yuan.

[0162] The system displays the generated complete sequence of adjustment records on a graphical interface. This interface not only displays the target value and remaining indicator value of each adjustment, but also displays the changing trend of the indicator value through charts or line graphs, so that users can intuitively view and understand the adjustment process. The system visualizes the adjustment records and displays them in the form of a list or chart on the user interface. For example, a line graph is used to show how the remaining indicator value changes after each adjustment, and the specific value of each adjustment is displayed in a list.

[0163] For example, the content that the user sees on the graphical interface may include:

[0164] Adjustment 1: Target value 100 yuan, remaining identification value 900 yuan;

[0165] Adjustment 2: Target value 50 yuan, remaining identification value 850 yuan;

[0166] Adjustment trend chart: a line chart with gradually decreasing label values.

[0167] This embodiment integrates current and historical adjustment parameters, generates a complete adjustment record sequence and displays it on a graphical interface. The system can provide users with a clear visualization of the adjustment process. Users can not only view the detailed information of each adjustment, but also intuitively understand the changing trend of the identification value through the trend chart. This display method enhances the user's sense of participation and understanding of the adjustment process, making the adjustment process transparent and easy to track.

[0168] In one embodiment, in the above S80, after outputting the non-first adjustment parameter of the current identification value adjustment, the method further includes:

[0169] S901, determining a target value for the next identification value adjustment based on the remaining identification value after the current identification value is adjusted and the non-first identification value adjustment ratio;

[0170] S902: Output the target value for the next identification value adjustment.

[0171] In this embodiment, after the current identification value adjustment is completed, the system will determine the target value for the next identification value adjustment in advance based on the remaining identification value after the current adjustment and the set non-first identification value adjustment ratio. This calculation helps the system prepare the recommended value for the next adjustment for the user, ensuring that the subsequent adjustment process is smoother. The system first obtains the remaining identification value after the current adjustment, which is the latest identification value after the user's last adjustment. Then, the system applies the non-first identification value adjustment ratio and calculates the target value for the next adjustment through the formula:

[0172] The next adjustment target value = remaining identification value × non-first identification value adjustment ratio.

[0173] For example, the current remaining identification value is 450 yuan, and the non-first adjustment ratio is 10%, then the target value for the next adjustment is 450 yuan × 10% = 45 yuan.

[0174] After the system calculates the target value for the next adjustment of the identification value, it needs to output the value so that the user can know the expected target of the next adjustment in advance. The output of this step can be displayed on the graphical interface, recorded in the background log, or returned to the user interface through the API. The system can choose multiple ways to output the target value for the next adjustment of the identification value:

[0175] User interface display: The system displays the target value for the next adjustment on the user interface, so that users can understand the next adjustment target in advance.

[0176] Background log recording: The system records the target value for the next adjustment in the log for easy follow-up tracking and system inspection.

[0177] API return: If the system has a front-end and back-end separated architecture, the system can return the next adjustment target to the client or other system modules through the API.

[0178] For example, the system outputs "the next adjustment target value is 45 yuan" and displays it on the user's bargaining interface.

[0179] In this embodiment, by determining the target value for the next identification value adjustment in advance, the system provides clear guidance for the user's next adjustment operation. This not only improves the continuity of the adjustment, but also helps the user to better plan the adjustment operation and ensure that each adjustment is smoother. At the same time, outputting the target value allows the user to anticipate the result of the next operation, increasing the user's sense of control and trust in the adjustment process.

[0180] In one embodiment, a device for analyzing identification value adjustment parameters is provided, and the device for analyzing identification value adjustment parameters corresponds one-to-one to the method for analyzing identification value adjustment parameters in the above embodiment. Figure 3 , Figure 3 This is a functional module diagram of a preferred embodiment of the identification value adjustment parameter analysis device of the present invention. Data acquisition module 10, request detection module 20, first adjustment analysis module 30, first adjustment parameter output module 40, non-first adjustment analysis module 50, adjustment range analysis module 60, random adjustment value generation module 70 and non-first adjustment parameter output module 80. The functional modules are described in detail as follows:

[0181] The data acquisition module 10 is used to obtain the preset total identification value, total adjustment times, first identification value adjustment ratio and non-first identification value adjustment ratio;

[0182] The request detection module 20 is used to determine whether the current identification value adjustment is the first identification value adjustment when an identification value adjustment request is detected;

[0183] The first adjustment analysis module 30 is used to determine the target value of the first adjustment according to the total identification value and the first identification value adjustment ratio if the current identification value adjustment is the first identification value adjustment, and update the current remaining remaining identification value and the remaining number of adjustments;

[0184] The first adjustment parameter output module 40 is used to output the first adjustment parameters of the current identification value adjustment, wherein the first adjustment parameters include the target value of the first adjustment, the identification value before adjustment, the first adjustment sequence number, the remaining identification value and the remaining adjustment times;

[0185] The non-first adjustment analysis module 50 is used to determine the average value of the current identification value adjustment according to the remaining identification values ​​and the remaining number of adjustments if the current identification value adjustment is not the first identification value adjustment;

[0186] An adjustment range analysis module 60, configured to determine a minimum value and a maximum value of an estimated adjustment range of a current identification value adjustment based on the average value and the non-first identification value adjustment ratio;

[0187] A random adjustment value generation module 70, configured to randomly determine a target value for adjusting the current identification value between the minimum value and the maximum value, and determine a non-first adjustment parameter for adjusting the current identification value based on the target value for adjusting the current identification value;

[0188] The non-first adjustment parameter output module 80 is used to update the current remaining remaining identification value and the remaining adjustment times, and output the non-first adjustment parameters for the current identification value adjustment. The non-first adjustment parameters include the target value for the current identification value adjustment, the identification value before adjustment, the adjustment times sequence number, the remaining identification value, the remaining adjustment times and the estimated adjustment range.

[0189] In one embodiment, the request detection module 20 is specifically configured to:

[0190] When an identification value adjustment request is detected, query the historical adjustment record to determine whether there is a historical adjustment operation;

[0191] If there is no historical adjustment operation in the historical adjustment record, the current identification value adjustment is marked as the first identification value adjustment;

[0192] If there is a historical adjustment operation in the historical adjustment record, the current identification value adjustment is marked as a non-first identification value adjustment;

[0193] After determining and marking the current identification value adjustment, the historical adjustment record is updated.

[0194] In one embodiment, the adjustment range analysis module 60 is specifically used to:

[0195] The minimum value is generated by subtracting the product of the average value and the adjustment ratio of the non-first identification value from the average value, and the minimum value is not less than zero;

[0196] The average value is added to the product of the average value and the adjustment ratio of the non-first identification value to generate a maximum value, where the maximum value is not greater than the remaining identification value currently remaining;

[0197] An estimated adjustment range for adjusting the current identification value is formed based on the minimum value and the maximum value.

[0198] In one embodiment, the random adjustment value generation module 70 is specifically used to:

[0199] Calling a random number generation module and setting a random number generation range based on the minimum and maximum values ​​of the estimated adjustment range;

[0200] Generate a random number by the random number generation module, and verify whether the random number is within the random number generation range;

[0201] The verified random number is used as the target value for adjusting the current identification value;

[0202] The target value is subtracted from the current remaining remaining identification value to determine the adjusted remaining identification value, and the remaining adjustment times are updated.

[0203] In one embodiment, the data acquisition module 10 is specifically used for:

[0204] Obtaining a target task, and extracting a preset total identification value from the target task;

[0205] Based on the complexity of the target task, generate a total number of adjustments;

[0206] Determine the type of the target task, obtain multiple historical adjustment tasks of the same type as the target task, analyze the first identification value adjustment ratios of the multiple historical adjustment tasks and corresponding feedback data, and determine the first identification value adjustment ratio of the target task according to the analysis results;

[0207] The non-first identification value adjustment ratio is generated based on the total identification value, the total number of adjustments and the first identification value adjustment ratio.

[0208] In one embodiment, the non-first adjustment parameter output module 80 is specifically used to:

[0209] Acquire adjustment parameters for each identification value adjustment before the current identification value is adjusted, where the adjustment parameters include first adjustment parameters and / or non-first adjustment parameters;

[0210] Integrate the non-first adjustment parameters of the current identification value adjustment with the adjustment parameters of each previous identification value adjustment to generate a complete adjustment record sequence;

[0211] Based on the complete adjustment record sequence, the target value of each identification value adjustment and the change trend of the remaining identification value are displayed on a graphical interface.

[0212] In one embodiment, the non-first adjustment parameter output module 80 is specifically used to:

[0213] Determine the target value for the next identification value adjustment based on the remaining identification value after the current identification value is adjusted and the non-first identification value adjustment ratio;

[0214] Output the target value for the next identification value adjustment.

[0215] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external user terminal through a network connection. When the computer program is executed by the processor, it realizes the functions or steps of a service-side side of an identification value adjustment parameter analysis method.

[0216] In one embodiment, a computer device is provided. The computer device may be a user terminal, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server through a network connection. When the computer program is executed by the processor, it realizes the functions or steps of a user-side method for adjusting parameter analysis of an identification value

[0217] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0218] Obtain the preset total identification value, total adjustment times, first identification value adjustment ratio, and non-first identification value adjustment ratio;

[0219] When an identification value adjustment request is detected, determining whether the current identification value adjustment is the first identification value adjustment;

[0220] If the current identification value adjustment is the first identification value adjustment, determine the target value of the first adjustment according to the total identification value and the first identification value adjustment ratio, and update the current remaining remaining identification value and the remaining number of adjustments;

[0221] Output the first adjustment parameters of the current identification value adjustment, wherein the first adjustment parameters include the target value of the first adjustment, the identification value before adjustment, the first adjustment sequence number, the remaining identification value and the remaining number of adjustments;

[0222] If the current identification value adjustment is not the first identification value adjustment, then the average value of the current identification value adjustment is determined according to the current remaining remaining identification value and the remaining number of adjustments;

[0223] Determine a minimum value and a maximum value of an estimated adjustment range of the current identification value adjustment based on the average value and the non-first identification value adjustment ratio;

[0224] Randomly determine a target value for adjusting the current identification value between the minimum value and the maximum value, and determine a non-first adjustment parameter for adjusting the current identification value based on the target value for adjusting the current identification value;

[0225] Update the current remaining remaining identification value and remaining adjustment times, and output the non-first adjustment parameters for the current identification value adjustment, wherein the non-first adjustment parameters include the target value for the current identification value adjustment, the identification value before adjustment, the adjustment times sequence number, the remaining identification value, the remaining adjustment times and the estimated adjustment range.

[0226] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0227] Obtain the preset total identification value, total adjustment times, first identification value adjustment ratio, and non-first identification value adjustment ratio;

[0228] When an identification value adjustment request is detected, determining whether the current identification value adjustment is the first identification value adjustment;

[0229] If the current identification value adjustment is the first identification value adjustment, determine the target value of the first adjustment according to the total identification value and the first identification value adjustment ratio, and update the current remaining remaining identification value and the remaining number of adjustments;

[0230] Output the first adjustment parameters of the current identification value adjustment, wherein the first adjustment parameters include the target value of the first adjustment, the identification value before adjustment, the first adjustment sequence number, the remaining identification value and the remaining number of adjustments;

[0231] If the current identification value adjustment is not the first identification value adjustment, then the average value of the current identification value adjustment is determined according to the current remaining remaining identification value and the remaining number of adjustments;

[0232] Determine a minimum value and a maximum value of an estimated adjustment range of the current identification value adjustment based on the average value and the non-first identification value adjustment ratio;

[0233] Randomly determine a target value for adjusting the current identification value between the minimum value and the maximum value, and determine a non-first adjustment parameter for adjusting the current identification value based on the target value for adjusting the current identification value;

[0234] Update the current remaining remaining identification value and remaining adjustment times, and output the non-first adjustment parameters for the current identification value adjustment, wherein the non-first adjustment parameters include the target value for the current identification value adjustment, the identification value before adjustment, the adjustment times sequence number, the remaining identification value, the remaining adjustment times and the estimated adjustment range.

[0235] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant descriptions on the server side and the user side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0236] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0237] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0238] It should be noted that if software tools or components other than those of the Company appear in the embodiments of the present application, they are only used for illustration and do not represent actual use. The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above-mentioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the above-mentioned embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for analyzing identification value adjustment parameters, characterized in that: The following steps are involved: Obtain the preset total identification value, total adjustment times, first identification value adjustment ratio, and non-first identification value adjustment ratio; When an identification value adjustment request is detected, determining whether the current identification value adjustment is the first identification value adjustment; If the current identification value adjustment is the first identification value adjustment, determine the target value of the first adjustment according to the total identification value and the first identification value adjustment ratio, and update the current remaining remaining identification value and the remaining number of adjustments; Output the first adjustment parameters of the current identification value adjustment, wherein the first adjustment parameters include the target value of the first adjustment, the identification value before adjustment, the first adjustment sequence number, the remaining identification value and the remaining number of adjustments; If the current identification value adjustment is not the first identification value adjustment, then the average value of the current identification value adjustment is determined according to the current remaining remaining identification value and the remaining number of adjustments; Determine a minimum value and a maximum value of an estimated adjustment range of the current identification value adjustment based on the average value and the non-first identification value adjustment ratio; Randomly determine a target value for adjusting the current identification value between the minimum value and the maximum value, and determine a non-first adjustment parameter for adjusting the current identification value based on the target value for adjusting the current identification value; Update the current remaining remaining identification value and remaining adjustment times, and output the non-first adjustment parameters for the current identification value adjustment, wherein the non-first adjustment parameters include the target value for the current identification value adjustment, the identification value before adjustment, the adjustment times sequence number, the remaining identification value, the remaining adjustment times and the estimated adjustment range.

2. The identification value adjustment parameter analysis method according to claim 1, characterized in that: When an identification value adjustment request is detected, determining whether the current identification value adjustment is the first identification value adjustment includes: When an identification value adjustment request is detected, query the historical adjustment record to determine whether there is a historical adjustment operation; If there is no historical adjustment operation in the historical adjustment record, the current identification value adjustment is marked as the first identification value adjustment; If there is a historical adjustment operation in the historical adjustment record, the current identification value adjustment is marked as a non-first identification value adjustment; After determining and marking the current identification value adjustment, the historical adjustment record is updated.

3. The identification value adjustment parameter analysis method according to claim 1, characterized in that: Determining a minimum value and a maximum value of an estimated adjustment range of a current identification value adjustment based on the average value and the non-first identification value adjustment ratio includes: The minimum value is generated by subtracting the product of the average value and the adjustment ratio of the non-first identification value from the average value, and the minimum value is not less than zero; The average value is added to the product of the average value and the adjustment ratio of the non-first identification value to generate a maximum value, where the maximum value is not greater than the remaining identification value currently remaining; An estimated adjustment range for adjusting the current identification value is formed based on the minimum value and the maximum value.

4. The identification value adjustment parameter analysis method according to claim 1, characterized in that: Randomly determining a target value for adjusting the current identification value between the minimum value and the maximum value, and determining a non-first adjustment parameter for adjusting the current identification value based on the target value for adjusting the current identification value, including: Calling a random number generation module and setting a random number generation range based on the minimum and maximum values ​​of the estimated adjustment range; Generate a random number by the random number generation module, and verify whether the random number is within the random number generation range; The verified random number is used as the target value for adjusting the current identification value; The target value is subtracted from the current remaining remaining identification value to determine the adjusted remaining identification value, and the remaining adjustment times are updated.

5. The identification value adjustment parameter analysis method as claimed in claim 1, characterized in that: Get the preset total identification value, total adjustment times, first identification value adjustment ratio, and non-first identification value adjustment ratio, including: Obtaining a target task, and extracting a preset total identification value from the target task; Based on the complexity of the target task, generate a total number of adjustments; Determine the type of the target task, obtain multiple historical adjustment tasks of the same type as the target task, analyze the first identification value adjustment ratios of the multiple historical adjustment tasks and corresponding feedback data, and determine the first identification value adjustment ratio of the target task according to the analysis results; The non-first identification value adjustment ratio is generated based on the total identification value, the total number of adjustments and the first identification value adjustment ratio.

6. The identification value adjustment parameter analysis method according to claim 1, characterized in that: Output the non-first adjustment parameters of the current identification value adjustment, including: Acquire adjustment parameters for each identification value adjustment before the current identification value is adjusted, where the adjustment parameters include first adjustment parameters and / or non-first adjustment parameters; Integrate the non-first adjustment parameters of the current identification value adjustment with the adjustment parameters of each previous identification value adjustment to generate a complete adjustment record sequence; Based on the complete adjustment record sequence, the target value of each identification value adjustment and the change trend of the remaining identification value are displayed on a graphical interface.

7. The identification value adjustment parameter analysis method according to claim 1, characterized in that: After outputting the non-first adjustment parameters of the current identification value adjustment, it also includes: Determine the target value for the next identification value adjustment based on the remaining identification value after the current identification value is adjusted and the non-first identification value adjustment ratio; Output the target value for the next identification value adjustment.

8. A device for analyzing identification value adjustment parameters, characterized in that: The identification value adjustment parameter analysis device comprises: A data acquisition module is used to obtain a preset total identification value, a total number of adjustments, a first identification value adjustment ratio, and a non-first identification value adjustment ratio; A request detection module, used to determine whether the current identification value adjustment is the first identification value adjustment when an identification value adjustment request is detected; A first adjustment analysis module, for determining a target value for the first adjustment according to the total identification value and the first identification value adjustment ratio if the current identification value adjustment is the first identification value adjustment, and updating the current remaining remaining identification value and the remaining number of adjustments; The first adjustment parameter output module is used to output the first adjustment parameters of the current identification value adjustment, wherein the first adjustment parameters include the target value of the first adjustment, the identification value before adjustment, the first adjustment sequence number, the remaining identification value and the remaining number of adjustments; A non-first adjustment analysis module, for determining an average value of the current identification value adjustment according to the currently remaining remaining identification value and the remaining number of adjustments if the current identification value adjustment is not the first identification value adjustment; An adjustment range analysis module, used to determine a minimum value and a maximum value of an estimated adjustment range of a current identification value adjustment based on the average value and the non-first identification value adjustment ratio; A random adjustment value generation module, used for randomly determining a target value for adjusting the current identification value between the minimum value and the maximum value, and determining a non-first adjustment parameter for adjusting the current identification value based on the target value for adjusting the current identification value; The non-first adjustment parameter output module is used to update the current remaining remaining identification value and the remaining adjustment times, and output the non-first adjustment parameters for the current identification value adjustment. The non-first adjustment parameters include the target value for the current identification value adjustment, the identification value before adjustment, the adjustment times sequence number, the remaining identification value, the remaining adjustment times and the estimated adjustment range.

9. A computer device, characterized in that: The computer device includes a memory, a processor, and an identification value adjustment parameter analysis program stored in the memory and executable on the processor. When the identification value adjustment parameter analysis program is executed by the processor, the steps of the identification value adjustment parameter analysis method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores an identification value adjustment parameter analysis program, which, when executed by a processor, implements the steps of the identification value adjustment parameter analysis method according to any one of claims 1 to 7.

Images