Method, device and equipment for dynamically adjusting rental fees based on use behaviors of users

Abstract

The invention relates to a lease fee dynamic adjustment method, device and equipment based on user use behaviors. The method comprises the following steps: acquiring basic information and lease transaction historical data of a user, and constructing a behavior model of the user; the behavior model of the user comprises quantitative indexes of at least one dimension; acquiring market data of the rental items, and determining a basic pricing model of the rental items; the basic pricing model comprises a basic price of the rental item and at least one dimension of pricing adjustment factor; based on the basic price of the rental item, combining the quantitative index of the behavior model and the pricing adjustment factor of the basic pricing model to perform pricing adjustment on the rental item; wherein the quantitative indexes of the at least one dimension are in one-to-one correspondence with the pricing adjustment factors of the at least one dimension. The method mainly solves the technical problems of low resource utilization rate, poor user experience and difficulty in adapting to market changes due to the fact that traditional static pricing of the leasing cost cannot be dynamically and flexibly adjusted according to user behaviors and markets.

Description

Technical Field

[0001] The present application relates to the technical field of rental services, and in particular to a method, device and equipment for dynamically adjusting rental fees based on user usage behavior. Background Art

[0002] With the development of the sharing economy, rental services have become an indispensable part of people's lives, covering a wide range of areas from transportation, housing to daily necessities. Traditionally, rental fees are pre-set based on factors such as the cost, depreciation, and average market price of the leased item, and are fixed in the lease contract. However, this static pricing method has some limitations, such as failing to fully consider individual differences and usage behavior patterns of users, failing to respond promptly to changes in market supply and demand, and difficulty in achieving personalized services.

[0003] On the one hand, when setting prices, leasing companies usually determine the base price based on the cost of the leased items, the average market price level, and simple supply and demand relationships, but ignore the significant differences among different users in terms of leasing frequency, leasing duration, leasing time preference, booking lead time, item return status, leasing item type preference, and related leasing behavior. For example, for users with a high leasing frequency, if a uniform price standard is used, it may not be possible to fully tap the potential value of these users, and may even cause them to turn to competitors due to price factors. Similarly, for users with longer leasing durations, not giving them appropriate price discounts may reduce their willingness to lease, increase the idle time of equipment, and affect the company's overall revenue.

[0004] On the other hand, the market environment is in a state of continuous dynamic change. Factors such as raw material price fluctuations, seasonal demand changes, and competitors' price strategy adjustments will all have an impact on the pricing of rental items. However, traditional pricing methods are difficult to respond to these market changes quickly and accurately. For example, during the peak tourist season, the demand for rental of outdoor equipment increases significantly. If companies fail to raise prices in a timely manner, they may suffer losses due to the shortage of equipment; conversely, if they fail to lower prices to attract users during the off-season, equipment utilization will continue to be sluggish. Moreover, due to the lack of effective integration and analysis of market data, rental companies are often slow to respond to competitors' price wars or new market trends, and are unable to adjust their pricing strategies in a timely manner to maintain their competitiveness. Summary of the invention

[0005] The main technical problem of this application is that the static pricing of traditional rental fees cannot be flexibly adjusted according to user behavior and market dynamics, resulting in low resource utilization, poor user experience and difficulty in adapting to market changes.

[0006] An embodiment of the present application provides a method for dynamically adjusting rental fees based on user usage behavior, and the adjustment method includes: Obtaining the user's basic information and rental transaction history data, and constructing a user behavior model; the user behavior model includes at least one dimensional quantitative indicator; Acquire market data of the leased object and determine a basic pricing model for the leased object; the basic pricing model includes a basic price of the leased object and a pricing adjustment factor of at least one dimension; Based on the base price of the leased object, combined with the quantitative indicators of the behavior model and the pricing adjustment factors of the basic pricing model, adjust the pricing of the leased object; Among them, the quantitative indicators of at least one dimension correspond one-to-one to the pricing adjustment factors of at least one dimension.

[0007] In the above-mentioned method for dynamically adjusting rental fees based on user usage behavior, as a preferred solution, the quantitative index of at least one dimension includes quantitative indexes of at least seven dimensions, and the pricing adjustment factor of at least one dimension includes adjustment factors of at least seven dimensions; The quantitative indicators of the seven dimensions include quantitative indicators of rental frequency dimension, quantitative indicators of rental duration dimension, quantitative indicators of rental time preference dimension, quantitative indicators of scheduled lead time dimension, quantitative indicators of item return dimension, quantitative indicators of item type preference dimension, and quantitative indicators of associated rental behavior dimension; The adjustment factors in seven dimensions include rental frequency adjustment factor, rental duration adjustment factor, rental time preference adjustment factor, booking lead time adjustment factor, item return adjustment factor, item type preference adjustment factor, and associated rental behavior adjustment factor.

[0008] In the above-mentioned method for dynamically adjusting the rental fee based on user usage behavior, as a preferred solution, the basic pricing model for determining the leased item includes: Determine the base price of the leased property; Determine the pricing adjustment factors of the seven dimensions of the leased property, and associate the pricing adjustment factors of the seven dimensions with the quantitative indicators of the seven dimensions respectively.

[0009] In the above-mentioned method for dynamically adjusting rental fees based on user usage behavior, as a preferred solution, the step of determining the base price of the rental item includes: The total cost of the lease is calculated using the following formula:

[0010] The base price of the leased property is determined by the following formula: ; in, is the total cost, As the base price, For the purchase cost, For maintenance costs, For storage costs, For transportation costs, is the expected profit margin, is the market competition adjustment coefficient.

[0011] In the above-mentioned method for dynamically adjusting rental fees based on user usage behavior, as a preferred solution, the step of constructing a user behavior model includes: The behavior model is expressed by the following formula: ; in, is the user behavior vector, They respectively represent the quantitative indicators of rental frequency dimension, rental duration dimension, rental time preference dimension, booking lead time dimension, item return dimension, item type preference dimension, and associated rental behavior dimension.

[0012] In the above-mentioned method for dynamically adjusting the rental fee based on the user's usage behavior, as a preferred solution, the pricing adjustment of the leased item is performed by combining the quantitative index of the behavior model and the pricing adjustment factor of the basic pricing model, including: The final price of the leased property is obtained by the following formula:

[0013] in, is the lease frequency adjustment factor, is the lease duration adjustment factor, is the rental time preference adjustment factor, is the scheduling lead time adjustment factor, The item return adjustment factor. is the item type preference adjustment factor, Adjustment factor for associated leasing activities.

[0014] In the above-mentioned method for dynamically adjusting rental fees based on user usage behavior, as a preferred solution, the rental frequency dimension quantitative index, rental duration dimension quantitative index, rental time preference dimension quantitative index, scheduled lead time dimension quantitative index, item return dimension quantitative index, item type preference dimension quantitative index, and associated rental behavior dimension quantitative index are respectively expressed by the following formulas: ; ; ; ; ; ; ;

[0015] in, is the total number of leases, is the number of monthly rentals, is the number of days in the month, is the duration of the i-th lease, is the number of rentals on weekdays, For weekend rentals, is the lease start time, For booking time, To return the number of times on time, is the number of damages, is the proportion of users renting the mth item type, For item set With item set The support between The transaction data set.

[0016] In the above-mentioned method for dynamically adjusting rental fees based on user usage behavior, as a preferred scheme, the maximum values ​​of the rental frequency adjustment factor, rental duration adjustment factor, rental time preference adjustment factor, scheduled lead time adjustment factor, item return adjustment factor, item type preference adjustment factor, and associated rental behavior adjustment factor are all 1.

[0017] The present application also provides a rental fee dynamic adjustment device based on user usage behavior, the rental fee dynamic adjustment device comprising: A user data acquisition module, which is used to acquire basic information and rental transaction history data of the user and construct a user behavior model; the user behavior model includes quantitative indicators of at least one dimension; A market data acquisition module, the market data acquisition module is used to acquire market data of the leased object and determine a basic pricing model for the leased object; the basic pricing model includes a basic price of the leased object and a pricing adjustment factor of at least one dimension; A pricing module, the pricing module is used to adjust the pricing of the leased object based on the base price of the leased object in combination with the quantitative indicators of the behavior model and the pricing adjustment factor of the basic pricing model; Among them, the quantitative indicators of at least one dimension correspond one-to-one to the pricing adjustment factors of at least one dimension.

[0018] The present application also provides a device for dynamically adjusting rental fees based on user usage behavior, the device comprising: a memory, a processor, and a dynamic adjustment program stored in the memory and executable on the processor, wherein the dynamic adjustment program, when executed by the processor, implements the dynamic adjustment method as described in any one of claims 1 to 8.

[0019] According to the method, device and equipment for dynamically adjusting rental fees based on user usage behavior of the above-mentioned embodiments, by obtaining the user's basic information and rental transaction history data and building a user's behavior model, the basic pricing model and adjustment factors of the leased item are determined in combination with market data; the rental fees are dynamically adjusted by analyzing the user's usage behavior patterns and market dynamic factors; the quantitative indicators of at least one dimension are matched one-to-one with the corresponding pricing adjustment factors, and the rental price can be flexibly adjusted according to the user's actual behavior characteristics and changes in market conditions. Through this personalized and dynamic pricing strategy, resource utilization is significantly improved, because the rental price is closer to market demand, avoiding idle or excessive use of resources, and providing a more reasonable and transparent pricing system, which promotes the healthy development of the rental market and ensures the balance of interests between rental service providers and users. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Flow chart (I) of the method for automatically transporting assembly parts provided in an embodiment of the present application; Figure 2 Flow chart (II) of the automatic handling method for assembly parts provided in the embodiment of the present application; Figure 3 This is a functional block diagram of the automatic assembly handling device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The present application is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are intended to enable the present application to be better understood. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, and methods. In some cases, some operations related to the present application are not shown or described in the specification, in order to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations based on the description in the specification and the general technical knowledge in the art.

[0022] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0023] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings).

[0024] Please refer to Figure 1 In order to solve the technical problems of insufficient personalization, inaccurate matching and lack of risk control in the existing lease adjustment system mainly by comprehensively applying user profiling technology and leased property matching, a dynamic adjustment method for lease fees based on user usage behavior is provided in an embodiment of the present application, and the adjustment method includes the following steps: Step S101: Obtain basic information and rental transaction history data of a user, and construct a user behavior model; the user behavior model includes quantitative indicators of at least one dimension.

[0025] In some embodiments, with the help of the database of the rental business system, basic user information is widely collected, covering age, gender, occupation, residential area, etc., and historical rental transaction data is accurately extracted, including the start and end time of each rental to the minute, the specific model and specifications of the rental items, and the details of the rental fees. Use data statistics and analysis methods to build a user behavior model. Among them, the quantitative indicators of the rental frequency dimension measure the frequency of user rentals by the ratio of the number of rentals to the duration in a specific period; the quantitative indicators of the rental duration dimension calculate the average rental duration and analyze its distribution; the quantitative indicators of the rental time preference dimension count the rental ratio of weekdays and weekends, daytime and nighttime, etc.; the quantitative indicators of the booking lead time dimension determine the average lead time and regularity of users booking rental items; the quantitative indicators of the item return situation dimension calculate the proportion of on-time return, early return, overdue return and item damage; the quantitative indicators of the item type preference dimension calculate the proportion of users' rental times of various types of rental items; the quantitative indicators of the associated rental behavior dimension use the association rule mining algorithm to find the rental association strength of the item combination.

[0026] Step S102: Obtain market data of the leased object and determine a basic pricing model for the leased object. The basic pricing model includes a basic price of the leased object and a pricing adjustment factor of at least one dimension.

[0027] In some embodiments, market data of leased items are collected from multiple channels, including but not limited to detailed research on competitors' leasing prices, covering their different leasing package prices, pricing strategies for different customer groups, etc.; pay close attention to fluctuations in raw material prices, such as the impact of price trends of raw materials such as metals and plastics on the cost of leased items; analyze seasonal demand changes, such as the substantial increase in demand for outdoor equipment leasing during the peak tourist season; consider the potential impact of the macroeconomic environment and policies and regulations on the leasing business, such as tax policy adjustments that may lead to cost changes. Based on these data, the base price of the leased item is determined, taking into account various expenses such as procurement costs, maintenance costs, warehousing costs, and transportation costs, and setting pricing adjustment factors in multiple dimensions. For example, the leasing frequency adjustment factor adjusts the price according to the frequency of user leasing; the leasing duration adjustment factor gives different price discounts to long-term or short-term users; the leasing time preference adjustment factor adjusts the price during peak or low leasing periods; the booking lead time adjustment factor provides price discounts to users who make advance reservations; the item return adjustment factor provides price rewards and penalties based on the situation of the user returning the item; the item type preference adjustment factor adjusts the price due to the market supply and demand and value differences of the item type; and the associated leasing behavior adjustment factor provides package discounts for users who rent associated items at the same time.

[0028] Step S103: Based on the base price of the leased object, combined with the quantitative index of the behavior model and the pricing adjustment factor of the basic pricing model, the pricing of the leased object is adjusted.

[0029] In some embodiments, starting from determining a good base price for the leased item, the quantitative indicators of each dimension in the user behavior model are combined with the corresponding pricing adjustment factors of the basic pricing model. When the user's rental frequency is high and meets the preferential conditions of the rental frequency adjustment factor, the rental price is reduced; if the rental duration is long, the rental fee per unit time is reduced according to the rental duration adjustment factor; during the peak period of the preferred rental time, the price is increased, and during the off-peak period, the price is reduced; for users with a long booking lead time, a price discount is given according to the booking lead time adjustment factor; users with good item return conditions enjoy the price reduction brought by the item return adjustment factor; for users who prefer high-value or popular rental items, the price is adjusted according to the item type preference adjustment factor; when a user rents a combination of items with strong correlation, a package preferential price is given by associating the rental behavior adjustment factor, thereby achieving accurate pricing adjustment of the leased items to better adapt to market demand and user behavior characteristics, improve the efficiency and competitiveness of the rental business, and at the same time meet the personalized needs of users and improve user satisfaction and loyalty.

[0030] Among them, the quantitative indicators of at least one dimension correspond one-to-one to the pricing adjustment factors of at least one dimension.

[0031] In the above-mentioned method for dynamically adjusting rental fees based on user usage behavior, as a preferred solution, the quantitative indicators of at least one dimension include quantitative indicators of at least seven dimensions, and the pricing adjustment factors of at least one dimension include adjustment factors of at least seven dimensions.

[0032] The quantitative indicators of the seven dimensions include quantitative indicators of rental frequency dimension, quantitative indicators of rental duration dimension, quantitative indicators of rental time preference dimension, quantitative indicators of booking lead time dimension, quantitative indicators of item return status dimension, quantitative indicators of item type preference dimension, and quantitative indicators of associated rental behavior dimension.

[0033] The adjustment factors in seven dimensions include rental frequency adjustment factor, rental duration adjustment factor, rental time preference adjustment factor, booking lead time adjustment factor, item return adjustment factor, item type preference adjustment factor, and associated rental behavior adjustment factor.

[0034] Please refer to Figure 2 In the above-mentioned method for dynamically adjusting the rental fee based on user usage behavior, as a preferred solution, the basic pricing model for the leased item is determined, including the following steps: Step S201: Determine the base price of the leased item.

[0035] Step S202: determine the pricing adjustment factors of the seven dimensions of the leased object, and associate the pricing adjustment factors of the seven dimensions with the quantitative indicators of the seven dimensions respectively.

[0036] In the above-mentioned method for dynamically adjusting rental fees based on user usage behavior, as a preferred solution, the step of determining the base price of the rental item includes: The total cost of the lease is calculated using the following formula: .

[0037] The base price of the leased property is determined by the following formula: .

[0038] in, is the total cost, As the base price, For the purchase cost, For maintenance costs, For storage costs, For transportation costs, is the expected profit margin, is the market competition adjustment coefficient.

[0039] In the above-mentioned method for dynamically adjusting rental fees based on user usage behavior, as a preferred solution, the step of constructing a user behavior model includes: The behavior model is expressed by the following formula: ; in, is the user behavior vector, They respectively represent the quantitative indicators of rental frequency dimension, rental duration dimension, rental time preference dimension, booking lead time dimension, item return dimension, item type preference dimension, and associated rental behavior dimension.

[0040] In the above-mentioned method for dynamically adjusting the rental fee based on the user's usage behavior, as a preferred solution, the pricing adjustment of the leased item is performed by combining the quantitative index of the behavior model and the pricing adjustment factor of the basic pricing model, including: The final price of the leased property is obtained by the following formula: ; in, is the lease frequency adjustment factor, is the lease duration adjustment factor, is the rental time preference adjustment factor, is the scheduling lead time adjustment factor, The item return adjustment factor. ) is the item type preference adjustment factor, Adjustment factor for associated leasing activities.

[0041] In the above-mentioned method for dynamically adjusting rental fees based on user usage behavior, as a preferred solution, the rental frequency dimension quantitative index, rental duration dimension quantitative index, rental time preference dimension quantitative index, scheduled lead time dimension quantitative index, item return dimension quantitative index, item type preference dimension quantitative index, and associated rental behavior dimension quantitative index are respectively expressed by the following formulas: ; ; ; ; ; ; ; in, is the total number of leases, is the number of monthly rentals, is the number of days in the month, is the duration of the i-th lease, is the number of rentals on weekdays, For weekend rentals, is the lease start time, For booking time, To return the number of times on time, is the number of damages, is the proportion of users renting the mth item type, For item set With item set The support between The transaction data set.

[0042] In the above-mentioned method for dynamically adjusting rental fees based on user usage behavior, as a preferred scheme, the maximum values ​​of the rental frequency adjustment factor, rental duration adjustment factor, rental time preference adjustment factor, scheduled lead time adjustment factor, item return adjustment factor, item type preference adjustment factor, and associated rental behavior adjustment factor are all 1.

[0043] In some embodiments, the adjustment factors of the seven dimensions are determined as follows: Rental frequency adjustment factor: It is set based on the ratio between the number of rentals and the duration of a user in a specific time period. When the user's rental frequency is higher than the market average by a certain margin, it indicates that the user has a strong demand for rental services and is highly loyal. At this time, the factor is set to a value less than 1, such as 0.8, and a corresponding price discount is given; if the rental frequency is at or below the average level, it is set to 1 and the original price is maintained, thereby encouraging high-frequency rental users and improving user stickiness and equipment utilization.

[0044] Lease duration adjustment factor: Determined based on the duration of a single lease by the user. If the lease duration reaches or exceeds the preset long-term lease duration standard, based on the cost advantage of long-term leases and market competition, this factor is set to a lower value, such as 0.7, to reduce the unit time lease price and attract long-term lease customers; conversely, for shorter lease durations, due to relatively high operating costs, it is set to a value greater than 1, such as 1.2, to ensure profit margins and reasonably reflect the cost structure and market value of short-term leases.

[0045] Leasing time preference adjustment factor: set according to the characteristics of the time period in which the leasing business is located. During peak periods with strong market demand, such as weekends, holidays, and specific seasons, in order to balance supply and demand and increase revenue, the factor is set to a value greater than 1, such as 1.3; and during non-peak periods with relatively low demand, such as daytime on weekdays and off-seasons, in order to stimulate leasing demand, it is set to a value less than 1, such as 0.8, to optimize resource allocation and improve overall efficiency through time-sharing pricing.

[0046] Booking lead time adjustment factor: based on the time interval between the user's booking of the rental item and the actual start of the rental. If the user makes a reservation long in advance, such as more than 5 days, this will help the rental company to arrange inventory and resource scheduling in advance and reduce operational risks. At this time, the factor is set to 0.9 or a value less than 1 to give a price discount; if the booking time is late and close to the rental time, it may put pressure on the company's resource allocation, so it is set to 1 or a value slightly greater than 1, such as 1.1, and the price is appropriately increased to ensure a balance between service quality and cost.

[0047] Item return adjustment factor: This factor is set by counting the return performance of items in previous rentals. If the user can always return the items on time and without damage, in order to encourage this good behavior, this factor will be set to 0.95 or other values ​​less than 1 for the next rental, and a certain price reduction will be given; if there is an overdue return, different intervals will be divided according to the number of overdue days, such as 1.1 for 1-3 days overdue, 1.3 for 3-7 days overdue, etc., and the fee will be increased accordingly; for damaged items, a higher value greater than 1 will be set according to the degree of damage, such as 1.5 for severe damage, in order to compensate the company for possible losses and additional management costs, and regulate user return behavior.

[0048] Item type preference adjustment factor: Determined based on the market demand, cost, and value of the rental item. For items with high market demand, relatively high cost or value, such as high-end electronic products and luxury cars, the factor is set to a value greater than 1, such as 1.4, to moderately increase the rental price, taking into account their scarcity and maintenance costs; for popular, highly competitive, and low-cost items, such as ordinary office desks and chairs, common household appliances, etc., in order to attract more users, it is set to a value less than 1, such as 0.8, to adapt to market supply and demand conditions and value laws, and achieve differentiated pricing.

[0049] Adjustment factor for associated rental behavior: set based on the strength of association between the user's rental item combination. When a user rents a combination of strongly associated items at the same time, such as "camera-lens-tripod" and "grill-charcoal-grill tools", after data analysis to determine the rental frequency and commercial value of these combinations, in order to promote such associated rental business, this factor is set to a value less than 1, such as 0.85, to give a package discount price; if the user only rents a single item, it is set to 1 and calculated at the normal price. By mining the association between items, the user's rental value and the company's average order value are increased, thereby improving overall competitiveness.

[0050] Please refer to Figure 3The present application also provides a rental fee dynamic adjustment device based on user usage behavior, and the rental fee dynamic adjustment device includes: A user data acquisition module 301, which is used to acquire basic information and rental transaction history data of the user and construct a user behavior model; the user behavior model includes quantitative indicators of at least one dimension; A market data acquisition module 302, the market data acquisition module 302 is used to acquire market data of the leased object and determine a basic pricing model for the leased object; the basic pricing model includes a basic price of the leased object and a pricing adjustment factor of at least one dimension; A pricing module 303, which is used to adjust the pricing of the leased object based on the base price of the leased object in combination with the quantitative indicators of the behavior model and the pricing adjustment factor of the basic pricing model; Among them, the quantitative indicators of at least one dimension correspond one-to-one to the pricing adjustment factors of at least one dimension.

[0051] The present application also provides a device for dynamically adjusting rental fees based on user usage behavior, the device comprising: a memory, a processor, and a dynamic adjustment program stored in the memory and executable on the processor, wherein the dynamic adjustment program, when executed by the processor, implements the dynamic adjustment method as described in any one of claims 1 to 8.

[0052] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above-mentioned embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above-mentioned functions can be implemented. In addition, when all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and can be downloaded or copied and saved in the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above-mentioned embodiments can be implemented.

[0053] The above specific examples are used to illustrate the present application, which is only used to help understand the present application and is not intended to limit the present application. For technicians in the technical field to which the present application belongs, they can also make some simple deductions, deformations or substitutions based on the ideas of the present application.

Claims

1. A method for dynamically adjusting rental fees based on user usage behavior, characterized in that: The adjustment method comprises: Obtaining the user's basic information and rental transaction history data, and constructing a user behavior model; the user behavior model includes at least one dimensional quantitative indicator; Acquire market data of the leased object and determine a basic pricing model for the leased object; the basic pricing model includes a basic price of the leased object and a pricing adjustment factor of at least one dimension; Based on the base price of the leased object, combined with the quantitative indicators of the behavior model and the pricing adjustment factors of the basic pricing model, adjust the pricing of the leased object; Among them, the quantitative indicators of at least one dimension correspond one-to-one to the pricing adjustment factors of at least one dimension.

2. The method for dynamically adjusting rental fees based on user usage behavior as claimed in claim 1, characterized in that: The quantitative indicators of at least one dimension include quantitative indicators of at least seven dimensions, and the pricing adjustment factors of at least one dimension include adjustment factors of at least seven dimensions; The quantitative indicators of the seven dimensions include quantitative indicators of rental frequency dimension, quantitative indicators of rental duration dimension, quantitative indicators of rental time preference dimension, quantitative indicators of scheduled lead time dimension, quantitative indicators of item return dimension, quantitative indicators of item type preference dimension, and quantitative indicators of associated rental behavior dimension; The adjustment factors in seven dimensions include rental frequency adjustment factor, rental duration adjustment factor, rental time preference adjustment factor, booking lead time adjustment factor, item return adjustment factor, item type preference adjustment factor, and associated rental behavior adjustment factor.

3. The method for dynamically adjusting rental fees based on user usage behavior as claimed in claim 2, characterized in that: The basic pricing model for determining the leased property includes: Determine the base price of the leased property; Determine the pricing adjustment factors of the seven dimensions of the leased property, and associate the pricing adjustment factors of the seven dimensions with the quantitative indicators of the seven dimensions respectively.

4. The method for dynamically adjusting rental fees based on user usage behavior as claimed in claim 3, characterized in that: The basic price of the leased property shall include: The total cost of the lease is calculated using the following formula: ； The base price of the leased property is determined by the following formula: ； in, is the total cost, As the base price, For the purchase cost, For maintenance costs, For storage costs, For transportation costs, is the expected profit margin, is the market competition adjustment coefficient.

5. The method for dynamically adjusting rental fees based on user usage behavior as claimed in claim 3, characterized in that: The constructing of the user's behavior model includes: The behavior model is expressed by the following formula: ； in, is the user behavior vector, They respectively represent the quantitative indicators of rental frequency dimension, rental duration dimension, rental time preference dimension, booking lead time dimension, item return dimension, item type preference dimension, and associated rental behavior dimension.

6. The method for dynamically adjusting rental fees based on user usage behavior as claimed in claim 5, characterized in that: The quantitative index of the combined behavior model and the pricing adjustment factor of the basic pricing model are used to adjust the pricing of the leased item, including: The final price of the leased property is obtained by the following formula: ； in, is the lease frequency adjustment factor, is the lease duration adjustment factor, is the rental time preference adjustment factor, is the scheduling lead time adjustment factor, The item return adjustment factor. is the item type preference adjustment factor, Adjustment factor for associated leasing activities.

7. The method for dynamically adjusting rental fees based on user usage behavior as claimed in claim 6, characterized in that: The quantitative indicators of the rental frequency dimension, the rental duration dimension, the rental time preference dimension, the scheduled lead time dimension, the item return dimension, the item type preference dimension, and the associated rental behavior dimension are respectively expressed by the following formulas: ； ； ， ， ； ， ； ， ， ； ； ； in, is the total number of leases, is the number of monthly rentals, is the number of days in the month, is the duration of the i-th lease, is the number of rentals on weekdays, For weekend rentals, is the lease start time, For booking time, To return the number of times on time, is the number of damages, is the proportion of users renting the mth item type, For item set With item set The support between The transaction data set.

8. The method for dynamically adjusting rental fees based on user usage behavior as claimed in claim 7, characterized in that: The maximum values ​​of the rental frequency adjustment factor, rental duration adjustment factor, rental time preference adjustment factor, booking lead time adjustment factor, item return adjustment factor, item type preference adjustment factor, and associated rental behavior adjustment factor are all 1.

9. A device for dynamically adjusting rental fees based on user usage behavior, characterized in that: The rental fee dynamic adjustment device comprises: A user data acquisition module, which is used to acquire basic information and rental transaction history data of the user and construct a user behavior model; the user behavior model includes quantitative indicators of at least one dimension; A market data acquisition module, the market data acquisition module is used to acquire market data of the leased object and determine a basic pricing model for the leased object; the basic pricing model includes a basic price of the leased object and a pricing adjustment factor of at least one dimension; A pricing module, the pricing module is used to adjust the pricing of the leased object based on the base price of the leased object, combined with the quantitative indicators of the behavior model and the pricing adjustment factor of the basic pricing model; Among them, the quantitative indicators of at least one dimension correspond one-to-one to the pricing adjustment factors of at least one dimension.

10. A device for dynamically adjusting rental fees based on user usage behavior, characterized in that: The device comprises: a memory, a processor, and a dynamic adjustment program stored in the memory and executable on the processor, wherein the dynamic adjustment program implements the dynamic adjustment method according to any one of claims 1 to 8 when executed by the processor.

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