Online office equipment leasing method and system

Abstract

The present application proposes an online office equipment leasing method and system, which relates to the technical field of leasing information analysis. The online office equipment leasing method includes: obtaining basic leasing information of the leased item and obtaining a user portrait of the user; determining the leasing relationship data between the user and the leased item based on the leasing record; training a leasing risk prediction model based on the leasing relationship data, the basic leasing information and the user portrait; receiving the target user portrait of the leasing user and the target leasing information of the target leasing item in real time; inputting the user portrait and the target leasing information into the leasing risk prediction model to obtain the risk value of the target leasing item corresponding to the target leasing information when the leasing user seeks to rent the target leasing item; and determining the leasing management strategy corresponding to the target leasing item based on the risk value. The present application can improve the accuracy of leasing information management.

Description

Technical Field

[0001] The present application relates to the technical field of leasing information management, and in particular to an online office equipment leasing method and system. Background Art

[0002] At present, organizations or individuals operating leasing businesses usually need to evaluate the leasing needs of users and manage information related to leasing behavior to reduce the risk probability of leasing behavior, thereby ensuring the interests of the individuals or organizations operating the leasing business and the interests of users.

[0003] However, since leasing risks are dynamically changing, failure to comprehensively consider the various attribute information of the tenant and the leased item, as well as the relationship between the tenant and the leased item, may be detrimental to leasing information management and reduce the accuracy of leasing information management. Summary of the invention

[0004] In view of the above, it is necessary to propose an online office equipment leasing method and system to solve the technical problem of low accuracy of leasing information management and control.

[0005] The present application provides an online office equipment leasing method, which is applied to electronic devices, and the method includes: obtaining basic leasing information of the leased item and obtaining a user portrait of the user; determining the leasing relationship data between the user and the leased item based on the leasing record; training a leasing risk prediction model based on the leasing relationship data, the basic leasing information and the user portrait; receiving in real time a target user portrait of a user seeking to lease and target leasing information of a target leasing item; inputting the user portrait and the target leasing information into the leasing risk prediction model to obtain a risk value when the user seeking to lease seeks to lease a target leasing item corresponding to the target leasing information; and determining a leasing management strategy corresponding to the target leasing item based on the risk value.

[0006] In some embodiments, determining the rental relationship data between the user and the leased item based on the rental record includes: determining the rental rate and vacancy rate of each of the leased items based on the rental record, and determining a first risk value of the corresponding leased item based on the rental rate and the vacancy rate; determining the renewal rate and termination rate of each of the user based on the rental record, and determining a second risk value of the corresponding user based on the renewal rate and the termination rate; and determining the rental relationship data between the user and the leased item based on the first risk value and the second risk value.

[0007] In some embodiments, determining the rental relationship data between the user and the rental item based on the first risk value and the second risk value includes: determining the average of the first risk value and the second risk value; and determining the average as the rental relationship data between the user and the rental item.

[0008] In some embodiments, the training of the leasing risk prediction model based on the leasing relationship data, the basic leasing information and the user portrait includes: constructing sample data based on the basic leasing information and the user portrait, and constructing label data based on the leasing relationship data; determining the confidence corresponding to each sample data based on the user portrait and the frequency of the leasing information in the leasing record; inputting the sample data into a pre-constructed initial prediction model to obtain predicted risk data corresponding to the sample data; determining the loss value of the initial prediction model based on the predicted risk data and the label data; when the loss value is greater than a preset termination threshold, updating the initial prediction model based on a back propagation algorithm until the loss value is less than or equal to the termination threshold, stopping the update and obtaining a leasing risk prediction model trained to a convergence state.

[0009] In some embodiments, determining the confidence level corresponding to each sample data according to the user portrait and the frequency of the rental information in the rental record includes: confirming a first frequency of the user appearing in the rental record; confirming a second frequency of the rental item corresponding to the rental information appearing in the rental record; confirming a third frequency of the user renting the rental item corresponding to the rental information in the rental record; determining the confidence level corresponding to the sample data according to the first frequency, the second frequency, and the third frequency includes: ; Wherein, A represents the first frequency; B represents the second frequency; C represents the third frequency; S represents the maximum value among the first frequency, the second frequency and the third frequency.

[0010] In some embodiments, determining the loss value of the initial prediction model according to the predicted risk data and the label data includes: ; Wherein, Loss represents the loss value of the initial prediction model; R represents the confidence level; e represents a preset natural constant; P represents the label data; and L represents the predicted risk data.

[0011] In some embodiments, determining the rental management strategy corresponding to the target rental item according to the risk value includes: when the risk value is greater than a preset first threshold, outputting a first warning message, wherein the first warning message is used to indicate that the renting user is prohibited from renting the target rental item.

[0012] In some embodiments, determining the rental management strategy corresponding to the target rental item based on the risk value includes: when the risk value is greater than a preset second threshold and the risk value is less than or equal to a preset first threshold, outputting a second warning message, wherein the second warning message is used to indicate that the duration for which the user seeking to rent the target rental item is less than or equal to a preset duration.

[0013] An embodiment of the present application also provides a rental management system, the device comprising: an acquisition module, used to acquire basic rental information of the rental item and a user portrait of the user; a determination module, used to determine the rental relationship data between the user and the rental item based on the rental record; the determination module is also used to train a rental risk prediction model based on the rental relationship data, the basic rental information and the user portrait; the acquisition module is also used to receive in real time the target user portrait of the user seeking to rent and the target rental information of the target rental item; the determination module is also used to input the user portrait and the target rental information into the rental risk prediction model to obtain the risk value when the user seeking to rent the target rental item corresponding to the target rental information; the determination module is also used to determine the rental management strategy corresponding to the target rental item based on the risk value.

[0014] In some embodiments, the determination module is also used to: determine the rental rate and vacancy rate of each of the leased items based on the rental records, and determine the first risk value of the corresponding leased item based on the rental rate and the vacancy rate; determine the renewal rate and termination rate of each of the users based on the rental records, and determine the second risk value of the corresponding user based on the renewal rate and the termination rate; determine the rental relationship data between the user and the leased item based on the first risk value and the second risk value.

[0015] It can be seen from the above technical solutions that the embodiment of the present application obtains and determines the rental relationship data between the user and the rental item according to the data related to the user and the rental item recorded in the rental record, so as to quantitatively characterize the risk level of the user's rental of each rental item, and improve the accuracy of rental information management. Then, the rental risk prediction model is trained based on the rental relationship data, the basic rental information of the rental item, and the user portrait, which is used to predict the user portrait of the user seeking to rent and the target rental information of the target rental item received in real time, so as to obtain the risk value of the target rental item corresponding to the target rental information when the user seeking to rent the rental information is rented. Finally, the rental management strategy corresponding to the target rental item is determined according to the risk value, so as to control the user's behavior of renting the rental item, and improve the accuracy of rental information control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1This is an application scenario diagram of an online office equipment leasing method provided in one embodiment of the present application.

[0017] Figure 2 This is a flow chart of an online office equipment leasing method provided in one embodiment of the present application.

[0018] Figure 3 It is a functional module diagram of a rental management system provided by one embodiment of the present application.

[0019] Figure 4 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to more clearly understand the purpose, features and advantages of the present application, the present application is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other. In the following description, many specific details are set forth to facilitate a full understanding of the present application, and the embodiments described are only a part of the embodiments of the present application, rather than all of the embodiments.

[0021] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used herein in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0023] The embodiment of the present application provides an online office equipment rental method, which can be applied to one or more electronic devices. The electronic device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0024] An electronic device can be any electronic product that can interact with customers, such as personal computers, tablet computers, smart phones, personal digital assistants (PDAs), game consoles, Internet Protocol Television (IPTV), smart wearable devices, etc.

[0025] The electronic device may also include a network device and / or a client device, wherein the network device includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud consisting of a large number of hosts or network servers based on cloud computing.

[0026] The networks where electronic devices are located include but are not limited to the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0027] like Figure 1 As shown, an online office equipment leasing method provided by the present application can be applied to an electronic device 100, and the electronic device 100 is communicatively connected with a server 200. The electronic device 100 is used to obtain basic leasing information of the leased item from the server 200, and obtain a user portrait of the user; determine the leasing relationship data between the user and the leased item according to the leasing record; train a leasing risk prediction model according to the leasing relationship data, the basic leasing information and the user portrait; receive the target user portrait of the leasing user and the target leasing information of the target leasing item in real time; input the user portrait and the target leasing information into the leasing risk prediction model, and obtain the risk value when the leasing user seeks to rent the target leasing item corresponding to the target leasing information; and determine the leasing management strategy corresponding to the target leasing item according to the risk value.

[0028] like Figure 2 The figure is a flowchart of an online office equipment leasing method provided in an embodiment of the present application. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted. The online office equipment leasing method provided in an embodiment of the present application includes the following steps.

[0029] S20, obtaining basic rental information of the rental item and obtaining a user profile of the user.

[0030] In one embodiment of the present application, in order to improve the efficiency and accuracy of the management and control of rental information, the basic rental information of the rental items and the user profile of the user can be obtained in advance. The rental information can be information related to the rental behavior in the rental platform. For example, the rental information can be information related to the rental behavior of user 1 for rental item 2; the rental information can also be information related to the rental behavior of user 2 when renting rental item 3.

[0031] In one embodiment of the present application, the basic rental information of the leased item is used to characterize the attribute information of the leased item itself. Exemplarily, the basic rental information can be a descriptive text related to the name, quantity, structure or appearance of the leased item. Exemplarily, the leased item can be various hardware equipment required for online office, for example, the hardware equipment can be electronic devices such as computers, mice, keyboards, monitors or tablets, and the hardware equipment can also be appliances such as desks and office chairs; the leased item can also be real estate such as venues or houses required for office. Exemplarily, when the leased item is hardware equipment required for online office, the description text includes detailed information such as the equipment name, equipment number, equipment value and equipment appearance of the hardware equipment; when the leased item is real estate such as houses, the description text includes detailed information such as the location of the house, the area of ​​the house, the structure of the house, the decoration of the house and the condition of the house facilities.

[0032] In an embodiment of the present application, the user may be a person or organization with leasing behavior. The user portrait corresponding to the user is a labeled data obtained by quantitative analysis based on the user's social attributes, living habits, leasing behavior and other information, which is used to characterize the various attributes of the user with leasing behavior.

[0033] In this way, by collecting information related to user portraits and leasing behaviors, data support is provided for the subsequent quantitative evaluation of leasing information management, which can improve the accuracy of leasing information management.

[0034] S21, determining the rental relationship data between the user and the rental item according to the rental record.

[0035] In one embodiment of the present application, in order to determine the possibility of risks incurred when a user rents each rental item, the rental relationship data between the user and the rental item can be determined based on the rental record, wherein the rental relationship data is used to characterize the possibility of risks incurred when the user rents the rental item. Among them, the rental record can be text data in any format, and the present application does not limit the form of the rental record. Specifically, the rental record is used to record information related to the behavior of all users in renting the rental items. Exemplarily, the rental record includes: user name, user ID, user renewal rate, return rate, rental item name, rental item ID, rental duration, rental rate, vacancy rate, and rental price.

[0036] In one embodiment of the present application, the determination of the rental relationship data between the user and the leased item based on the rental record includes: determining the rental rate and vacancy rate of each of the leased items based on the rental record, and determining the first risk value of the corresponding leased item based on the rental rate and the vacancy rate; determining the renewal rate and the termination rate of each user based on the rental record, and determining the second risk value of the corresponding user based on the renewal rate and the termination rate; determining the rental relationship data between the user and the leased item based on the first risk value and the second risk value. Specifically, the rental rate, vacancy rate, renewal rate, termination rate and other data in the rental record can be determined using a text recognition algorithm, wherein the text recognition algorithm can be any neural network model or optical character recognition algorithm, which is not limited in the present application.

[0037] Specifically, the higher the rental rate of the leased item, the higher the probability that the user will rent the leased item, and therefore the lower the risk of the user renting the leased item; the higher the vacancy rate of the leased item, the lower the probability that the user will rent the leased item, and therefore the higher the risk of the user renting the leased item. Therefore, the first risk value of the leased item can be determined according to the rental rate and the lease rate of the leased item, and the rental rate is inversely proportional to the first risk value, and the vacancy rate is proportional to the first risk value. The first risk value is used to characterize the probability of a risk situation occurring when the user rents the leased item. For example, the risk situation may be that the leased item is damaged or the leased item is used improperly, causing harm to the user.

[0038] Specifically, the higher the renewal rate of the user, the stronger the user's willingness to rent the leased item, and therefore the lower the risk of the user renting the leased item; the higher the return rate of the user, the lower the probability of the user renting the leased item, and therefore the higher the risk of the user renting the leased item. Therefore, the second risk value of the user can be determined according to the renewal rate and return rate of the user, and the renewal rate is inversely proportional to the second risk value, and the return rate is proportional to the second risk value. The second risk value is used to characterize the probability of a risk situation occurring when the user rents the leased item. For example, the risk situation may be that the leased item is damaged or the leased item is used improperly, causing harm to the user.

[0039] In an embodiment of the present application, determining the rental relationship data between the user and the rental item according to the first risk value and the second risk value includes: determining the average of the first risk value and the second risk value; and determining the average as the rental relationship data between the user and the rental item. Specifically, the rental relationship data is used to characterize the possibility of risk when the user rents the rental item.

[0040] S22, training a leasing risk prediction model according to the leasing relationship data, the basic leasing information and the user portrait.

[0041] In one embodiment of the present application, the training of the rental risk prediction model according to the rental relationship data, the basic rental information and the user portrait includes: constructing sample data according to the basic rental information and the user portrait, and constructing label data according to the rental relationship data; determining the confidence corresponding to each of the sample data according to the user portrait and the frequency of the rental information in the rental record; inputting the sample data into the pre-constructed initial prediction model to obtain the predicted risk data corresponding to the sample data; determining the loss value of the initial prediction model according to the predicted risk data and the label data; when the loss value is greater than a preset termination threshold, updating the initial prediction model based on the back propagation algorithm until the loss value is less than or equal to the termination threshold, stopping the update and obtaining the rental risk prediction model trained to a convergence state. Specifically, when the loss value is large, it indicates that the accuracy of the predicted risk data predicted by the initial prediction model is low, so the initial prediction model can be updated based on the back propagation algorithm, thereby improving the performance of the initial prediction model. Among them, the initial prediction model can be a pre-constructed fully connected neural network, convolutional neural network or long short-term memory model, etc., and the present application does not limit the specific category of the initial prediction model.

[0042] In an embodiment of the present application, determining the confidence level corresponding to each sample data according to the user portrait and the frequency of the rental information in the rental record includes: confirming a first frequency of the user appearing in the rental record; confirming a second frequency of the rental item corresponding to the rental information appearing in the rental record; confirming a third frequency of the user renting the rental item corresponding to the rental information in the rental record; determining the confidence level corresponding to the sample data according to the first frequency, the second frequency, and the third frequency, includes: ; Wherein, A represents the first frequency; B represents the second frequency; C represents the third frequency; S represents the maximum value among the first frequency, the second frequency and the third frequency.

[0043] In an embodiment of the present application, determining the loss value of the initial prediction model according to the predicted risk data and the label data includes: ; Wherein, Loss represents the loss value of the initial prediction model; R represents the confidence; e represents the preset natural constant; P represents the label data; L represents the predicted risk data. When the loss value is large, it indicates that the accuracy of the predicted risk data predicted by the initial prediction model is low. Therefore, the initial prediction model can be updated based on the back propagation algorithm, thereby improving the accuracy of subsequent predictions of user portraits and target rental information received in real time.

[0044] S23, receiving in real time the target user portrait of the rental user and the target rental information of the target rental item.

[0045] In one embodiment of the present application, the user seeking to rent can be any individual or organization with rental needs, and the user profile of the user seeking to rent is used to characterize the various attribute characteristics of the user seeking to rent. The target rental item can be the item that the user seeking to rent needs to rent, and the target rental information can be the attribute data corresponding to the target rental item. For example, the target rental information can be a descriptive text related to the name, quantity, structure or appearance of the target rental item.

[0046] S24, inputting the user portrait and the target rental information into the rental risk prediction model to obtain the risk value when the renting user rents the target rental item corresponding to the target rental information.

[0047] In one embodiment of the present application, in order to use the rental risk prediction model to quantitatively analyze the risk of a user renting a target rental item, the target user profile and target rental information of the renting user can be input into the rental risk prediction model to obtain the risk value of the target rental item corresponding to the target rental information of the renting user. This can improve the accuracy of rental information management.

[0048] S25, determining a rental management strategy corresponding to the target rental item according to the risk value.

[0049] In one embodiment of the present application, the determining of the rental management strategy corresponding to the target rental item according to the risk value includes: when the risk value is greater than a preset first threshold, outputting a first warning message, the first warning message being used to indicate that the renting user is prohibited from renting the target rental item. Specifically, when the risk value is greater than the preset first threshold, it indicates that the risk generated when the renting user rents the target rental item is relatively high, and the renting user may be prohibited from renting the target rental item.

[0050] In one embodiment of the present application, the rental management strategy corresponding to the target rental item is determined according to the risk value, including: when the risk value is greater than a preset second threshold value, and the risk value is less than or equal to a preset first threshold value, a second warning message is output, and the second warning message is used to indicate that the duration of the rental of the target rental item by the user seeking the rental is less than or equal to the preset duration. Specifically, when the risk value is less than or equal to the first threshold value and greater than the second threshold value, it indicates that there is a risk when the user seeking the rental rents the target rental item, and the probability of risk can be reduced by limiting the user's rental duration.

[0051] In one embodiment of the present application, when the risk value is less than or equal to the preset second threshold, it indicates that the risk of the renting user renting the target rental item is small, and there is no need to control the information of the renting user renting the target rental item.

[0052] It can be seen from the above technical solutions that the embodiment of the present application obtains and determines the rental relationship data between the user and the rental item according to the data related to the user and the rental item recorded in the rental record, so as to quantitatively characterize the risk level of the user's rental of each rental item, and improve the accuracy of rental information management. Then, the rental risk prediction model is trained based on the rental relationship data, the basic rental information of the rental item, and the user portrait, which is used to predict the user portrait of the user seeking to rent and the target rental information of the target rental item received in real time, so as to obtain the risk value of the target rental item corresponding to the target rental information when the user seeking to rent the rental information is rented. Finally, the rental management strategy corresponding to the target rental item is determined according to the risk value, so as to control the user's behavior of renting the rental item, and improve the accuracy of rental information control.

[0053] See also Figure 3 , Figure 3 3 is a functional module diagram of a rental management system provided in an embodiment of the present application. A rental management system 31 includes an acquisition module 310 and a determination module 311. The module / unit referred to in the present application refers to a series of computer-readable instruction segments that can be executed by the processor 13 and can complete fixed functions, which are stored in the memory 12. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0054] The acquisition module 310 is used to acquire basic rental information of the rental items and acquire a user profile of the user.

[0055] The determining module 311 is used to determine the rental relationship data between the user and the rental item according to the rental record.

[0056] The determination module 311 is further used to train a leasing risk prediction model based on the leasing relationship data, the basic leasing information and the user portrait.

[0057] The acquisition module 310 is also used to receive the target user portrait of the rental user and the target rental information of the target rental item in real time.

[0058] The determination module 311 is further used to input the user portrait and the target rental information into the rental risk prediction model to obtain the risk value when the renting user rents the target rental item corresponding to the target rental information.

[0059] The determination module 311 is further configured to determine a rental management strategy corresponding to the target rental item according to the risk value.

[0060] In some embodiments, the determination module 311 is also used to: determine the rental rate and vacancy rate of each of the leased items based on the rental records, and determine the first risk value of the corresponding leased item based on the rental rate and the vacancy rate; determine the renewal rate and termination rate of each of the users based on the rental records, and determine the second risk value of the corresponding user based on the renewal rate and the termination rate; determine the rental relationship data between the user and the leased item based on the first risk value and the second risk value.

[0061] See also Figure 4 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 100 includes a memory 12 and a processor 13. The memory 12 is used to store computer-readable instructions, and the processor 13 is used to execute the computer-readable instructions stored in the memory to implement an online office equipment rental method described in any of the above embodiments.

[0062] In an embodiment of the present application, the electronic device 100 further includes a bus, a computer program stored in the memory 12 and executable on the processor 13, such as a rental management program.

[0063] Figure 4 Only the electronic device 100 having the memory 12 and the processor 13 is shown, and those skilled in the art can understand that Figure 4 The structure shown does not constitute a limitation on the electronic device 100 , and the electronic device 100 may include fewer or more components than shown in the figure, or combine some components, or arrange the components differently.

[0064] Combination Figure 2The memory 12 in the electronic device 100 stores a plurality of computer-readable instructions to implement an online office equipment leasing method, and the processor 13 can execute the plurality of instructions to implement: obtaining basic leasing information of the leased item and obtaining a user portrait of the user; determining the leasing relationship data between the user and the leased item based on the leasing record; training a leasing risk prediction model based on the leasing relationship data, the basic leasing information and the user portrait; receiving in real time the target user portrait of the user seeking to lease and the target leasing information of the target leasing item; inputting the user portrait and the target leasing information into the leasing risk prediction model to obtain the risk value when the user seeking to lease seeks to lease the target leasing item corresponding to the target leasing information; and determining the leasing management strategy corresponding to the target leasing item based on the risk value.

[0065] Specifically, the specific implementation method of the processor 13 for the above instructions can refer to Figure 2 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0066] Those skilled in the art will appreciate that the schematic diagram is merely an example of the electronic device 100 and does not constitute a limitation on the electronic device 100. The electronic device 100 may be a bus-type structure or a star-type structure. The electronic device 100 may also include more or less other hardware or software than shown in the diagram, or a different arrangement of components. For example, the electronic device 100 may also include input and output devices, network access devices, etc.

[0067] It should be noted that the electronic device 100 is only an example, and other existing or future electronic products that are suitable for the present application should also be included in the protection scope of the present application and included here by reference.

[0068] Among them, the memory 12 includes at least one type of readable storage medium, and the readable storage medium can be non-volatile or volatile. The readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the electronic device 100, such as a mobile hard disk of the electronic device 100. In other embodiments, the memory 12 can also be an external storage device of the electronic device 100, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the electronic device 100. The memory 12 can not only be used to store application software and various types of data installed in the electronic device 100, such as a code of a rental management program, etc., but also can be used to temporarily store data that has been output or is to be output.

[0069] In some embodiments, the processor 13 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 13 is the control core (Control Unit) of the electronic device 100, and uses various interfaces and lines to connect various components of the entire electronic device 100, and executes various functions and processes data of the electronic device 100 by running or executing programs or modules stored in the memory 12 (for example, executing a rental management program, etc.), and calling data stored in the memory 12.

[0070] The processor 13 executes the operating system of the electronic device 100 and various installed applications. The processor 13 executes the applications to implement the steps in each of the above-mentioned embodiments of the online office equipment rental method, for example Figure 2 Steps shown.

[0071] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to complete the present application. The one or more modules / units may be a series of computer-readable instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the electronic device 100. For example, the computer program may be divided into an acquisition module 310 and a determination module 311.

[0072] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor to execute a part of an online office equipment leasing method described in each embodiment of the present application.

[0073] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also instruct the relevant hardware devices to complete through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented.

[0074] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory and other memory, etc.

[0075] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0076] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. Figure 4 Only one arrow is used in the figure, but it does not mean that there is only one bus or one type of bus. The bus is configured to realize the connection and communication between the memory 12 and at least one processor 13, etc.

[0077] An embodiment of the present application also provides a computer-readable storage medium (not shown), in which computer-readable storage medium is stored computer-readable instructions, and the computer-readable instructions are executed by a processor in an electronic device to implement an online office equipment rental method described in any of the above embodiments.

[0078] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0079] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0080] In addition, each functional module in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0081] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the specification can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any specific order.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical solution of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present application.

Claims

1. An online office equipment leasing method, applied to electronic equipment, characterized in that: The method comprises: Obtain basic rental information of rental items and user profile of users; Determining the rental relationship data between the user and the rental item according to the rental record; The leasing risk prediction model is trained according to the leasing relationship data, the basic leasing information and the user portrait, including: constructing sample data according to the basic leasing information and the user portrait, and constructing label data according to the leasing relationship data; determining the confidence corresponding to each sample data according to the user portrait and the frequency of the leasing information in the leasing record; inputting the sample data into a pre-constructed initial prediction model to obtain the predicted risk data corresponding to the sample data; determining the loss value of the initial prediction model according to the predicted risk data and the label data; when the loss value is greater than a preset termination threshold, updating the initial prediction model based on a back propagation algorithm Model, until the loss value is less than or equal to the termination threshold, stop updating and obtain the rental risk prediction model trained to a convergent state; wherein, the confidence corresponding to each sample data is determined according to the user portrait and the frequency of the rental information in the rental record, including: confirming the first frequency of the user appearing in the rental record; confirming the second frequency of the rental item corresponding to the rental information appearing in the rental record; confirming the third frequency of the rental item corresponding to the rental information rented by the user in the rental record; determining the confidence corresponding to the sample data according to the first frequency, the second frequency and the third frequency, including: , wherein A represents the first frequency, B represents the second frequency, C represents the third frequency, and S represents the maximum value among the first frequency, the second frequency and the third frequency; Receive the target user portrait of the user seeking to rent and the target rental information of the target rental items in real time; Input the user portrait and the target rental information into the rental risk prediction model to obtain the risk value when the renting user rents the target rental item corresponding to the target rental information; A rental management strategy corresponding to the target rental item is determined according to the risk value.

2. The online office equipment leasing method according to claim 1, characterized in that: The determining of the rental relationship data between the user and the rental item according to the rental record includes: Determine the rental rate and vacancy rate of each of the leased items according to the rental records, and determine the first risk value of the corresponding leased item according to the rental rate and the vacancy rate; Determine a renewal rate and a rent termination rate of each user according to the rental record, and determine a second risk value of the corresponding user according to the renewal rate and the rent termination rate; The rental relationship data between the user and the rental item is determined according to the first risk value and the second risk value.

3. The online office equipment leasing method according to claim 2, characterized in that: The determining of the rental relationship data between the user and the rental item according to the first risk value and the second risk value comprises: determining an average of the first risk value and the second risk value; The average is determined as rental relationship data between the user and the rental item.

4. The online office equipment leasing method according to claim 1, characterized in that: The determining the loss value of the initial prediction model according to the predicted risk data and the label data includes: ； Among them, Loss represents the loss value of the initial prediction model; R represents the confidence; e represents the preset natural constant; P represents the label data; L represents the predicted risk data.

5. The online office equipment leasing method according to claim 1, characterized in that: The determining of the rental management strategy corresponding to the target rental item according to the risk value includes: when the risk value is greater than a preset first threshold, outputting first warning information, wherein the first warning information is used to indicate that the renting user is prohibited from renting the target rental item.

6. The online office equipment leasing method according to claim 5, characterized in that: The step of determining the rental management strategy corresponding to the target rental item according to the risk value includes: when the risk value is greater than a preset second threshold and the risk value is less than or equal to a preset first threshold, outputting a second warning message, wherein the second warning message is used to indicate that the duration for which the target rental item is rented by the renting user is less than or equal to a preset duration.

7. A rental management system, characterized in that: The system includes a module for implementing the online office equipment leasing method according to any one of claims 1 to 6, and the system includes: The acquisition module is used to obtain the basic rental information of the rental items and the user profile of the user; A determination module, configured to determine the rental relationship data between the user and the rental item according to the rental record; The determination module is further used to train a leasing risk prediction model based on the leasing relationship data, the basic leasing information and the user portrait; The acquisition module is also used to receive the target user portrait of the rental user and the target rental information of the target rental item in real time; The determination module is further used to input the user portrait and the target rental information into the rental risk prediction model to obtain the risk value when the renting user rents the target rental item corresponding to the target rental information; The determination module is further configured to determine a rental management strategy corresponding to the target rental item according to the risk value.

8. The rental management system according to claim 7, characterized in that: The determination module is further used to: determine the rental rate and vacancy rate of each of the leased items according to the rental records, and determine the first risk value of the corresponding leased item according to the rental rate and the vacancy rate; Determine a renewal rate and a rent termination rate of each user according to the rental record, and determine a second risk value of the corresponding user according to the renewal rate and the rent termination rate; The rental relationship data between the user and the rental item is determined according to the first risk value and the second risk value.

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