CN117172891A - Equipment lease order data supervision system and method based on artificial intelligence - Google Patents
Equipment lease order data supervision system and method based on artificial intelligence Download PDFInfo
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Abstract
The invention relates to the technical field of lease data supervision. The equipment lease order data supervision system comprises a data acquisition module, a data analysis module, a lease order supervision module and an alarm reminding module; the data acquisition module is used for acquiring a lease request sent by a user and information of a lease order in the platform and the state of lease equipment; the data analysis module is used for analyzing the binding degree of the user portrait and the user and the lease order and screening lease equipment; the lease order supervision module is used for updating lease equipment in the platform and constructing lease equipment alternatives for equipment meeting the lease requirements of users; the alarm reminding module is used for reminding an alarm when the alternative scheme of the leasing equipment does not meet the leasing requirement of the user and rejecting the leasing request sent by the user; according to the invention, the accurate state of the leasing equipment in the platform is obtained by analyzing the special condition of the leasing equipment in the platform, so that the loss of a passenger source is avoided.
Description
Technical Field
The invention relates to the technical field of lease data supervision, in particular to an artificial intelligence-based equipment lease order data supervision system and method.
Background
The equipment leasing refers to a technical means for managing and optimizing equipment leasing business by using an information technology and an Internet of things technology; along with the progress of science and technology and the development of equipment, equipment renting businesses are innovated and developed continuously, and related technical requirements are improved continuously; the hardware system of the Internet of things is used for monitoring, controlling and alarming equipment states, operation parameters and the like in the equipment renting process in real time, so that the utilization rate and maintenance level of equipment renting are improved;
under the prior art, when the leasing platform receives the leasing request of the user, the platform can only lease the equipment in the idle state in the platform according to the leasing requirement of the user, and the equipment in the idle state cannot be accurately analyzed and judged, for example: the equipment lease contract is released to enable the equipment to be in an idle state or equipment to be leased continuously; the status of leasing equipment in the platform is inaccurate, so that the situations of passenger source loss and the like can occur.
Disclosure of Invention
The invention aims to provide an artificial intelligence-based equipment lease order data supervision system and method, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an equipment lease order data supervision method based on artificial intelligence comprises the following specific steps:
s100, acquiring a user leasing requirement, and acquiring whether idle leasing equipment in a platform meets the user leasing requirement according to the equipment leasing state; the user leasing requirements comprise the number of leasing equipment, the leasing date and the leasing duration, and the equipment leasing states comprise four states of leasing expiration, normal use, idle and contract release;
s200, when the renting equipment in an idle state does not meet the renting requirement of a user, the platform acquires information of a renting order, analyzes the renting state of the equipment according to the information of the renting order, analyzes the user portrait by combining with the historical renting order data information of the user and judges the binding degree of the user and the renting order of the equipment;
s300, predicting and updating the leasing equipment in the platform according to the equipment leasing state and the binding degree of the user and the equipment leasing order, and constructing a leasing equipment alternative scheme;
and S400, when the alternative scheme of the leasing equipment cannot meet the leasing requirement of the user, carrying out alarm reminding and rejecting the leasing request of the user.
Further, the specific method for obtaining whether the idle rental equipment in the platform meets the user rental requirement according to the equipment rental status in S100 is as follows:
a user sends a lease request to a platform through a user terminal, the platform obtains the lease requirements of the user from the lease request as M, t ' and t0 respectively, wherein M is represented as the number of the lease devices of the user, t0 is represented as a lease starting time point, t ' is represented as a time period of the lease devices of the user, the time period of the lease devices in an idle state obtained from a lease order of the equipment in the platform is respectively t ' S, s=1, 2 and 3..S, and S is represented as the number of the lease devices in the platform; when M is less than or equal to K (t's ) representing that rental equipment in the platform meets the rental requirement of a user; when M > K (t's ) is greater than K, the idle leasing equipment in the platform cannot meet the leasing requirement of the user, wherein K (t's ) is represented as the number of leasing equipment in the idle state in the period of time meeting the leasing requirement of the user.
Further, the specific method for analyzing the user portrait and judging the initial binding degree of the equipment rental order by the collected historical data in S200 is as follows:
s201, randomly acquiring data information Q1 of a user and equipment rentals, wherein the equipment rentals in a platform are in normal use or the rentals are expired, marking the user as a, traversing the platform according to the marked user a, acquiring historical data information Q ' of the rentals of the user in the platform, acquiring behavior data information generated in the process of renting the equipment of the user a in the platform, and respectively generating feature vector sets Q '1, Q '2 and Q '3, wherein Q '1 is represented as an equipment renting historical data information set of a user who continuously rents at the rentals expiration date, Q '2 is represented as an equipment renting historical data information set of the user who stops the continuous rentals at the rentals expiration date, and Q '3 is represented as an equipment renting historical data information set which is normally used but relieved by a user contract;
s202, constructing a user portrait model:
；
can obtain the user portrait vector of the marked user a, if the current equipment renting state is in normal use,is 0; if the current equipment rental status is at rental expiration, the device is left on the first device>Is 0; wherein U is 0 Feature vector representing initial user, ++>、/>、/>、/>Expressed as weights; the |q1|, |q2|, and |q3| respectively represent the number of elements in the equipment lease history data information set that the user generates a renewal, stops the renewal, and the contract release behavior;feature vector represented as equipment rental history data information j +.>Feature vector represented as equipment rental history data information i,/->Feature vectors expressed as equipment rental history data information z, j e Q '1, i e Q '2, z e Q '3;
s203, according to the formula:
；
calculating to obtain the initial binding degree of the user a to the equipment lease order, wherein the probability of expiration of the equipment lease is required to be analyzed when the current equipment lease state is in normal use, and thenIf the equipment lease status is at lease expiration, if the equipment lease status is 0, the analysis is to analyze whether the lease duration probability is continuous at lease expiration, thenIs 0; />Is the modulus value of the user portrait vector of user a, R qj Represented as similarity of current equipment rental data information Q1 to equipment rental history data information j in collection Q'1, R qi Represented as a similarity of the current equipment rental data information Q1 to the equipment rental history data information i in the collection Q'2, R qz Represented as similarity of the current equipment rental data information Q1 to the equipment rental history data information z in the set Q'3, +.>、/>And->A weight of similarity between the current equipment lease data information and the equipment lease history data information; performing similarity analysis according to the current equipment lease data information and the equipment lease history data information of the user which respectively generates lease renewing or contract releasing behaviors, and renewing lease or contract release with the userAnd after the similarity of the equipment lease historical data information of the contract release behavior of the user is accumulated and subtracted, the probability of renewing lease or contract release of the user and the current equipment lease order can be calculated.
Further, the specific method for predicting and updating the rental equipment in the platform in S300 is as follows:
s301, optionally acquiring lease order information of equipment lease state in a platform before a time period t', and obtaining the number mv of lease equipment at lease expiration according to the lease order information at lease expiration, wherein v=1, 2 and 3. When fv=f (sv, qv) is less than or equal to F ', marking the equipment with the lease-full, releasing the marked lease-expiration equipment and accumulating to obtain M1=K (Fv is less than or equal to F'), wherein Fv is represented as a binding value between a v-th lease order and a user, F 'is represented as a preset binding threshold, and K (Fv is less than or equal to F') is represented as the number of orders with which the binding degree of the user and the lease-full equipment is less than or equal to the preset binding threshold; releasing the equipment means that when the original leasing state is in normal use or the leasing is expired but the locked equipment which is continuously leased is unlocked, so that a user can select the leased equipment through the platform;
s302, optionally acquiring lease order information in a normal use state in a platform, and obtaining the number mc of lease equipment in normal use according to the lease order information in normal use, wherein c=1, 2 and 3. When fc=f (sc, qc)/t is less than or equal to F' and t0 e (tc-t), marking the normally used device, releasing the normally used device and accumulating to obtain M2; wherein Fc is expressed as a binding value between the c-th lease order and the user, t is expressed as a leased duration of the lease equipment in a normal use state, tc is expressed as a lease time period of the lease equipment in a normal use state, and the binding value between the user and the equipment lease order decreases with the passage of the lease duration;
s303, traversing equipment lease orders in the platform, which are in normal use and lease expiration, and predicting and updating lease equipment in the platform.
Further, the step S300 specifically includes: when M is less than or equal to M1+M2+K (t's epsilon t's), the platform receives a lease request sent by a user; when M > M1+M2+K (t '∈t's), the system sends an alert to the terminal and refuses to receive the lease request sent by the user.
The equipment lease order data supervision system based on the artificial intelligence comprises a data acquisition module, a data analysis module, a lease order supervision module and an alarm reminding module; the output end of the data acquisition module is connected with the input end of the data analysis module, the output end of the data analysis module is connected with the input end of the lease order supervision module, and the output end of the lease order supervision module is connected with the input end of the alarm reminding module; the data acquisition module is used for acquiring a lease request sent by a user through a user terminal, information of a lease order in a platform and the state of lease equipment; the data analysis module is used for analyzing the binding degree of the user portrait and the user and the lease order and screening lease equipment; the lease order supervision module is used for updating lease equipment in the platform and constructing lease equipment alternatives for lease equipment meeting the lease requirements of users; the alarm reminding module is used for carrying out alarm reminding and refusing to receive the leasing request sent by the user when the leasing equipment alternative scheme does not meet the leasing requirement of the user.
Further, the data acquisition module comprises a user terminal demand acquisition unit, a lease order information acquisition unit and a lease equipment state acquisition unit; the user terminal demand acquisition unit is used for sending a lease request to the platform by a user through the user terminal, and the platform obtains the user lease demand from the lease request; the lease order information acquisition unit acquires lease order information in a platform; the rental equipment state acquisition unit acquires the state of the platform rental equipment, wherein the equipment rental state comprises four states of rental expiration, normal use, idling and contract release, and the contract release refers to ending of a rental contract with the platform due to personal reasons of a user.
Further, the data analysis module comprises a user portrait analysis unit, a user and lease order binding degree analysis unit and a device lease screening unit; the user portrait analysis unit is used for analyzing user portraits of equipment lease orders in the platform, and the user portraits are embodied for the labels by abstracting specific information of the users into the labels; the binding degree analysis unit for the user and the lease order is used for judging the binding degree between the user and the lease order in the platform according to user portrait analysis, wherein the binding degree is mainly divided into whether the user can continue to lease the lease equipment or not and whether the lease contract is released during lease or not; the equipment lease screening unit screens lease equipment which does not meet the lease requirement of the user.
Further, the lease order supervision module comprises a lease equipment prediction updating unit and a lease equipment alternative construction unit, wherein the lease equipment prediction updating unit is used for performing prediction updating on equipment which can be leased in the platform; the lease equipment alternative construction unit is used for constructing lease equipment alternatives meeting the lease requirements of users through the lease equipment predicted to be updated in the platform.
Further, the alarm reminding module comprises an alarm reminding unit and a lease request rejecting unit; the alarm reminding unit is used for sending alarm reminding to the terminal by the system when the alternative scheme of the leasing equipment does not meet the leasing request of the user; the lease request refusal unit is used for sending an alarm prompt to the terminal by the system and refusing to receive the lease request sent by the user.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, through carrying out portrait analysis on the user in the order of the leasing equipment in the platform, the probability of renewing leasing the leasing equipment, stopping renewing the leasing equipment or releasing the leasing contract of the user is analyzed and judged, and the state information of the leasing equipment in the platform is accurately analyzed and judged, so that the situation that the number of the leasing equipment in the platform does not meet the leasing requirement of the user, and therefore, the loss of a passenger source is avoided.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of an artificial intelligence based equipment rental order data administration system.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions: the equipment lease order data supervision system based on the artificial intelligence comprises a data acquisition module, a data analysis module, a lease order supervision module and an alarm reminding module; the output end of the data acquisition module is connected with the input end of the data analysis module, the output end of the data analysis module is connected with the input end of the lease order supervision module, and the output end of the lease order supervision module is connected with the input end of the alarm reminding module; the data acquisition module is used for acquiring a lease request sent by a user through a user terminal, information of a lease order in a platform and the state of lease equipment; the data analysis module is used for analyzing the binding degree of the user portrait and the user and the lease order and screening lease equipment; the lease order supervision module is used for updating lease equipment in the platform and constructing lease equipment alternatives for lease equipment meeting the lease requirements of users; the alarm reminding module is used for carrying out alarm reminding and refusing to receive the leasing request sent by the user when the leasing equipment alternative scheme does not meet the leasing requirement of the user.
Further, the data acquisition module comprises a user terminal demand acquisition unit, a lease order information acquisition unit and a lease equipment state acquisition unit; the user terminal demand acquisition unit is used for sending a lease request to the platform by a user through the user terminal, and the platform obtains the user lease demand from the lease request; the lease order information acquisition unit acquires lease order information in a platform; the rental equipment state acquisition unit acquires the state of the platform rental equipment, wherein the equipment rental state comprises four states of rental expiration, normal use, idling and contract release, and the contract release refers to ending of a rental contract with the platform due to personal reasons of a user.
Further, the data analysis module comprises a user portrait analysis unit, a user and lease order binding degree analysis unit and a device lease screening unit; the user portrait analysis unit is used for analyzing user portraits of equipment lease orders in the platform, and the user portraits are embodied for the labels by abstracting specific information of the users into the labels; the binding degree analysis unit for the user and the lease order is used for judging the binding degree between the user and the lease order in the platform according to user portrait analysis, wherein the binding degree is mainly divided into whether the user can continue to lease the lease equipment or not and whether the lease contract is released during lease or not; the equipment lease screening unit screens lease equipment which does not meet the lease requirement of the user.
Further, the lease order supervision module comprises a lease equipment prediction updating unit and a lease equipment alternative construction unit, wherein the lease equipment prediction updating unit is used for performing prediction updating on equipment which can be leased in the platform; the lease equipment alternative construction unit is used for constructing lease equipment alternatives meeting the lease requirements of users through the lease equipment predicted to be updated in the platform.
Further, the alarm reminding module comprises an alarm reminding unit and a lease request rejecting unit; the alarm reminding unit is used for sending alarm reminding to the terminal by the system when the alternative scheme of the leasing equipment does not meet the leasing request of the user; the lease request refusal unit is used for sending an alarm prompt to the terminal by the system and refusing to receive the lease request sent by the user.
An equipment lease order data supervision method based on artificial intelligence comprises the following specific steps:
s100, acquiring a user leasing requirement, and acquiring whether idle leasing equipment in a platform meets the user leasing requirement according to the equipment leasing state; the user leasing requirements comprise the number of leasing equipment, the leasing date and the leasing duration, and the equipment leasing states comprise four states of leasing expiration, normal use, idle and contract release;
s200, when the renting equipment in an idle state does not meet the renting requirement of a user, the platform acquires information of a renting order, analyzes the renting state of the equipment according to the information of the renting order, analyzes the user portrait by combining with the historical renting order data information of the user and judges the binding degree of the user and the renting order of the equipment;
s300, predicting and updating the leasing equipment in the platform according to the equipment leasing state and the binding degree of the user and the equipment leasing order, and constructing a leasing equipment alternative scheme;
and S400, when the alternative scheme of the leasing equipment cannot meet the leasing requirement of the user, carrying out alarm reminding and rejecting the leasing request of the user.
Further, the specific method for obtaining whether the idle rental equipment in the platform meets the user rental requirement according to the equipment rental status in S100 is as follows:
a user sends a lease request to a platform through a user terminal, the platform obtains the lease requirements of the user from the lease request as M, t ' and t0 respectively, wherein M is represented as the number of the lease devices of the user, t0 is represented as a lease starting time point, t ' is represented as a time period of the lease devices of the user, the time period of the lease devices in an idle state obtained from a lease order of the equipment in the platform is respectively t ' S, s=1, 2 and 3..S, and S is represented as the number of the lease devices in the platform; when M is less than or equal to K (t's ) representing that rental equipment in the platform meets the rental requirement of a user; when M > K (t's ) is greater than K, the idle leasing equipment in the platform cannot meet the leasing requirement of the user, wherein K (t's ) is represented as the number of leasing equipment in the idle state in the period of time meeting the leasing requirement of the user.
Further, the specific method for analyzing the user portrait and judging the initial binding degree of the equipment rental order by the collected historical data in S200 is as follows:
s201, randomly acquiring data information Q1 of a user and equipment rentals, wherein the equipment rentals in a platform are in normal use or the rentals are expired, marking the user as a, traversing the platform according to the marked user a, acquiring historical data information Q ' of the rentals of the user in the platform, acquiring behavior data information generated in the process of renting the equipment of the user a in the platform, and respectively generating feature vector sets Q '1, Q '2 and Q '3, wherein Q '1 is represented as an equipment renting historical data information set of a user who continuously rents at the rentals expiration date, Q '2 is represented as an equipment renting historical data information set of the user who stops the continuous rentals at the rentals expiration date, and Q '3 is represented as an equipment renting historical data information set which is normally used but relieved by a user contract;
s202, constructing a user portrait model:
；
can obtain the user portrait vector of the marked user a, if the current equipment renting state is in normal use,is 0; if the current equipment rental status is at rental expiration, the device is left on the first device>Is 0; wherein U is 0 Feature vector representing initial user, ++>、/>、/>、/>Expressed as weights; the |q1|, |q2|, and |q3| respectively represent the number of elements in the equipment lease history data information set that the user generates a renewal, stops the renewal, and the contract release behavior;feature vector represented as equipment rental history data information j +.>Feature vector represented as equipment rental history data information i,/->Feature vectors expressed as equipment rental history data information z, j e Q '1, i e Q '2, z e Q '3;
s203, according to the formula:
；
calculating to obtain the initial binding degree of the user a to the equipment lease order, wherein the probability of expiration of the equipment lease is required to be analyzed when the current equipment lease state is in normal use, and thenIf the equipment lease status is at lease expiration, if the equipment lease status is 0, the analysis is to analyze whether the lease duration probability is continuous at lease expiration, thenIs 0; />Is the modulus value of the user portrait vector of user a, R qj Represented as similarity of current equipment rental data information Q1 to equipment rental history data information j in collection Q'1, R qi Represented as current equipment rental data information Q1 and equipment rentals within set Q'2Similarity of history data information i, R qz Represented as similarity of the current equipment rental data information Q1 to the equipment rental history data information z in the set Q'3, +.>、/>And->A weight of similarity between the current equipment lease data information and the equipment lease history data information; and carrying out similarity analysis according to the current equipment lease data information and the equipment lease historical data information of the user which respectively generates the renewing or contract release actions, accumulating and subtracting the similarity of the current equipment lease data information and the equipment lease historical data information of the user renewing or contract release actions, and then calculating to obtain the probability of renewing or contract release of the user and the current equipment lease order.
Further, the specific method for predicting and updating the rental equipment in the platform in S300 is as follows:
s301, optionally acquiring lease order information of equipment lease state in a platform before a time period t', and obtaining the number mv of lease equipment at lease expiration according to the lease order information at lease expiration, wherein v=1, 2 and 3. When fv=f (sv, qv) is less than or equal to F ', marking the equipment with the lease-full, releasing the marked lease-expiration equipment and accumulating to obtain M1=K (Fv is less than or equal to F'), wherein Fv is represented as a binding value between a v-th lease order and a user, F 'is represented as a preset binding threshold, and K (Fv is less than or equal to F') is represented as the number of orders with which the binding degree of the user and the lease-full equipment is less than or equal to the preset binding threshold; releasing the equipment means that when the original leasing state is in normal use or the leasing is expired but the locked equipment which is continuously leased is unlocked, so that a user can select the leased equipment through the platform;
s302, optionally acquiring lease order information in a normal use state in a platform, and obtaining the number mc of lease equipment in normal use according to the lease order information in normal use, wherein c=1, 2 and 3. When fc=f (sc, qc)/t is less than or equal to F' and t0 e (tc-t), marking the normally used device, releasing the normally used device and accumulating to obtain M2; wherein Fc is expressed as a binding value between the c-th lease order and the user, t is expressed as a leased duration of the lease equipment in a normal use state, tc is expressed as a lease time period of the lease equipment in a normal use state, and the binding value between the user and the equipment lease order decreases with the passage of the lease duration;
s303, traversing equipment lease orders in the platform, which are in normal use and lease expiration, and predicting and updating lease equipment in the platform.
Further, the step S300 specifically includes: when M is less than or equal to M1+M2+K (t's epsilon t's), the platform receives a lease request sent by a user; when M > M1+M2+K (t '∈t's), the system sends an alert to the terminal and refuses to receive the lease request sent by the user.
In this embodiment:
the user sends a lease request to a platform through a user terminal, the platform obtains that the lease demands of the user are M=20, t '= [2023.8.25, 2023.9.16] and t0= 2023.8.25 from the lease request, and obtains that the time period of obtaining that the lease equipment is in an idle state from the equipment lease order in the platform is t' s= { [2023.7.28,2023.9.1], [2023.8.20,2029.9.20], [2023.9.1,2023.9.28], [2023.8.15,2023.9.10]. The user is provided with a first time period;
example 1: m=20 < K (t '∈t's) =50, indicating that the rental equipment in the platform meets the user rental requirement;
example 2: when m=20 > K (t '∈t's) =15, it indicates that the idle rental equipment in the platform cannot meet the user rental requirement;
optionally acquiring lease order information of equipment lease status in a platform before 2023.8.25, and obtaining the number mv of lease equipment at lease expiration according to the lease order information at lease expiration, wherein v=1, 2, 3..v, v=16; when fv=f (sv, qv) +.f ', marking the rental-expiration device, releasing the marked rental expiration device and accumulating to obtain m1=k (fv+.f')=6; the method comprises the steps of randomly acquiring lease order information in a normal use state in a platform, and obtaining the number mc of lease equipment in normal use according to the lease order information in normal use, wherein c=1, 2 and 3..C, and C=13; when fc=f (sc, qc)/t is less than or equal to F' and t0 e (tc-t), marking the normally used device will release the normally used device and accumulate to m2=4;
when m=20 < m1+m2+k (t '∈t's) =6+4+15=25, the platform receives the lease request sent by the user;
example 3: when m=20 > K (t '∈t's) =15, it indicates that the idle rental equipment in the platform cannot meet the user rental requirement;
optionally acquiring lease order information of equipment lease status in a platform before 2023.8.25, and obtaining the number mv of lease equipment at lease expiration according to the lease order information at lease expiration, wherein v=1, 2, 3..v, v=16; when fv=f (sv, qv) +.f ', marking the rental-expiration device, releasing the marked rental expiration device and accumulating to obtain m1=k (fv+.f')=2; the method comprises the steps of randomly acquiring lease order information in a normal use state in a platform, and obtaining the number mc of lease equipment in normal use according to the lease order information in normal use, wherein c=1, 2 and 3..C, and C=13; when fc=f (sc, qc)/t is less than or equal to F' and t0 e (tc-t), marking the normally used device will release the normally used device and accumulate to m2=2;
m=20 > m1+m2+k (t '∈t's) =2+2+15=19, the system sends an alert to the terminal and refuses to receive the lease request sent by the user.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An equipment lease order data supervision method based on artificial intelligence is characterized in that: the equipment lease order data supervision method comprises the following specific steps:
s100, acquiring a user leasing requirement, and acquiring whether idle leasing equipment in a platform meets the user leasing requirement according to the equipment leasing state; the user leasing requirements comprise the number of leasing equipment, the leasing date and the leasing duration, and the equipment leasing states comprise four states of leasing expiration, normal use, idle and contract release;
s200, when the renting equipment in an idle state does not meet the renting requirement of a user, the platform acquires information of a renting order, analyzes the renting state of the equipment according to the information of the renting order, analyzes the user portrait by combining with the historical renting order data information of the user and judges the binding degree of the user and the renting order of the equipment;
s300, predicting and updating the leasing equipment in the platform according to the equipment leasing state and the binding degree of the user and the equipment leasing order, and constructing a leasing equipment alternative scheme;
and S400, when the alternative scheme of the leasing equipment cannot meet the leasing requirement of the user, carrying out alarm reminding and rejecting the leasing request of the user.
2. The method for supervising equipment rental order data based on artificial intelligence according to claim 1, wherein: the specific method for acquiring whether the idle rental equipment in the platform meets the user rental requirement according to the equipment rental state in S100 is as follows:
a user sends a lease request to a platform through a user terminal, the platform obtains the lease requirements of the user from the lease request as M, t ' and t0 respectively, wherein M is represented as the number of the lease devices of the user, t0 is represented as a lease starting time point, t ' is represented as a time period of the lease devices of the user, the time period of the lease devices in an idle state obtained from a lease order of the equipment in the platform is respectively t ' S, s=1, 2 and 3..S, and S is represented as the number of the lease devices in the platform; when M is less than or equal to K (t's ) representing that rental equipment in the platform meets the rental requirement of a user; when M > K (t's ) is greater than K, the idle leasing equipment in the platform cannot meet the leasing requirement of the user, wherein K (t's ) is represented as the number of leasing equipment in the idle state in the period of time meeting the leasing requirement of the user.
3. The equipment renting order data supervision method based on artificial intelligence according to claim 2, wherein the method comprises the following steps: the specific method for analyzing the user portrait and judging the initial binding degree of the equipment lease order by the collected historical data in the S200 is as follows:
s201, randomly acquiring data information Q1 of a user and equipment rentals, wherein the equipment rentals in a platform are in normal use or the rentals are expired, marking the user as a, traversing the platform according to the marked user a, acquiring historical data information Q ' of the rentals of the user in the platform, acquiring behavior data information generated in the process of renting the equipment of the user a in the platform, and respectively generating feature vector sets Q '1, Q '2 and Q '3, wherein Q '1 is represented as an equipment renting historical data information set of a user who continuously rents at the rentals expiration date, Q '2 is represented as an equipment renting historical data information set of the user who stops the continuous rentals at the rentals expiration date, and Q '3 is represented as an equipment renting historical data information set which is normally used but relieved by a user contract;
s202, constructing a user portrait model:
；
a user portrait vector of a marked user a can be obtained, wherein U 0 A feature vector representing the initial user is presented,、/>、/>、/>expressed as weights; the |q1|, |q2|, and |q3| respectively represent the number of elements in the equipment lease history data information set that the user generates a renewal, stops the renewal, and the contract release behavior; />Feature vector represented as equipment rental history data information j +.>Feature vector represented as equipment rental history data information i,/->Feature vectors expressed as equipment rental history data information z, j e Q '1, i e Q '2, z e Q '3;
s203, according to the formula:
；
the initial binding degree of the user a to the current equipment lease order is calculated,is the modulus value of the user portrait vector of user a, R qj Represented as similarity of current equipment rental data information Q1 to equipment rental history data information j in collection Q'1, R qi Represented as a similarity of the current equipment rental data information Q1 to the equipment rental history data information i in the collection Q'2, R qz Represented as similarity of the current equipment rental data information Q1 to the equipment rental history data information z in the set Q'3, +.>、/>And->And (5) weighting the similarity between the current equipment lease data information and the equipment lease history data information.
4. The equipment renting order data supervision method based on artificial intelligence according to claim 3, wherein the method comprises the following steps: the specific method for predicting and updating the leasing equipment in the platform in the S300 is as follows:
s301, optionally acquiring lease order information of equipment lease state in a platform before a time period t', and obtaining the number mv of lease equipment at lease expiration according to the lease order information at lease expiration, wherein v=1, 2 and 3. When fv=f (sv, qv) is less than or equal to F ', marking the equipment with the lease-full, releasing the marked lease-expiration equipment and accumulating to obtain M1=K (Fv is less than or equal to F'), wherein Fv is represented as a binding value between a v-th lease order and a user, F 'is represented as a preset binding threshold, and K (Fv is less than or equal to F') is represented as the number of orders with which the binding degree of the user and the lease-full equipment is less than or equal to the preset binding threshold;
s302, optionally acquiring lease order information in a normal use state in a platform, and obtaining the number mc of lease equipment in normal use according to the lease order information in normal use, wherein c=1, 2 and 3. When fc=f (sc, qc)/t is less than or equal to F' and t0 e (tc-t), marking the normally used device, releasing the normally used device and accumulating to obtain M2; wherein Fc is expressed as a binding value between the c-th rental order and the user, t is expressed as a rented duration of the rental device in a normal use state, and tc is expressed as a rental period of the rental device in a normal use state;
s303, traversing equipment lease orders in the platform, which are in normal use and lease expiration, and predicting and updating lease equipment in the platform.
5. The method for supervising equipment rental order data based on artificial intelligence according to claim 4, wherein: the step S300 specifically includes: when M is less than or equal to M1+M2+K (t's epsilon t's), the platform receives a lease request sent by a user; when M > M1+M2+K (t '∈t's), the system sends an alert to the terminal and refuses to receive the lease request sent by the user.
6. An artificial intelligence-based equipment lease order data supervision system is characterized in that: the equipment lease order data supervision system comprises a data acquisition module, a data analysis module, a lease order supervision module and an alarm reminding module; the output end of the data acquisition module is connected with the input end of the data analysis module, the output end of the data analysis module is connected with the input end of the lease order supervision module, and the output end of the lease order supervision module is connected with the input end of the alarm reminding module; the data acquisition module is used for acquiring a lease request sent by a user through a user terminal, information of a lease order in a platform and the state of lease equipment; the data analysis module is used for analyzing the binding degree of the user portrait and the user and the lease order and screening lease equipment; the lease order supervision module is used for updating lease equipment in the platform and constructing lease equipment alternatives for lease equipment meeting the lease requirements of users; the alarm reminding module is used for carrying out alarm reminding and refusing to receive the leasing request sent by the user when the leasing equipment alternative scheme does not meet the leasing requirement of the user.
7. The artificial intelligence based equipment rental order data administration system of claim 6, wherein: the data acquisition module comprises a user terminal demand acquisition unit, a lease order information acquisition unit and a lease equipment state acquisition unit; the user terminal demand acquisition unit is used for acquiring user leasing demands from the leasing request by the platform; the lease order information acquisition unit acquires lease order information in a platform; the rental equipment state acquisition unit is used for acquiring the state of the platform rental equipment.
8. The artificial intelligence based equipment rental order data administration system of claim 7, wherein: the data analysis module comprises a user portrait analysis unit, a user and lease order binding degree analysis unit and a device lease screening unit; the user portrait analysis unit is used for analyzing the user portrait of the equipment lease order in the platform; the user and lease order binding degree analysis unit is used for judging the binding degree between the user and the lease order in the platform according to user portrait analysis; the equipment lease screening unit screens lease equipment which does not meet the lease requirement of the user.
9. The artificial intelligence based equipment rental order data administration system of claim 8, wherein: the lease order supervision module comprises a lease equipment prediction updating unit and a lease equipment standby scheme construction unit, wherein the lease equipment prediction updating unit is used for performing prediction updating on equipment which can be leased in a platform; the lease equipment alternative construction unit is used for constructing lease equipment alternatives meeting the lease requirements of users through the lease equipment predicted to be updated in the platform.
10. The artificial intelligence based equipment rental order data administration system of claim 9, wherein: the alarm reminding module comprises an alarm reminding unit and a lease request refusal unit; the alarm reminding unit is used for sending alarm reminding to the terminal by the system when the alternative scheme of the leasing equipment does not meet the leasing request of the user; the lease request refusal unit is used for sending an alarm prompt to the terminal by the system and refusing to receive the lease request sent by the user.
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| JP2014153958A (en) * | 2013-02-08 | 2014-08-25 | Japan Research Institute Ltd | Lease contract proposal device, lease contract proposal method and lease contract proposal program |
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