CN113435975B

CN113435975B - Wheelchair leasing processing method and device and related equipment

Info

Publication number: CN113435975B
Application number: CN202110723237.2A
Authority: CN
Inventors: 张旗
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2021-06-29
Filing date: 2021-06-29
Publication date: 2023-09-26
Anticipated expiration: 2041-06-29
Also published as: CN113435975A

Abstract

The application relates to a data processing technology, and provides a wheelchair leasing processing method, a device, computer equipment and a storage medium, wherein the wheelchair leasing processing method comprises the following steps: binding a target salesman according to a user source; acquiring the acquisition frequency corresponding to the target leasing video, and acquiring facial data of a user in the playing process of the target leasing video according to the acquisition frequency; detecting whether the emotion of the user is in a target emotion according to the face data; when the detection result is yes, acquiring a target timestamp corresponding to the target emotion, and determining a video node of the target leased video corresponding to the target timestamp; playing target decomposed video content corresponding to the video node; determining a target wheelchair type according to the demand information; when the wheelchair leasing service corresponding to the target wheelchair type is detected to be started, calculating service cost, and attributing the service cost to a target salesman. The wheelchair renting method and the wheelchair renting device can improve the accuracy of wheelchair renting, can be used for all functional modules of the smart city, and promote the rapid development of the smart city.

Description

Wheelchair leasing processing method and device and related equipment

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a wheelchair leasing processing method, apparatus, computer device, and medium.

Background

With the continuous development of Internet of things big data cloud computing artificial intelligence technology, convenience and richness of work and life are greatly improved, and daily life of people is also greatly facilitated by various types of wheelchair leases.

In the process of implementing the present invention, the inventor finds that at least the following technical problems exist in the prior art: most of the existing wheelchair leasing platforms provide text explanation functions in the use process of users, the functions are applicable to young users, but for older users, the problem that the wheelchair leasing process is not fully known possibly exists, so that the applicability of the wheelchair leasing platform is low; in addition, for the situation that the types of wheelchairs are more, users mostly rely on intuition of themselves or recommendation of operators when renting wheelchairs, but include more subjective factors by virtue of intuition of themselves or recommendation of operators, and the type of wheelchairs which cannot be rented is the most suitable, so that the accuracy of wheelchairs renting is lower.

Therefore, it is necessary to provide a wheelchair renting method, which can improve the applicability and accuracy of wheelchair renting.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a wheelchair renting method, a wheelchair renting device, a computer device, and a medium, which can improve the applicability and accuracy of wheelchair renting.

An embodiment of the present application provides a wheelchair lease processing method, including:

when a registration request is received, analyzing the registration request to obtain a user source, and binding a target salesman according to the user source;

after registration is completed according to the registration request, acquiring a target leasing video set and acquisition frequencies corresponding to each target leasing video in the target leasing video set, and acquiring facial data of a user in the playing process of the target leasing video according to the acquisition frequencies;

detecting whether the emotion of the user is in a target emotion according to the face data;

when the detection result shows that the emotion of the user is in the target emotion, acquiring a target timestamp corresponding to the target emotion, and determining a video node corresponding to the target leased video;

acquiring and playing target decomposed video content corresponding to the video node;

after the target decomposed video content is played, obtaining the demand information of a user, and determining the type of the target wheelchair according to the demand information;

when the wheelchair leasing service corresponding to the target wheelchair type is detected to be started, service cost is calculated according to the use data of the user, and the service cost is attributed to the target salesman.

Further, in the wheelchair leasing processing method provided by the embodiment of the present application, the analyzing the registration request includes:

analyzing the registration request to obtain structured registration information;

acquiring target information at a preset position in the registration information, wherein the target information comprises a mobile phone number;

traversing a mapping relation between a preset mobile phone number and a salesman code according to the target information, and detecting whether the salesman code matched with the target information exists or not;

when the detection result is that the salesman code matched with the target information exists, determining that the user source is a salesman recommended source;

and when the detection result is that the service personnel code matched with the target information does not exist, determining that the user source is a user self-query source.

Further, in the wheelchair leasing processing method provided by the embodiment of the present application, the obtaining the target leasing video set and the collection frequency corresponding to each target leasing video in the target leasing video set includes:

determining an evaluation index of importance degree dimension of the target leasing video and probability dimension of the target leasing video that a user is in a target emotion in a history playing process;

Establishing a corresponding evaluation algorithm aiming at the evaluation indexes of different dimensions, and calling the evaluation algorithm to calculate index evaluation results of each dimension;

distributing preset weights to index evaluation results of all dimensions to obtain comprehensive evaluation results;

and determining the acquisition frequency of the target leasing video according to the comprehensive evaluation result.

Further, in the wheelchair leasing processing method provided by the embodiment of the present application, the collecting facial data of the user during the playing process of the target leasing video according to the collection frequency includes:

collecting the target leasing video according to the collection frequency to obtain a target leasing sub-video;

reading each frame of face image in the target leasing sub-video, and extracting facial organ characteristics of the face image;

and integrating the facial organ characteristics according to a preset data format to obtain facial data of the user.

Further, in the wheelchair renting processing method provided by the embodiment of the present application, the detecting whether the emotion of the user is at the target emotion according to the face data includes:

collecting target emotion data stored in a preset database;

acquiring target characteristic information of a facial organ corresponding to the target emotion data;

Taking the target characteristic information as sample data, and splitting the sample data into a training sample and a test sample;

inputting the training sample into an initial target emotion judgment model for training to obtain a trained target emotion judgment model;

inputting the test sample into a trained target emotion judgment model to obtain model judgment accuracy, and determining that the training of the target emotion judgment model is completed when the model judgment accuracy is higher than a preset judgment accuracy threshold;

and calling the trained target emotion judgment model to process the facial data so as to detect whether the user is in a target emotion.

Further, in the wheelchair leasing processing method provided by the embodiment of the present application, the obtaining the target timestamp corresponding to the target emotion, and determining the video node corresponding to the target leasing video by the target timestamp includes:

acquiring target face data corresponding to the target emotion;

determining a time stamp carried by the target face data as a target time stamp;

acquiring time periods corresponding to all the leasing videos in the target leasing video set, and determining the leasing video corresponding to the time period containing the target time stamp as the target leasing video;

Traversing a preset video node set corresponding to the target leased video to obtain a video node corresponding to the target time stamp.

Further, in the above wheelchair renting processing method provided by the embodiment of the present application, the obtaining the requirement information of the user, and determining the target wheelchair type according to the requirement information includes:

processing the demand information according to a preset data format to obtain structured target demand information;

classifying the target demand information to obtain a hard demand, a soft demand and a negative demand;

traversing a preset wheelchair function knowledge graph according to the hard requirement, the soft requirement and the negative requirement to obtain an initial wheelchair type;

and calculating the matching degree of the initial wheelchair type and the target demand information, and selecting the wheelchair type with the highest matching degree as the target wheelchair type.

The second aspect of the embodiment of the present application also provides a wheelchair lease processing apparatus, including:

the request analysis module is used for analyzing the registration request to obtain a user source when the registration request is received, and binding a target service member according to the user source;

The face acquisition module is used for acquiring a target leasing video set and acquisition frequency corresponding to each target leasing video in the target leasing video set after registration is completed according to the registration request, and acquiring face data of a user in the playing process of the target leasing video according to the acquisition frequency;

the emotion detection module is used for detecting whether the emotion of the user is in a target emotion according to the face data;

the node determining module is used for acquiring a target timestamp corresponding to a target emotion when the detection result is that the emotion of the user is in the target emotion, and determining a video node corresponding to the target leased video by the target timestamp;

the video acquisition module is used for acquiring and playing target decomposition video content corresponding to the video node;

the type determining module is used for acquiring the requirement information of the user after the target decomposed video content is played, and determining the type of the target wheelchair according to the requirement information;

and the expense calculation module is used for calculating service expense according to the use data of the user when detecting that the wheelchair leasing service corresponding to the target wheelchair type is started, and attributing the service expense to the target salesman.

A third aspect of the embodiment of the present application further provides a computer device, where the computer device includes a processor, and the processor is configured to implement the wheelchair rental processing method according to any one of the above when executing a computer program stored in a memory.

The fourth aspect of the embodiment of the present application further provides a computer readable storage medium, where a computer program is stored, where the computer program when executed by a processor implements the wheelchair rental treatment method according to any one of the above.

According to the wheelchair leasing processing method, the wheelchair leasing processing device, the computer equipment and the computer readable storage medium, when the detected result is that the emotion of the user is in the target emotion, a target timestamp corresponding to the target emotion is obtained, and a video node corresponding to the target leasing video is determined; the target decomposition video content corresponding to the video node is obtained and played so that a user can fully know the video content, and the applicability of the wheelchair leasing platform can be improved; according to the application, through carrying out multidimensional comprehensive consideration on each target leasing video in the target leasing video set, the corresponding facial data acquisition frequency is matched for each target leasing video, so that the frequency of facial data acquisition can be improved aiming at the target leasing video with higher importance degree or more target emotion, and the problems of excessive data acquisition quantity, low data acquisition efficiency and the like caused by adopting the same acquisition frequency to acquire the facial data are avoided; in addition, the application determines the type of the target wheelchair according to the target demand information of the user, avoids the user from determining the type of the wheelchair by intuition of the user or recommendation of a salesman, and improves the accuracy of wheelchair leasing. The intelligent city intelligent management system can be applied to various functional modules of intelligent cities such as intelligent government affairs, intelligent transportation and the like, such as wheelchair leasing processing modules of the intelligent government affairs and the like, and can promote rapid development of the intelligent cities.

Drawings

Fig. 1 is a flowchart of a wheelchair leasing method according to an embodiment of the present application.

Fig. 2 is a block diagram of a wheelchair leasing device according to a second embodiment of the present application.

Fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present application.

The application will be further described in the following detailed description in conjunction with the above-described figures.

Detailed Description

In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, the described embodiments are examples of some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

The wheelchair leasing processing method provided by the embodiment of the application is executed by the computer equipment, and correspondingly, the wheelchair leasing processing device runs in the computer equipment.

Fig. 1 is a flowchart of a wheelchair rental processing method according to a first embodiment of the present application. As shown in fig. 1, the wheelchair renting method may include the following steps, the order of the steps in the flowchart may be changed according to different needs, and some may be omitted.

S11, when a registration request is received, the registration request is analyzed to obtain a user source, and a target salesman is bound according to the user source.

In at least one embodiment of the present application, the registration request refers to a request of a new user for registering a wheelchair leasing processing platform, the registration request may be unstructured and/or structured request information, and the registration request may include information of a mobile phone number, a name, an age, a geographic location and the like of the registered user. The user sources may include sources such as attendant recommendations and user self-searches. The target salesman is a salesman corresponding to the user source. For example, when the user source is a recommendation for a salesman, the target salesman refers to the recommended salesman; when the user source is the user, the target salesman is the salesman matched according to the preset allocation rule, and the preset allocation rule is a preset rule for binding the salesman and the registered user.

Optionally, the parsing the registration request to obtain a user source includes:

The structured registration information is information such as mobile phone number, name, age, geographical position and the like of the registered user contained in the registration request are arranged according to a certain data format to form structured information. The preset position refers to a position where the mobile phone number is located, and for example, a preset mark may be added to the position where the mobile phone number is located, and the preset position may be determined by querying the preset mark. The preset mark can be a color mark, a numerical mark, an alphabetical mark or the like. The application sets a preset database for storing the mobile phone number of the user, the service code serving the user and other information. The preset database may be a preset node in the blockchain in consideration of confidentiality and privacy of data, which is not limited herein.

Optionally, the binding the target salesman according to the user source includes:

when the user source is a salesman recommendation source, determining a target salesman and a target salesman code corresponding to the salesman recommendation source, and binding a mobile phone number of a user with the target salesman code;

when the user source is a self-query source of the user, determining the geographic position information of the user, determining a target service network point according to the geographic position information, randomly acquiring a target salesman and a target salesman code in the target service network point, and binding a mobile phone number of the user and the target salesman code.

When the user source is a recommended source of the salesman, the salesman providing business service for the user can be directly determined as a target salesman, and the mobile phone number of the user and the salesman code of the target salesman are bound; when the user source is the source of the user self-inquiry, the service network point closest to the user is preferentially selected, one salesman in the service network point is randomly selected as a target salesman, and the mobile phone number of the user and the salesman code of the target salesman are bound.

And S12, after registration is completed according to the registration request, acquiring a target leasing video set and acquisition frequency corresponding to each target leasing video in the target leasing video set, and acquiring facial data of a user in the playing process of the target leasing video according to the acquisition frequency.

In at least one embodiment of the present application, the user typically completes the registration of the wheelchair rental processing platform at a mobile terminal, such as a mobile phone, tablet computer, or the like, that includes a camera device. The target leasing video set refers to a video set comprising leasing processes such as wheelchair leasing time, expense type and standard, responsibility clause, confirmed borrowing and returning, damage compensation, fund payment and the like. And the target leasing video set is stored in a preset database. And calling camera equipment of the mobile terminal to collect facial data of a user in the playing process of the target leased video. The facial data may include eye closure, eyebrow curvature, mouth closure, nostril opening and closing, and the like. The face data may be obtained by recognizing a face image of the user based on a face recognition technique.

In an embodiment, for each target rental video in the target rental video set, there is a corresponding collection frequency, where the collection frequency refers to a frequency at which user face data is collected during a playing process of the target rental video. And the acquisition frequency and the target leasing video have a mapping relation, and the acquisition frequency corresponding to the target leasing video can be obtained by inquiring the mapping relation. Different targets lease videos, corresponding acquisition frequencies may be different, and the acquisition frequencies may be comprehensively considered according to multiple dimensions. For example, the dimension of consideration may include, but is not limited to, the importance of the target rental video and the probability that the target rental video is in the target emotion of the user during the historical play. The target emotion is a preset emotion, for example, the target emotion may be a puzzled emotion.

Optionally, the method further comprises:

When the importance degree of the target leasing video is higher and the probability that the target leasing video is in the target emotion in the history playing process is higher, the corresponding acquisition frequency is higher; when the importance degree of the target leasing video is lower and the probability that the target leasing video is in the target emotion in the history playing process is smaller, the corresponding acquisition frequency is lower. The evaluation index may include a importance degree dimension of the target rental video and a probability dimension of the target rental video that the user is in a target emotion during the history playing process.

For importance degree dimension of the target rental video, establishing a corresponding evaluation algorithm may include: analyzing the target leasing videos, and determining the number of preset content identification factors in each target leasing video; acquiring the weight of each preset content identification factor; and comprehensively calculating the importance degree of each target rental video based on the number and the weight of each preset content recognition factor. The preset content recognition factor may be determined according to an application scenario, for example, a plurality of content recognition factors may be preset: cost type, damage reimbursement, funds payment, and the like. The weight of each preset content identification factor can be set by a system staff according to the importance of the factor, and the higher the importance of the factor is, the larger the corresponding weight is; the lower the importance of a factor, the less its corresponding weight. The method comprises the steps of presetting a calculation model of the number, the weight and the importance degree of the preset content recognition factors, and obtaining the importance degree of each target leased video by inputting the number and the weight of the preset content recognition factors into the calculation model.

For the probability dimension of the target rental video that the user is in the target emotion in the history playing process, establishing the corresponding evaluation algorithm may include: acquiring the times of the target leasing video set in the target emotion of the user in the history playing process in a preset time period; acquiring the number of users who acquire face data; and calculating the probability that the user is in the target emotion in each target leasing video according to the times and the number of the users.

The preset weight can be a weight preset by a system personnel. And the comprehensive evaluation result and the acquisition frequency have a corresponding relation, and the acquisition frequency of the target leased video corresponding to the comprehensive evaluation result can be obtained by inquiring the corresponding relation.

According to the application, through carrying out multidimensional comprehensive consideration on each target leasing video in the target leasing video set, the corresponding facial data acquisition frequency is matched for each target leasing video, so that the frequency of facial data acquisition can be improved aiming at the target leasing video with higher importance degree or more target emotion, and the problems of excessive data acquisition quantity, low data acquisition efficiency and the like caused by adopting the same acquisition frequency to acquire the facial data are avoided.

Optionally, the collecting facial data of the user during the playing of the target rental video according to the collecting frequency includes:

Wherein the facial organ comprises chin, eyes, eyebrows, teeth, lips, nose, ears, etc. The facial organ features may be coordinate information of each facial organ, including a transverse coordinate and a longitudinal coordinate, and the coordinate information of each facial organ is arranged according to a preset data format, so as to obtain facial data corresponding to the facial image of the frame. Aiming at the target leasing sub-video, a preset number of face images can be continuously extracted, and facial organ characteristics of each frame of face images are combined to obtain facial data of a user corresponding to the target leasing sub-video. The preset number may be a number preset by a system person, for example, the preset number may be 5 or 7.

Optionally, the reading each frame of face image in the target rental sub-video, and extracting facial organ features from the face image further includes:

determining that each frame of face image is effective according to a face detection algorithm; or alternatively

And determining that the brightness of each frame of face image meets the preset brightness requirement according to a brightness detection algorithm.

After the face image is obtained, but the computer does not necessarily need to automatically identify whether a face exists in the image, whether the face is provided with a reasonable angle or not is needed to be checked, if the face is provided with a side face facing the camera, the computer equipment may not be able to identify the face; or the information of the face is incomplete, the computer device cannot recognize the face. The collected face image is ensured to be effective through a face detection algorithm from the face image, effective face data can be ensured to be obtained from the face image, and when the face in the face image is invalid, the frame of the face image is deleted. The preset brightness requirement refers to brightness meeting the face recognition technology requirement, and when the brightness of the face image does not meet the preset brightness requirement, the interference of the side light source on the face image and the interference of the front light source on the face image can be weakened through illuminance correction and illumination correction processing.

S13, detecting whether the emotion of the user is in a target emotion according to the face data.

In at least one embodiment of the present application, the user's emotion may include happy, surprised or confusing. The target emotion refers to an confused emotion. The face data is a vector composed of facial organ characteristics contained in continuous multi-frame face images, can reflect continuous emotion expression of a user, and can improve recognition accuracy of target emotion. Optionally, the detecting whether the emotion of the user is at the target emotion according to the face data includes:

collecting target emotion data stored in a preset database;

The target emotion data may include that eye closure degree is lower than preset closure degree, eyebrow curvature is higher than preset curvature, mouth closure degree is higher than preset closure degree, and nostril opening and closing degree is higher than preset opening and closing degree. Because the face data is a vector composed of facial organ characteristics contained in continuous multi-frame face images, when the training target emotion judgment model is called to process the face data, emotion information corresponding to each frame of face image can be obtained. Illustratively, when the facial data includes facial organ features corresponding to 5 frames of face images, the target emotion judgment model is invoked to judge whether the 5 frames of face images are all in the target emotion. When the judgment result shows that the 5 frames of face images are in the target emotion and the obtained quantity is large, determining that the emotion of the user is in the target emotion; and when the judgment result shows that the 5 frames of face images are in the target emotion and the obtained quantity is smaller than the proportion, determining that the emotion of the user is not in the target emotion.

And S14, when the detected result is that the emotion of the user is in the target emotion, acquiring a target time stamp corresponding to the target emotion, and determining a video node corresponding to the target leased video by the target time stamp.

In at least one embodiment of the present application, for each frame of image in the target rental video, there is a corresponding timestamp, and when the target emotion judgment model detects that the emotion of the user is in the target emotion, the target face data in the target emotion is collected, and the timestamp of each frame of face image of the target face data is determined. The target rental video may be a video including wheelchair rental time, cost type and criteria, liability terms, validation of loans, damage compensation, and fund payments. The target leasing video comprises a plurality of video nodes, each video node is provided with a corresponding time stamp, and each video node corresponds to each service chapter of the leasing flow. Taking the target rental video as a fee type and a standard as an example, the target rental video includes video nodes A, B and C corresponding to each fee type and video nodes corresponding to each fee standard. The video node A comprises explanation contents with the cost type A, the video node B comprises explanation contents with the cost type B, and the video node C comprises explanation contents with the cost type C.

Optionally, the obtaining the target timestamp corresponding to the target emotion, and determining that the target timestamp corresponds to the video node of the target rental video includes:

Acquiring target face data corresponding to the target emotion;

traversing a preset video node set corresponding to the target leased video to obtain a video node corresponding to the target time stamp. And setting video nodes according to service chapters contained in each rental video set aiming at each rental video in the target rental video set, and storing each video node in a form of a structural tree, for example, taking the rental video as a father node and each service chapter contained in the rental video set as a child node to construct the structural tree. By traversing the structure tree, the video node corresponding to the target timestamp can be obtained.

S15, obtaining and playing the target decomposition video content corresponding to the video node.

In at least one embodiment of the present application, the target rental video may be a video including multi-level information, for example, the target rental video is a video including two levels of information a and B, and for level information a, the target rental video includes a whole rental video including wheelchair rental time, fee type and standard, responsibility clause, confirmation of borrowing and returning, damage compensation, and fund payment; the hierarchical information B includes the decomposed video content of each flow, for example, the decomposed video of the fee type and standard, the decomposed video of the responsibility clause, and the like. When the detected result is that the emotion of the user is in the target emotion, determining the video node, matching a plurality of decomposed video contents corresponding to the video node, and playing the decomposed video contents so that the user can fully know the video contents, and the applicability of the wheelchair leasing platform can be improved.

Optionally, an association relationship map exists between the video node and the decomposed video content, and the association relationship map marks association relationships between the video node and a plurality of decomposed video content. In an embodiment, the obtaining and playing the target decomposed video content corresponding to the video node includes:

acquiring a first label corresponding to the video node;

inquiring a preset association relation map according to the first label to obtain a plurality of second labels corresponding to the first label;

and determining the target decomposed video content corresponding to the second label.

The first label and the second label may be a digital label or an alphabetic label, which is not limited herein.

S16, after the target decomposed video content is played, obtaining the requirement information of the user, and determining the type of the target wheelchair according to the requirement information.

In at least one embodiment of the present application, the wheelchair types include electric wheelchair, sports wheelchair, walking assist wheelchair, stair climbing wheelchair, and intelligent wheelchair. For example, an electric wheelchair refers to a wheelchair capable of moving by itself, a sports wheelchair refers to a wheelchair for disabled athletes, an assisted walking wheelchair refers to a wheelchair for assisting patients to walk, a stair climbing wheelchair refers to a wheelchair capable of conveniently climbing stairs, and an intelligent wheelchair refers to a wheelchair comprising multiple functions. Different types of wheelchairs are suitable for different crowds, and the most suitable wheelchair type is determined according to target behavior information of different crowds. The user's demand information refers to the user's demand for wheelchair functionality. According to the wheelchair type determining method and device, the type of the target wheelchair is determined according to the demand information of the user, the user is prevented from determining the wheelchair type intuitively or according to the recommendation of a salesman, the most proper type of the rented wheelchair can be ensured, and the accuracy of wheelchair renting is improved.

Optionally, the obtaining the requirement information of the user and determining the target wheelchair type according to the requirement information include:

The requirement information may be information in a natural language form, after normalization processing is performed on the requirement information, irrelevant words (for example, words such as' we, what you like) are removed, and then the requirement information is processed by word segmentation to obtain target requirement information. And classifying the target demand information, namely extracting preset keywords in the target demand. The preset keywords comprise preset hard requirement keywords, preset soft requirement keywords and preset negative requirement keywords. For example, "do not want, go, dislike" and the like are preset negative demand keywords. Hard requirements refer to wheelchair services that need to be preferentially met; soft requirements refer to wheelchair services that users wish to obtain; negative demand refers to wheelchair service that the user does not want. The calculating the matching degree of the initial wheelchair type and the target demand information, that is, calculating the satisfaction rates of the initial wheelchair type for the hard demand, the soft demand and the negative demand, it is understood that the matching degree is 100% when the initial wheelchair type satisfies all of the hard demand, the soft demand and the negative demand.

And S17, when the wheelchair leasing service corresponding to the target wheelchair type is detected to be started, calculating service cost according to the use data of the user, and attributing the service cost to the target salesman.

In at least one embodiment of the present application, the number of wheelchairs corresponding to the target wheelchair type may be one or more, and a unique identification code may be set for each wheelchair. When the identification code corresponding to the wheelchair is detected to be scanned, the wheelchair leasing service corresponding to the wheelchair can be determined to be started. And when the wheelchair leasing service is started, acquiring the use data of the user, calculating service cost based on the use data of the user after detecting that the wheelchair leasing service is closed, and attributing the service cost to the account of the corresponding target salesman. The usage data includes wheelchair lease time, cost type, whether damaged, etc. Setting a preset service charge calculation rule according to the data such as the wheelchair lease time, the cost type and whether the wheelchair is damaged, and obtaining the service charge of wheelchair lease based on the preset service charge calculation rule.

According to the wheelchair leasing processing method provided by the embodiment of the application, the target timestamp corresponding to the target emotion is obtained, and the video node corresponding to the target leasing video is determined; the target decomposition video content corresponding to the video node is obtained and played so that a user can fully know the video content, and the applicability of the wheelchair leasing platform can be improved; according to the application, through carrying out multidimensional comprehensive consideration on each target leasing video in the target leasing video set, the corresponding facial data acquisition frequency is matched for each target leasing video, so that the frequency of facial data acquisition can be improved aiming at the target leasing video with higher importance degree or more target emotion, and the problems of excessive data acquisition quantity, low data acquisition efficiency and the like caused by adopting the same acquisition frequency to acquire the facial data are avoided; in addition, the application determines the type of the target wheelchair according to the target demand information of the user, avoids the user from determining the type of the wheelchair by intuition of the user or recommendation of a salesman, and improves the accuracy of wheelchair leasing. The intelligent city intelligent management system can be applied to various functional modules of intelligent cities such as intelligent government affairs, intelligent transportation and the like, such as wheelchair leasing processing modules of the intelligent government affairs and the like, and can promote rapid development of the intelligent cities.

In some embodiments, the wheelchair rental processing device 20 may include a plurality of functional modules comprised of computer program segments. The computer program of each program segment in the wheelchair rental treatment apparatus 20 may be stored in a memory of a computer device and executed by at least one processor to perform the functions of the wheelchair rental treatment (described in detail with reference to fig. 1).

In this embodiment, the wheelchair rental processing device 20 may be divided into a plurality of functional modules according to the functions it performs. The functional module may include: a request parsing module 201, a face acquisition module 202, an emotion detection module 203, a node determination module 204, a video acquisition module 205, a type determination module 206, and a fee calculation module 207. The module referred to in the present application refers to a series of computer program segments capable of being executed by at least one processor and of performing a fixed function, stored in a memory. In the present embodiment, the functions of the respective modules will be described in detail in the following embodiments.

The request parsing module 201 is configured to parse the registration request to obtain a user source when the registration request is received, and bind a target salesman according to the user source.

The face collection module 202 is configured to obtain a target rental video set and a collection frequency corresponding to each target rental video in the target rental video set after registration is completed according to the registration request, and collect face data of a user during playing of the target rental video according to the collection frequency.

Optionally, the method further comprises:

The emotion detection module 203 is configured to detect whether an emotion of a user is in a target emotion according to the face data.

collecting target emotion data stored in a preset database;

The node determining module 204 is configured to obtain a target timestamp corresponding to a target emotion when the detected result is that the emotion of the user is in the target emotion, and determine that the target timestamp corresponds to a video node of the target rental video.

Acquiring target face data corresponding to the target emotion;

The video acquisition module 205 is configured to acquire and play the target decomposed video content corresponding to the video node.

acquiring a first label corresponding to the video node;

The type determining module 206 is configured to obtain requirement information of a user after the target decomposed video content is played, and determine a target wheelchair type according to the requirement information.

The fee calculation module 207 is configured to calculate a service fee according to usage data of a user when it is detected that a wheelchair rental service corresponding to the target wheelchair type is opened, and attribute the service fee to the target salesman.

Fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present application. In the preferred embodiment of the present application, the computer device 3 includes a memory 31, at least one processor 32, at least one communication bus 33, and a transceiver 34.

It will be appreciated by those skilled in the art that the configuration of the computer device shown in fig. 3 is not limiting of the embodiments of the present application, and that either a bus-type configuration or a star-type configuration is possible, and that the computer device 3 may include more or less other hardware or software than that shown, or a different arrangement of components.

In some embodiments, the computer device 3 is a device capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The computer device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client by way of a keyboard, mouse, remote control, touch pad, or voice control device, such as a personal computer, tablet, smart phone, digital camera, etc.

It should be noted that the computer device 3 is only used as an example, and other electronic products that may be present in the present application or may be present in the future are also included in the scope of the present application by way of reference.

In some embodiments, the memory 31 has stored therein a computer program that when executed by the at least one processor 32 performs all or part of the steps in the wheelchair rental treatment method as described. The Memory 31 includes Read-Only Memory (ROM), programmable Read-Only Memory (PROM), erasable programmable Read-Only Memory (EPROM), one-time programmable Read-Only Memory (One-time Programmable Read-Only Memory, OTPROM), electrically erasable rewritable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic tape Memory, or any other medium that can be used for computer-readable carrying or storing data.

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.

The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

In some embodiments, the at least one processor 32 is a Control Unit (Control Unit) of the computer device 3, connects the various components of the entire computer device 3 using various interfaces and lines, and performs various functions and processes of the computer device 3 by running or executing programs or modules stored in the memory 31, and invoking data stored in the memory 31. For example, the at least one processor 32, when executing the computer program stored in the memory, implements all or some of the steps of the wheelchair rental treatment method described in embodiments of the present application; or to implement all or part of the functionality of the wheelchair rental treatment device. The at least one processor 32 may be comprised of integrated circuits, such as a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functionality, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like.

In some embodiments, the at least one communication bus 33 is arranged to enable connected communication between the memory 31 and the at least one processor 32 or the like.

Although not shown, the computer device 3 may further comprise a power source (such as a battery) for powering the various components, preferably the power source is logically connected to the at least one processor 32 via a power management means, whereby the functions of managing charging, discharging, and power consumption are performed by the power management means. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The computer device 3 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described in detail herein.

The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or processor (processor) to perform portions of the methods described in the various embodiments of the application.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over 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.

In addition, each functional module in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.

It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it will be obvious that the term "comprising" does not exclude other elements or that the singular does not exclude a plurality. Several of the elements or devices recited in the specification may be embodied by one and the same item of software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.

Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present application without departing from the spirit and scope of the technical solution of the present application.

Claims

1. A wheelchair lease processing method, characterized in that the wheelchair lease processing method comprises:

after registration is completed according to the registration request, a target rental video set is acquired, an acquisition frequency corresponding to each target rental video in the target rental video set is determined according to the importance degree of the target rental video and the probability that a user is in a target emotion in the history playing process of the target rental video, and face data of the user is acquired in the playing process of the target rental video according to the acquisition frequency, wherein the determining of the importance degree of the target rental video comprises the following steps: analyzing the target leasing videos, and determining the number of preset content identification factors in each target leasing video; acquiring the weight of each preset content identification factor; comprehensively calculating the importance degree of each target leasing video based on the number and the weight of each preset content identification factor; and determining the probability that the user is in the target emotion in the history playing process of the target rental video, comprising: acquiring the times of the target leasing video set in the target emotion of the user in the history playing process in a preset time period; acquiring the number of users who acquire face data; calculating the probability that the user is in the target emotion in each target leasing video according to the times and the number of the users;

2. The wheelchair rental processing method of claim 1, wherein the parsing the registration request to obtain a user source comprises:

3. The wheelchair leasing method of claim 1, wherein determining the collection frequency corresponding to each target leasing video in the target leasing video set according to the importance level of the target leasing video and the probability that the target leasing video is in a target emotion during the history playing process comprises:

4. The wheelchair rental processing method of claim 1, wherein the capturing facial data of the user during the playing of the target rental video according to the capture frequency comprises:

5. The wheelchair rental processing method of claim 1, wherein the detecting whether the emotion of the user is at a target emotion from the face data comprises:

collecting target emotion data stored in a preset database;

6. The wheelchair rental processing method of claim 1, wherein the obtaining a target timestamp corresponding to the target emotion, determining that the target timestamp corresponds to a video node of the target rental video, comprises:

acquiring target face data corresponding to the target emotion;

7. The wheelchair rental processing method of claim 1, wherein the obtaining the user demand information and determining the target wheelchair type based on the demand information comprises:

8. A wheelchair lease processing apparatus, characterized in that the wheelchair lease processing apparatus includes:

the face acquisition module is used for acquiring a target leasing video set after registration is completed according to the registration request, determining the acquisition frequency corresponding to each target leasing video in the target leasing video set according to the importance degree of the target leasing video and the probability that a user is in a target emotion in the historical playing process of the target leasing video, and acquiring face data of the user in the playing process of the target leasing video according to the acquisition frequency, wherein the determining the importance degree of the target leasing video comprises the following steps: analyzing the target leasing videos, and determining the number of preset content identification factors in each target leasing video; acquiring the weight of each preset content identification factor; comprehensively calculating the importance degree of each target leasing video based on the number and the weight of each preset content identification factor; and determining the probability that the user is in the target emotion in the history playing process of the target rental video, comprising: acquiring the times of the target leasing video set in the target emotion of the user in the history playing process in a preset time period; acquiring the number of users who acquire face data; calculating the probability that the user is in the target emotion in each target leasing video according to the times and the number of the users;

9. A computer device, characterized in that it comprises a processor for implementing the wheelchair rental treatment method according to any one of claims 1 to 7 when executing a computer program stored in a memory.

10. A computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the wheelchair rental treatment method of any one of claims 1 to 7.