SaaS platform-based identification method and device for united coupon swiping of driver and passenger

Abstract

The invention relates to the technical field of Internet, and discloses a driver and passenger joint coupon swiping identification method based on an SaaS platform, and the method comprises the steps: responding to the situation that a passenger cancels a coupon after verification on the SaaS platform and pays a commission to the driver, and obtaining the commission amount of the driver and the payment amount of the passenger; according to the commission amount and the payment amount, determining whether the driver and the passenger have behavior suspicion of united coupon swiping; if the behavior suspicion exists, constructing a multi-dimensional feature vector according to the driver information, the passenger information and order information related to the behavior suspicion; and based on the multi-dimensional feature vector, identifying the behavior of united coupon swiping of the driver and the passenger. According to the technical scheme of the invention, the method solves a problem that the timeliness and accuracy of the behavior recognition of the joint coupon brushing of a designated driver and a passenger are insufficient in the related technology, and improves the timeliness and accuracy of the behavior recognition of the joint coupon brushing of the designated driver and the passenger.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to a method and device for identifying a driver and a passenger who jointly swipe a ticket based on a SaaS platform. Background Art

[0002] As the designated driver service becomes increasingly popular, Software as a Service (SaaS)-based designated driver platforms often offer coupons to passengers to attract repeat purchases. In real-world scenarios, when the coupons are large, for example, when the passenger's actual payment exceeds the driver's performance earnings, drivers can act as both "driver" and "passenger," profiting from these fraudulent services and harming the interests of passengers on these SaaS-based designated driver platforms.

[0003] In related technologies, designated driver platforms have a relatively simple detection dimension, relying primarily on historical matching between drivers and passengers to determine whether coupon swiping has occurred. This makes it impossible to accurately identify when drivers use related devices to register users with different mobile phone numbers and then swipe coupons. Most platforms use offline data warehouses to construct coupon swiping feature data to identify risks, but by the time risks are identified, significant economic losses may have already occurred. In other words, related technologies lack the timeliness and accuracy to identify the behavior of designated drivers and passengers swiping coupons together. Summary of the Invention

[0004] In view of this, the present invention provides a method and device for identifying the joint swiping of coupons by drivers and passengers based on a SaaS platform, so as to solve the problem of insufficient timeliness and accuracy in the related art in identifying the joint swiping of coupons by designated drivers and passengers.

[0005] In a first aspect, the present invention provides a method for identifying a driver and a passenger who jointly swipes a coupon based on a SaaS platform, the method comprising:

[0006] In response to the passenger redeeming the coupon on the SaaS platform and paying the commission to the driver, obtaining the driver's commission amount and the passenger's payment amount;

[0007] Based on the commission amount and payment amount, determine whether the driver and passenger are suspected of colluding to use coupons;

[0008] If there is suspicious behavior, a multi-dimensional feature vector is constructed based on the driver information, passenger information, and order information related to the suspected behavior;

[0009] Based on multi-dimensional feature vectors, the joint coupon swiping behavior of drivers and passengers is identified.

[0010] In an optional embodiment, determining whether the driver and the passenger are suspected of jointly using coupons based on the commission amount and the payment amount includes:

[0011] If the payment amount is greater than the commission amount, it is suspected that the driver and the passenger have engaged in a joint voucher swiping operation;

[0012] If the payment amount is less than or equal to the commission amount, it is determined that there is no suspicion of joint voucher swiping between the driver and the passenger.

[0013] In an optional embodiment, a multi-dimensional feature vector is constructed based on driver information, passenger information, and order information related to suspicious behavior, including:

[0014] Extracting a first feature vector based on the driver account, the driver's first login device information, and the driver's first order information in the driver information;

[0015] Extracting a second feature vector based on the passenger account, the passenger's second login device information, and the passenger's second order information in the passenger information, where the second order information corresponds to the first order information;

[0016] The first eigenvector and the second eigenvector are fused into a multi-dimensional eigenvector.

[0017] In an optional embodiment, identifying the driver and passenger's joint coupon swiping behavior based on a multi-dimensional feature vector includes:

[0018] The multi-dimensional feature vector is used to identify the joint ticket swiping behavior of the driver and the passenger through the joint ticket swiping recognition model, wherein the joint ticket swiping recognition model is used to indicate whether the multi-dimensional feature vector is mapped to the behavior of the driver and the passenger jointly swiping the ticket.

[0019] In an optional embodiment, before using the multi-dimensional feature vector to identify the joint ticket swiping behavior of the driver and the passenger through the joint ticket swiping recognition model, the method further includes:

[0020] The confirmed joint coupon swiping behaviors recorded on the SaaS platform are used as sample labels, and the features extracted from the passenger information, driver information, and order information corresponding to the sample labels are used as multi-dimensional feature vector samples;

[0021] Multi-dimensional feature vector samples and sample labels are used to train the deep neural network for classification to obtain a joint coupon swiping recognition model.

[0022] In an optional embodiment, after identifying the driver and passenger's joint coupon swiping behavior based on the multi-dimensional feature vector, the method further includes:

[0023] If the joint coupon swiping behavior of the driver and passenger is successfully identified, the order corresponding to the behavior will be frozen.

[0024] In a second aspect, the present invention provides a driver and passenger joint ticket swiping identification device based on a SaaS platform, the device comprising:

[0025] The acquisition module is used to obtain the driver's commission amount and the passenger's payment amount on the SaaS platform;

[0026] The suspicion determination module is used to determine whether the driver and passenger are suspected of colluding to use coupons based on the commission amount and payment amount;

[0027] A feature construction module is used to construct a multi-dimensional feature vector based on driver information, passenger information, and order information related to the suspected behavior if there is suspected behavior;

[0028] The behavior recognition module is used to identify the joint ticket swiping behavior of drivers and passengers based on multi-dimensional feature vectors.

[0029] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, computer instructions being stored in the memory, and the processor executing the computer instructions to execute the driver and passenger joint ticket swiping identification method based on the SaaS platform of the above-mentioned first aspect or any corresponding embodiment.

[0030] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the driver and passenger joint ticket swiping identification method based on a SaaS platform of the above-mentioned first aspect or any corresponding embodiment thereof.

[0031] In a fifth aspect, the present invention provides a computer program product comprising computer instructions for enabling a computer to execute the method for identifying joint ticket swiping by a driver and a passenger based on a SaaS platform according to the first aspect or any corresponding embodiment thereof.

[0032] The present invention provides a method for identifying a driver and a passenger who jointly swipes a ticket based on a SaaS platform, which can achieve the following beneficial technical effects compared to the existing technology:

[0033] In response to the passenger redeeming the coupon on the SaaS platform and paying the commission to the driver, the driver's commission amount and the passenger's payment amount are obtained, providing data for preliminary judgment on whether the driver and passenger have engaged in joint coupon swiping; based on the commission amount and the payment amount, it is determined whether the driver and passenger are suspected of jointly swiping coupons, thereby achieving a quick judgment on the possible suspicion of such behavior; if there is suspected behavior, a multi-dimensional feature vector is constructed based on the driver information, passenger information and order information related to the suspected behavior, and the current orders of the driver and passenger on the designated driver platform, as well as the feature data of the driver and passenger, are obtained, providing reliable reference feature data for the identification of coupon swiping behavior; based on the multi-dimensional feature vector, the joint coupon swiping behavior of the driver and passenger is identified, the joint coupon swiping behavior of the driver and passenger is accurately identified, and the interests of the passengers are avoided from being damaged. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 This is a flow chart of a method for identifying a driver and a passenger who jointly swipes a voucher based on a SaaS platform according to an embodiment of the present invention;

[0036] Figure 2 This is a flow chart of another method for identifying a driver and a passenger who jointly swipes a coupon based on a SaaS platform according to an embodiment of the present invention;

[0037] Figure 3 This is a flowchart of another method for identifying a driver and passenger's joint voucher swiping based on a SaaS platform according to an embodiment of the present invention;

[0038] Figure 4 This is a structural block diagram of a driver and passenger joint ticket swiping identification device based on a SaaS platform according to an embodiment of the present invention;

[0039] Figure 5 Schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative efforts shall fall within the scope of protection of the present invention.

[0041] The embodiment of the present invention provides a method for identifying the joint swiping of coupons by drivers and passengers based on a SaaS platform, which can be applied to scenarios such as online car-hailing platforms and designated driver platforms.

[0042] According to an embodiment of the present invention, an embodiment of a method for identifying joint ticket swiping by drivers and passengers on a SaaS platform is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0043] In this embodiment, a SaaS platform driver and passenger joint coupon identification method is provided, which can be used on the server side. Figure 1 is a flow chart of a method for identifying a driver and a passenger who jointly swipes a voucher on a SaaS platform according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0044] Step S101, in response to the passenger redeeming the coupon on the SaaS platform and paying the commission to the driver, the driver's commission amount and the passenger's payment amount are obtained.

[0045] In this embodiment, the passenger summons a driver on a SaaS-based designated driver platform and completes the trip. After paying the driver online, the designated driver platform obtains the driver's commission amount and the passenger's payment amount.

[0046] Step S102: Determine whether the driver and the passenger are suspected of jointly swiping coupons based on the commission amount and the payment amount.

[0047] In this embodiment, based on the commission amount that the driver should receive and the payment amount that the passenger has paid, by comparing the two, it can be preliminarily determined whether the driver and the passenger are suspected of jointly swiping coupons.

[0048] Step S103: If there is suspected behavior, a multi-dimensional feature vector is constructed based on the driver information, passenger information, and order information related to the suspected behavior.

[0049] In this embodiment, a multidimensional feature vector is a feature vector composed of features extracted from the three dimensions of driver, passenger, and order. If a driver and passenger are suspected of collaborating on coupons, a feature vector is extracted based on the driver's information, passenger information, and information on the designated driver platform regarding the driver and passenger's current orders suspected of coupon swiping, and a multidimensional feature vector is constructed.

[0050] Step S104: Identify the driver and passenger's joint coupon swiping behavior based on the multi-dimensional feature vector.

[0051] In this embodiment, multi-dimensional feature vectors are used to identify the joint ticket swiping behavior of drivers and passengers through a neural network algorithm.

[0052] The present embodiment provides a method for identifying joint coupon swiping by drivers and passengers based on a SaaS platform. In response to a passenger redeeming a coupon on the SaaS platform and paying a commission to the driver, the driver's commission amount and the passenger's payment amount are obtained, providing data for preliminary judgment on whether the driver and passenger have engaged in joint coupon swiping. Based on the commission amount and the payment amount, it is determined whether there is suspicion of joint coupon swiping by the driver and the passenger, thereby enabling a rapid judgment on possible suspicion of such behavior. If there is suspicion of behavior, a multi-dimensional feature vector is constructed based on the driver information, passenger information, and order information related to the suspected behavior, thereby obtaining the current orders of the driver and passenger on the designated driver platform, as well as the feature data of the driver and passenger, providing reliable reference feature data for identifying coupon swiping behavior. Based on the multi-dimensional feature vector, the joint coupon swiping behavior of the driver and passenger is identified, and the joint coupon swiping behavior of the driver and passenger is accurately identified, thereby avoiding damage to the interests of the passengers.

[0053] In this embodiment, a method for identifying a driver and a passenger who jointly swipes a voucher based on a SaaS platform is provided, which can be used on the server side. Figure 2 is a flow chart of another method for identifying a driver and a passenger who jointly swipes a coupon based on a SaaS platform according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0054] Step S201, in response to the passenger redeeming the coupon on the SaaS platform and paying the commission to the driver, the driver's commission amount and the passenger's payment amount are obtained.

[0055] For details, please see Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0056] Step S202: Determine whether the driver and the passenger are suspected of jointly swiping coupons based on the commission amount and the payment amount.

[0057] Specifically, the above step S202 includes:

[0058] Step S2021: If the payment amount is greater than the commission amount, it is determined that the driver and the passenger are suspected of jointly swiping coupons.

[0059] In this embodiment, if the payment amount is greater than the commission amount, it means that the amount paid by the passenger is greater than the commission amount that the driver should receive, and then it can be determined that there is suspicion of joint voucher swiping by the driver and the passenger.

[0060] Step S2022: If the payment amount is less than or equal to the commission amount, it is determined that there is no suspicion of joint coupon swiping between the driver and the passenger.

[0061] In this example, if the payment amount is less than or equal to the commission amount, it means that the amount paid by the passenger is not greater than the commission amount that the driver should receive. In other words, the total amount paid by the passenger and the redeemed coupons is equal to the driver's commission amount. Therefore, it can be determined that there is no suspicion of joint coupon swiping between the driver and the passenger. The current order is a normal order.

[0062] Step S203: If there is suspected behavior, a multi-dimensional feature vector is constructed based on the driver information, passenger information, and order information related to the suspected behavior.

[0063] In this embodiment, driver information includes the driver's account on the designated driver platform. Passenger information includes the passenger's account on the designated driver platform. Order information includes the order number, payment amount, commission amount, and coupons redeemed for the order. If there is suspicion of a driver and passenger collaborating on coupons, a multi-dimensional feature vector is constructed based on the driver information, passenger information, and order information related to the suspected behavior.

[0064] Specifically, the above step S203 includes:

[0065] Step S2031: extract a first feature vector based on the driver account, the driver's first login device information, and the driver's first order information in the driver information.

[0066] In this embodiment, the first login device information refers to the device used by the driver on the designated driver platform, including information such as the device code, device model, and device IP address. The first order information refers to the order information in the driver-side software of the designated driver platform, including information such as the distance from the starting point, the commission amount, and the driving time corresponding to the order. The first feature vector refers to a vector composed of features extracted from the driver-side data. Through a neural network model, such as a recurrent neural network, the first feature vector can be extracted from the driver account in the driver information, the driver's first login device information, and the driver's first order information.

[0067] Step S2032: extract a second feature vector based on the passenger account, the passenger's second login device information, and the passenger's second order information in the passenger information, where the second order information corresponds to the first order information.

[0068] In this embodiment, the second login device information refers to the device used by the passenger to use the designated driver platform, including information such as the device code, device model, and device IP address. The second order information is the order information in the passenger-side software of the designated driver platform, including information such as the distance from the starting point, the payment amount, the coupon amount, and the ride duration corresponding to the order. The second feature vector refers to a vector composed of features extracted from the data on the passenger side. Similar to the above steps, a neural network model, such as a recurrent neural network, can be used to extract the second feature vector from the passenger account in the passenger information, the passenger's second login device information, and the passenger's second order information.

[0069] Step S2033: Merge the first eigenvector and the second eigenvector into a multi-dimensional eigenvector.

[0070] In this embodiment, a matrix fusion method is used to fuse the first eigenvector and the second eigenvector into a multi-dimensional feature vector, wherein the multi-dimensional feature vector includes features reflecting driver information, passenger information, and order information.

[0071] Step S204: Identify the driver and passenger's joint coupon swiping behavior based on the multi-dimensional feature vector.

[0072] Specifically, the above step S204 includes: using a multi-dimensional feature vector to identify the joint ticket swiping behavior of the driver and the passenger through a joint ticket swiping recognition model, wherein the joint ticket swiping recognition model is used to indicate whether the multi-dimensional feature vector is mapped to whether the driver and the passenger have the joint ticket swiping behavior.

[0073] In this embodiment, the joint ticket swiping identification model refers to a model that uses a neural network algorithm to identify the behavior of drivers and passengers swiping tickets together. It is used to map a multi-dimensional feature vector to indicate whether the driver and passenger have engaged in joint ticket swiping. Using the multi-dimensional feature vector as input to the model, the joint ticket swiping identification model can identify whether the behavior is joint ticket swiping.

[0074] Through the technical solution of this embodiment, data related to the suspected joint ticket swiping behavior of drivers and passengers are extracted to obtain a multi-dimensional feature vector, and the behavior is identified through a joint ticket swiping recognition model, thereby improving the recognition rate and accuracy of the joint ticket swiping behavior of drivers and passengers.

[0075] In some optional implementations, before step S2041, the following steps are further included:

[0076] Step a1: The confirmed joint coupon swiping behavior recorded by the SaaS platform is used as a sample label, and the features extracted from the passenger information, driver information and order information corresponding to the sample label are used as multi-dimensional feature vector samples.

[0077] In step a2, the deep neural network for classification is trained using multi-dimensional feature vector samples and sample labels to obtain a joint coupon swiping recognition model.

[0078] In this example, a training dataset was generated by constructing sample labels and multi-dimensional feature vector samples for joint ticket swiping. Based on this training dataset, a deep neural network for classification was trained to develop a joint ticket swiping recognition model capable of identifying joint ticket swiping behavior. This improved the accuracy of detecting joint ticket swiping by drivers and passengers.

[0079] In some optional implementations, after step S204, the process further includes: if the driver and passenger's joint coupon swiping behavior is successfully identified, freezing the order corresponding to the behavior.

[0080] In this embodiment, the designated driver platform successfully identifies the joint coupon swiping behavior of the driver and the passenger, and immediately freezes the current order, thereby preventing the joint coupon swiping behavior from occurring and avoiding losses to the passengers.

[0081] Figure 3 This is a flow chart of another method for identifying a driver and a passenger who jointly swipes a voucher based on a SaaS platform according to an embodiment of the present invention. Figure 3 As shown, a passenger places a ride order on a SaaS-based designated driver platform. The platform obtains user information and device information. As the driver receives the order and completes the fulfillment, the platform also obtains the driver's device information. The passenger pays online and redeems the coupon. The designated driver platform's settlement center completes the commission settlement according to the settlement rules configured by the SaaS customer and sends a "calculation completed" message to the RocketMQ distributed messaging middleware message queue. This message queue includes key fields such as the order number and the driver's commission income. Based on the settlement information, the designated driver platform's risk control center determines whether there is suspicion of illegal profiting through coupon swiping. The judgment strategy is to determine whether the payment amount is less than the driver's commission amount. If not, the order is ignored. If so, a multidimensional feature vector is constructed using the driver and passenger IDs, the driver and passenger devices, and the order information. In response to the stored feature vector, the multidimensional feature vector is stored in Redis. Using the multidimensional feature vector, a neural network is used to predict the likelihood of coupon swiping and determine whether it exceeds a preset threshold. If so, the commission for the order involved in the coupon swiping is frozen. Otherwise, the judgment ends. It can realize timely recognition of the joint ticket swiping behavior of drivers and passengers and improve recognition accuracy.

[0082] In this embodiment, a device for identifying the joint swipe of a driver and a passenger based on a SaaS platform is also provided. The device is used to implement the above-mentioned embodiments and preferred implementations, and the details that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation by hardware, or a combination of software and hardware, is also possible and conceivable.

[0083] This embodiment provides a SaaS platform-based identification device for drivers and passengers to jointly swipe tickets. Figure 4 Shown, including:

[0084] An acquisition module 401 is used to obtain the driver's commission amount and the passenger's payment amount on the SaaS platform;

[0085] Suspicion determination module 402, for determining whether the driver and the passenger are suspected of jointly swiping coupons based on the commission amount and the payment amount;

[0086] Feature construction module 403, for constructing a multi-dimensional feature vector based on driver information, passenger information, and order information related to the suspected behavior if there is suspected behavior;

[0087] The behavior recognition module 404 is used to recognize the joint ticket swiping behavior of the driver and the passenger based on the multi-dimensional feature vector.

[0088] In some optional implementations, the suspicion determination module 402 includes:

[0089] The first determining unit is configured to determine that the driver and the passenger are suspected of jointly using coupons if the payment amount is greater than the commission amount;

[0090] The second determining unit is used to determine that there is no suspicion of joint coupon swiping between the driver and the passenger if the payment amount is less than or equal to the commission amount.

[0091] In some optional implementations, the feature construction module 403 includes:

[0092] A first feature extraction unit is configured to extract a first feature vector based on the driver account, the driver's first login device information, and the driver's first order information in the driver information;

[0093] a second feature extraction unit, configured to extract a second feature vector based on the passenger account, the passenger's second login device information, and the passenger's second order information in the passenger information, where the second order information corresponds to the first order information;

[0094] The feature fusion unit is used to fuse the first feature vector and the second feature vector into a multi-dimensional feature vector.

[0095] In some optional implementations, the behavior recognition module 404 includes:

[0096] The identification unit is used to use the multi-dimensional feature vector to identify the joint ticket swiping behavior of the driver and the passenger through the joint ticket swiping identification model, wherein the joint ticket swiping identification model is used to indicate whether the multi-dimensional feature vector is mapped to whether the driver and the passenger have the joint ticket swiping behavior.

[0097] In some optional implementations, the behavior recognition module 404 further includes:

[0098] A sample collection unit is used to record confirmed joint coupon swiping behaviors through the SaaS platform as sample labels, and extract features from the passenger information, driver information, and order information corresponding to the sample labels as multi-dimensional feature vector samples;

[0099] The model training unit is used to train the deep neural network used for classification using multi-dimensional feature vector samples and sample labels to obtain a joint coupon swiping recognition model.

[0100] In some optional implementations, the behavior recognition module 404 further includes:

[0101] The freezing unit is used to freeze the order corresponding to the behavior if the joint coupon swiping behavior of the driver and the passenger is successfully identified.

[0102] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0103] The SaaS platform-based identification device for joint ticket swiping by drivers and passengers in this embodiment is presented in the form of a functional unit, where the unit refers to an application-specific integrated circuit (ASIC) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0104] The embodiment of the present invention also provides a computer device having the above Figure 5 The illustrated device is a SaaS platform-based identification device for joint ticket swiping by drivers and passengers.

[0105] See also Figure 5 , Figure 5 is a structural diagram of a computer device provided by an optional embodiment of the present invention, such as Figure 5As shown, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components utilize different buses to communicate with each other and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in the memory or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Equally, multiple computer devices can be connected, and each device provides part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0106] The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic, or any combination thereof.

[0107] The memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0108] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0109] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0110] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0111] The embodiment of the present invention also provides a computer-readable storage medium. The above-mentioned method according to the embodiment of the present invention can be implemented in hardware, firmware, or implemented as a computer code that can be recorded in a storage medium, or implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memory. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

[0112] A portion of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the form in which the computer program instruction exists in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc. Accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium that can be accessed by the computer.

[0113] Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention. Such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A driver and passenger joint voucher recognition method based on a SaaS platform, characterized in that: The method comprises: In response to the passenger redeeming the coupon on the SaaS platform and paying the commission to the driver, obtaining the driver's commission amount and the passenger's payment amount; Determining whether the driver and the passenger are suspected of colluding on coupons based on the commission amount and the payment amount; If the suspected behavior exists, a multi-dimensional feature vector is constructed based on the driver information, passenger information, and order information related to the suspected behavior; Based on the multi-dimensional feature vector, the joint ticket swiping behavior of the driver and the passenger is identified.

2. The method according to claim 1, characterized in that The determining, based on the commission amount and the payment amount, whether the driver and the passenger are suspected of jointly swiping coupons includes: If the payment amount is greater than the commission amount, it is determined that the driver and the passenger are suspected of jointly swiping the coupons; If the payment amount is less than or equal to the commission amount, it is determined that there is no suspicion of joint voucher swiping between the driver and the passenger.

3. The method according to claim 2, characterized in that The multi-dimensional feature vector is constructed based on the driver information, passenger information, and order information related to the suspected behavior, including: Extracting a first feature vector based on the driver account, the driver's first login device information, and the driver's first order information in the driver information; extracting a second feature vector based on the passenger account number, the passenger's second login device information, and the passenger's second order information in the passenger information, where the second order information corresponds to the first order information; The first feature vector and the second feature vector are fused into a multi-dimensional feature vector.

4. The method according to claim 1, wherein The identifying the joint ticket swiping behavior of the driver and the passenger based on the multi-dimensional feature vector includes: The multi-dimensional feature vector is used to identify the joint ticket swiping behavior of the driver and the passenger through a joint ticket swiping recognition model, wherein the joint ticket swiping recognition model is used to indicate whether the multi-dimensional feature vector is mapped to whether the driver and the passenger have engaged in joint ticket swiping behavior.

5. The method according to claim 4, characterized in that Before using the multi-dimensional feature vector to identify the joint ticket swiping behavior of the driver and the passenger through a joint ticket swiping recognition model, the method further includes: The confirmed joint coupon swiping behavior recorded by the SaaS platform is used as a sample label, and the features extracted from the passenger information, driver information, and order information corresponding to the sample label are used as a multi-dimensional feature vector sample; The multi-dimensional feature vector samples and the sample labels are used to train a deep neural network for classification to obtain the combined coupon swiping recognition model.

6. The method according to claim 1, characterized in that After identifying the joint ticket-swiping behavior of the driver and the passenger based on the multi-dimensional feature vector, the method further includes: If the joint coupon swiping behavior of the driver and the passenger is successfully identified, the order corresponding to the behavior will be frozen.

7. A SaaS platform-based identification device for joint ticket swiping by drivers and passengers, characterized by: The device comprises: An acquisition module, used to obtain the driver's commission amount and the passenger's payment amount on the SaaS platform; A suspicion determination module, configured to determine whether the driver and the passenger are suspected of jointly swiping coupons based on the commission amount and the payment amount; a feature construction module for constructing a multi-dimensional feature vector based on driver information, passenger information, and order information related to the suspected behavior if the suspected behavior exists; A behavior recognition module is used to identify the joint ticket swiping behavior of the driver and the passenger based on the multi-dimensional feature vector.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, computer instructions are stored in the memory, and the processor executes the driver and passenger joint ticket swiping identification method based on the SaaS platform as described in any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the driver and passenger joint ticket swiping identification method based on the SaaS platform as described in any one of claims 1 to 6.

10. A computer program product, characterized in that It includes computer instructions, which are used to enable a computer to execute the driver and passenger joint ticket swiping identification method based on the SaaS platform as described in any one of claims 1 to 6.

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