CN117874606A - Network taxi driver verification method and device, computer equipment and storage medium - Google Patents
Network taxi driver verification method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN117874606A CN117874606A CN202311798722.1A CN202311798722A CN117874606A CN 117874606 A CN117874606 A CN 117874606A CN 202311798722 A CN202311798722 A CN 202311798722A CN 117874606 A CN117874606 A CN 117874606A
- Authority
- CN
- China
- Prior art keywords
- information
- target object
- passing
- verification
- preset
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012795 verification Methods 0.000 title claims abstract description 107
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000012549 training Methods 0.000 claims description 47
- 230000015654 memory Effects 0.000 claims description 26
- 238000012360 testing method Methods 0.000 claims description 24
- 230000003068 static effect Effects 0.000 claims description 12
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 4
- 230000006870 function Effects 0.000 description 6
- 238000001514 detection method Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000006399 behavior Effects 0.000 description 4
- 238000013527 convolutional neural network Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000001815 facial effect Effects 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000004913 activation Effects 0.000 description 2
- 238000013475 authorization Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011176 pooling Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of computers and discloses a method, a device, computer equipment and a storage medium for verifying a network taxi driver, wherein the method acquires a data information set and attribute information of a target object in a preset historical time period, and provides a data basis for subsequent verification; secondly, corresponding verification information and order information in each data information subset are analyzed, and departure information of the target object is determined, so that the behavior mode and habit of the target object can be known; on the basis, according to the departure information, the attribute information and the preset model, the passing information of the target object is determined, and the passing information of the target object can be accurately determined; according to the passing information and the preset threshold value, whether the target object is an object to be verified or not is determined, the target object is verified according to the verification conditions of historical data and the like, the expansibility is strong, and the identification efficiency is improved without depending on a third party interface.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for verifying a network taxi driver, computer equipment and a storage medium.
Background
With the rapid development of net-bound vehicles, a large number of drivers are rushing into the platform, but with the following safety problems, including false drivers, who themselves do not qualify as net-bound vehicles, but rather use or substitute the identity of other legitimate drivers in an attempt to gain advantage.
The traditional driving safety detection is dependent on a third-party face recognition interface and is used for full detection, so that the driver is a legal and compliant driver, but high recognition cost is brought along with the increasing driver quantity.
The above test method has the following three problems: 1. the practicability is poor, the traditional situation is full detection, and the cost is gradually overdrawn along with the increase of the magnitude, so that an priori strategy is urgently needed for improvement; 2. poor pertinence, no abnormal sensing capability is provided, and detection is carried out without difference; 3. the expansibility is poor, the method is a full-detection strategy, the method does not have autonomous consciousness, and the method is not preferable to rely on interfaces of third parties completely;
disclosure of Invention
In view of the above, the present invention provides a method, apparatus, computer device and storage medium for verifying a network taxi driver, so as to solve the problem of difficult verification of the existing network taxi driver.
In a first aspect, the present invention provides a method for verifying a driver of a network taxi, the method comprising:
acquiring a data information set and attribute information of a target object in a preset historical time period, wherein the data information set comprises at least one data information subset, and each data information subset comprises verification information and order information of the target object;
analyzing the corresponding verification information and order information in each data information subset, and determining the departure information of the target object;
determining passing information of the target object according to the departure information, the attribute information and the preset model;
and determining whether the target object is an object to be verified or not according to the passing information and a preset threshold value.
The method has the beneficial effects that the data information set and the attribute information of the target object in the preset historical time period are obtained, and a data basis is provided for subsequent verification; secondly, corresponding verification information and order information in each data information subset are analyzed, and departure information of the target object is determined, so that the behavior mode and habit of the target object can be known; on the basis, according to the departure information, the attribute information and the preset model, the passing information of the target object is determined, and the passing information of the target object can be accurately determined; according to the passing information and the preset threshold value, whether the target object is an object to be verified or not is determined, the target object is verified according to the verification conditions of historical data and the like, the expansibility is strong, and the identification efficiency is improved without depending on a third party interface.
In an alternative embodiment, the verification information includes dynamic verification, static verification and failure reasons, the order information includes completion information and income information, corresponding verification information and order information in each data information subset are analyzed, and departure information of the target object is determined, specifically including:
determining a first pass rate of the target object according to the dynamic verification in each data information subset;
determining a second pass rate of the target object based on static verification in each data information subset;
determining the final passing rate of the target object according to the failure reason, the first passing rate and the second passing rate;
determining the driver attribute of the target object according to the completion information and the income information;
and determining the departure information according to the driver attribute and the final passing rate.
In an alternative embodiment, the preset model includes a first preset model and a second preset model, and the determining the passing information of the target object according to the departure information, the attribute information and the preset model specifically includes:
inputting the departure information into a first preset model for prediction to obtain initial passing information of a target object;
and inputting the initial passing information and the attribute information into a second preset model for prediction to obtain the passing information of the target object.
In an optional implementation manner, the preset threshold includes a first preset threshold and a second preset threshold, and determining whether the target object is an object to be verified according to the passing information and the preset threshold specifically includes:
when the passing information is smaller than a first preset threshold, the target object is not the object to be verified, and verification failure information is generated according to the passing information and the first preset threshold;
when the passing information is larger than or equal to the first preset threshold value and smaller than the second preset threshold value, acquiring the face information of the target object, wherein the face information is used for repeatedly verifying the target object;
when the passing information is larger than a second preset threshold, the target object is the object to be verified.
In an alternative embodiment, the pre-set model is trained by:
acquiring a training sample set, wherein the training sample set comprises a data information set and attribute information of each training object and passing information of the corresponding training object;
analyzing a data information set corresponding to the training sample set, and determining the departure information of the corresponding eye sample object;
dividing the departure information and the attribute information into a training set and a testing set;
inputting the training set into a preset model, determining training passing information of a target object until the training passing information meets a first preset condition of the preset model, and determining an initial preset model;
inputting the test set into the initial preset model to obtain test passing information, and determining the initial preset model as a trained preset model when the test passing information meets a second preset condition.
In an alternative embodiment, the method further comprises:
and when the test passing information is not satisfied with the second preset condition, adjusting the parameters of the initial preset model to continue training until the test passing information satisfies the second preset condition. .
In a second aspect, the present invention provides a network vehicle driver verification apparatus, the apparatus comprising:
the data acquisition module is used for acquiring a data information set and attribute information of a target object in a preset historical time period, wherein the data information set comprises at least one data information subset, and each data information subset comprises verification information and order information of the target object;
the analysis data module is used for analyzing the corresponding verification information and order information in each data information subset and determining the departure information of the target object;
the prediction module is used for determining the passing information of the target object according to the departure information, the attribute information and the preset model;
and the verification module is used for determining whether the target object is an object to be verified or not according to the passing information and a preset threshold value.
In a third aspect, the present invention provides a computer device comprising: the system comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the network bus driver verification method according to the first aspect or any implementation mode corresponding to the first aspect.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon computer instructions for causing a computer to perform the network vehicle driver verification method of the first aspect or any of its corresponding embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method of network about vehicle driver verification in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a net restraint vehicle driver verification method according to an embodiment of the present invention;
FIG. 3 is a block diagram of a network vehicle driver verification device according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In this embodiment, a method for verifying a driver of a network bus is provided, and fig. 1 is a flowchart of a method for verifying a driver of a network bus according to an embodiment of the present invention. In accordance with an embodiment of the present invention, there is provided an embodiment of a method for network vehicle driver verification, it being noted that the steps shown in the flowchart of the figures may be performed in a computer system, such as a set of computer executable instructions, and that although a logical sequence is shown in the flowchart, in some cases the steps shown or described may be performed in a different order than what is shown or described herein.
As shown in fig. 1, the flow of the network vehicle driver verification method includes the following steps:
step S101, acquiring a data information set and attribute information of a target object in a preset historical time period, wherein the data information set comprises at least one data information subset, and each data information subset comprises verification information and order information of the target object.
For example, the platform database may be used to obtain the data information set and attribute information of the target object in a historical preset time period, where the preset historical time period is exemplified by the past 15 days, the data information set is data information corresponding to each day, the data of each day corresponds to a subset, and the corresponding data information of each day may be whether verification of the target object in one day passes or not, and the order completion condition of the target object. The attribute information may be identity information of the target object, in particular, sex, name, registration source, employment type, and the like.
After the data information set is obtained, the data information set can be cleaned to remove repeated, erroneous or incomplete data. Meanwhile, preprocessing is carried out on the data, such as missing value filling, abnormal value processing and the like, so that the accuracy and the reliability of data analysis are improved.
Step S102, corresponding verification information and order information in each data information subset are analyzed, and departure information of the target object is determined.
Illustratively, the characteristics associated with the departure are extracted from the verification information and the order information. These characteristics may include the start time of the order, the completion time, the order type (whether it is windward), the number of orders, and the status of the authentication in the authentication information, the authorization verification results, etc.
Pattern recognition: by analyzing the extracted features, patterns associated with the departure behavior are identified. This may be achieved by means of statistical methods, machine learning algorithms, etc. For example, if a target object receives orders frequently or ends orders within a certain period of time, the object's departure behavior during that period of time may be analyzed.
Logical reasoning: and according to the identified mode, carrying out logic reasoning by combining the identity verification and the authorization verification state of the target object so as to determine the departure information of the target object. For example, if a target object passes authentication within a certain period of time and the corresponding order information is completed within the corresponding period of time, it may be inferred that the departure information of the object within this period of time is normal.
In a preferred embodiment, the verification information includes dynamic verification, static verification, and failure reasons, the order information includes completion information and income information, and the step S102 specifically includes:
determining a first pass rate of the target object according to the dynamic verification in each data information subset;
determining a second pass rate of the target object based on static verification in each data information subset;
determining the final passing rate of the target object according to the failure reason, the first passing rate and the second passing rate;
determining the driver attribute of the target object according to the completion information and the income information;
and determining the departure information according to the driver attribute and the final passing rate.
The verification information is illustratively verification passing conditions of the target object in a preset historical time period (in the past 15 days), and specifically, verification time is classified into static verification and dynamic verification, wherein the reliability of the dynamic verification is higher than that of the static verification. In the process of real-time verification, there may be a case where verification fails due to a network or a device, etc., which may appropriately increase the passing rate of subsequent verification.
The order information comprises the completion information corresponding to the completion of the target object, the order type corresponding to the completion information and the income situation corresponding to the completion information, and further, whether the target object is a full-time driver or a part-time driver can be determined according to the completion information and the income situation, and further, the target object can be an object to be verified.
Specifically, as shown in table 1, in order to verify the details of the information and the order information, the embodiment of the present invention does not limit the details of the order information and the verification information, and those skilled in the art can determine the details according to the actual situation.
The reliability of the dynamic verification is greater than the reliability of the static verification, so that the first passing rate and the second passing rate can be determined according to the magnitude of the weight, and particularly the weight of the dynamic verification is greater than the weight of the static verification.
When the verification failure is caused by the factors such as equipment or network, the first passing rate and the second passing rate are correspondingly improved by a certain range of amplitude. Specifically, the passing rate before adjustment can be determined according to the first passing rate, the second passing rate and the corresponding weight, and finally the passing rate before adjustment is finely adjusted according to the failure reason, so that the final passing rate is obtained.
The corresponding driver attribute can be determined according to the completion information and the income information, wherein the driver attribute is a full-time driver or a part-time driver, or when the completion information and the income information are larger than a certain threshold value, a registration network vehicle can exist while a plurality of people run, the attribute information of the corresponding target object is not matched with the attribute information of the target object during registration, and the follow-up probability is extremely low.
Based on the determination of the final pass rate and the driver attribute, final departure information can be determined, wherein the departure information comprises the related information of the final pass rate, the driver attribute and the like of the driver.
Step S103, determining the passing information of the target object according to the departure information, the attribute information and the preset model.
Illustratively, the preset model may be a convolutional neural network, a BP neural network, or the like predictive model. And inputting the departure information and the attribute information into a corresponding preset model, and predicting the passing information of the target object.
In a preferred embodiment, the preset models include a first preset model and a second preset model, and the step S103 specifically includes:
and inputting the departure information into a first preset model for prediction to obtain initial passing information of the target object.
And inputting the initial passing information and the attribute information into a second preset model for prediction to obtain the passing information of the target object.
The first preset model and the second preset model may be two different models, or may be two sub-models respectively corresponding to one large model.
Specifically, the departure information is input into a first preset model, and initial passing information obtained according to the departure information in a prediction mode is obtained. On the basis, the initial passing information and the attribute information are input into a second preset model for prediction, and the passing information of the target object is obtained through prediction.
Specifically, the preset model is obtained through training the following steps:
acquiring a training sample set, wherein the training sample set comprises a data information set and attribute information of each training object and passing information of the corresponding training object;
analyzing a data information set corresponding to the training sample set, and determining the departure information of the corresponding eye sample object;
dividing the departure information and the attribute information into a training set and a testing set;
inputting the training set into a preset model, determining training passing information of a target object until the training passing information meets a first preset condition of the preset model, and determining an initial preset model;
inputting the test set into the initial preset model to obtain test passing information, and determining the initial preset model as a trained preset model when the test passing information meets a second preset condition.
Illustratively, as shown in fig. 2, the above implementation is described by taking a training process of a convolutional neural network as an example.
Acquiring a sample data set, and dividing the sample data into a training set and a testing set according to a certain proportion;
1) Loading a training set and a testing set to generate a data iterator for gradually calling an iteration model;
2) Initializing a model and related super parameters, wherein the model comprises an Input Layer (Input Layer), a convolution Layer (Convolutional Layer), an activation function (Activation Function), a Pooling Layer (Pooling Layer), a full connection Layer (Fully Connected Layer), an Output Layer (Output Layer), a training round number, a training data size of each time and the like;
3) Training is started until the loss function converges, and a CNN inference model is generated, wherein the model can check the feature importance of each input dimension according to the influence/contribution degree of each dimension on the result Y in model training.
4) Reading data of the verification set, inputting the data into the model 3, and verifying the effect of the trained model on the verification set;
5) Repeating the steps 3) and 4) to generate an optimal CNN model, wherein the evaluation indexes of the model mainly comprise:
accuracy (Accuracy): the accuracy is the ratio of the number of correctly classified samples to the total number of samples, and can measure the accuracy of the overall classification of the model.
The formula: accuracy= (tp+tn)/(tp+tn+fp+fn).
Where TP (True Positive) represents a true example, TN (True Negative) represents a true negative example, FP (False Positive) represents a false positive example, and FN (False Negative) represents a false negative example.
Precision (Precision): the accuracy rate refers to the proportion of the actual positive examples in the positive examples, and the judgment accuracy degree of the model on the positive examples can be measured.
The formula: accuracy = TP/(tp+fp)
Recall (Recall): recall is the proportion of the actual positive cases that are correctly classified as positive cases, and can measure the coverage of the model on the positive cases.
The formula: recall = TP/(tp+fn)
F1 Score (F1 Score): the F1 score comprehensively considers the accuracy rate and the recall rate and is the harmonic mean value of the accuracy rate and the recall rate. It is able to evaluate the overall performance of the model.
The formula: f1 Score=2 x (precision x recall)/(precision + recall)
Step S104, determining whether the target object is an object to be verified according to the passing information and a preset threshold value.
For example, after determining the passing information, comparing the passing information with a preset threshold value, and when the passing information is larger than the preset threshold value, indicating that the target object is the object to be verified, and obtaining verification passing; if the information is smaller than the predetermined value, the information is not the object to be verified, verification is not passed, corresponding non-passing information can be generated on the basis of the information to form log information for recording, or alarm information is sent out, and the like.
In a preferred embodiment, the preset threshold includes a first preset threshold and a second preset threshold, and determining whether the target object is an object to be verified according to the passing information and the preset threshold specifically includes:
when the passing information is smaller than a first preset threshold, the target object is not the object to be verified, and verification failure information is generated according to the passing information and the first preset threshold;
when the passing information is larger than or equal to the first preset threshold value and smaller than the second preset threshold value, acquiring the face information of the target object, wherein the face information is used for repeatedly verifying the target object;
when the passing information is larger than a second preset threshold, the target object is the object to be verified.
Illustratively, when the preset threshold includes a first preset threshold and a second preset threshold, determining whether the target object is a verification object based on a relationship between the pass information and the first preset threshold and the second preset threshold,
when the passing information of the target object is smaller than a first preset threshold value, the system judges that the target object is not the object to be verified. At this time, the system generates verification failure information, which indicates that the object fails to pass the preliminary verification; when the passing information of the target object is larger than or equal to a first preset threshold value but smaller than a second preset threshold value, the system can acquire the face information of the target object, and the face information can be used for repeatedly verifying the target object to ensure the identity or the accuracy of the information; when the passing information of the target object is larger than a second preset threshold value, the system judges that the target object is an object to be verified. This may mean that the object passes a higher level of authentication or that certain specific conditions are met. This is a step-by-step in-depth verification process. First, a possible target object is screened out through simple threshold judgment. For those objects that fail the preliminary verification, the system may give the result of the verification failure directly. For those subjects who pass the preliminary verification but remain questionable, the system further collects facial information for repeated verification. And only those objects that pass all of the verification steps will be determined as objects to be verified.
According to the network taxi driver verification method, the data information set and the attribute information of the target object in the preset historical time period are obtained, and a data basis is provided for subsequent verification; secondly, corresponding verification information and order information in each data information subset are analyzed, and departure information of the target object is determined, so that the behavior mode and habit of the target object can be known; on the basis, according to the departure information, the attribute information and the preset model, the passing information of the target object is determined, and the passing information of the target object can be accurately determined; according to the passing information and the preset threshold value, whether the target object is an object to be verified or not is determined, the target object is verified according to the verification conditions of historical data and the like, the expansibility is strong, and the identification efficiency is improved without depending on a third party interface.
The embodiment also provides a device for verifying the driver of the network bus, which is used for realizing the embodiment and the preferred implementation, and the description is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a network vehicle driver verification device, as shown in fig. 3, including:
the acquiring data module 301 is configured to acquire a data information set and attribute information of a target object in a preset historical time period, where the data information set includes at least one data information subset, and each data information subset includes verification information and order information of the target object;
the analysis data module 302 is configured to analyze the verification information and the order information corresponding to each data information subset, and determine departure information of the target object;
the prediction module 303 is configured to determine passing information of the target object according to the departure information, the attribute information and the preset model;
the verification module 304 is configured to determine whether the target object is an object to be verified according to the passing information and a preset threshold.
In some alternative embodiments, the verification information includes dynamic verification, static verification, and failure reasons, the order information includes completion information and income information, and the analysis data module specifically includes:
a first pass determination unit for determining a first pass rate of the target object according to the dynamic verification in each data information subset;
a second pass determination unit configured to determine a second pass rate of the target object according to the static verification in each of the data information subsets;
a third pass determination unit, configured to determine a final pass of the target object according to the failure cause, the first pass, and the second pass;
the attribute determining unit is used for determining the driver attribute of the target object according to the completion information and the income information;
and the taxi information determining unit is used for determining taxi information according to the driver attribute and the final passing rate.
In some optional embodiments, the preset model includes a first preset model and a second preset model, and the prediction module specifically includes:
the first prediction unit is used for inputting the departure information into a first preset model to predict so as to obtain initial passing information of the target object;
and the second prediction unit is used for inputting the initial passing information and the attribute information into a second preset model to perform prediction so as to obtain the passing information of the target object.
In some optional embodiments, the preset threshold includes a first preset threshold and a second preset threshold, and the verification module specifically includes:
the first verification unit is used for generating verification failure information according to the passing information and a first preset threshold when the passing information is smaller than the first preset threshold and the target object is not the object to be verified;
the second verification unit is used for collecting the facial information of the target object when the passing information is larger than or equal to the first preset threshold value and smaller than the second preset threshold value, and the facial information is used for repeatedly verifying the target object;
and the second verification unit is used for verifying that the target object is the object to be verified when the passing information is larger than a second preset threshold value.
In some alternative embodiments, the pre-set model is trained by:
acquiring a training sample set, wherein the training sample set comprises a data information set and attribute information of each training object and passing information of the corresponding training object;
analyzing a data information set corresponding to the training sample set, and determining the departure information of the corresponding eye sample object;
dividing the departure information and the attribute information into a training set and a testing set;
inputting the training set into a preset model, determining training passing information of a target object until the training passing information meets a first preset condition of the preset model, and determining an initial preset model;
inputting the test set into the initial preset model to obtain test passing information, and determining the initial preset model as a trained preset model when the test passing information meets a second preset condition.
In some alternative embodiments, the apparatus is further for:
and when the test passing information is not satisfied with the second preset condition, adjusting the parameters of the initial preset model to continue training until the test passing information satisfies the second preset condition.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The net jockey vehicle driver verification device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit ) circuit, a processor and memory executing one or more software or fixed programs, and/or other devices that can provide the above functions.
The embodiment of the invention also provides computer equipment, which is provided with the network bus driver verification device shown in the figure 3.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 4, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 4.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. 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.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform a method for implementing the embodiments described above.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.
Claims (9)
1. A method of network vehicle driver verification, the method comprising:
acquiring a data information set and attribute information of a target object in a preset historical time period, wherein the data information set comprises at least one data information subset, and each data information subset comprises verification information and order information of the target object;
analyzing the corresponding verification information and order information in each data information subset, and determining the departure information of the target object;
determining passing information of the target object according to the departure information, the attribute information and a preset model;
and determining whether the target object is an object to be verified or not according to the passing information and a preset threshold value.
2. The method according to claim 1, wherein the verification information includes dynamic verification, static verification, and failure reasons, the order information includes completion information and income information, and the analyzing the corresponding verification information and order information in each data information subset determines departure information of the target object, specifically includes:
determining a first pass rate of the target object according to dynamic verification in each data information subset;
determining a second pass rate of the target object based on static verification in each data information subset;
determining the final passing rate of the target object according to the failure reason, the first passing rate and the second passing rate;
determining a driver attribute of the target object according to the completion information and the income information;
and determining the departure information according to the driver attribute and the final passing rate.
3. The method according to claim 2, wherein the preset model includes a first preset model and a second preset model, and the determining the passing information of the target object according to the departure information, the attribute information and the preset model specifically includes:
inputting the departure information into the first preset model for prediction to obtain initial passing information of the target object;
and inputting the initial passing information and the attribute information into a second preset model for prediction to obtain the passing information of the target object.
4. The method according to claim 1, wherein the preset threshold includes a first preset threshold and a second preset threshold, and the determining whether the target object is an object to be verified according to the passing information and the preset threshold specifically includes:
when the passing information is smaller than a first preset threshold, the target object is not the object to be verified, and verification failure information is generated according to the passing information and the first preset threshold;
when the passing information is larger than or equal to the first preset threshold value and smaller than the second preset threshold value, acquiring the face information of the target object, wherein the face information is used for repeatedly verifying the target object;
and when the passing information is larger than the second preset threshold, the target object is the object to be verified.
5. The method according to any one of claims 1-4, wherein the pre-set model is trained by:
acquiring a training sample set, wherein the training sample set comprises a data information set and attribute information of each training object and passing information of the corresponding training object;
analyzing a data information set corresponding to the training sample set, and determining the departure information of a corresponding eye sample object;
dividing the departure information and the attribute information into a training set and a testing set;
inputting the training set into the preset model, determining training passing information of the target object until the training passing information meets a first preset condition of the preset model, and determining an initial preset model;
inputting the test set into the initial preset model to obtain test passing information, and determining the initial preset model as a trained preset model when the test passing information meets a second preset condition.
6. The method of claim 5, wherein the method further comprises:
and when the test passing information is not satisfied with the second preset condition, adjusting the parameters of the initial preset model to continue training until the test passing information satisfies the second preset condition.
7. A net restraint vehicle driver verification device, the device comprising:
the data acquisition module is used for acquiring a data information set and attribute information of a target object in a preset historical time period, wherein the data information set comprises at least one data information subset, and each data information subset comprises verification information and order information of the target object;
the analysis data module is used for analyzing the corresponding verification information and order information in each data information subset and determining the departure information of the target object;
the prediction module is used for determining the passing information of the target object according to the departure information, the attribute information and a preset model;
and the verification module is used for determining whether the target object is an object to be verified or not according to the passing information and a preset threshold value.
8. A computer device, comprising:
a memory and a processor in communication with each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the net-jockey vehicle driver verification method of any one of claims 1 to 6.
9. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the network vehicle driver verification method of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311798722.1A CN117874606A (en) | 2023-12-25 | 2023-12-25 | Network taxi driver verification method and device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311798722.1A CN117874606A (en) | 2023-12-25 | 2023-12-25 | Network taxi driver verification method and device, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117874606A true CN117874606A (en) | 2024-04-12 |
Family
ID=90580347
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311798722.1A Pending CN117874606A (en) | 2023-12-25 | 2023-12-25 | Network taxi driver verification method and device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117874606A (en) |
-
2023
- 2023-12-25 CN CN202311798722.1A patent/CN117874606A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11734353B2 (en) | Multi-sampling model training method and device | |
| US10805151B2 (en) | Method, apparatus, and storage medium for diagnosing failure based on a service monitoring indicator of a server by clustering servers with similar degrees of abnormal fluctuation | |
| WO2019214309A1 (en) | Model test method and device | |
| Bodik et al. | Fingerprinting the datacenter: automated classification of performance crises | |
| CN111177714B (en) | Abnormal behavior detection method and device, computer equipment and storage medium | |
| CN109816200B (en) | Task pushing method, device, computer equipment and storage medium | |
| CN110807491A (en) | License plate image definition model training method, definition detection method and device | |
| CN111340144B (en) | Risk sample detection method and device, electronic equipment and storage medium | |
| CN112884092A (en) | AI model generation method, electronic device, and storage medium | |
| CN109145030B (en) | Abnormal data access detection method and device | |
| CN110378258A (en) | A kind of vehicle seat information detecting method and equipment based on image | |
| US11120308B2 (en) | Vehicle damage detection method based on image analysis, electronic device and storage medium | |
| CN113592649A (en) | Data asset value determination method and device and electronic equipment | |
| CN115952081A (en) | Software testing method, device, storage medium and equipment | |
| CN113110961B (en) | Equipment abnormality detection method and device, computer equipment and readable storage medium | |
| CN117197591B (en) | Data classification method based on machine learning | |
| CN113822336A (en) | Cloud hard disk fault prediction method, device and system and readable storage medium | |
| CN117874606A (en) | Network taxi driver verification method and device, computer equipment and storage medium | |
| CN117437019A (en) | Credit card overdue risk prediction method, apparatus, device, medium and program product | |
| KR102481583B1 (en) | Method and apparatus for forming inspection criteria data with cloud outsorcing work by using artificial intelligent | |
| CN113239075A (en) | Construction data self-checking method and system | |
| KR102470856B1 (en) | Method and apparatus for cloud outsorcing task checking by using artificial intelligent | |
| CN114547640A (en) | Method and device for judging sensitive operation behaviors, electronic equipment and storage medium | |
| CN114707420A (en) | Credit fraud behavior identification method, device, equipment and storage medium | |
| CN113298182A (en) | Early warning method, device and equipment based on certificate image |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |