CN112017005A - Service maintenance method, device, server and storage medium - Google Patents
Service maintenance method, device, server and storage medium Download PDFInfo
- Publication number
- CN112017005A CN112017005A CN202010891418.1A CN202010891418A CN112017005A CN 112017005 A CN112017005 A CN 112017005A CN 202010891418 A CN202010891418 A CN 202010891418A CN 112017005 A CN112017005 A CN 112017005A
- Authority
- CN
- China
- Prior art keywords
- service
- abnormal
- parameter
- state
- order
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
- G06Q30/0635—Processing of requisition or of purchase orders
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application provides a service maintenance method, a service maintenance device, a server and a storage medium, which relate to the technical field of Internet.A service maintenance operation corresponding to a service abnormal state is output to maintain a service order by acquiring service state parameters of a service provider when the service order is executed and judging that the service provider is in the service abnormal state according to the service state parameters; therefore, the service orders with abnormal service states can be rapidly searched out, the service orders with abnormal service states are automatically maintained, and the maintenance efficiency of the service orders is improved.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to a service maintenance method, an apparatus, a server, and a storage medium.
Background
With the development of internet technology, some service platforms can provide services such as online taxi taking and the like for users, and the online taxi taking has the advantage of being more flexible than taxi service and the like because the online taxi taking allows the users to reserve taxi taking at any place; in addition, the service platform can monitor the service state of the service provider providing the network taxi taking service, so that the service content of the service provider is restricted, and safer and more stable taxi taking service can be provided for users.
In some scenes, when a service providing end has a traffic accident, operations such as rescue and maintenance of the accident are performed in time, so that the loss caused by the accident can be reduced; however, some service maintenance schemes can only be manually maintained, and the maintenance efficiency is low.
Disclosure of Invention
In view of the above, an object of the present application is to provide a service maintenance method, device, server and storage medium, which can improve the maintenance efficiency of service orders.
In a first aspect, an embodiment of the present application provides a service maintenance method, where the method includes:
acquiring service state parameters of a service provider when executing a service order;
detecting whether the service providing end is in an abnormal service state or not according to the service state parameters;
and when the service providing end is in the abnormal service state, outputting service maintenance operation corresponding to the abnormal service state.
Optionally, as a possible implementation manner, the outputting the service maintenance operation corresponding to the service abnormal state includes:
determining a target service abnormal grade of the service provider according to the service state parameter;
determining a target service maintenance operation corresponding to the target service abnormal level in a preset abnormal operation strategy; the abnormal operation strategy comprises a corresponding relation between a plurality of service abnormal levels and a plurality of service maintenance operations;
and sending the target service maintenance operation to the service provider.
Optionally, as a possible implementation manner, the service state parameter includes service recording data, an audio parameter of the service recording data, text data corresponding to the service recording data, and a driving parameter corresponding to the service provider;
the determining the target service abnormal grade of the service provider according to the service state parameter comprises the following steps:
and inputting the service recording data, the audio parameters of the service recording data, the text data corresponding to the service recording data and the driving parameters corresponding to the service providing end into a preset accident risk level model so that the accident risk level model outputs the target service abnormity level of the service providing end.
Optionally, as a possible implementation, the method further includes:
receiving a service rescue request sent by the service providing terminal aiming at the target service maintenance operation;
and responding to the service rescue request, determining a target rescue service terminal from a plurality of rescue service terminals according to the position information of the service providing terminal, and sending a rescue instruction to the target rescue service terminal.
Optionally, as a possible implementation, the method further includes:
according to the target service abnormal grade, improving the maintenance priority corresponding to the service order; wherein the maintenance priority characterizes an order of the corresponding service orders in an exception troubleshooting queue.
Optionally, as a possible implementation manner, the detecting, according to the service state parameter, whether the service provider is in an abnormal service state includes:
judging whether the service state parameters meet preset abnormal detection conditions or not;
when the service state parameter meets the abnormal detection condition, inputting the service state parameter into a preset accident detection model so that the accident detection model outputs the service state of the service provider; the service state is used for indicating whether the service providing end is in a service abnormal state or not.
Optionally, as a possible implementation manner, the service status parameter includes a driving speed parameter and a driving stop parameter of the service provider;
the judging whether the service state parameter meets a preset abnormal detection condition includes:
judging whether the running speed parameter and/or the running stopping parameter are/is matched with a preset abnormal triggering strategy;
when the running speed parameter and/or the running stopping parameter is matched with the abnormal triggering strategy, determining that the service state parameter meets the abnormal detection condition;
and when the running speed parameter and the running stopping parameter are not matched with the abnormal triggering strategy, determining that the service state parameter does not meet the abnormal detection condition.
Optionally, as a possible implementation manner, the service status parameter includes a driving speed parameter and a driving stop parameter of the service provider;
the judging whether the service state parameter meets a preset abnormal detection condition includes:
inputting the running speed parameter and the running stopping parameter into a preset trigger detection model so that the trigger detection model outputs an abnormal trigger state of the service state parameter;
wherein the exception triggering state is used to indicate whether the service state parameter satisfies the exception detection condition.
Optionally, as a possible implementation manner, the acquiring service state parameters of the service provider when executing the service order includes:
receiving order service data of a service providing terminal when executing a service order;
and extracting characteristic information in the order service data to obtain service state parameters of the service provider when executing the service order.
Optionally, as a possible implementation manner, the extracting feature information in the order service data includes:
extracting service recording data of the service provider, audio parameters of the service recording data, text data corresponding to the service recording data, and driving speed parameters and driving stopping parameters corresponding to the service provider from the order service data.
Optionally, as a possible implementation manner, the acquiring service state parameters of the service provider when executing the service order includes:
and receiving service state parameters sent by the service provider when the service provider executes a service order.
In a second aspect, an embodiment of the present application provides a service maintenance apparatus, where the apparatus includes:
the processing module is used for acquiring service state parameters of the service providing end when executing the service order;
the processing module is further used for detecting whether the service providing end is in an abnormal service state according to the service state parameter;
and the output module is used for outputting the service maintenance operation corresponding to the abnormal service state when the service providing end is in the abnormal service state.
Optionally, as a possible implementation manner, when outputting the service maintenance operation corresponding to the abnormal service state, the output module is specifically configured to:
determining a target service abnormal grade of the service provider according to the service state parameter;
determining a target service maintenance operation corresponding to the target service abnormal level in a preset abnormal operation strategy; the abnormal operation strategy comprises a corresponding relation between a plurality of service abnormal levels and a plurality of service maintenance operations;
and sending the target service maintenance operation to the service provider.
Optionally, as a possible implementation manner, the service state parameter includes service recording data, an audio parameter of the service recording data, text data corresponding to the service recording data, and a driving parameter corresponding to the service provider;
when determining the target service abnormal level of the service provider according to the service state parameter, the output module is specifically configured to:
and inputting the service recording data, the audio parameters of the service recording data, the text data corresponding to the service recording data and the driving parameters corresponding to the service providing end into a preset accident risk level model so that the accident risk level model outputs the target service abnormity level of the service providing end.
Optionally, as a possible implementation manner, the processing module is further configured to:
receiving a service rescue request sent by the service providing terminal aiming at the target service maintenance operation;
and responding to the service rescue request, determining a target rescue service terminal from a plurality of rescue service terminals according to the position information of the service providing terminal, and sending a rescue instruction to the target rescue service terminal.
Optionally, as a possible implementation manner, the processing module is further configured to:
according to the target service abnormal grade, improving the maintenance priority corresponding to the service order; wherein the maintenance priority characterizes an order of the corresponding service orders in an exception troubleshooting queue.
Optionally, as a possible implementation manner, when detecting whether the service provider is in the abnormal service state according to the service state parameter, the processing module is specifically configured to:
judging whether the service state parameters meet preset abnormal detection conditions or not;
when the service state parameter meets the abnormal detection condition, inputting the service state parameter into a preset accident detection model so that the accident detection model outputs the service state of the service provider; the service state is used for indicating whether the service providing end is in a service abnormal state or not.
Optionally, as a possible implementation manner, the service status parameter includes a driving speed parameter and a driving stop parameter of the service provider;
when judging whether the service state parameter meets a preset abnormal detection condition, the processing module is specifically configured to:
judging whether the running speed parameter and/or the running stopping parameter are/is matched with a preset abnormal triggering strategy;
when the running speed parameter and/or the running stopping parameter is matched with the abnormal triggering strategy, determining that the service state parameter meets the abnormal detection condition;
and when the running speed parameter and the running stopping parameter are not matched with the abnormal triggering strategy, determining that the service state parameter does not meet the abnormal detection condition.
Optionally, as a possible implementation manner, the service status parameter includes a driving speed parameter and a driving stop parameter of the service provider;
when judging whether the service state parameter meets a preset abnormal detection condition, the processing module is specifically configured to:
inputting the running speed parameter and the running stopping parameter into a preset trigger detection model so that the trigger detection model outputs an abnormal trigger state of the service state parameter;
wherein the exception triggering state is used to indicate whether the service state parameter satisfies the exception detection condition.
Optionally, as a possible implementation manner, when acquiring the service state parameter of the service provider when executing the service order, the processing module is specifically configured to:
receiving order service data of a service providing terminal when executing a service order;
and extracting characteristic information in the order service data to obtain service state parameters of the service provider when executing the service order.
Optionally, as a possible implementation manner, when extracting the feature information in the order service data, the processing module is specifically configured to:
extracting service recording data of the service provider, audio parameters of the service recording data, text data corresponding to the service recording data, and driving speed parameters and driving stopping parameters corresponding to the service provider from the order service data.
Optionally, as a possible implementation manner, when acquiring the service state parameter of the service provider when executing the service order, the processing module is specifically configured to:
and receiving service state parameters sent by the service provider when the service provider executes a service order.
In a third aspect, the present application provides a server comprising a memory for storing one or more programs; a processor; the one or more programs, when executed by the processor, implement the service maintenance method described above.
In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the service maintenance method described above.
According to the service maintenance method, the service maintenance device, the server and the storage medium, the service maintenance operation corresponding to the service abnormal state is output by acquiring the service state parameter of the service provider when executing the service order and judging that the service provider is in the service abnormal state according to the service state parameter, so that the service order is maintained; therefore, the service orders with abnormal service states can be rapidly searched out, the service orders with abnormal service states are automatically maintained, and the maintenance efficiency of the service orders is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a schematic application scenario diagram illustrating a service maintenance method provided by the present application;
FIG. 2 is a block diagram of a schematic structure of a server provided in the present application;
FIG. 3 illustrates an exemplary flow chart of a service maintenance method provided herein;
FIG. 4 is an exemplary flowchart of the substeps of step 201 of FIG. 3;
FIG. 5 is an exemplary flowchart of the substeps of step 203 of FIG. 3;
FIG. 6 is an exemplary flowchart of the substeps of step 205 of FIG. 3;
fig. 7 shows an exemplary structural block diagram of a service maintenance apparatus provided in an embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.
In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but is merely representative of some alternative embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
To enable those skilled in the art to use the present disclosure, the following embodiments are presented in conjunction with a specific application scenario, "network taxi". It will be apparent to those skilled in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application. Although the present application is described primarily in the context of "network taxi," it should be understood that this is merely one exemplary embodiment.
It should be noted that in the embodiments of the present application, the term "comprising" is used to indicate the presence of the features stated hereinafter, but does not exclude the addition of further features.
Referring to fig. 1, fig. 1 is a schematic application scenario diagram illustrating a service maintenance method provided in the present application; in some embodiments of the present application, the server, the service request end and the service providing end may be located in a network, and the server, the service request end and the service providing end may perform data interaction through the network.
It should be noted that fig. 1 is only an example, and shows that one service request end and one service providing end establish communication with a server; in some possible application scenarios of the present application, the server may also establish communication with more service request terminals and more service providing terminals, and the present application does not limit the number of service request terminals establishing communication with the server through the network, and does not limit the number of service providing terminals establishing communication with the server through the network.
For example, in a scenario such as "taxi taking over from the internet", drivers who provide taxi taking services can respectively access the server of the service platform through the service providing terminal, and passengers who use taxi taking services can respectively access the server through the service requesting terminal; the method comprises the steps that communication is established between a service platform and a large number of service providing terminals and a large number of service requesting terminals, when the service platform receives a taxi taking service request of the service requesting terminal, the service platform can send a to-be-serviced order to one schedulable service providing terminal according to respective position information of the service requesting terminal and each schedulable service providing terminal (namely the service providing terminal which can provide taxi taking service required for passengers), and the to-be-serviced order can comprise service information of the service requesting terminal which sends the taxi taking service request, such as a taxi taking starting point, a taxi taking terminal, estimated duration, estimated cost and the like; after receiving the to-be-serviced order, the service providing terminal can execute the service order and provide the passenger with the taxi taking service according to the service information in the service order.
In some possible application scenarios, the service platform may monitor the service state of the service order executed by the service provider, for example, compare the real-time travel track of the service provider with the estimated planned travel track, so as to ensure that the service provider can provide the taxi taking service for the service requester according to the estimated planned travel track when the service provider provides the service, thereby ensuring the service quality of the service provider.
In application scenarios such as "taxi taking over via internet", emergencies such as traffic accidents may occur; when abnormal service conditions such as traffic accidents occur, the service providing terminal and the service requesting terminal of the corresponding service orders are timely rescued and maintained, and the loss caused by the accidents can be reduced.
For example, in some possible embodiments, a manager at the service platform side may monitor a service state of the service order based on the obtained service parameters of the service order, so that when it is determined that the service order has a service abnormality, the manager may provide the rescue service to the service provider by requesting a public rescue resource or delegating a rescue team based on parameters such as location information of the service provider having the service abnormality.
However, in the rescue method, for example, the manager on the service platform side needs to manually check the service orders with abnormal service states one by one among a large number of service orders, which is slow in checking speed and low in maintenance efficiency.
Therefore, based on the above defects, the embodiments of the present application provide a possible implementation manner as follows: the service order is maintained by acquiring service state parameters of a service provider when executing the service order and outputting service maintenance operation corresponding to the service abnormal state when judging that the service provider is in the service abnormal state according to the service state parameters; thus, the maintenance efficiency of the service orders can be improved.
Referring to fig. 2, fig. 2 is a schematic block diagram of a server 100 provided in the present application; in some embodiments, server 100 may include memory 101, processor 102, and communication interface 103, with memory 101, processor 102, and communication interface 103 being electrically connected to one another, directly or indirectly, to enable the transfer or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines.
The memory 101 may be used to store software programs and modules, such as program instructions/modules corresponding to the service maintenance apparatus provided in the present application, and the processor 102 executes the software programs and modules stored in the memory 101 to execute various functional applications and data processing, thereby executing the steps of the service maintenance method provided in the present application. The communication interface 103 may be used for communicating signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Programmable Read-Only Memory (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. The Processor 102 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
The following takes the server 100 shown in fig. 2 as an exemplary execution subject to exemplarily describe the service maintenance method provided in the present application.
Referring to fig. 3, fig. 3 shows an exemplary flowchart of a service maintenance method provided in the present application, which may include the following steps in some embodiments:
Taking the scenario of the network taxi taking as an example, after receiving a taxi taking service request sent by a service request terminal, the server can send a to-be-served order to a service providing terminal; when the service provider confirms to receive the to-be-serviced order, the server can establish a service relationship between the service provider and the service requester, and maintain the service provided by the service provider for the service requester by obtaining the service state parameters of the service provider when executing the service order.
For example, in some embodiments, the server may obtain the driving parameters, such as longitude and latitude, angular velocity, speed, acceleration rate of change of acceleration, moving distance, staying time, and the like, of the service provider when executing the service order; or, acquiring service recording data of the service provider when executing the service order, and audio parameters extracted from the service recording data or text contents converted from the service recording data.
Next, the server may detect whether the service providing end is in an abnormal service state according to the obtained service state parameter, for example, the server determines whether a vehicle fault or a traffic accident occurs at the service providing end.
Then, when the server determines that the service provider is in the abnormal service state, the server may output a service maintenance operation corresponding to the abnormal service state, such as dialing an emergency call, reporting an accident location, or scheduling a rescue team.
Therefore, according to the service maintenance method provided by the application, the service maintenance operation corresponding to the service abnormal state is output by acquiring the service state parameter of the service provider when executing the service order and judging that the service provider is in the service abnormal state according to the service state parameter, so that the service order is maintained; therefore, the service orders with abnormal service states can be rapidly searched out, the service orders with abnormal service states are automatically maintained, and the maintenance efficiency of the service orders is improved.
In some embodiments, when the service provider executes the service order and provides service for the service requester, the service provider may collect service data in real time and send the collected service data to the server; thereby enabling the server to extract the required service state parameters based on the obtained service data.
For example, referring to fig. 4 based on fig. 3, fig. 4 is an exemplary flowchart of sub-steps of step 201 in fig. 3; as a possible implementation, step 201 may include the following sub-steps:
step 201-1, receiving order service data of a service provider when executing a service order.
Step 201-3, extracting characteristic information in the order service data to obtain service state parameters of the service provider when executing the service order.
In some embodiments, the service provider may collect order service data of the service provider when executing a service order by using a mounted device such as a vehicle-mounted orange equipment, a vehicle-mounted IMU (Inertial Measurement Unit) device, a sound collection device, and send the collected order service data to the server in real time.
For example, in some possible scenarios, the service provider may collect, in real time, driving data of the service provider, such as longitude and latitude, speed, and heading angle, by using the onboard vehicle-mounted viewing device; in addition, the service provider can also acquire collision detection signals and the like of the service provider in real time by using the loaded IMU equipment and send the detected collision detection signals to the server in real time; and the service providing end can acquire the audio data in real time when the service order is executed under the condition of obtaining the authorization of the user to obtain the service recording data and send the obtained service recording data to the server in real time.
In contrast, when the server receives the order service data sent by the service provider, the server may extract the characteristic information in the order service data to obtain the service state parameters of the service provider when executing the service order.
For example, in some embodiments, the server may calculate, based on the travel data such as longitude and latitude, speed, and heading angle in the order service data, the travel speed parameter corresponding to the service provider, such as acceleration, change rate of acceleration, speed, angular velocity, moving distance, distance information at the time when the acceleration is minimum, and the like, in each sliding time window with a set time duration (e.g., 1min) as a sliding time window, and may also calculate the travel stop parameter such as stop time in each sliding time window.
It is understood that, in some embodiments, the above-mentioned staying time may refer to a time period when the service provider executes the service order, and the corresponding speed is less than a set speed threshold (e.g. 0).
For another example, in some embodiments, the server may extract information such as an order start time, a service start point, a service end point, a distance from the service provider in the service order to the order start point, a distance from the order end point, or a travel time period for the service order.
For another example, in some embodiments, the server may further determine, based on the obtained acceleration of the service provider, when the value of the acceleration is greater than a set threshold and is a negative value, a time corresponding to the acceleration as a rapid deceleration time of the service provider; based on this, the server may extract service recording data of the service providing end in a time period adjacent to the rapid deceleration moment, for example, extract service recording data within 1min before and after the rapid deceleration moment as recording data to be detected, extract audio feature vectors of the recording data to be detected, process the audio feature vectors of the recording data to be detected by using an abnormal sound detection model trained in advance, so as to detect whether abnormal audio such as collision sound, scream sound, door opening and closing sound, and distress sound exists in the recording data to be detected, and add an abnormal type tag to the recording data to be detected when determining that the abnormal audio exists, for example, the added type tag may be "collision sound audio", "scream sound audio", "door opening and closing sound audio", "distress sound audio", and the like.
In some embodiments, the abnormal sound detection model may be a machine learning model constructed based on MFCC (Mel Frequency Cepstral coefficients), CNN (Convolutional Neural network), and the like, and the abnormal sound detection model is trained by using a plurality of abnormal sounds as training samples until the abnormal sound detection model reaches a set convergence condition, so as to obtain a trained abnormal sound detection model, and the trained abnormal sound detection model is used for detecting the abnormal sounds.
In addition, for the extracted recording data to be detected, for example, asr (Automatic Speech Recognition) technology may be adopted, and a track text feature vector is constructed in an accident keyword matching manner, so as to extract text data corresponding to the recording data to be detected.
Of course, it can be understood that, in the above implementation manner provided in the embodiment of the present application, the service state parameter of the service provider when executing the service order is obtained in a manner that the server extracts the feature information in the order service data sent by the service provider; in some other implementation manners of the embodiment of the present application, the service providing end may further perform an operation of extracting feature information in the service order data, and send the service providing end obtained service state parameters to the server; in contrast, the server may receive the service state parameter sent by the service provider when the service provider executes the service order, without performing the operation of extracting the feature information.
Referring additionally to fig. 5 on the basis of fig. 3, fig. 5 shows a schematic flow chart of the sub-steps of step 203, and as a possible implementation, step 203 may include the following sub-steps:
step 203-1, judging whether the service state parameter meets a preset abnormal detection condition;
step 203-3, when the service state parameter meets the abnormal detection condition, inputting the service state parameter into a preset accident detection model, so that the accident detection model outputs the service state of the service provider.
In some embodiments, an accident detection model may be pre-saved in the server based on a machine learning manner, and the accident detection model may be trained to output a corresponding service state according to the input service state parameter, and the service state may be used to indicate whether the service provider is in a service abnormal state.
In addition, the server may preset an abnormal detection condition, and when the server executes step 203, the server may first determine whether the obtained service state parameter meets the preset abnormal detection condition; when the service state parameter satisfies the abnormal detection condition, the server determines that the service provider may have an abnormal state such as a traffic accident, and the server inputs the service state parameter into the preset accident detection model, so that the accident detection model outputs the service state of the service provider, for example, the service state may indicate that the service provider is in the abnormal service state such as the traffic accident, or the service state may also indicate that the service provider is in the normal service state.
In some embodiments, the accident detection model may be constructed based on algorithms such as XGBoost, SVM (Support Vector Machine), LR (Logistic Regression), GBDT (Gradient Boosting Decision Tree); when the accident detection model is trained, the audio data with the label of the abnormal service state can be used as a training sample to train the accident detection model until the accident detection model reaches the set convergence condition, and then the trained accident detection model is obtained; in this way, when the service state is identified by using the accident detection model, the server may use an audio parameter of abnormal sounds such as collision sound, door opening and closing sound, screaming sound, and the like among the service state parameters as an input of the accident detection model, so that the accident detection model outputs the service state of the service provider, thereby determining whether the service provider is in an abnormal service state according to the service state.
In addition, as a possible implementation manner, when the server determines whether the service state parameter satisfies the preset abnormal detection condition, the server may determine whether at least one of the running speed parameter and the running stop parameter matches a preset abnormal triggering policy by using the running speed parameter and the running stop parameter of the service provider included in the service state parameter; when the running speed parameter is matched with the abnormal triggering strategy, the running stopping parameter is matched with the abnormal triggering strategy, or the running speed parameter and the running stopping parameter are both matched with the abnormal triggering strategy, the server determines that the service state parameter meets the abnormal detection condition; when the driving speed parameter and the driving stopping parameter are not matched with the abnormal triggering strategy, the server can determine that the service state parameter does not meet the abnormal triggering condition. Therefore, the detection flexibility of the abnormity detection condition can be improved by flexibly setting different abnormity triggering strategies.
Of course, it is understood that the above is only an example, and a manner in which the server determines whether the service state parameter meets the preset abnormal detection condition is illustrated; in some other possible scenarios in the embodiment of the present application, the server may further use some other implementation apparatuses to determine whether the service state parameter meets a preset abnormal detection condition.
For example, the server may input the obtained running speed parameter and the obtained stopping parameter into a preset trigger detection model based on the running speed parameter and the stopping parameter of the service provider included in the service state parameter, so that the trigger detection model outputs an abnormal trigger state of the service state parameter, and thus, the abnormal trigger state indicates whether the service state parameter satisfies an abnormal detection condition.
The trigger detection model may be constructed based on XGBoost, SVM, LR, for example.
Referring additionally to fig. 6 on the basis of fig. 3, fig. 6 shows a schematic flow chart of the sub-steps of step 205, and as a possible implementation, step 205 may include the following sub-steps:
step 205-1, determining the target service abnormal grade of the service provider according to the service state parameter.
And step 205-3, determining the target service maintenance operation corresponding to the target service abnormal level in the preset abnormal operation strategy.
Step 205-5, the target service maintenance operation is sent to the service provider.
In the case of a traffic accident such as that described above, the driver or passenger may require different rescue methods depending on the severity of the traffic accident.
Therefore, the server may pre-configure an abnormal operation policy, which may include a correspondence between a plurality of service exception levels and a plurality of service maintenance operations.
Based on this, when the server executes step 205, the target service exception level of the service provider may be determined according to the service state parameter of the service provider.
For example, an accident risk level model may be preset in the server, and when step 205 is executed, the server may input, for example, the service recording data, the audio parameter of the service recording data, the text data corresponding to the service recording data, and the driving parameter corresponding to the service provider, which are included in the service state parameter, into the accident risk level model, so that the accident risk level model outputs the target service abnormality level of the service provider.
For example, parameters such as abnormal sounds in service status parameters, scream sound energy values, driver and passenger records, keywords of call contents (such as dialing 120, saving, etc.), speed, acceleration, etc. may be input into the accident risk level model, so that the accident risk level model outputs a target service abnormality level of the service provider, for example, the target service abnormality level may be a dead person, a heavy injury, a light injury, a car damage, etc.
The accident risk level model may be constructed based on XGBoost, LightGradient hoisting Machine (LightGradient hoisting Machine), and the like.
Next, the server may determine a target service maintenance operation corresponding to the target service abnormal level in the abnormal operation policy of, for example, the sprinkle-hu; for example, the service maintenance operation corresponding to the "heavy injury" may be "call a traffic police team and ask whether emergency rescue is needed", or the service maintenance operation corresponding to the "car damage" may be "whether claim settlement service is needed".
Then, the server may send the determined target service maintenance operation to the service provider, so that the service provider may perform maintenance based on the obtained target service maintenance operation.
For example, in some possible scenarios, assuming that the target service maintenance operation obtained by the service provider is "whether emergency rescue is needed", when the service provider receives an input command "determine call emergency rescue", the service provider may send a service rescue request representing "determine call emergency rescue" to the server.
Accordingly, when the server receives a service rescue request sent by the service provider for the target service maintenance operation, the server can provide emergency rescue service to the service provider in response to the service rescue request; for example, as a possible implementation manner, the server may determine a target rescue service end from the plurality of rescue service ends according to the position information of the service providing end, and send a rescue instruction to the target rescue service end, so that the target rescue service end goes to the position of the service providing end based on the obtained rescue instruction, and provides rescue service for the service providing end.
For example, in some embodiments, the server may select, according to the location information of the service provider, a rescue service provider closest to the service provider from among the plurality of rescue services as a target rescue service provider; or determining the priority order of each rescue service end according to the emergency degree of the service providing end, the rescue distance of each rescue service end and the rescue standard, and selecting the rescue service end with the highest corresponding priority as the target rescue service end.
In addition, in some possible scenes, the service state of each service order can be rechecked in combination with a manual checking mode, so that the service orders with abnormal service states which are not checked due to misjudgment and the like when the server performs machine checking are avoided.
In some possible embodiments, for a conventional service order, the server may configure a maintenance priority for each service order according to a time sequence generated by each service order; the corresponding service orders are added into the abnormal investigation queue, so that maintenance personnel perform manual investigation on each service order from the abnormal investigation queue according to the maintenance priority, and the service orders with abnormal service states are prevented from being missed; wherein, the maintenance priority represents the sequence of the corresponding service orders in the abnormal investigation queue.
In addition, in some possible embodiments, for the service provider that the server determines is in the abnormal service state, the server may further increase the maintenance priority corresponding to the service order of the service provider according to the target abnormal service level of the service provider, so that the service provider that is in the abnormal service state can be manually checked and verified more quickly, thereby increasing the maintenance efficiency of the service.
Based on the same inventive concept, the embodiment of the application also provides a service maintenance device corresponding to the service maintenance method; referring to fig. 7, fig. 7 is a block diagram illustrating an exemplary structure of a service maintenance apparatus provided in an embodiment of the present application, where the service maintenance apparatus 300 may include a processing module 301 and an output module 303; wherein:
a processing module 301, configured to obtain a service state parameter when a service provider executes a service order;
the processing module 301 is further configured to detect whether the service provider is in an abnormal service state according to the service state parameter;
the output module 303 is configured to output a service maintenance operation corresponding to the abnormal service state when the service provider is in the abnormal service state.
Optionally, as a possible implementation manner, when outputting the service maintenance operation corresponding to the abnormal service state, the output module 303 is specifically configured to:
determining a target service abnormal grade of a service provider according to the service state parameters;
determining a target service maintenance operation corresponding to the target service abnormal level in a preset abnormal operation strategy; the abnormal operation strategy comprises a corresponding relation between a plurality of service abnormal levels and a plurality of service maintenance operations;
and sending the target service maintenance operation to the service provider.
Optionally, as a possible implementation manner, the service state parameter includes service recording data, an audio parameter of the service recording data, text data corresponding to the service recording data, and a driving parameter corresponding to the service provider;
the output module 303, when determining the target service exception level of the service provider according to the service state parameter, is specifically configured to:
and inputting the service recording data, the audio parameters of the service recording data, the text data corresponding to the service recording data and the driving parameters corresponding to the service providing terminal into a preset accident risk level model so that the accident risk level model outputs the target service abnormity level of the service providing terminal.
Optionally, as a possible implementation, the processing module 301 is further configured to:
receiving a service rescue request sent by a service provider aiming at target service maintenance operation;
and responding to the service rescue request, determining a target rescue service terminal from the plurality of rescue service terminals according to the position information of the service providing terminal, and sending a rescue instruction to the target rescue service terminal.
Optionally, as a possible implementation, the processing module 301 is further configured to:
according to the target service abnormal grade, improving the maintenance priority corresponding to the service order; wherein the maintenance priority represents an order of the corresponding service orders in the exception investigation queue.
Optionally, as a possible implementation manner, when detecting whether the service providing end is in the abnormal service state according to the service state parameter, the processing module 301 is specifically configured to:
judging whether the service state parameters meet preset abnormal detection conditions or not;
when the service state parameters meet the abnormal detection conditions, inputting the service state parameters into a preset accident detection model so that the accident detection model outputs the service state of the service providing end; the service state is used for indicating whether the service providing end is in the abnormal service state.
Optionally, as a possible implementation manner, the service state parameter includes a driving speed parameter and a driving stop parameter of the service provider;
when determining whether the service state parameter satisfies the preset abnormal detection condition, the processing module 301 is specifically configured to:
judging whether the running speed parameter and/or the running stopping parameter are/is matched with a preset abnormal triggering strategy;
when the running speed parameter and/or the running stopping parameter is matched with the abnormal triggering strategy, determining that the service state parameter meets the abnormal detection condition;
and when the running speed parameter and the running stopping parameter are not matched with the abnormal triggering strategy, determining that the service state parameter does not meet the abnormal detection condition.
Optionally, as a possible implementation manner, the service state parameter includes a driving speed parameter and a driving stop parameter of the service provider;
when determining whether the service state parameter satisfies the preset abnormal detection condition, the processing module 301 is specifically configured to:
inputting the running speed parameter and the running stopping parameter into a preset trigger detection model so as to enable the trigger detection model to output an abnormal trigger state of the service state parameter;
the abnormal triggering state is used for indicating whether the service state parameter meets an abnormal detection condition.
Optionally, as a possible implementation manner, when acquiring the service state parameter of the service provider when executing the service order, the processing module 301 is specifically configured to:
receiving order service data of a service providing terminal when executing a service order;
and extracting characteristic information in the order service data to obtain service state parameters of the service provider when executing the service order.
Optionally, as a possible implementation manner, when extracting the feature information in the order service data, the processing module 301 is specifically configured to:
and extracting service recording data of the service provider in the order service data, audio parameters of the service recording data, text data corresponding to the service recording data, and driving speed parameters and driving stopping parameters corresponding to the service provider.
Optionally, as a possible implementation manner, when acquiring the service state parameter of the service provider when executing the service order, the processing module 301 is specifically configured to:
and receiving the service state parameters sent by the service provider when the service provider executes the service order.
Because the principle of the service maintenance device in the embodiment of the present application for solving the problem is similar to that of the service maintenance method in the embodiment of the present application, the implementation of the service maintenance device can refer to the implementation of the method, and repeated details are not repeated.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to some embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in some embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to some embodiments of the present application. And the aforementioned storage medium includes: u disk, removable hard disk, read only memory, random access memory, magnetic or optical disk, etc. for storing program codes.
The above description is only a few examples of the present application and is not intended to limit the present application, and those skilled in the art will appreciate that various modifications and variations can be made in the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (24)
1. A method of service maintenance, the method comprising:
acquiring service state parameters of a service provider when executing a service order;
detecting whether the service providing end is in an abnormal service state or not according to the service state parameters;
and when the service providing end is in the abnormal service state, outputting service maintenance operation corresponding to the abnormal service state.
2. The method of claim 1, wherein outputting the service maintenance operation corresponding to the service exception state comprises:
determining a target service abnormal grade of the service provider according to the service state parameter;
determining a target service maintenance operation corresponding to the target service abnormal level in a preset abnormal operation strategy; the abnormal operation strategy comprises a corresponding relation between a plurality of service abnormal levels and a plurality of service maintenance operations;
and sending the target service maintenance operation to the service provider.
3. The method of claim 2, wherein the service status parameters comprise service recording data, audio parameters of the service recording data, text data corresponding to the service recording data, and driving parameters corresponding to the service provider;
the determining the target service abnormal grade of the service provider according to the service state parameter comprises the following steps:
and inputting the service recording data, the audio parameters of the service recording data, the text data corresponding to the service recording data and the driving parameters corresponding to the service providing end into a preset accident risk level model so that the accident risk level model outputs the target service abnormity level of the service providing end.
4. The method of claim 2, further comprising:
receiving a service rescue request sent by the service providing terminal aiming at the target service maintenance operation;
and responding to the service rescue request, determining a target rescue service terminal from a plurality of rescue service terminals according to the position information of the service providing terminal, and sending a rescue instruction to the target rescue service terminal.
5. The method of claim 2, further comprising:
according to the target service abnormal grade, improving the maintenance priority corresponding to the service order; wherein the maintenance priority characterizes an order of the corresponding service orders in an exception troubleshooting queue.
6. The method according to claim 1, wherein the detecting whether the service provider is in an abnormal service state according to the service state parameter comprises:
judging whether the service state parameters meet preset abnormal detection conditions or not;
when the service state parameter meets the abnormal detection condition, inputting the service state parameter into a preset accident detection model so that the accident detection model outputs the service state of the service provider; the service state is used for indicating whether the service providing end is in a service abnormal state or not.
7. The method of claim 6, wherein the service status parameters include a travel speed parameter and a travel stop parameter of the service provider;
the judging whether the service state parameter meets a preset abnormal detection condition includes:
judging whether the running speed parameter and/or the running stopping parameter are/is matched with a preset abnormal triggering strategy;
when the running speed parameter and/or the running stopping parameter is matched with the abnormal triggering strategy, determining that the service state parameter meets the abnormal detection condition;
and when the running speed parameter and the running stopping parameter are not matched with the abnormal triggering strategy, determining that the service state parameter does not meet the abnormal detection condition.
8. The method of claim 6, wherein the service status parameters include a travel speed parameter and a travel stop parameter of the service provider;
the judging whether the service state parameter meets a preset abnormal detection condition includes:
inputting the running speed parameter and the running stopping parameter into a preset trigger detection model so that the trigger detection model outputs an abnormal trigger state of the service state parameter;
wherein the exception triggering state is used to indicate whether the service state parameter satisfies the exception detection condition.
9. The method of claim 1, wherein the obtaining the service state parameters of the service provider when executing the service order comprises:
receiving order service data of a service providing terminal when executing a service order;
and extracting characteristic information in the order service data to obtain service state parameters of the service provider when executing the service order.
10. The method of claim 9, wherein said extracting characteristic information from said order service data comprises:
extracting service recording data of the service provider, audio parameters of the service recording data, text data corresponding to the service recording data, and driving speed parameters and driving stopping parameters corresponding to the service provider from the order service data.
11. The method of claim 1, wherein the obtaining the service state parameters of the service provider when executing the service order comprises:
and receiving service state parameters sent by the service provider when the service provider executes a service order.
12. A service maintenance device, the device comprising:
the processing module is used for acquiring service state parameters of the service providing end when executing the service order;
the processing module is further used for detecting whether the service providing end is in an abnormal service state according to the service state parameter;
and the output module is used for outputting the service maintenance operation corresponding to the abnormal service state when the service providing end is in the abnormal service state.
13. The apparatus according to claim 12, wherein the output module, when outputting the service maintenance operation corresponding to the abnormal service state, is specifically configured to:
determining a target service abnormal grade of the service provider according to the service state parameter;
determining a target service maintenance operation corresponding to the target service abnormal level in a preset abnormal operation strategy; the abnormal operation strategy comprises a corresponding relation between a plurality of service abnormal levels and a plurality of service maintenance operations;
and sending the target service maintenance operation to the service provider.
14. The apparatus of claim 13, wherein the service status parameter comprises service recording data, an audio parameter of the service recording data, text data corresponding to the service recording data, and a driving parameter corresponding to the service provider;
when determining the target service abnormal level of the service provider according to the service state parameter, the output module is specifically configured to:
and inputting the service recording data, the audio parameters of the service recording data, the text data corresponding to the service recording data and the driving parameters corresponding to the service providing end into a preset accident risk level model so that the accident risk level model outputs the target service abnormity level of the service providing end.
15. The apparatus of claim 13, wherein the processing module is further configured to:
receiving a service rescue request sent by the service providing terminal aiming at the target service maintenance operation;
and responding to the service rescue request, determining a target rescue service terminal from a plurality of rescue service terminals according to the position information of the service providing terminal, and sending a rescue instruction to the target rescue service terminal.
16. The apparatus of claim 13, wherein the processing module is further configured to:
according to the target service abnormal grade, improving the maintenance priority corresponding to the service order; wherein the maintenance priority characterizes an order of the corresponding service orders in an exception troubleshooting queue.
17. The apparatus according to claim 12, wherein the processing module, when detecting whether the service provider is in the abnormal service state according to the service state parameter, is specifically configured to:
judging whether the service state parameters meet preset abnormal detection conditions or not;
when the service state parameter meets the abnormal detection condition, inputting the service state parameter into a preset accident detection model so that the accident detection model outputs the service state of the service provider; the service state is used for indicating whether the service providing end is in a service abnormal state or not.
18. The apparatus of claim 17, wherein the service status parameters comprise a travel speed parameter and a travel stop parameter of the service provider;
when judging whether the service state parameter meets a preset abnormal detection condition, the processing module is specifically configured to:
judging whether the running speed parameter and/or the running stopping parameter are/is matched with a preset abnormal triggering strategy;
when the running speed parameter and/or the running stopping parameter is matched with the abnormal triggering strategy, determining that the service state parameter meets the abnormal detection condition;
and when the running speed parameter and the running stopping parameter are not matched with the abnormal triggering strategy, determining that the service state parameter does not meet the abnormal detection condition.
19. The apparatus of claim 17, wherein the service status parameters comprise a travel speed parameter and a travel stop parameter of the service provider;
when judging whether the service state parameter meets a preset abnormal detection condition, the processing module is specifically configured to:
inputting the running speed parameter and the running stopping parameter into a preset trigger detection model so that the trigger detection model outputs an abnormal trigger state of the service state parameter;
wherein the exception triggering state is used to indicate whether the service state parameter satisfies the exception detection condition.
20. The apparatus according to claim 12, wherein the processing module, when acquiring the service state parameter of the service provider when executing the service order, is specifically configured to:
receiving order service data of a service providing terminal when executing a service order;
and extracting characteristic information in the order service data to obtain service state parameters of the service provider when executing the service order.
21. The apparatus according to claim 20, wherein the processing module, when extracting the feature information in the order service data, is specifically configured to:
extracting service recording data of the service provider, audio parameters of the service recording data, text data corresponding to the service recording data, and driving speed parameters and driving stopping parameters corresponding to the service provider from the order service data.
22. The apparatus according to claim 12, wherein the processing module, when acquiring the service state parameter of the service provider when executing the service order, is specifically configured to:
and receiving service state parameters sent by the service provider when the service provider executes a service order.
23. A server, comprising:
a memory for storing one or more programs;
a processor;
the one or more programs, when executed by the processor, implement the method of any of claims 1-11.
24. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-11.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010891418.1A CN112017005A (en) | 2020-08-30 | 2020-08-30 | Service maintenance method, device, server and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010891418.1A CN112017005A (en) | 2020-08-30 | 2020-08-30 | Service maintenance method, device, server and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112017005A true CN112017005A (en) | 2020-12-01 |
Family
ID=73503304
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010891418.1A Pending CN112017005A (en) | 2020-08-30 | 2020-08-30 | Service maintenance method, device, server and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112017005A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190238581A1 (en) * | 2017-07-06 | 2019-08-01 | Zhongan Information Technology Service Co., Ltd. | Method, apparatus and system for detecting abnormal behavior of user |
| CN110751586A (en) * | 2019-02-21 | 2020-02-04 | 北京嘀嘀无限科技发展有限公司 | Order travel abnormity identification method and system |
| CN110992119A (en) * | 2019-02-21 | 2020-04-10 | 北京嘀嘀无限科技发展有限公司 | Method and system for sequencing risk orders |
| CN111259935A (en) * | 2020-01-09 | 2020-06-09 | 斑马网络技术有限公司 | Vehicle accident recognition method, device, equipment and storage medium |
| CN111598368A (en) * | 2019-02-21 | 2020-08-28 | 北京嘀嘀无限科技发展有限公司 | Risk identification method, system and device based on stopping abnormity after stroke ends |
| CN111599164A (en) * | 2019-02-21 | 2020-08-28 | 北京嘀嘀无限科技发展有限公司 | Driving abnormity identification method and system |
| CN111598371A (en) * | 2019-02-21 | 2020-08-28 | 北京嘀嘀无限科技发展有限公司 | Risk prevention method, system, device and storage medium |
| CN111598642A (en) * | 2019-02-21 | 2020-08-28 | 北京嘀嘀无限科技发展有限公司 | Risk judgment method, system, device and storage medium |
-
2020
- 2020-08-30 CN CN202010891418.1A patent/CN112017005A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190238581A1 (en) * | 2017-07-06 | 2019-08-01 | Zhongan Information Technology Service Co., Ltd. | Method, apparatus and system for detecting abnormal behavior of user |
| CN110751586A (en) * | 2019-02-21 | 2020-02-04 | 北京嘀嘀无限科技发展有限公司 | Order travel abnormity identification method and system |
| CN110992119A (en) * | 2019-02-21 | 2020-04-10 | 北京嘀嘀无限科技发展有限公司 | Method and system for sequencing risk orders |
| CN111598368A (en) * | 2019-02-21 | 2020-08-28 | 北京嘀嘀无限科技发展有限公司 | Risk identification method, system and device based on stopping abnormity after stroke ends |
| CN111599164A (en) * | 2019-02-21 | 2020-08-28 | 北京嘀嘀无限科技发展有限公司 | Driving abnormity identification method and system |
| CN111598371A (en) * | 2019-02-21 | 2020-08-28 | 北京嘀嘀无限科技发展有限公司 | Risk prevention method, system, device and storage medium |
| CN111598642A (en) * | 2019-02-21 | 2020-08-28 | 北京嘀嘀无限科技发展有限公司 | Risk judgment method, system, device and storage medium |
| CN111259935A (en) * | 2020-01-09 | 2020-06-09 | 斑马网络技术有限公司 | Vehicle accident recognition method, device, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11021166B2 (en) | Systems and methods for driver scoring with machine learning | |
| US10065652B2 (en) | Method and system for driver monitoring by fusing contextual data with event data to determine context as cause of event | |
| CN104916138B (en) | The processing method of information of vehicles, system, mobile unit and server | |
| JP7160454B2 (en) | Method, apparatus and system, electronic device, computer readable storage medium and computer program for outputting information | |
| CN108985530A (en) | Vehicle risk behavior management method and device | |
| US12106670B2 (en) | Accident reporter | |
| CN112329963A (en) | Data processing method, data processing apparatus, storage medium, and electronic device | |
| CN114677848B (en) | Perception early warning system, method, device and computer program product | |
| CN116572984A (en) | Dangerous driving management and control method and system based on multi-feature fusion | |
| CN111862386A (en) | Accident recording method, device, medium and server for vehicle | |
| CN113312958B (en) | Method and device for adjusting dispatch priority based on driver state | |
| US11263837B2 (en) | Automatic real-time detection of vehicular incidents | |
| CN115188184B (en) | Vehicle speed limit processing method, device and apparatus | |
| CN112017005A (en) | Service maintenance method, device, server and storage medium | |
| CN113393643B (en) | Abnormal behavior early warning method and device, vehicle-mounted terminal and medium | |
| CN115257771B (en) | Intersection identification method, electronic equipment and storage medium | |
| CN110807899A (en) | Driver state comprehensive monitoring method and system | |
| CN110059650A (en) | Information processing method, device, computer storage medium and electronic equipment | |
| US10896598B1 (en) | Ambient situational abnormality detection and response | |
| CN111341106B (en) | Traffic early warning method, device and equipment | |
| RU2703341C1 (en) | Method for determining hazardous conditions on public roads based on monitoring the situation in the cabin of a vehicle | |
| CN110956143A (en) | Abnormal behavior detection method and device, electronic equipment and storage medium | |
| CN117325874A (en) | Accident identification method, device, vehicle and storage medium | |
| CN115081545B (en) | Driver rotation identification method and identification model construction method | |
| Hong et al. | SLICE-based Trustworthiness Analysis system |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201201 |