CN115187071A - Order risk identification method and device, computer equipment and readable storage medium - Google Patents
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Abstract
The application provides an order risk identification method and device, computer equipment and a readable storage medium. The method comprises the following steps: respectively carrying out risk assessment on the ordering user and the driver waiting for ordering to obtain corresponding risk assessment results; responding to a user ordering request, generating an order according to risk evaluation results of an ordering user and a driver to be ordered, and matching a corresponding driver to be ordered for each ordering user; in the order carrying process, carrying out real-time risk monitoring on each order to generate a corresponding order risk identification result; and carrying out risk avoidance processing according to the order risk identification result. According to the method and the system, the optimal order is generated after risk assessment is carried out on the user and the driver, and real-time risk monitoring is carried out on the order in the process, so that various potential risks in the whole waybill process are actively and effectively identified, the active risk avoiding effect is realized, the accuracy and comprehensiveness of risk identification are improved, disputes and risks in the order carrying process are reduced, and the safety of the driver and the passenger is effectively ensured.
Description
Technical Field
The present application relates to the field of computer and data processing technologies, and in particular, to an order risk identification method and apparatus, a computer device, and a readable storage medium.
Background
Nowadays, with the vigorous development of mobile terminal technology, various taxi taking software or platforms appear, and great convenience is brought to the life of people. Along with this, various disputes and risks in the waybill process are brought. At present, in the prior art, disputes and hidden dangers are mainly reduced by using some service functions on software by a user, for example, recording in the whole process, reporting and positioning, software one-key alarming and automatic contact of an emergency contact person, or disputes or hidden dangers which may occur are solved by user evaluation and the like after an order is completed.
However, in the course of research and practice on the prior art, the inventors of the present application found that the prior art has the following disadvantages: in the prior art, disputes and hidden dangers are mainly reduced through a software service function, related data are mainly collected, some services for improving user safety are provided, the method belongs to a passive danger avoiding mode essentially, various potential risks in the whole waybill process cannot be actively and effectively identified, active danger avoiding cannot be achieved, the probability of disputes occurring in the waybill process cannot be reduced, and rear liability judgment cannot be carried out, so that the safety of drivers and passengers cannot be effectively ensured.
The foregoing description is provided for general background information and is not admitted to be prior art.
Disclosure of Invention
In view of the above technical problems, the present application provides an order risk identification method, an order risk identification device, a computer device, and a readable storage medium, which can actively and effectively identify various potential risks in the entire order taking process, achieve an active risk avoidance effect, improve accuracy and comprehensiveness of risk identification, reduce disputes and risks in the order taking process, and effectively ensure safety of drivers and passengers.
In order to solve the technical problem, the present application provides an order risk identification method, including the following steps:
respectively carrying out risk assessment on each ordering user and each order waiting driver to obtain corresponding risk assessment results;
responding to a user order request, generating an order according to the risk evaluation results of each order placing user and each order waiting driver, and matching corresponding order waiting drivers for each order placing user;
in the order carrying process, carrying out real-time risk monitoring on each order to generate a corresponding order risk identification result;
and carrying out risk avoiding treatment according to the order risk identification result.
Optionally, the performing risk assessment on each order issuing user and each order waiting driver respectively to obtain corresponding risk assessment results includes:
emotion recognition is respectively carried out on the order placing user and the driver waiting for receiving the order, and corresponding initial emotion stable values are obtained;
calculating corresponding first risk parameters by adopting a weight method according to the data information of the order placing user and the driver waiting to receive the order;
and evaluating by integrating the initial emotional stability value and the first risk parameter to obtain a risk evaluation result corresponding to the order placing user and the driver waiting to receive the order.
Optionally, the generating an order according to the risk assessment results of each order placing user and each driver waiting to receive an order, and matching a corresponding driver waiting to receive an order for each order placing user includes:
acquiring the position information of a current order-placing user, and matching a plurality of drivers to be ordered in a preset range according to the position information;
matching every two current ordering users with a plurality of drivers to be ordered in a preset range respectively, and calculating corresponding comprehensive risk assessment results among all combinations;
and generating an order according to the combination with the lowest comprehensive risk assessment result, and matching a corresponding driver to be subjected to order taking for each order placing user.
Optionally, after the generating an order according to the risk assessment results of the order placing users and the order waiting drivers, the method further includes:
scoring the risk assessment results to obtain corresponding risk assessment scores;
and when the risk evaluation score is larger than a first preset risk value, marking the order and then carrying out risk avoidance processing.
Optionally, the performing real-time risk monitoring on each order during the order performing process to generate a corresponding order risk identification result includes:
identifying an order emotion value corresponding to the order, wherein the order emotion value is the sum of emotion stable values of an order placing user and an order taking driver of the order;
distributing a corresponding risk monitoring strategy according to the order emotion value;
and carrying out real-time risk monitoring according to the risk monitoring strategy to generate a corresponding order risk identification result.
Optionally, the allocating a corresponding risk monitoring policy according to the order emotion value includes:
if the order emotion value is smaller than a first preset emotion value, the risk monitoring strategy comprises track offset analysis, audio data analysis and video data analysis;
if the order emotion value is larger than a first preset emotion value and smaller than a second preset emotion value, the risk monitoring strategy comprises track offset analysis and audio data analysis;
and if the order emotion value is greater than a second preset emotion value, the risk monitoring strategy comprises track deviation analysis.
Optionally, the method further comprises:
after the order is finished, obtaining a feedback result of the order placing user to the order;
performing authenticity filtering processing on the feedback result;
and correcting the risk evaluation result and the order risk identification result according to the filtered feedback result.
Correspondingly, this application still provides an order risk identification device, includes:
the evaluation module is used for respectively carrying out risk evaluation on each ordering user and each order waiting driver to obtain corresponding risk evaluation results;
the order module is used for responding to the order placing request of the user, generating an order according to the risk evaluation results of each order placing user and each order waiting driver, and matching the corresponding order waiting driver for each order placing user;
the monitoring module is used for carrying out real-time risk monitoring on each order in the order carrying process to generate a corresponding order risk identification result;
and the risk avoiding module is used for carrying out risk avoiding treatment according to the order risk identification result.
The present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the order risk identification method according to any one of the above items when executing the computer program.
The present application also proposes a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the order risk identification method of any of the preceding claims.
The embodiment of the invention has the following beneficial effects:
as described above, the method, the apparatus, the computer device and the readable storage medium for order risk identification provided by the present application include: firstly, respectively carrying out risk assessment on each ordering user and each order waiting driver to obtain corresponding risk assessment results; then, responding to the order placing request of the user, generating an order according to the risk evaluation results of each order placing user and each driver to be subjected to order taking, and matching the corresponding driver to be subjected to order taking for each order placing user; then, in the order carrying process, carrying out real-time risk monitoring on each order to generate a corresponding order risk identification result; and finally, carrying out risk avoiding treatment according to the order risk identification result. According to the method and the system, the optimal order is generated after risk assessment is carried out on the user and the driver, and real-time risk monitoring is carried out on the order in the process, so that various potential risks in the whole order taking process are actively and effectively identified, the active risk avoiding effect is realized, the accuracy and comprehensiveness of risk identification are improved, disputes and risks in the order carrying process are reduced, and the safety of the driver and the passenger is effectively ensured.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the description of the embodiments will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive step.
Fig. 1 is a schematic flowchart of a first implementation manner of an order risk identification method provided in an embodiment of the present application;
FIG. 2 is a flowchart illustrating a second implementation manner of an order risk identification method according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating a third implementation manner of an order risk identification method according to an embodiment of the present application;
fig. 4 is a schematic flowchart of a fourth implementation manner of an order risk identification method according to an embodiment of the present application;
fig. 5 is a schematic flowchart of a fifth implementation manner of an order risk identification method according to an embodiment of the present application;
fig. 6 is a schematic flowchart of a sixth implementation manner of an order risk identification method according to an embodiment of the present application;
FIG. 7 is a schematic structural diagram of an order risk identification apparatus according to an embodiment of the present disclosure;
FIG. 8 is a schematic structural diagram of a crash alert order risk identification system according to an embodiment of the present disclosure;
fig. 9 is a block diagram schematically illustrating a structure of a computer device according to an embodiment of the present disclosure.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings. With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. The drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the concepts of the application by those skilled in the art with reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the recitation of a claim "comprising a" 8230a "\8230means" does not exclude the presence of additional identical elements in the process, method, article or apparatus in which the element is incorporated, and further, similarly named components, features, elements in different embodiments of the application may have the same meaning or may have different meanings, the specific meaning of which should be determined by its interpretation in the specific embodiment or by further combination with the context of the specific embodiment.
It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope herein. The word "if" as used herein may be interpreted as "at \8230; \8230when" or "when 8230; \8230, when" "or" in response to a determination ", depending on the context. Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, species, and/or groups thereof. The terms "or," "and/or," "including at least one of the following," and the like, as used herein, are to be construed as inclusive or mean any one or any combination. For example, "includes at least one of: A. b, C "means" any of the following: a; b; c; a and B; a and C; b and C; a and B and C ", again for example," a, B or C "or" a, B and/or C "means" any one of the following: a; b; c; a and B; a and C; b and C; a and B and C'. An exception to this definition will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some way.
It should be understood that, although the steps in the flowcharts in the embodiments of the present application are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless otherwise indicated herein. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, in different orders, and may be performed alternately or at least partially with respect to other steps or sub-steps of other steps.
The words "if", as used herein may be interpreted as "at \8230; \8230whenor" when 8230; \8230when or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
It should be noted that step numbers such as S1 and S2 are used herein for the purpose of more clearly and briefly describing the corresponding contents, and do not constitute a substantial limitation on the sequence, and those skilled in the art may perform S2 first and then S1 in the specific implementation, but these should be within the scope of the present application.
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In the following description, suffixes such as "module", "component", or "unit" used to indicate elements are used only for facilitating the description of the present application, and have no particular meaning in themselves. Thus, "module", "component" or "unit" may be used mixedly.
The embodiment of the application can be applied to a server, and the server can be an independent server, or a cloud server providing basic cloud computing services such as cloud service, a cloud database, cloud computing, a cloud function, cloud storage, network service, cloud communication, middleware service, domain name service, security service, content Delivery Network (CDN), big data and an artificial intelligence platform.
First, the application scenarios that can be provided by the present application are introduced, for example, an order risk identification method, an order risk identification device, a computer device, and a readable storage medium are provided, which can actively and effectively identify various potential risks in the whole order taking process, achieve an active risk avoidance effect, improve the accuracy and comprehensiveness of risk identification, reduce disputes and risks in the order taking process, and effectively ensure the safety of drivers and passengers.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a first implementation manner of an order risk identification method according to an embodiment of the present disclosure. The order risk identification method may specifically include:
s1, risk assessment is respectively carried out on each ordering user and each driver waiting for ordering, and corresponding risk assessment results are obtained.
Specifically, in step S1, before the order is placed by the user or before the order is taken by the driver, risk assessment is performed on each order placing user and each driver waiting to take the order on the online platform, respectively, so as to obtain a risk assessment result corresponding to the user and the driver.
Optionally, as shown in fig. 2, in some embodiments, step S1 may specifically include:
s11, emotion recognition is respectively carried out on the order placing user and the order waiting driver to obtain corresponding initial emotion stable values;
s12, calculating corresponding first risk parameters by adopting a weight method according to the data information of the order placing user and the order waiting driver;
and S13, evaluating by integrating the initial emotional stability value and the first risk parameter to obtain a risk evaluation result corresponding to the order placing user and the driver waiting for receiving the order.
Specifically, before order transaction, namely before ordering by a user or before order taking by a driver, emotion recognition is respectively carried out on each order taking user and the driver to be ordered on the online platform, so that an initial emotion stable value corresponding to each order taking user and an initial emotion stable value of each driver to be ordered are obtained; in addition, the method can also obtain the data information of the order placing user and the driver waiting for receiving the order, including but not limited to various parameters such as area, age and credit, and adopts a weight method to calculate a first risk parameter corresponding to the data information, so as to evaluate the risk parameters of the order placing user and the driver waiting for receiving the order; and finally, integrating the initial emotional stability value and the first risk parameter of the order placing user and the initial emotional stability value and the first risk parameter of the order waiting driver to respectively obtain the risk assessment results corresponding to the order placing user and the order waiting driver. According to the method and the system, before order transaction and order placing by a user and order taking by a driver are carried out, an order risk identification link is added, risk evaluation results of each order placing user and the driver to be subjected to order taking are identified, and matching of a proper driver after an order is generated subsequently is facilitated.
Optionally, in some embodiments, the emotion recognition in step S1 may specifically include:
performing emotion recognition on each ordering user to obtain a corresponding initial emotion stable value;
and performing emotion recognition on each driver waiting for receiving the order at least once until the corresponding initial emotion stable value is greater than a first preset emotion stable value.
Specifically, for emotion recognition of the ordering user, a corresponding initial emotion stable value can be obtained only once; for emotion recognition of the order waiting driver, performing emotion recognition at least once, judging whether an emotion stable value obtained by the emotion recognition is larger than a first preset emotion stable value or not, and if so, starting order dispatching after taking the emotion stable value as an initial emotion stable value of the order waiting driver; if not, a driver waiting for order taking needs to be subjected to warm prompt, the state is adjusted, then the driver starts to work, after the driver waiting for order taking confirms that the adjustment is performed, the emotion recognition link is started again until the obtained emotion stable value is larger than the first preset emotion stable value, and therefore the influence on riding safety of a user caused by the unstable emotion of the driver waiting for order taking is avoided. Optionally, the emotion recognition method in this embodiment includes, but is not limited to, interactive video question answering, facial micro-expression recognition, audio emotion recognition, and the like.
And S2, responding to the order placing request of the user, generating an order according to the risk evaluation results of each order placing user and each driver to be subjected to order taking, and matching the corresponding driver to be subjected to order taking for each order placing user.
Specifically, for step S2, an order placing request initiated by a user is responded, a corresponding order is generated according to risk assessment results of the order placing user and each driver to be placed on the online platform, a plurality of drivers to be placed are matched for the order placing user, priority ranking is performed on the matched drivers to be placed, and an order dispatching process is completed after the drivers with the front priorities place orders.
Optionally, as shown in fig. 3, in some embodiments, the step S2 may specifically include:
s21, acquiring the position information of the current order issuing user, and matching a plurality of drivers to receive orders in a preset range according to the position information;
s22, matching every two current ordering users with a plurality of drivers to be ordered in a preset range, and calculating corresponding comprehensive risk assessment results among all combinations;
and S23, generating an order according to the combination with the lowest comprehensive risk assessment result, and matching corresponding drivers to be subjected to order taking for each order taking user.
Specifically, the method comprises the steps of firstly obtaining position information of a user who initiates a request for placing an order currently, and searching a plurality of drivers to receive the order within a preset range according to the position information; then, combining the ordering user with a plurality of drivers to be ordered in a preset range in pairs, calculating corresponding comprehensive risk assessment results among the combinations, and sequencing the comprehensive risk assessment results of all the combinations from low to high; and dispatching orders according to the sequence from low to high, preferentially selecting the combination with low comprehensive risk assessment result as much as possible to generate the corresponding base order, and matching the corresponding driver to be subjected to order taking for each order taking user.
In a specific embodiment, the emotion value of the single user and the driver waiting for taking the order is taken as an example of the order sending index, and the emotion range is set to be 1-10 points, wherein 1 represents that the emotion is extremely unstable, and 10 represents that the emotion is extremely stable. Firstly, searching a plurality of order waiting drivers in a preset range of position information of different order placing users, calculating the emotion value sum between the order placing users and the order waiting drivers, and preferentially assigning orders to a combination with the emotion value sum between the order placing users and the order waiting drivers larger than or equal to 10 through an order assigning model, so that the risk is reduced; and after the order is generated, sending the driver of the order to the driver meeting the conditions until a certain driver to receive the order successfully receives the order.
Optionally, in some embodiments, after step S2, the method may further include:
grading the risk assessment result to obtain a corresponding risk assessment score;
and when the risk evaluation score is larger than a first preset risk value, marking the order and then carrying out risk avoidance processing.
Specifically, after the driver successfully takes the order, the risk assessment results of the order placing user and the order taking driver in the order are graded to obtain corresponding risk assessment scores; judging whether the risk evaluation score is larger than a first preset risk value, if so, marking the order so as to enable the platform to pay key attention to the order; if the risk assessment result is abnormal, corresponding risk avoiding measures need to be taken for the order, such as customer service communication confirmation or platform re-matching of the order.
And S3, in the order carrying process, carrying out real-time risk monitoring on each order to generate a corresponding order risk identification result.
Specifically, in step S3, in the order process, real-time risk monitoring needs to be performed on each order, so as to generate a corresponding order risk identification result, and different measures are taken according to the order risk identification result.
Optionally, as shown in fig. 4, in some embodiments, step S3 may specifically include:
s31, identifying an order emotion value corresponding to the order, wherein the order emotion value is the sum of emotion stability values of an order placing user and an order taking driver of the order;
s32, distributing a corresponding risk monitoring strategy according to the emotion value of the order;
and S33, carrying out real-time risk monitoring according to a risk monitoring strategy to generate a corresponding order risk identification result.
Specifically, taking an order emotion value as an index of risk monitoring as an example, in an order making process, identifying the order emotion value of the order, wherein the order emotion value is the sum of emotion stable values of an order placing user and an order taking driver corresponding to the order, and distributing different risk monitoring strategies according to the order emotion value, so that the order is subjected to real-time risk monitoring according to the distributed risk monitoring strategies to generate a corresponding order risk identification result. By carrying out real-time risk monitoring on the orders in the process of carrying out the orders, the method is beneficial to determining responsibility for follow-up disputes and reducing the probability of potential safety hazards.
Optionally, step S32 may specifically include:
if the order emotion value is smaller than a first preset emotion value, the risk monitoring strategy comprises track offset analysis, audio data analysis and video data analysis;
if the order emotion value is larger than the first preset emotion value and smaller than the second preset emotion value, the risk monitoring strategy comprises track offset analysis and audio data analysis;
and if the order emotion value is greater than a second preset emotion value, the risk monitoring strategy comprises track deviation analysis.
Specifically, allocating different risk monitoring strategies according to the order emotion value specifically includes: if the emotion value of the order is smaller than a first preset emotion value, allocating a risk monitoring strategy comprising track offset analysis, audio data analysis and video data analysis for the order to carry out real-time risk monitoring, and guiding to start a camera on the basis of starting travel recording and positioning reporting so as to carry out track offset analysis, audio data analysis and video data analysis; if the order emotion value is larger than the first preset emotion value and smaller than the second preset emotion value, allocating a risk monitoring strategy comprising track offset analysis and audio data analysis to the order to carry out real-time risk monitoring; and if the emotion value of the order is greater than the second preset emotion value, allocating a risk monitoring strategy only comprising track deviation analysis to the order for real-time risk monitoring. Optionally, the real-time risk monitoring includes, but is not limited to, keyword recognition, quarrel recognition, fighting recognition, emotional overstimulation recognition, and the like.
And S4, carrying out risk avoiding treatment according to the order risk identification result.
Specifically, in step S4, in the order processing process, relevant measures are taken according to the order risk identification result obtained by real-time risk monitoring to perform risk avoidance processing, for example, for an order with a high risk, a platform customer service immediately performs telephone intervention; or the user is timely reminded to confirm whether the order is normal or not in a platform message pushing mode; or the platform adopts a real-time monitoring mode, and contacts the user, the driver or related departments for processing after data abnormity is found, so that the active risk avoiding effect is realized, the accuracy and the comprehensiveness of risk identification are improved, disputes and risks in the order carrying process are reduced, and the safety of the driver and passengers is effectively ensured.
Optionally, the step S4 may be specifically followed by:
after the order is finished, obtaining a feedback result of the order placing user to the order;
performing authenticity filtering processing on the feedback result;
and correcting the risk evaluation result and the order risk identification result according to the filtered feedback result.
Specifically, after the order is completed, the platform can guide the user to perform order experience feedback to obtain a feedback result of the order placing user to the order; and further performing authenticity filtration on the obtained feedback result, and performing evidence making and correction on a risk evaluation result before the order is performed and an order risk identification result when the order is performed according to the feedback result after the authenticity filtration, so that the success rate of risk identification is continuously optimized, a benign closed loop is formed, and the accuracy of order risk identification is favorably improved.
As shown in fig. 5, this embodiment further provides a flowchart of a fifth implementation manner of the order risk identification method, and the specific steps include: before the order begins, after a driver starts to enter an application, an emotion recognition link is started to generate an emotion stable value corresponding to the driver, whether the emotion is stable or not is judged according to the emotion stable value, if yes, the driver starts to pick up the order after the emotion stable value of the driver is recorded; if not, the driver is warm to prompt that the work is restarted after the state is adjusted, and after the driver confirms that the adjustment is finished, the emotion recognition link is restarted until the emotion of the driver is stable. After a user starts to enter an application, an emotion recognition link is started, an emotion stable value of the user is generated, ordering is started, and the emotion stable value of the user is recorded; and finally, ordering and online drivers are carried out through the platform mobile phone user. Before order transaction, namely before a user places an order and a driver takes an order, the platform adds an order risk identification link, scores an identification result, and the result exceeds a certain threshold value, so that the system automatically marks the order and needs to pay attention. And aiming at the abnormal recognition result, the platform can take corresponding measures. And risk identification is carried out before the order, so that the probability of high-risk order pairing is reduced, and precaution is taken.
As shown in fig. 6, this embodiment further provides a flowchart of a sixth implementation manner of the order risk identification method, and the specific steps include: after a platform mobile phone online driver and a user order, taking the emotion value of the user and the driver as one of order sending indexes through an order sending model to send an order, and setting the emotion range to be 1-10 minutes, wherein 1 represents that the emotion is extremely unstable, and 10 represents that the emotion is extremely stable; the order is preferentially delegated to a combination that the sum of emotion values between the order placing user and the order waiting driver is larger than or equal to 10 through an order delegating model, so that the risk is reduced; waiting for the drivers to take orders, sending order information to drivers meeting the conditions, and determining an order emotion value (a user emotion value + a driver emotion value) after a certain driver successfully takes an order; performing data analysis of different strengths according to the order emotion value, for example, only analyzing whether the track is deviated or not when the order emotion value is greater than 15, performing track deviation analysis and audio analysis when the order emotion value is between 10 and 15, performing track deviation analysis, audio analysis and video analysis simultaneously when the order emotion value is less than 10, and adopting a multi-dimensional identification means including product interaction, audio and video, geographic position and the like, thereby improving the risk identification success rate; analyzing the obtained risk data, and processing by the platform according to the risk condition of the order, for example, for the high-risk order, the platform customer service immediately calls to intervene; or the user is timely reminded to confirm whether the order is normal or not in a platform message pushing mode; or the platform adopts a real-time monitoring mode, and contacts a user, a driver or a related department for processing after data abnormality is found; until the order is completed. When the order is carried out, the camera is guided to be started on the basis of current travel recording and positioning reporting, and the platform analyzes audio and video data and positions of different orders according to a first-step risk identification result to monitor risks in real time; and after the order is finished, guiding the user to perform order experience feedback, further performing authenticity filtering on a feedback result, and finally feeding the feedback result to a risk failure model for continuous optimization. According to the method, the risk identification model is introduced, and the order dispatching matching factors such as newly increased risk and emotion value are added, so that the order experience of the user is better; risk is analyzed and identified from multiple dimensions such as travel records, videos and tracks, so that risk identification is more accurate, and a platform can effectively identify and intervene in advance; after the recognition model is introduced, the platform can further understand the driver and the user, can more accurately distinguish the user and the driver, and further carry out differentiated product iteration.
As can be seen from the above, the order risk identification method provided in the embodiment of the present application includes: firstly, respectively carrying out risk assessment on each ordering user and each order waiting driver to obtain corresponding risk assessment results; then, responding to the order placing request of the user, generating an order according to the risk evaluation results of each order placing user and each driver to be subjected to order taking, and matching the corresponding driver to be subjected to order taking for each order placing user; then, in the order carrying process, carrying out real-time risk monitoring on each order to generate a corresponding order risk identification result; and finally, carrying out risk avoiding treatment according to the order risk identification result. According to the method and the device, the optimal order is generated after risk assessment is carried out on the user and the driver, and when the order is carried out, through multi-dimensional real-time risk monitoring, various potential risks in the whole order carrying process are actively and effectively identified, the active risk avoiding effect is achieved, the accuracy and the comprehensiveness of risk identification are improved, disputes and risks in the order carrying process are reduced, and the safety of the driver and the passenger is effectively guaranteed.
Correspondingly, the present application further provides an order risk identification apparatus, please refer to fig. 7, where fig. 7 is a schematic structural diagram of the order risk identification apparatus provided in the present application, and specifically, the order risk identification apparatus may include an evaluation module 100, an order module 200, a monitoring module 300, and a risk avoidance module 400;
the evaluation module 100 is configured to perform risk evaluation on each order placing user and each order waiting driver respectively to obtain corresponding risk evaluation results.
Specifically, for the evaluation module 100, before the order is placed by the user or before the order is received by the driver, the risk evaluation is performed on each order placing user and each order receiving driver on the online platform respectively, so as to obtain the risk evaluation result corresponding to the user and the driver.
Optionally, in some embodiments, the evaluation module 100 may specifically include:
the first evaluation unit is used for respectively carrying out emotion recognition on the order placing user and the order waiting driver to obtain corresponding initial emotion stable values;
the second evaluation unit is used for calculating corresponding first risk parameters by adopting a weight method according to the data information of the ordering user and the order waiting driver;
and the third evaluation unit is used for evaluating by integrating the initial emotional stability value and the first risk parameter to obtain a risk evaluation result corresponding to the order placing user and the driver waiting to receive the order.
And the order module 200 is used for responding to the order placing request of the user, generating an order according to the risk evaluation results of each order placing user and each driver to receive the order, and matching the corresponding driver to receive the order for each order placing user.
Specifically, for the order module 200, an order placing request initiated by a user is responded, a corresponding order is generated according to the risk assessment results of the order placing user and each driver to be ordered on the online platform, a plurality of drivers to be ordered are matched for the order placing user, the matched drivers to be ordered are subjected to priority ranking, and the order dispatching process is completed after the drivers with the front priorities receive orders.
Optionally, in some embodiments, the order module 200 may specifically include:
the first matching unit is used for acquiring the position information of the current ordering user and matching a plurality of drivers waiting for ordering in a preset range according to the position information;
the second matching unit is used for matching every two current ordering users with a plurality of drivers to be ordered in a preset range respectively and calculating corresponding comprehensive risk assessment results among all combinations;
and the third matching unit is used for generating an order according to the combination with the lowest comprehensive risk assessment result and matching the corresponding driver to be subjected to the order taking for each order placing user.
The monitoring module 300 is configured to perform real-time risk monitoring on each order during the order process, and generate a corresponding order risk identification result.
Specifically, for the monitoring module 300, in the order proceeding process, real-time risk monitoring needs to be performed on each order, so as to generate a corresponding order risk identification result, and different measures are taken according to the order risk identification result.
Optionally, in some embodiments, the monitoring module 300 may specifically include:
the first monitoring unit is used for identifying an order emotion value corresponding to the order, wherein the order emotion value is the sum of emotion stability values of an order placing user and an order taking driver of the order;
the second monitoring unit is used for distributing a corresponding risk monitoring strategy according to the emotion value of the order;
and the third monitoring unit is used for carrying out real-time risk monitoring according to the risk monitoring strategy and generating a corresponding order risk identification result.
And the risk avoiding module 400 is used for carrying out risk avoiding processing according to the order risk identification result.
Specifically, for the risk avoiding module 400, in the order performing process, relevant measures are taken according to the order risk identification result obtained by real-time risk monitoring to perform risk avoiding processing, for example, for an order with a high risk, a platform customer service immediately performs telephone intervention; or the user is timely reminded to confirm whether the order is normal or not in a platform message pushing mode; or the platform adopts a real-time monitoring mode, and contacts the user, the driver or related departments to process after data abnormality is found.
Optionally, in some embodiments, the order risk identification device further comprises:
the correction module is used for acquiring a feedback result of the order placing user to the order after the order is completed; performing authenticity filtering processing on the feedback result; and correcting the risk evaluation result and the order risk identification result according to the filtered feedback result.
To sum up, the order risk identification device provided in the embodiment of the present application performs risk assessment on each order placing user and each order waiting driver through the assessment module 100, respectively, to obtain corresponding risk assessment results; then, responding to the order placing request of the user through the order module 200, generating an order according to the risk evaluation results of each order placing user and each driver to receive the order, and matching the corresponding driver to receive the order for each order placing user; then, real-time risk monitoring is carried out on each order by the monitoring module 300 in the order process, and a corresponding order risk identification result is generated; finally, risk avoiding processing is carried out through the risk avoiding module 400 according to the order risk identification result.
Therefore, the order risk identification device of the embodiment of the application generates the optimal order after risk assessment is carried out on the user and the driver, and actively and effectively identifies various potential risks in the whole order transportation process through multi-dimensional real-time risk monitoring when the order is carried out, so that the active risk avoiding effect is realized, the accuracy and the comprehensiveness of risk identification are improved, disputes and risks in the order carrying process are reduced, and the safety of the driver and the passenger is effectively ensured.
As shown in fig. 8, the embodiment of the present application further provides an order risk identification system, which includes a user application 10, an online platform 20, and a driver application 30;
the user application end 10 is used for performing emotion recognition after a user initiates an order placing request, matching a corresponding driver according to an emotion stable value after an order is generated, and feeding back the order after the order is completed;
the online platform 20 is used for respectively carrying out risk assessment on each ordering user and each driver waiting for ordering to obtain corresponding risk assessment results; the system comprises a client, a client and a system server, wherein the client is used for responding to a user order placing request, generating an order according to risk evaluation results of each order placing user and each driver to receive the order, and matching a corresponding driver to receive the order for each order placing user; the order risk monitoring system is used for monitoring the risk of each order in real time in the order process and generating a corresponding order risk identification result; carrying out risk avoiding treatment according to the order risk identification result;
and the driver application end 30 is used for performing emotion recognition, matching the corresponding order according to the emotion stable value and further completing the order.
Referring to fig. 9, an embodiment of the present application further provides a computer device, where the computer device may be a server, and an internal structure of the computer device may be as shown in fig. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer designed processor is used to provide computational and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used for storing data such as order risk identification methods and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an order risk identification method. The order risk identification method comprises the following steps: firstly, respectively carrying out risk assessment on each ordering user and each order waiting driver to obtain corresponding risk assessment results; then, responding to the order placing request of the user, generating an order according to the risk evaluation results of each order placing user and each driver to be subjected to order taking, and matching the corresponding driver to be subjected to order taking for each order placing user; then, in the order carrying process, carrying out real-time risk monitoring on each order to generate a corresponding order risk identification result; and finally, carrying out risk avoiding treatment according to the order risk identification result.
An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements an order risk identification method, including the steps of: firstly, respectively carrying out risk assessment on each ordering user and each order waiting driver to obtain corresponding risk assessment results; then, responding to a user ordering request, generating an order according to risk evaluation results of each ordering user and each order waiting driver, and matching a corresponding order waiting driver for each ordering user; then, in the order carrying process, carrying out real-time risk monitoring on each order to generate a corresponding order risk identification result; and finally, carrying out risk avoiding treatment according to the order risk identification result.
According to the executed order risk identification method, firstly, an optimal order is generated after risk evaluation is carried out on a user and a driver, and when the order is carried out, various potential risks in the whole order carrying process are actively and effectively identified through multi-dimensional real-time risk monitoring, the active risk avoiding effect is realized, the accuracy and the comprehensiveness of risk identification are improved, disputes and risks in the order carrying process are reduced, and the safety of the driver and passengers is effectively ensured.
It is to be understood that the foregoing scenarios are only examples, and do not constitute a limitation on application scenarios of the technical solutions provided in the embodiments of the present application, and the technical solutions of the present application may also be applied to other scenarios. For example, as can be known by those skilled in the art, with the evolution of system architecture and the emergence of new service scenarios, the technical solution provided in the embodiments of the present application is also applicable to similar technical problems.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The steps in the method of the embodiment of the application can be sequentially adjusted, combined and deleted according to actual needs.
The units in the device in the embodiment of the application can be merged, divided and deleted according to actual needs.
In the present application, the same or similar descriptions of terms, technical solutions and/or application scenarios will generally be described in detail only when they occur for the first time, and when they occur repeatedly later, they will not be repeated again for brevity, and in understanding the technical solutions and the like of the present application, reference may be made to the related detailed descriptions and the like before the same or similar descriptions of terms, technical solutions and/or application scenarios and the like which are not described in detail later.
In the present application, each embodiment is described with an emphasis on the description, and reference may be made to the description of other embodiments for parts that are not described or recited in any embodiment.
The technical features of the technical solution of the present application may be arbitrarily combined, and for brevity of description, all possible combinations of the technical features in the embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, the scope of the present application should be considered as being described in the present application.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, a controlled terminal, or a network device) to execute the method of each embodiment of the present application.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the embodiments of the present application are all or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, digital subscriber line) or wirelessly (e.g., infrared, wireless, microwave, etc.). Computer-readable storage media can be any available media that can be accessed by a computer or a data storage device, such as a server, data center, etc., that includes one or more available media. The usable medium may be a magnetic medium (e.g., floppy Disk, storage Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (10)
1. An order risk identification method is characterized by comprising the following steps:
respectively carrying out risk assessment on each ordering user and each order waiting driver to obtain corresponding risk assessment results;
responding to a user ordering request, generating an order according to the risk evaluation results of each ordering user and each driver to be ordered, and matching the corresponding driver to be ordered for each ordering user;
in the order carrying process, carrying out real-time risk monitoring on each order to generate a corresponding order risk identification result;
and carrying out risk avoidance processing according to the order risk identification result.
2. The order risk identification method according to claim 1, wherein the step of performing risk assessment on each order placing user and each order waiting driver respectively to obtain corresponding risk assessment results comprises:
performing emotion recognition on the order placing user and the order waiting driver respectively to obtain corresponding initial emotion stable values;
calculating corresponding first risk parameters by adopting a weight method according to the data information of the order placing user and the order waiting driver;
and evaluating by integrating the initial emotional stability value and the first risk parameter to obtain a risk evaluation result corresponding to the order placing user and the driver waiting to receive the order.
3. The order risk identification method according to claim 1, wherein the step of generating an order according to the risk assessment results of each ordering user and each order waiting driver, and matching the corresponding order waiting driver for each ordering user comprises:
acquiring the position information of a current order-placing user, and matching a plurality of drivers to be subjected to order taking in a preset range according to the position information;
matching every two of the current ordering users with a plurality of drivers to be ordered in a preset range respectively, and calculating corresponding comprehensive risk assessment results among all combinations;
and generating an order according to the combination with the lowest comprehensive risk assessment result, and matching a corresponding driver to be subjected to order taking for each order placing user.
4. The order risk identification method according to claim 1, wherein after said generating an order according to the risk assessment results of said each order placing user and each order waiting driver, said method further comprises:
scoring the risk assessment results to obtain corresponding risk assessment scores;
and when the risk evaluation score is larger than a first preset risk value, marking the order and then carrying out risk avoidance processing.
5. The order risk identification method according to claim 1, wherein the step of performing real-time risk monitoring on each order during the order process to generate a corresponding order risk identification result comprises:
identifying an order emotion value corresponding to the order, wherein the order emotion value is the sum of emotion stable values of an order placing user and an order taking driver of the order;
distributing a corresponding risk monitoring strategy according to the order emotion value;
and carrying out real-time risk monitoring according to the risk monitoring strategy to generate a corresponding order risk identification result.
6. The order risk identification method of claim 5, wherein assigning a corresponding risk monitoring policy according to the order sentiment value comprises:
if the order emotion value is smaller than a first preset emotion value, the risk monitoring strategy comprises track offset analysis, audio data analysis and video data analysis;
if the order emotion value is larger than a first preset emotion value and smaller than a second preset emotion value, the risk monitoring strategy comprises track offset analysis and audio data analysis;
and if the order emotion value is greater than a second preset emotion value, the risk monitoring strategy comprises track deviation analysis.
7. The order risk identification method of claim 1, further comprising:
after the order is finished, obtaining a feedback result of the order placing user to the order;
performing authenticity filtering processing on the feedback result;
and correcting the risk evaluation result and the order risk identification result according to the filtered feedback result.
8. An order risk identification device, comprising:
the evaluation module is used for respectively carrying out risk evaluation on each ordering user and each order waiting driver to obtain corresponding risk evaluation results;
the order module is used for responding to the order placing request of the user, generating an order according to the risk evaluation results of each order placing user and each order waiting driver, and matching the corresponding order waiting driver for each order placing user;
the monitoring module is used for carrying out real-time risk monitoring on each order in the order carrying process to generate a corresponding order risk identification result;
and the risk avoiding module is used for carrying out risk avoiding treatment according to the order risk identification result.
9. A computer arrangement comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, carries out the steps of the order risk identification method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the order risk identification method of any one of claims 1 to 7.
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| CN115345533A (en) * | 2022-10-20 | 2022-11-15 | 阿里健康科技(杭州)有限公司 | Order data processing method, device, equipment and storage medium |
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| CN115345533A (en) * | 2022-10-20 | 2022-11-15 | 阿里健康科技(杭州)有限公司 | Order data processing method, device, equipment and storage medium |
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