CN112085612B - Vehicle total loss detection method and device, terminal equipment and storage medium - Google Patents
Vehicle total loss detection method and device, terminal equipment and storage medium Download PDFInfo
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Abstract
The application is suitable for the technical field of artificial intelligence, and provides a vehicle total loss detection method, a device, terminal equipment and a storage medium, wherein the method comprises the following steps: acquiring accessory damage information according to vehicle information of the vehicle in danger; calculating the corresponding damage quantity in different accessory groups according to the accessory damage information, and calculating the loss evidence weight according to the corresponding damage quantity in the different accessory groups; calculating a loss weight threshold according to the vehicle information, and calculating total loss probability according to the loss evidence weight and the loss weight threshold; and if the loss probability is greater than the probability threshold value, judging that the vehicle in danger is in a full loss state. According to the method and the device, the total loss probability of the vehicle in danger is calculated according to the loss evidence weight and the loss weight threshold, whether the corresponding vehicle in danger is in the total loss state or not can be effectively judged based on the total loss probability, namely, the influence of business experience or subjective factors of people on total loss state identification is prevented, and the accuracy of vehicle total loss detection is improved. In addition, the application also relates to a block chain technology.
Description
Technical Field
The present application relates to the field of artificial intelligence, and in particular, to a vehicle total loss detection method, apparatus, terminal device, and storage medium.
Background
In the field of vehicle damage assessment, whether the vehicle in danger reaches the total damage is a very controversial problem, which is related to the interests of many parties such as insurance companies, insurance applicants, repair shops and the like. On the one hand, for vehicles with maintenance cost higher than the actual value of the vehicle, the insurance company hopes to estimate the total loss and reduce the loss of the insurance company; on the other hand, the applicant hopes to maintain self benefits and obtain more compensation; in addition, a repair shop wants to directly recycle and refit dangerous vehicles without disassembling the dangerous vehicles, and obtains great benefits. Because the difference between the dismantling value and the non-dismantling value of the vehicle is huge, under the condition of not dismantling the vehicle, how to accurately and quickly judge whether the vehicle reaches the full-loss standard is a problem which needs to be solved urgently in the insurance industry.
In the existing vehicle total loss detection process, whether an dangerous vehicle reaches a total loss state or not is detected according to business experience of loss assessment personnel, so that influence factors of personnel subjectivity exist in vehicle total loss detection, and the accuracy of vehicle total loss detection is reduced.
Disclosure of Invention
In view of this, embodiments of the present application provide a vehicle total loss detection method, apparatus, terminal device and storage medium, so as to solve the problem in the prior art that in the vehicle total loss detection process, since whether an insurance vehicle reaches a total loss state is detected according to business experience of loss assessment personnel, the vehicle total loss detection accuracy is low.
A first aspect of an embodiment of the present application provides a vehicle total loss detection method, including:
acquiring vehicle information of an emergency vehicle, and acquiring accessory damage information of the emergency vehicle according to the vehicle information;
calculating the corresponding damage number of the vehicle in different component groups according to the component damage information, and calculating the loss evidence weight of the vehicle according to the corresponding damage number in different component groups, wherein the loss evidence weight is used for representing the whole vehicle loss degree of the vehicle;
calculating a loss weight threshold of the vehicle in danger according to the vehicle information, and calculating a total loss probability of the vehicle in danger according to the loss evidence weight and the loss weight threshold;
and if the total loss probability of the vehicle in danger is greater than the probability threshold value, determining that the vehicle in danger is in a total loss state.
Further, the calculating the corresponding damage number of the vehicle in different component groups according to the component damage information includes:
respectively acquiring accessory identification of a damaged accessory in the accessory damage information, and inquiring the corresponding accessory group according to the accessory identification of the damaged accessory;
and adding different damaged accessories into the correspondingly inquired accessory groups, and respectively obtaining the quantity of the damaged accessories in the different accessories to obtain the corresponding damaged quantity of the different accessory groups.
Further, the calculating the loss evidence weight of the vehicle in danger according to the corresponding damage number in different component groups comprises:
respectively obtaining quantity thresholds corresponding to different component groups, and calculating loss sub-weights corresponding to the component groups according to the quantity thresholds and the damage quantity in the component groups;
and calculating the sum of the loss sub-weights among different component groups to obtain the loss evidence weight.
Further, the calculation formula for calculating the loss sub-weight corresponding to the component group according to the quantity threshold and the damage quantity in the component group is as follows:
wherein thrdiIs the number threshold, thrd, corresponding to the ith said component groupi upA weight upper limit value set for the ith one of the component groups,gis the number of defects in the ith said set of fittings,W i (g)is the loss sub-weight corresponding to the ith said component group.
Further, the calculating a loss weight threshold of the vehicle in danger according to the vehicle information comprises:
acquiring the vehicle age of the vehicle in the vehicle information, and determining the vehicle age classification of the vehicle in danger according to the vehicle age;
and calculating a value-keeping coefficient of the vehicle in danger according to the determined vehicle age classification, and performing regression calculation according to the value-keeping coefficient to obtain the loss weight threshold.
Further, the acquiring the accessory damage information of the vehicle in danger according to the vehicle information includes:
acquiring shot pictures corresponding to different vehicle accessories in the vehicle information, and inquiring corresponding standard pictures according to accessory identifications of the vehicle accessories;
respectively calculating the image similarity between the shot picture and the standard picture corresponding to different vehicle accessories;
if the image similarity between the shot picture corresponding to the vehicle accessory and the standard picture is smaller than a similarity threshold value, marking the vehicle accessory as a damaged accessory;
and acquiring the shot picture corresponding to the damaged accessory to obtain the accessory damage information.
Further, the calculating a total loss probability of the vehicle in danger according to the loss evidence weight and the loss weight threshold value comprises:
and performing exponential operation on the loss evidence weight and the loss weight threshold according to an exponential life function to obtain the total loss probability of the vehicle in danger.
A second aspect of the embodiments of the present application provides a vehicle total loss detection apparatus, including:
the damage information acquisition unit is used for acquiring vehicle information of an emergency vehicle and acquiring accessory damage information of the emergency vehicle according to the vehicle information;
the loss evidence weight calculation unit is used for calculating the corresponding damage number of the vehicle in different component groups according to the component damage information, and calculating the loss evidence weight of the vehicle according to the corresponding damage number in different component groups, wherein the loss evidence weight is used for representing the whole vehicle loss degree of the vehicle in danger;
the total loss probability calculation unit is used for calculating a loss weight threshold value of the vehicle in danger according to the vehicle information and calculating the total loss probability of the vehicle in danger according to the loss evidence weight and the loss weight threshold value;
and the total loss judging unit is used for judging that the vehicle in danger is in a total loss state if the total loss probability of the vehicle in danger is greater than a probability threshold value.
A third aspect of the embodiments of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and operable on the terminal device, where the processor implements the steps of the vehicle total loss detection method provided by the first aspect when executing the computer program.
A fourth aspect of the embodiments of the present application provides a storage medium storing a computer program that, when executed by a processor, implements each step of the vehicle total loss detection method provided by the first aspect.
The implementation of the vehicle total loss detection method, the vehicle total loss detection device, the terminal equipment and the storage medium provided by the embodiment of the application has the following beneficial effects:
according to the vehicle total loss detection method provided by the embodiment of the application, the accessory damage information of the vehicle to be insured is obtained according to the vehicle information, the corresponding damage number of the vehicle to be insured in different accessory groups is calculated according to the accessory damage information, the corresponding damage number in different accessory groups can be effectively calculated, the loss evidence weight of the vehicle to be insured is calculated according to the corresponding damage number in different accessory groups, the loss evidence weight is used for representing the whole vehicle loss degree of the vehicle to be insured, therefore, the whole vehicle loss degree of the vehicle to be insured can be effectively obtained based on the loss evidence weight, the total loss probability of the vehicle to be insured is calculated according to the loss evidence weight and the loss weight threshold, whether the corresponding vehicle to be insured is in the total loss state can be effectively judged based on the total loss probability, namely, if the total loss probability of the vehicle to be insured is greater than the probability threshold, the vehicle to be insured, therefore, the influence of business experience or subjective factors of personnel on the overall damage state identification is prevented, the accuracy of vehicle overall damage detection is improved, the disassembly and damage assessment of vehicles in danger are not needed, and the detection efficiency of vehicle overall damage detection is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flowchart of an implementation of a vehicle total loss detection method provided in an embodiment of the present application;
FIG. 2 is a flowchart illustrating an implementation of a vehicle total loss detection method according to another embodiment of the present disclosure;
fig. 3 is a block diagram of a vehicle total loss detection apparatus according to an embodiment of the present application;
fig. 4 is a block diagram of a terminal device according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. 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.
The vehicle total loss detection method according to the embodiment of the present application may be executed by a control device or a terminal (hereinafter referred to as a "mobile terminal").
Referring to fig. 1, fig. 1 shows a flowchart of an implementation of a vehicle total loss detection method provided in an embodiment of the present application, including:
and step S10, vehicle information of the vehicle in danger is obtained, and accessory damage information of the vehicle in danger is obtained according to the vehicle information.
The vehicle information comprises shot pictures or text descriptions of damage degrees corresponding to different vehicle accessories on the vehicle in danger, when the vehicle information comprises the shot pictures corresponding to the different vehicle accessories, picture analysis is carried out on the shot pictures corresponding to the vehicle accessories so as to judge whether the vehicle accessories corresponding to the shot pictures are damaged accessories, the picture analysis is used for calculating the damage degrees of the corresponding vehicle accessories according to the shot pictures, and if the calculated damage degrees are larger than a damage threshold value, the vehicle accessories corresponding to the damage degrees are judged to be damaged accessories.
Specifically, in this step, the shot pictures corresponding to the damaged accessories on the vehicle in danger are stored in the accessory damage information, for example, the accessory damage information may include shot pictures corresponding to a damaged door panel and a damaged front bumper on the vehicle in danger.
Step S20, calculating the corresponding damage quantity of the vehicle in different component groups according to the component damage information, and calculating the loss evidence weight of the vehicle in danger according to the corresponding damage quantity in different component groups.
The corresponding damage number of the vehicle in different component groups is calculated according to the component damage information, so that the accuracy of calculating the loss evidence weight of the vehicle in danger is effectively improved, and the loss evidence weight is used for representing the whole vehicle loss degree of the vehicle in danger.
In this step, when the number of damages corresponding to different component groups in the emergency vehicle is larger, the calculated loss evidence weight of the emergency vehicle is larger, and the loss evidence weight of the emergency vehicle is larger, so that the overall vehicle loss degree of the emergency vehicle is larger.
Specifically, in this step, corresponding quantity thresholds are set for different component groups, weight calculation is performed based on the quantity thresholds and the damage quantities in the corresponding component groups to obtain loss sub-weights corresponding to the different component groups, and loss evidence weights of the vehicle in danger are obtained based on the loss sub-weights corresponding to the different component groups.
Optionally, in this step, the calculating, according to the accessory damage information, the number of damages of the vehicle in danger to different accessory groups includes:
acquiring accessory identifications of damaged accessories in the accessory damage information respectively, and inquiring the corresponding accessory groups according to the accessory identifications of the damaged accessories, wherein an accessory group inquiry table is prestored in the embodiment, the accessory group inquiry table stores corresponding relations between different accessory identifications and the corresponding accessory groups, the accessory identifications and the accessory groups are stored in a one-to-one or one-to-many mode, and the accessory groups comprise an interior accessory group, an exterior accessory group, a power accessory group or a driving accessory group and the like, so that the accessory identifications of different damaged accessories in the accessory damage information are matched with the accessory group inquiry table respectively to obtain the accessory groups corresponding to different damaged accessories.
Adding different damaged accessories into the correspondingly inquired accessory groups, and respectively obtaining the number of the damaged accessories in the different accessories to obtain the damaged number corresponding to the different accessory groups, wherein the different damaged accessories are added into the correspondingly inquired accessory groups, so that the calculation accuracy of the number of the damaged accessories in the different accessories is effectively improved, for example, when the matching result between the accessory identification of the damaged accessory a1 and the accessory group query table is accessory group A, the matching result between the accessory identification of the damaged accessory a2 and the accessory group query table is accessory group B, and the matching result between the accessory identification of the damaged accessory a3 and the accessory group query table is accessory group A, the damaged accessories a1 and a3 are added into the accessory group A, the damaged accessory a2 is added into the accessory group B, and at this time, the number of the damaged accessories corresponding to the accessory group A is 2, the number of damages corresponding to the component group B is 1.
Optionally, in this step, the calculating the loss evidence weight of the vehicle in danger according to the corresponding damage number in different component groups includes:
respectively obtaining quantity thresholds corresponding to different accessory groups, and calculating loss sub-weights corresponding to the accessory groups according to the quantity thresholds and the damage quantity in the accessory groups, wherein the loss sub-weights are used for representing the damage degree of the corresponding accessory groups;
and calculating the sum of the loss sub-weights among different component groups to obtain the loss evidence weight.
Further, the calculation formula for calculating the loss sub-weight corresponding to the component group according to the quantity threshold and the damage quantity in the component group is as follows:
wherein thrdiIs the number threshold, thrd, corresponding to the ith said component groupi upA weight upper limit value set for the ith one of the component groups,gis the number of defects in the ith said set of fittings,W i (g)is the loss sub-weight corresponding to the ith said component group.
Step S30, calculating a loss weight threshold value of the vehicle in danger according to the vehicle information, and calculating the total loss probability of the vehicle in danger according to the loss evidence weight and the loss weight threshold value;
the loss evidence weight is used for representing the current loss degree of the vehicle in danger, and the loss weight threshold is used for representing the corresponding loss weight when the vehicle in danger is in a full loss state, so that the full loss probability of the vehicle in danger can be effectively calculated according to the loss evidence weight and the loss weight threshold, and the full loss probability is the probability value that the vehicle in danger is in the full loss state.
Optionally, in this step, the calculating a loss weight threshold of the vehicle in danger according to the vehicle information includes:
acquiring the vehicle age of the vehicle in the vehicle information, and determining the vehicle age classification of the vehicle in danger according to the vehicle age, wherein a vehicle age classification query table is pre-stored in the embodiment, and the vehicle age classification query table stores the corresponding relationship between different vehicle age groups and corresponding vehicle age classifications, so that the vehicle age classification corresponding to the vehicle in danger is obtained by matching the vehicle age of the vehicle in danger with the vehicle age classification query table;
the insurance coefficient of the vehicle in danger is calculated according to the determined vehicle age classification, regression calculation is carried out according to the insurance coefficient, and the loss weight threshold is obtained.
Optionally, in this step, the calculating a total loss probability of the vehicle in danger according to the loss evidence weight and the loss weight threshold includes: and performing exponential operation on the loss evidence weight and the loss weight threshold according to an exponential life function to obtain the total loss probability of the vehicle in danger.
And step S40, if the total loss probability of the vehicle in danger is larger than the probability threshold, determining that the vehicle in danger is in a total loss state.
In this step, if it is determined that the vehicle in danger is in a full loss state, a full loss mark may be performed on the vehicle in danger, and the full loss mark may mark the vehicle in danger in a text, voice or image manner to prompt a corresponding user or loss assessment person that the vehicle in danger is determined to be in the full loss state.
Optionally, in this step, if the total loss probability of the vehicle in danger is less than or equal to the probability threshold, it is determined that the vehicle in danger is not in a total loss state, and no total loss claim is required for the vehicle in danger.
In the embodiment, the accessory damage information of the vehicle to be insured is obtained according to the vehicle information, the corresponding damage number of the vehicle to be insured in different accessory groups is calculated according to the accessory damage information, the corresponding damage number in different accessory groups can be effectively calculated, the loss evidence weight of the vehicle to be insured is calculated according to the corresponding damage number in different accessory groups, the loss evidence weight is used for representing the whole vehicle loss degree of the vehicle to be insured, the whole vehicle loss degree of the vehicle to be insured can be effectively obtained based on the loss evidence weight, the total loss probability of the vehicle to be insured is calculated according to the loss evidence weight and the loss weight threshold, whether the corresponding vehicle to be insured is in the total loss state can be effectively judged based on the total loss probability, namely, if the total loss probability of the vehicle to be insured is greater than the probability threshold, the vehicle to be insured is judged to be in the total loss state, and further, the influence of service experience or subjective factors, the accuracy of vehicle total loss detection is improved, the disassembly and damage assessment of the vehicle in danger are not needed, and the detection efficiency of the vehicle total loss detection is improved.
Referring to fig. 2, fig. 2 is a flowchart illustrating an implementation of a vehicle total loss detection method according to another embodiment of the present application. With respect to the embodiment corresponding to fig. 1, the vehicle total loss detection method provided in this embodiment is further detailed in step S10 in the embodiment corresponding to fig. 1, and includes:
and step S11, acquiring shot pictures corresponding to different vehicle accessories in the vehicle information, and inquiring corresponding standard pictures according to the accessory identifications of the vehicle accessories.
The corresponding relation between different vehicle accessories and the corresponding shot pictures is stored in the vehicle information, so that the accessory identifications of the different vehicle accessories are matched with the accessory identifications stored in the vehicle information to obtain the standard pictures corresponding to the different vehicle accessories, wherein the standard pictures are pictures of the corresponding vehicle accessories in an undamaged state.
Step S12, calculating image similarities between the photographed pictures and the standard pictures corresponding to the different vehicle accessories, respectively.
The method comprises the steps of obtaining a standard pixel matrix and a shooting pixel matrix by respectively obtaining a standard picture and a matrix of pixel points in a shooting picture corresponding to the same vehicle accessory, calculating the distance between the standard pixel matrix and the shooting pixel matrix according to an Euclidean distance formula to obtain the image similarity between the shooting picture and the standard picture, and if the image similarity between the shooting picture and the standard picture is larger, the damage degree of the vehicle accessory is lower.
Step S13, if the image similarity between the shot picture corresponding to the vehicle accessory and the standard picture is smaller than a similarity threshold, the vehicle accessory is marked as a damaged accessory.
The similarity threshold may be set as required, for example, the similarity threshold may be set to 10%, 5%, or 3% in this step, and the similarity threshold is used to determine whether the vehicle accessory corresponding to the image similarity is a damaged accessory.
Specifically, in this step, if the image similarity between the shot picture corresponding to the vehicle accessory and the standard picture is smaller than the similarity threshold, that is, the difference between the current state and the non-damaged state of the vehicle accessory is greater than the threshold difference, it is determined that the current damaged degree of the vehicle accessory is large, and the vehicle accessory is marked as a damaged accessory.
And step S14, acquiring the shot picture corresponding to the damaged accessory to obtain the accessory damage information.
In the embodiment, the shot pictures corresponding to different vehicle accessories in the vehicle information are obtained, the corresponding standard pictures are inquired according to the accessory identifications of the vehicle accessories, the accuracy of the image similarity calculation corresponding to the different vehicle accessories is effectively improved, whether the vehicle accessories are damaged accessories can be effectively judged based on the calculated image similarity of the different vehicle accessories, and the accessory damage information corresponding to the vehicle in danger is obtained by obtaining the shot pictures corresponding to the different damaged accessories.
In all embodiments of the present application, the total loss probability of the vehicle in danger is calculated based on the loss evidence weight and the loss weight threshold, and specifically, the total loss probability of the vehicle in danger is calculated by the loss evidence weight and the loss weight threshold. Uploading the total loss probability of the vehicle in danger to the block chain can ensure the safety and the fair transparency to users. The user equipment can download the total loss probability of the vehicle in danger from the blockchain so as to check whether the total loss probability of the vehicle in danger is tampered. The blockchain referred to in this example is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm, and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Referring to fig. 3, fig. 3 is a block diagram of a vehicle total loss detection apparatus 100 according to an embodiment of the present application. In the present embodiment, the vehicle total loss detection apparatus 100 includes units for executing the steps in the embodiment corresponding to fig. 1 and 2. Please refer to fig. 1 and fig. 2 and the related descriptions in the embodiments corresponding to fig. 1 and fig. 2. For convenience of explanation, only the portions related to the present embodiment are shown. Referring to fig. 3, the vehicle total loss detection apparatus 100 includes: a damage information acquisition unit 10, a loss evidence weight calculation unit 11, an all-loss probability calculation unit 12, and an all-loss determination unit 13, wherein:
the damage information acquiring unit 10 is used for acquiring vehicle information of an emergency vehicle and acquiring accessory damage information of the emergency vehicle according to the vehicle information.
Wherein the damage information acquiring unit 10 is further configured to: acquiring shot pictures corresponding to different vehicle accessories in the vehicle information, and inquiring corresponding standard pictures according to accessory identifications of the vehicle accessories;
respectively calculating the image similarity between the shot picture and the standard picture corresponding to different vehicle accessories;
if the image similarity between the shot picture corresponding to the vehicle accessory and the standard picture is smaller than a similarity threshold value, marking the vehicle accessory as a damaged accessory;
and acquiring the shot picture corresponding to the damaged accessory to obtain the accessory damage information.
And the loss evidence weight calculating unit 11 is used for calculating the corresponding damage number of the emergent vehicle in different component groups according to the component damage information, and calculating the loss evidence weight of the emergent vehicle according to the corresponding damage number in different component groups, wherein the loss evidence weight is used for representing the whole vehicle loss degree of the emergent vehicle.
Wherein, the loss evidence weight calculation unit 11 is further configured to: respectively acquiring accessory identification of a damaged accessory in the accessory damage information, and inquiring the corresponding accessory group according to the accessory identification of the damaged accessory;
and adding different damaged accessories into the correspondingly inquired accessory groups, and respectively obtaining the quantity of the damaged accessories in the different accessories to obtain the corresponding damaged quantity of the different accessory groups.
Optionally, the loss evidence weight calculating unit 11 is further configured to: respectively obtaining quantity thresholds corresponding to different component groups, and calculating loss sub-weights corresponding to the component groups according to the quantity thresholds and the damage quantity in the component groups;
and calculating the sum of the loss sub-weights among different component groups to obtain the loss evidence weight.
Further, the calculation formula for calculating the loss sub-weight corresponding to the component group according to the quantity threshold and the damage quantity in the component group is as follows:
wherein thrdiIs the number threshold, thrd, corresponding to the ith said component groupi upA weight upper limit value set for the ith one of the component groups,gis the number of defects in the ith said set of fittings,W i (g)is the loss sub-weight corresponding to the ith said component group.
And the total loss probability calculating unit 12 is used for calculating a loss weight threshold value of the vehicle in danger according to the vehicle information, and calculating the total loss probability of the vehicle in danger according to the loss evidence weight and the loss weight threshold value.
Wherein the total loss probability calculation unit 12 is further configured to: acquiring the vehicle age of the vehicle in the vehicle information, and determining the vehicle age classification of the vehicle in danger according to the vehicle age;
and calculating a value-keeping coefficient of the vehicle in danger according to the determined vehicle age classification, and performing regression calculation according to the value-keeping coefficient to obtain the loss weight threshold.
And the total loss determination unit 13 is used for determining that the vehicle in danger is in a total loss state if the total loss probability of the vehicle in danger is greater than a probability threshold value.
Wherein, the total loss determining unit 13 is further configured to: and performing exponential operation on the loss evidence weight and the loss weight threshold according to an exponential life function to obtain the total loss probability of the vehicle in danger.
It can be seen from the above that, by obtaining the accessory damage information of the vehicle under emergency according to the vehicle information, and calculating the corresponding damage number of the vehicle under emergency in different accessory groups according to the accessory damage information, the corresponding damage number in different accessory groups can be effectively calculated, by calculating the loss evidence weight of the vehicle under emergency according to the corresponding damage number in different accessory groups, since the loss evidence weight is used for representing the entire vehicle loss degree of the vehicle under emergency, the entire vehicle loss degree of the vehicle under emergency can be effectively obtained based on the loss evidence weight, by calculating the entire loss probability of the vehicle under emergency according to the loss evidence weight and the loss weight threshold, whether the corresponding vehicle under emergency is in the entire loss state can be effectively judged based on the entire loss probability, that is, if the entire loss probability of the vehicle under emergency is greater than the probability threshold, the vehicle under emergency is judged to be in the entire loss state, and further, the influence of business experience or subjective factors of personnel on the entire loss state identification can be prevented, the accuracy of vehicle total loss detection is improved, the disassembly and damage assessment of the vehicle in danger are not needed, and the detection efficiency of the vehicle total loss detection is improved.
Fig. 4 is a block diagram of a terminal device 2 according to another embodiment of the present application. As shown in fig. 4, the terminal device 2 of this embodiment includes: a processor 20, a memory 21 and a computer program 22 stored in said memory 21 and operable on said processor 20, for example a program of a vehicle total loss detection method. The processor 20, when executing the computer program 23, implements the steps in each embodiment of the vehicle total loss detection method described above, such as S10 to S40 shown in fig. 1, or S11 to S14 shown in fig. 2. Alternatively, when the processor 20 executes the computer program 22, the functions of the units in the embodiment corresponding to fig. 3, for example, the functions of the units 10 to 13 shown in fig. 3, are implemented, for which reference is specifically made to the relevant description in the embodiment corresponding to fig. 4, which is not repeated herein.
Illustratively, the computer program 22 may be divided into one or more units, which are stored in the memory 21 and executed by the processor 20 to accomplish the present application. The one or more units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 22 in the terminal device 2. For example, the computer program 22 may be divided into a damage information acquisition unit 10, a loss evidence weight calculation unit 11, an all-loss probability calculation unit 12, and an all-loss determination unit 13, each of which functions as described above.
The terminal device may include, but is not limited to, a processor 20, a memory 21. It will be appreciated by those skilled in the art that fig. 4 is merely an example of a terminal device 2 and does not constitute a limitation of the terminal device 2 and may include more or less components than those shown, or some components may be combined, or different components, for example the terminal device may also include input output devices, network access devices, buses, etc.
The Processor 20 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 21 may be an internal storage unit of the terminal device 2, such as a hard disk or a memory of the terminal device 2. The memory 21 may also be an external storage device of the terminal device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 2. Further, the memory 21 may also include both an internal storage unit and an external storage device of the terminal device 2. The memory 21 is used for storing the computer program and other programs and data required by the terminal device. The memory 21 may also be used to temporarily store data that has been output or is to be output.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (9)
1. A vehicle total loss detection method is characterized by comprising the following steps:
acquiring vehicle information of an emergency vehicle, and acquiring shot pictures and standard pictures corresponding to different vehicle accessories in the vehicle information;
respectively acquiring matrixes of pixel points in the standard picture and the shot picture corresponding to the same vehicle accessory to obtain a standard pixel matrix and a shot pixel matrix, and calculating the distance between the standard pixel matrix and the shot pixel matrix to obtain image similarity;
if the image similarity is smaller than a similarity threshold value, marking the vehicle accessory corresponding to the image similarity as a damaged accessory, and acquiring the shot picture corresponding to the damaged accessory to obtain accessory damage information;
calculating the corresponding damage number of the vehicle in different accessory groups according to the accessory damage information, and respectively obtaining the corresponding number threshold values of the different accessory groups;
calculating a loss sub-weight corresponding to the component group according to the quantity threshold and the damage quantity in the component group, wherein the loss sub-weight is used for representing the damage degree corresponding to the component group, and calculating a loss evidence weight of the vehicle in danger according to the loss sub-weight, and the loss evidence weight is used for representing the whole vehicle loss degree of the vehicle in danger;
calculating a loss weight threshold of the vehicle in danger according to the vehicle information, and calculating a total loss probability of the vehicle in danger according to the loss evidence weight and the loss weight threshold;
if the total loss probability of the vehicle in danger is greater than the probability threshold value, determining that the vehicle in danger is in a total loss state;
the calculation formula for calculating the loss sub-weight corresponding to the component group according to the quantity threshold and the damage quantity in the component group is as follows:
wherein thrdiIs the number threshold, thrd, corresponding to the ith said component groupi upA weight upper limit value set for the ith one of the component groups,gis the number of defects in the ith said set of fittings,W i (g)is the loss sub-weight corresponding to the ith said component group.
2. The vehicle total loss detection method according to claim 1, wherein the calculating the corresponding damage number of the vehicle in different component groups according to the component damage information comprises:
respectively acquiring accessory identification of a damaged accessory in the accessory damage information, and inquiring the corresponding accessory group according to the accessory identification of the damaged accessory;
and adding different damaged accessories into the correspondingly inquired accessory groups, and respectively obtaining the quantity of the damaged accessories in the different accessories to obtain the corresponding damaged quantity of the different accessory groups.
3. The vehicle total loss detection method according to claim 1, wherein the calculating the loss evidence weight of the vehicle in danger according to the loss sub-weight comprises:
and calculating the sum of the loss sub-weights among different component groups to obtain the loss evidence weight.
4. The vehicle total loss detection method according to claim 1, wherein the calculating a loss weight threshold of the vehicle in danger from the vehicle information comprises:
acquiring the vehicle age of the vehicle in the vehicle information, and determining the vehicle age classification of the vehicle in danger according to the vehicle age;
and calculating a value-keeping coefficient of the vehicle in danger according to the determined vehicle age classification, and performing regression calculation according to the value-keeping coefficient to obtain the loss weight threshold.
5. The vehicle total loss detection method according to claim 1, wherein the acquiring of the shot picture and the standard picture corresponding to different vehicle accessories in the vehicle information comprises:
and acquiring shot pictures corresponding to different vehicle accessories in the vehicle information, and inquiring the corresponding standard picture according to the accessory identification of the vehicle accessory.
6. The vehicle total loss detection method according to claim 1, wherein the calculating the total loss probability of the vehicle in danger from the loss evidence weight and the loss weight threshold comprises:
and performing exponential operation on the loss evidence weight and the loss weight threshold according to an exponential life function to obtain the total loss probability of the vehicle in danger.
7. A vehicle total loss detection device, comprising:
the damage information acquisition unit is used for acquiring vehicle information of the vehicle in danger and acquiring shot pictures and standard pictures corresponding to different vehicle accessories in the vehicle information; respectively acquiring matrixes of pixel points in the standard picture and the shot picture corresponding to the same vehicle accessory to obtain a standard pixel matrix and a shot pixel matrix, and calculating the distance between the standard pixel matrix and the shot pixel matrix to obtain image similarity; if the image similarity is smaller than a similarity threshold value, marking the vehicle accessory corresponding to the image similarity as a damaged accessory, and acquiring the shot picture corresponding to the damaged accessory to obtain accessory damage information;
the loss evidence weight calculation unit is used for calculating the corresponding damage number of the vehicle in different component groups according to the component damage information and respectively acquiring the corresponding number threshold values of the different component groups; calculating a loss sub-weight corresponding to the component group according to the quantity threshold and the damage quantity in the component group, wherein the loss sub-weight is used for representing the damage degree corresponding to the component group, and calculating a loss evidence weight of the vehicle in danger according to the loss sub-weight, and the loss evidence weight is used for representing the whole vehicle loss degree of the vehicle in danger;
the calculation formula for calculating the loss sub-weight corresponding to the component group according to the quantity threshold and the damage quantity in the component group is as follows:
wherein thrdiIs the number threshold, thrd, corresponding to the ith said component groupi upA weight upper limit value set for the ith one of the component groups,gis the number of defects in the ith said set of fittings,W i (g)is the loss sub-weight corresponding to the ith component group;
the total loss probability calculation unit is used for calculating a loss weight threshold value of the vehicle in danger according to the vehicle information and calculating the total loss probability of the vehicle in danger according to the loss evidence weight and the loss weight threshold value;
and the total loss judging unit is used for judging that the vehicle in danger is in a total loss state if the total loss probability of the vehicle in danger is greater than a probability threshold value.
8. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 6 when executing the computer program.
9. A storage medium storing a computer program, characterized in that the computer program realizes the steps of the method according to any one of claims 1 to 6 when executed by a processor.
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| CN202011265076.9A CN112085612B (en) | 2020-11-13 | 2020-11-13 | Vehicle total loss detection method and device, terminal equipment and storage medium |
| PCT/CN2021/127259 WO2022100455A1 (en) | 2020-11-13 | 2021-10-29 | Vehicle total loss detection method and apparatus, terminal device and storage medium |
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| CN202011265076.9A CN112085612B (en) | 2020-11-13 | 2020-11-13 | Vehicle total loss detection method and device, terminal equipment and storage medium |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109146834A (en) * | 2017-06-13 | 2019-01-04 | 上海擎感智能科技有限公司 | Car damage identification method and device, computer readable storage medium, terminal |
| CN110084708A (en) * | 2019-04-15 | 2019-08-02 | 华侨大学 | A kind of insurance management platform based on Internet of Things and block chain technology and Claims Resolution method |
| CN111507484A (en) * | 2019-01-31 | 2020-08-07 | 北京嘀嘀无限科技发展有限公司 | Prompting method, prompting device, electronic equipment and storage medium |
| CN111784522A (en) * | 2020-05-29 | 2020-10-16 | 中国平安财产保险股份有限公司 | Vehicle total loss judgment method and device, computer equipment and readable storage medium |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017095425A1 (en) * | 2015-12-03 | 2017-06-08 | Allison Transmission, Inc. | System and method to control the operation of a transmission using engine patterns |
| CN107403424B (en) * | 2017-04-11 | 2020-09-18 | 阿里巴巴集团控股有限公司 | Vehicle loss assessment method and device based on image and electronic equipment |
| CN109443301B (en) * | 2018-09-25 | 2021-09-10 | 平安科技（深圳）有限公司 | Vehicle loss assessment method, computer readable storage medium and server |
| CN110796487A (en) * | 2019-10-18 | 2020-02-14 | 上海钧正网络科技有限公司 | Quantity prediction method, device, computer equipment and storage medium |
| CN112085612B (en) * | 2020-11-13 | 2021-03-16 | 深圳壹账通智能科技有限公司 | Vehicle total loss detection method and device, terminal equipment and storage medium |
-
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109146834A (en) * | 2017-06-13 | 2019-01-04 | 上海擎感智能科技有限公司 | Car damage identification method and device, computer readable storage medium, terminal |
| CN111507484A (en) * | 2019-01-31 | 2020-08-07 | 北京嘀嘀无限科技发展有限公司 | Prompting method, prompting device, electronic equipment and storage medium |
| CN110084708A (en) * | 2019-04-15 | 2019-08-02 | 华侨大学 | A kind of insurance management platform based on Internet of Things and block chain technology and Claims Resolution method |
| CN111784522A (en) * | 2020-05-29 | 2020-10-16 | 中国平安财产保险股份有限公司 | Vehicle total loss judgment method and device, computer equipment and readable storage medium |
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