CN112085610B - Target damage assessment method, target damage assessment device, electronic equipment and computer readable storage medium - Google Patents
Target damage assessment method, target damage assessment device, electronic equipment and computer readable storage medium Download PDFInfo
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Abstract
The application relates to artificial intelligence technology, and discloses a target damage assessment method, which comprises the following steps: extracting features of an original damaged accessory set to obtain an accessory feature set, cleaning the original damaged accessory set based on similarity between accessory features in the accessory feature set to obtain a standard damaged accessory set, dividing the standard damaged accessory set into a core accessory damaged set and a non-core accessory damaged set, training an original damage assessment model by using the core accessory damaged set, the non-core accessory damaged set and a maintenance time length set to obtain a standard damage assessment model, and executing damage assessment on a damaged accessory of a target object to be damaged by using the standard damage assessment model to obtain the maintenance time length of the damaged accessory. The application also provides a target damage assessment device, electronic equipment and a computer readable storage medium. The application combines the relation between the fittings of the target object, and can improve the accuracy of damage assessment to the target object.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to a method and apparatus for object damage assessment, an electronic device, and a computer readable storage medium.
Background
With rapid development of technology, a method for gradually changing damage assessment of a target object from manual damage assessment to automatic damage assessment, such as vehicle insurance claim service, is that a traditional damage assessment method relies on personal experience to manually assess the damage degree of a vehicle or uses a classification algorithm to predict the maintenance time spent on damaged accessories of the vehicle, and a vehicle damage assessment result is estimated according to the predicted time.
The two methods can achieve the purpose of target object damage assessment, but the manual damage assessment is low in efficiency and high in error rate due to excessive time of workers, and the relation between different accessories in the target object cannot be effectively divided by utilizing the automatic damage assessment of a classification algorithm, so that when the target object damage assessment is carried out, the phenomenon that the damage assessment result is far from the actual result is sometimes sent.
Disclosure of Invention
The application provides a method and a device for damage assessment of a target object, electronic equipment and a computer readable storage medium, and mainly aims to combine the relation between accessories of the target object and the accessories and improve the accuracy of damage assessment of the target object.
In order to achieve the above object, the present application provides a method for determining loss of a target object, including:
acquiring a historical maintenance data set of a target object, and extracting an original damaged fitting set and a maintenance duration set from the historical maintenance data set;
extracting features of the original damaged accessory set to obtain an accessory feature set;
calculating the similarity between the fitting features in the fitting feature set according to the maintenance duration set, and cleaning the original damaged fitting set based on the similarity to obtain a standard damaged fitting set;
dividing the standard damaged fitting set into a core fitting damaged set and a non-core fitting damaged set;
constructing an original damage assessment model based on a forward feedback neural network, and training the original damage assessment model by using the core accessory damage set, the non-core accessory damage set and the maintenance duration set to obtain a standard damage assessment model;
and performing damage assessment on the damaged accessory of the target object to be subjected to damage assessment by using the standard damage assessment model to obtain the maintenance time of the damaged accessory.
Optionally, the constructing the original impairment model based on the feed-forward neural network includes:
integrating a preset first classification function and a preset second classification function to obtain an output layer, wherein the first classification function and the second classification function are probability functions;
and connecting the output layer to the tail end of the feed-forward neural network to obtain the original loss assessment model.
Optionally, training the original damage assessment model by using the core accessory damage set, the non-core accessory damage set and the maintenance duration set to obtain a standard damage assessment model, including:
performing calculation on the non-core accessory damage set by using the first classification function to obtain a core accessory prediction set;
calculating an accessory error value between the core accessory prediction set and the core accessory damage set;
judging the size relation between the accessory error value and a preset accessory error threshold value;
if the fitting error value is greater than or equal to the fitting error threshold, adjusting an internal parameter value of the original loss assessment model, and re-predicting a core fitting prediction set;
if the fitting error value is smaller than the fitting error threshold, calculating a duration prediction set of the non-core fitting damage set by using the second classification function;
calculating maintenance time length error values of the time length prediction set and the maintenance time length set;
judging the size relation between the maintenance time error value and a preset maintenance time threshold;
if the maintenance time error value is greater than or equal to the maintenance time threshold, continuously adjusting the internal parameter value of the original loss assessment model, and recalculating a core accessory prediction set;
and if the maintenance time error value is smaller than the maintenance time threshold value, obtaining the standard loss assessment model.
Optionally, the calculating the non-core accessory damage set by using the first classification function to obtain a core accessory prediction set includes:
extracting a feature set of the non-core accessory damage set by utilizing the feed-forward neural network;
and calculating a probability value set corresponding to the feature set by using the first classification function, and predicting by using the probability value set to obtain a core accessory prediction set.
Optionally, said adjusting the internal parameter values of the original impairment model comprises:
modifying the weight value and the bias value of the original loss assessment model;
and modifying the step length and the training batch number of the training of the original loss assessment model.
Optionally, the feature extraction of the original damaged fitting set to obtain a fitting feature set includes:
performing pretreatment operation including analysis and abnormal word removal on the original damaged fitting set to obtain a primary damaged fitting set;
and carrying out word vector conversion on each damaged accessory in the primary damaged accessory set to obtain the accessory characteristic set.
Optionally, the calculating the similarity between the accessory features within the accessory feature set includes:
and calculating the similarity between the fitting features in the fitting feature set by adopting the following similarity calculation method:
wherein sim (d, T) represents the similarity between the fitting feature d and the fitting feature T in the fitting feature set, w represents the weight coefficients of the d, T and other fitting features k in the fitting feature set, n is the total data in the fitting feature set, α, β are bias coefficients, and T is the repair time corresponding to the fitting feature.
In order to solve the above problems, the present application further provides a target damage assessment device, which includes:
the accessory characteristic calculation module is used for acquiring a historical maintenance data set of the target object, extracting an original damaged accessory set and a maintenance duration set from the historical maintenance data set, and carrying out characteristic extraction on the original damaged accessory set to obtain an accessory characteristic set;
the accessory dividing module is used for calculating the similarity between accessory features in the accessory feature set according to the maintenance time length set, cleaning the original damaged accessory set based on the similarity to obtain a standard damaged accessory set, and dividing the standard damaged accessory set into a core accessory damage set and a non-core accessory damage set;
the model training module is used for constructing an original damage assessment model based on a forward feedback neural network, and training the original damage assessment model by utilizing the core accessory damage set, the non-core accessory damage set and the maintenance duration set to obtain a standard damage assessment model;
and the target object damage assessment module is used for carrying out damage assessment on the damaged accessory of the target object to be subjected to damage assessment by using the standard damage assessment model, so as to obtain the maintenance time of the damaged accessory.
In order to solve the above-mentioned problems, the present application also provides an electronic apparatus including:
a memory storing at least one instruction; a kind of electronic device with high-pressure air-conditioning system
And the processor executes the instructions stored in the memory to realize the target object damage assessment method according to any one of the above.
In order to solve the above-mentioned problems, the present application also provides a computer-readable storage medium including a storage data area storing created data and a storage program area storing a computer program; wherein the computer program when executed by the processor implements the object damage assessment method of any one of the above.
According to the embodiment of the application, the original damaged accessory set is extracted according to the historical maintenance data set of the target object, and the standard damaged accessory set is obtained by carrying out feature extraction and similarity calculation on the original damaged accessory set, so that the damaged features of each accessory can be effectively extracted by the feature extraction, the interference of non-accessory damaged features is reduced, and meanwhile, the similar accessory features are removed by a similarity calculation method, so that the interference of data quantity, non-accessory features and similar features on damage of subsequent target objects can be effectively reduced, and the accuracy of damage assessment is improved; meanwhile, in the embodiment of the application, the standard damaged accessory set is divided into the core accessory damaged set and the non-core accessory damaged set, and the damage assessment model is trained through the relevance of the non-core accessory and the core accessory. Therefore, the target damage assessment method, the target damage assessment device and the computer readable storage medium can be combined with the relation between the fittings of the target, so that the damage assessment accuracy of the target is improved.
Drawings
FIG. 1 is a flow chart of a method for determining damage to a target object according to an embodiment of the application;
FIG. 2 is a detailed flowchart of S2 in the method for determining loss of a target object according to an embodiment of the present application;
FIG. 3 is a detailed flowchart of training an original impairment model in an objective impairment method according to an embodiment of the present application;
FIG. 4 is a schematic block diagram of a target damage assessment device according to an embodiment of the present application;
fig. 5 is a schematic diagram of an internal structure of an electronic device for implementing a method for object damage assessment according to an embodiment of the present application;
the achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The execution main body of the target object damage assessment method provided by the embodiment of the application comprises at least one of an electronic device which can be configured to execute the method provided by the embodiment of the application, such as a server side, a terminal and the like. In other words, the objective damage assessment method may be performed by software or hardware installed in a terminal device or a server device, where the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
The application provides a target damage assessment method. Referring to fig. 1, a flow chart of a method for determining loss of a target object according to an embodiment of the application is shown. The method may be performed by an apparatus, which may be implemented in software and/or hardware.
In this embodiment, the method for target damage assessment includes:
s1, acquiring a historical maintenance data set of a target object, and extracting an original damaged fitting set and a maintenance duration set from the historical maintenance data set.
In a preferred embodiment of the present application, the target object may be an object such as an automobile, which is often required to be damaged. Further, maintenance data of the target object which is arranged in advance by the user in the historical time can be obtained, so that a historical maintenance data set is obtained, for example, a plurality of pieces of vehicle insurance claim data accepted in a specified time period are obtained from an automobile insurance company, damaged accessories of each vehicle are extracted from the vehicle insurance claim data, an original damaged accessory set is obtained, maintenance time corresponding to the damaged accessories is extracted, and a maintenance time set is obtained. The original damaged accessory set corresponding to the automobile insurance field comprises headlight replacement, engine oil leakage repair, automobile tire replacement and the like, and the maintenance duration is concentrated to record the headlight replacement duration of 2 hours, the engine oil leakage repair duration of 5 hours, the automobile tire replacement of 0.6 hour and the like.
S2, extracting features of the original damaged accessory set to obtain an accessory feature set.
In the preferred embodiment of the application, as various damaged parts have similar words in the original damaged parts, such as replacing automobile tires, replacing automobile front wheel tires and the like, the characteristics of the damaged parts are extracted from the original damaged parts by utilizing a characteristic extraction method, so that the characteristic set of the parts is obtained.
In detail, referring to fig. 2, the step S2 includes:
s21, carrying out pretreatment operation comprising word segmentation and abnormal word removal on the original damaged accessory set to obtain a primary damaged accessory set;
because in the Chinese representation, no clear separation mark exists between words, if word segmentation and abnormal word removal pretreatment are not performed before word vector conversion, the phenomenon of lengthy accessory features corresponding to each damaged accessory occurs, and the subsequent similarity calculation is directly affected, so that word segmentation and abnormal word removal are performed on the original damaged accessory set.
In a preferred implementation of the present application, the word segmentation process may use jieba word segmentation program based on programming languages such as Python and JAVA, where, if the original damaged accessory is concentrated with a damaged accessory, the method includes: "air leakage of rear tire of automobile, change the rear tire". The processing based on the jieba segmentation results in the following steps: [ automobile ] [ rear wheel ] [ tire ] [ air leakage ] [ and ] [ replacement ] [ rear wheel ] [ tire ].
From the above, when the word segmentation is finished, a plurality of special-shaped words such as repeated words, punctuation marks, stop words and the like are found, so that the special-shaped words are needed to be removed, and the purpose of conciseness is achieved, such as the following steps of removing the special-shaped words to obtain the tire with the rear wheel.
S22, carrying out word vector conversion on each damaged accessory in the primary damaged accessory set to obtain an accessory characteristic set.
Preferably, the word vector transformation may employ the presently disclosed one-hot model, word2vec model, etc., such as using the word2vec model to transform the [ change ] [ rear wheel ] [ tire ] described above into a matrix-form fitting feature.
S3, calculating the similarity between the fitting features in the fitting feature set, and cleaning the original damaged fitting set based on the similarity to obtain a standard damaged fitting set.
As can be seen from the feature extraction of S2, if the original damaged fitting set includes replacing a tire of an automobile and a tire of a front wheel of an automobile, two similar fitting features can be obtained by using the feature extraction method of S2, but since the damage assessment results of the similar fitting features are almost the same when the damage assessment is performed subsequently, the similar fitting features need to be cleaned by further calculating the similarity between the fitting features in the fitting feature set, so as to obtain the standard damaged fitting set.
In the preferred embodiment of the application, the following similarity calculation method can be used to calculate the similarity between the fitting features:
wherein sim (d, t) represents similarity between accessory feature d and accessory feature t in an accessory feature set, w represents weight coefficients of d and t and other accessory features k in the accessory feature set, n is total data in the accessory feature set, α and β are bias coefficients, α+β=1, and t is maintenance time length corresponding to an accessory feature, which can be found from the maintenance time length set.
Preferably, the cleaning method for cleaning the original damaged fitting set is multiple, for example, each similarity obtained by sequencing according to a similarity sequencing method from big to small, whether the difference value of every two adjacent groups of similarities is smaller than a specified threshold value is judged, and if the difference value is smaller than the specified threshold value, the damaged fitting corresponding to one of the similarities is removed until the standard damaged fitting set is obtained.
S4, dividing the standard damaged fitting set into a core fitting damaged set and a non-core fitting damaged set.
According to the principle that the part of the fittings and other fittings generally have relevance, the standard damaged fitting set can be divided into a core fitting damaged set and a non-core fitting damaged set, for example, when the fitting A is damaged, the accompanying fitting B and the fitting C are damaged at the same time, but when the fitting B is damaged, the other fittings are not affected under the general condition, the fitting A is divided into the non-core fittings, the fitting B is divided into the core fittings, for example, in the field of automobile insurance, the steering wheel of an automobile breaks down and generally has problems along with the bearing of the steering wheel, so the steering wheel of the automobile is the non-core fitting, and the bearing of the steering wheel is the core fitting.
S5, constructing an original damage assessment model based on a forward feedback neural network, and training the original damage assessment model by using the core accessory damage set, the non-core accessory damage set and the maintenance duration set to obtain a standard damage assessment model.
Because the conventional machine learning algorithm is simpler for processing the damage of the accessory, in the preferred embodiment of the application, the damage assessment model is constructed by combining the model with the neural network, wherein the constructing the original damage assessment model based on the feed-forward neural network comprises:
integrating a preset first classification function and a preset second classification function to obtain an output layer, wherein the first classification function and the second classification function are probability functions;
and connecting the output layer to the tail end of the feed-forward neural network to obtain the original loss assessment model.
The first classification function is used for predicting the core accessories related to the non-core accessory damage set to obtain a core accessory prediction set. Preferably, the first classification function may employ a probability function such as a softmax function. And the second classification function is used for predicting the maintenance duration corresponding to each non-core accessory in the non-core accessory damage set to obtain a duration prediction set. Likewise, the second classification function may use the same classification function as the first classification function, or may use a different classification function. For example, when a different classification function is employed, the second classification function may be a sigmoid function.
Further, the feed-forward neural network includes an input layer, an activation layer and an output layer, and in the preferred embodiment of the present application, the output layer obtained by using the first classification function and the second classification function is used to replace the original output layer of the feed-forward neural network, so as to obtain the original loss assessment model.
Further, after the original impairment model is constructed, the internal parameters of the original impairment model need to be further trained. Embodiments of the present application also include training the original impairment model. Preferably, the embodiment of the application trains the original damage assessment model by using the core accessory damage set, the non-core accessory damage set and the maintenance duration set to obtain a standard damage assessment model.
In detail, referring to fig. 3, the training the original impairment model includes:
s511, calculating the non-core accessory damage set by using the first classification function to obtain a core accessory prediction set;
in detail, the S511 includes: and extracting a feature set of the non-core accessory damage set by using the forward feedback neural network, calculating a probability value set corresponding to the feature set by using the first classification function, and predicting by using the probability value set to obtain a core accessory prediction set. In the preferred embodiment of the application, because the feedforward neural network is a multi-layer feedforward neural network trained according to an error reverse propagation algorithm, the characteristic set of the non-core accessory damage set can be effectively extracted by using the feedforward neural network, meanwhile, the probability value corresponding to each characteristic set is calculated by using a first classification function, for example, the non-core accessory damage set and the pull rod accessory damage are respectively calculated, the characteristic value of the pull rod accessory is obtained by calculation, and when the pull rod accessory is calculated according to the first classification function, the damage probability of the corresponding transverse pull rod is 78%, the damage probability of the steering driven arm is 17% and the damage probability of the power steering oil tank is 31%, so that when the pull rod accessory is damaged, the transverse pull rod is correspondingly damaged, and the core accessory prediction set is obtained by summarizing.
S512, calculating fitting error values of the core fitting prediction set and the core fitting damage set;
s513, judging the size relation between the accessory error value and a preset accessory error threshold;
s514, if the fitting error value is greater than or equal to the fitting error threshold, adjusting the internal parameter value of the original loss assessment model, and returning to S511;
in detail, the internal parameter values of the original impairment model include weight values, bias values, training steps, batch numbers, iteration cycles and the like of the feed-forward neural network.
S515, if the fitting error value is smaller than the fitting error threshold value, calculating a duration prediction set of the non-core fitting damage set by using the second classification function;
in a preferred embodiment of the present application, the second classification function has the same function as the first classification function, and as mentioned above, when the pull rod fitting is damaged, the tie rod is correspondingly damaged, so that the second classification function is used to calculate that when the pull rod fitting is damaged, the corresponding maintenance time is 3 hours.
S516, calculating maintenance duration error values of the duration prediction set and the maintenance duration set;
s517, judging the size relation between the maintenance time error value and a preset maintenance time threshold;
s518, if the maintenance duration error value is greater than or equal to the maintenance duration threshold, continuing to adjust the internal parameter value of the original loss assessment model, and returning to S511;
and S519, if the maintenance time error value is smaller than the maintenance time threshold value, obtaining the standard loss assessment model.
In detail, the adjusting the internal parameter values of the original impairment model includes: modifying the weight value and the bias value of the original loss assessment model; and modifying the step length and the batch number of the original loss assessment model training.
In the preferred embodiment of the present application, the weight and bias values are internal parameter values of the feed-forward neural network.
S6, obtaining a damaged fitting to be subjected to damage assessment, and obtaining maintenance time of the damaged fitting by means of damage assessment by using the standard damage assessment model.
If a car with traffic accident needs to be damaged, and the car engine cover is folded in a large area, the standard damage assessment model predicts that if the car engine cover is folded in a large area, the corresponding bumper is damaged, so that compared with the traditional damage assessment, the application can obtain a damage assessment result with longer maintenance time.
According to the embodiment of the application, the original damaged accessory set is extracted according to the historical maintenance data set of the target object, and the standard damaged accessory set is obtained by carrying out feature extraction and similarity calculation on the original damaged accessory set, so that the damaged features of each accessory can be effectively extracted by the feature extraction, the interference of non-accessory damaged features is reduced, and meanwhile, the similar accessory features are removed by a similarity calculation method, so that the interference of data quantity, non-accessory features and similar features on damage of subsequent target objects can be effectively reduced, and the accuracy of damage assessment is improved; meanwhile, in the embodiment of the application, the standard damaged accessory set is divided into the core accessory damaged set and the non-core accessory damaged set, and the damage assessment model is trained through the relevance of the non-core accessory and the core accessory. Therefore, the target damage assessment method, the target damage assessment device and the computer readable storage medium can be combined with the relation between the fittings of the target, so that the damage assessment accuracy of the target is improved.
Fig. 4 is a schematic block diagram of the object damage assessment device of the present application.
The object damage assessment device 100 of the present application may be installed in an electronic apparatus. Depending on the functions implemented, the object impairment determination device may include an accessory feature calculation module 101, an accessory classification module 102, a model training module 103, and an object impairment determination module 104. The module of the present application may also be referred to as a unit, meaning a series of computer program segments capable of being executed by the processor of the electronic device and of performing fixed functions, stored in the memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the accessory feature calculation module 101 is configured to obtain a historical maintenance data set of a target object, extract an original damaged accessory set and a maintenance duration set from the historical maintenance data set, and perform feature extraction on the original damaged accessory set to obtain an accessory feature set;
the accessory dividing module 102 is configured to calculate a similarity between accessory features in the accessory feature set according to the maintenance duration set, clean the original damaged accessory set based on the similarity, obtain a standard damaged accessory set, and divide the standard damaged accessory set into a core accessory damage set and a non-core accessory damage set;
the model training module 103 is configured to construct an original damage assessment model based on a feed-forward neural network, and train the original damage assessment model by using the core accessory damage set, the non-core accessory damage set and the maintenance duration set to obtain a standard damage assessment model;
the target damage assessment module 104 is configured to perform damage assessment on a damaged accessory of the target to be damaged by using the standard damage assessment model, so as to obtain a maintenance duration of the damaged accessory.
The module in the device provided by the application can be used for improving the damage assessment accuracy of the target object by combining the relation between the accessory of the target object and the accessory based on the damage assessment method of the target object.
Fig. 5 is a schematic structural diagram of an electronic device for implementing the object damage assessment method according to the present application.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as an object damage assessment program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the object damage determination program 12, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules (for example, an object damage determination program or the like) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 5 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The object damage determination program 12 stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, which, when executed in the processor 10, can implement:
acquiring a historical maintenance data set of a target object, and extracting an original damaged fitting set and a maintenance duration set from the historical maintenance data set;
extracting features of the original damaged accessory set to obtain an accessory feature set;
calculating the similarity between the fitting features in the fitting feature set according to the maintenance duration set, and cleaning the original damaged fitting set based on the similarity to obtain a standard damaged fitting set;
dividing the standard damaged fitting set into a core fitting damaged set and a non-core fitting damaged set;
constructing an original damage assessment model based on a forward feedback neural network, and training the original damage assessment model by using the core accessory damage set, the non-core accessory damage set and the maintenance duration set to obtain a standard damage assessment model;
and performing damage assessment on the damaged accessory of the target object to be subjected to damage assessment by using the standard damage assessment model to obtain the maintenance time of the damaged accessory.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any accompanying diagram representation in the claims should not be considered as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present application without departing from the spirit and scope of the technical solution of the present application.
Claims (7)
1. A method for target damage assessment, the method comprising:
acquiring a historical maintenance data set of a target object, and extracting an original damaged fitting set and a maintenance duration set from the historical maintenance data set;
extracting features of the original damaged accessory set to obtain an accessory feature set;
calculating the similarity between the fitting features in the fitting feature set according to the maintenance duration set, and cleaning the original damaged fitting set based on the similarity to obtain a standard damaged fitting set;
dividing the standard damaged fitting set into a core fitting damaged set and a non-core fitting damaged set;
constructing an original damage assessment model based on a forward feedback neural network, and training the original damage assessment model by using the core accessory damage set, the non-core accessory damage set and the maintenance duration set to obtain a standard damage assessment model;
performing damage assessment on a damaged accessory of the target object to be subjected to damage assessment by using the standard damage assessment model to obtain maintenance time of the damaged accessory;
the constructing the original impairment model based on the feed-forward neural network comprises the following steps: integrating a preset first classification function and a preset second classification function to obtain an output layer, wherein the first classification function and the second classification function are probability functions; connecting the output layer to the tail end of the forward feedback neural network to obtain the original loss assessment model;
training the original damage assessment model by using the core accessory damage set, the non-core accessory damage set and the maintenance duration set to obtain a standard damage assessment model, wherein the method comprises the following steps of: performing calculation on the non-core accessory damage set by using the first classification function to obtain a core accessory prediction set; calculating an accessory error value between the core accessory prediction set and the core accessory damage set; judging the size relation between the accessory error value and a preset accessory error threshold value; if the fitting error value is greater than or equal to the fitting error threshold, adjusting an internal parameter value of the original loss assessment model, and re-predicting a core fitting prediction set; if the fitting error value is smaller than the fitting error threshold, calculating a duration prediction set of the non-core fitting damage set by using the second classification function; calculating maintenance time length error values of the time length prediction set and the maintenance time length set; judging the size relation between the maintenance time error value and a preset maintenance time threshold; if the maintenance time error value is greater than or equal to the maintenance time threshold, continuously adjusting the internal parameter value of the original loss assessment model, and recalculating a core accessory prediction set; if the maintenance time error value is smaller than the maintenance time threshold value, the standard loss assessment model is obtained;
the computing of the similarity between the accessory features within the accessory feature set includes: and calculating the similarity between the fitting features in the fitting feature set by adopting the following similarity calculation method:
wherein ,representing fitting features within said fitting feature set +.>Fitting feature->Similarity between->Representing said->Is +.>Weight coefficient of>Representing said->Is +.>Weight coefficient of>For the total number of data in the fitting feature set, < >>For bias factor +.>And (5) maintaining the time length corresponding to the accessory characteristic.
2. The method of claim 1, wherein performing a calculation on the non-core part damage set using the first classification function to obtain a core part prediction set comprises:
extracting a feature set of the non-core accessory damage set by utilizing the feed-forward neural network;
and calculating a probability value set corresponding to the feature set by using the first classification function, and predicting by using the probability value set to obtain a core accessory prediction set.
3. The method of claim 1, wherein said adjusting the internal parameter values of the original impairment model comprises:
modifying the weight value and the bias value of the original loss assessment model;
and modifying the step length and the training batch number of the training of the original loss assessment model.
4. The method for object damage assessment according to claim 1, wherein said feature extraction of said original damaged fitting set to obtain a fitting feature set comprises:
performing pretreatment operations including analysis and abnormal word removal on the original damaged fitting set to obtain a primary damaged fitting set;
and carrying out word vector conversion on each damaged accessory in the primary damaged accessory set to obtain the accessory characteristic set.
5. A target damage assessment device for implementing the target damage assessment method according to any one of claims 1 to 4, characterized in that the device comprises:
the accessory characteristic calculation module is used for acquiring a historical maintenance data set of the target object, extracting an original damaged accessory set and a maintenance duration set from the historical maintenance data set, and carrying out characteristic extraction on the original damaged accessory set to obtain an accessory characteristic set;
the accessory dividing module is used for calculating the similarity between accessory features in the accessory feature set according to the maintenance time length set, cleaning the original damaged accessory set based on the similarity to obtain a standard damaged accessory set, and dividing the standard damaged accessory set into a core accessory damage set and a non-core accessory damage set;
the model training module is used for constructing an original damage assessment model based on a forward feedback neural network, and training the original damage assessment model by utilizing the core accessory damage set, the non-core accessory damage set and the maintenance duration set to obtain a standard damage assessment model;
and the target object damage assessment module is used for carrying out damage assessment on the damaged accessory of the target object to be subjected to damage assessment by using the standard damage assessment model, so as to obtain the maintenance time of the damaged accessory.
6. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the object damage determination method of any one of claims 1 to 4.
7. A computer-readable storage medium comprising a storage data area and a storage program area, characterized in that the storage data area stores created data, the storage program area storing a computer program; wherein the computer program, when executed by a processor, implements the object impairment estimation method according to any one of claims 1 to 4.
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