CN115587896B - Method, device and equipment for processing canine medical insurance data - Google Patents
Method, device and equipment for processing canine medical insurance data Download PDFInfo
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Abstract
The application relates to the technical field of canine management, in particular to a method for canine medical insurance management, and specifically relates to a method, a device and equipment for canine medical insurance data processing; the method comprises the following steps: acquiring identity information of dogs to be reimbursed; acquiring historical treatment information and application historical information based on the identity information of the dogs to be reimbursed; acquiring corresponding reimbursement classifications based on the application history information, and comparing the history treatment information with reimbursement classifications to obtain reimbursement results; the identity of the dogs is accurately identified by setting an identity identification model, the acquisition of insurance information and historical treatment information is realized based on a configuration-based management method for obtaining accurate dog information, and the acquisition of reimbursement information is realized based on the determination of the information. By the method provided by the embodiment, quick and accurate information management can be realized, management cost is reduced, and management safety is improved.
Description
Technical Field
The application relates to the technical field of canine management, in particular to a method for canine medical insurance management, and specifically relates to a method, a device and equipment for canine medical insurance data processing.
Background
At present, a user can apply insurance to the own pets, and when the own pets are ill or unexpected, the insurance company can be applied for claim settlement, so that the safety of the pets can be ensured and the economic loss of the own pets can be reduced. When the pet is ensured, identity authentication needs to be carried out on the pet, and the existing means such as DNA authentication, chip authentication and the like are complex in operation and high in cost.
The current medical insurance management for dogs is greatly different from that of human beings for insurance companies. Firstly, the insurance management for human beings is based on the determination of human identities, and the current confirmation for human identities is simpler, because human beings have a higher social administration system, namely, the identification is performed by means of a configured identity recognition card such as an identity card and a medical insurance card, the insurance management for human beings is mature and has higher insurance, so that insurance companies can manage insurance policies more easily.
The medical insurance management difficulty for dogs is high, and firstly, a social unified management system and method for dogs are not available, but the management for dogs is mainly managed through an ID (identity) card. But unlike humans, canine ID identities can be spoofed by replacing and making false IDs, and information management for dogs is also in a state of hysteresis. Therefore, it is necessary to provide an accurate and safe management method for managing the medical insurance information of dogs.
The medical insurance data processing system and the corresponding method provided by aiming at the problems at present have the technical problems of low processing efficiency and poor processed data relevance.
Disclosure of Invention
In order to solve the technical problems, the application provides a method, a device and equipment for processing canine medical insurance data, which can accurately identify the identity of a canine through constructing an identity identification model by a computer technology, and realize the identification of the identity of the canine and the management of canine information based on the identification by constructing a corresponding medical insurance data processing mode.
In order to achieve the above purpose, the technical scheme adopted by the embodiment of the application is as follows:
in a first aspect, a method for processing canine medical insurance data comprises the following steps: acquiring identity information of dogs to be reimbursed; acquiring historical treatment information and application history information based on the identity information of the dogs to be reimbursed, wherein the historical treatment information is configured in a preset treatment information database and ordered according to time, and the application history information is configured in the preset application information database and ordered according to time; acquiring corresponding reimbursement classifications based on the application history information, and comparing the history treatment information with reimbursement classifications to obtain reimbursement results; the method for acquiring the identity information of the dog to be reimbursed comprises the following steps: extracting the characteristics of the dogs to be reimbursed based on a preset identity recognition model; comparing the characteristics with a preset characteristic database to obtain a comparison result; determining the identity information of the to-be-reimbursed dogs based on the comparison result; the identification model comprises a first identification model and a first characteristic database, wherein the first identification model is used for identifying the type of the dog to be reimbursed, and the specific method comprises the following steps: extracting first characteristics of the dogs to be reimbursed based on the first identity recognition model, and comparing the first characteristics with a preset first characteristic database to obtain category information of the dogs to be reimbursed; the identification model also comprises a second identification model and a second characteristic database, wherein the second characteristic database is configured in the first characteristic database, and the second identification model is used for identifying the identification information of the dog to be reimbursed, and the specific method comprises the following steps: and extracting a second characteristic of the to-be-reimbursed dog based on the second identity recognition model, and comparing the second characteristic with the second characteristic database to obtain the identity information of the to-be-reimbursed dog.
In a first implementation manner of the first aspect, the acquiring historical treatment information and the application historical information based on the identity information of the to-be-reimbursed dog includes the following steps: acquiring a treatment time interval in the historical treatment information and an application time interval in the application historical information; a treatment time interval within the application time interval is determined, and historical treatment information within an interval is determined based on the treatment time interval.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner, the obtaining a corresponding reimbursement classification based on the application history information, and comparing the history treatment information with the reimbursement classification to obtain a reimbursement result includes the following steps: acquiring unreliability treatment information in the historical treatment information based on a preset reimbursement tag; comparing the unreferenced therapeutic information with reimbursement categories to obtain reimbursement results, wherein the reimbursement results comprise the following steps of: extracting treatment keywords in the unreferenced treatment information and reimbursement keywords in reimbursement categories; and comparing the similarity of the treatment keywords and the reimbursement keywords, and determining the similarity of the unreported medical information and reimbursement categories based on a similarity threshold value to obtain reimbursement results.
In a third implementation manner of the first aspect, extracting a first feature of the to-be-reimbursed dog based on the first identity recognition model, and comparing the first feature with a preset first feature database to obtain category information of the to-be-reimbursed dog, where the method includes the following steps: acquiring facial image information of a dog to be reimbursed; acquiring a target feature map based on the facial image information; acquiring a plurality of target detection points in the target feature map; acquiring coordinate parameters of a plurality of target detection points, and acquiring the relative distances of the plurality of target detection points based on the plurality of coordinate parameters; comparing the relative distances of a plurality of target detection points with the relative distances of a plurality of target detection points in a preset first characteristic database to obtain the kind information of the reimbursement dogs, wherein the method specifically comprises the following steps: comparing the similarity of the relative distance of any target detection point with the relative distance of the corresponding target detection point in a preset first characteristic database, and obtaining the similarity of the relative distances of a plurality of target detection points; fusing the similarity of the relative distances of a plurality of target detection points to obtain final similarity; and determining the category information of the corresponding reimbursement dogs based on the final similarity.
With reference to the third possible implementation manner of the first aspect, in a fourth possible implementation manner, the extracting, based on the second identification model, the second feature of the to-be-reimbursed dog, and comparing, based on the second feature and the second feature database, the identity information of the to-be-reimbursed dog, includes the following method: acquiring a plurality of facial images of dogs to be reimbursed; processing the face images based on a binary method to obtain a plurality of preprocessed face image outlines, obtaining area values of the preprocessed face image outlines, and comparing the area values to determine an optimal face image; and extracting a second feature of the to-be-reimbursed dog based on the optimal facial image, and comparing the second feature with the second feature database to obtain identity information of the to-be-reimbursed dog.
With reference to the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner, the extracting the second feature of the to-be-reimbursed dog based on the optimal facial image, and comparing the second feature with the second feature database to obtain identity information of the to-be-reimbursed dog, includes the following method: acquiring a target feature map of the optimal face image; extracting key target detection points in the target feature map; extracting the feature to be identified of the key target detection point, comparing the similarity between the feature to be identified and the second feature database based on the feature to be identified and the second feature database, and obtaining the identity information of the dog to be reimbursed based on the optimal similarity.
With reference to the fifth possible implementation manner, in a sixth possible implementation manner, the first recognition model and the second recognition model include a convolutional neural network, where the convolutional neural network includes a feature extraction layer, a first convolutional layer, a first stitching layer, a first upsampling layer, a second convolutional layer, a second stitching layer, a second upsampling layer, a third convolutional layer, and a third stitching layer; the acquisition of the target feature map comprises the following steps: inputting a first feature image output by a feature extraction layer into the first convolution layer for convolution to obtain a fourth feature image, and inputting the fourth feature image into the fourth convolution layer for convolution to obtain a first target feature image; inputting the fourth feature map into the first upsampling layer to perform upsampling to obtain a fifth feature map, inputting the second feature map and the fifth feature map which are output by the feature extraction layer into a first splicing layer to perform splicing to obtain a first spliced feature map, inputting the first spliced feature map into the second convolution layer to perform convolution to obtain a sixth feature map, and inputting the sixth feature map into a fifth convolution layer to perform convolution to obtain a second target feature map; inputting the sixth feature map into the second upsampling layer to perform upsampling to obtain a seventh feature map, inputting the third feature map output by the feature extraction layer and the seventh feature map into the second splicing layer to perform splicing to obtain a second spliced feature map, inputting the second spliced feature map into the third convolution layer to perform convolution to obtain an eighth feature map, and inputting the eighth feature map into the sixth convolution layer to perform convolution to obtain a third target feature map; and inputting the first target feature map, the second target feature map and the third target feature map to the third splicing layer for splicing processing to obtain the target feature map.
In a second aspect, a canine medical insurance management device includes: the identity information acquisition module is used for acquiring the identity information of the dog to be reimbursed; the historical information acquisition module is used for acquiring the historical treatment information and the application historical information of the dogs to be reimbursed; the reimbursement result acquisition module is used for acquiring corresponding reimbursement classifications based on the application history information, and comparing the history treatment information with the reimbursement classifications to obtain reimbursement results; the historical treatment information is configured in a preset treatment information database to be ordered according to time, and the application history information is configured in the preset application information database to be ordered according to time.
In a third aspect, 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 method according to any of the preceding claims when executing the computer program.
In a fourth aspect, a computer readable storage medium is provided, the computer readable storage medium storing a computer program which, when executed by a processor, implements the method of any of the above.
In the technical scheme provided by the embodiment of the application, the identity of the dogs is accurately identified by arranging the identity identification model, the acquisition of insurance information and historical treatment information is realized based on the configuration-based management method for obtaining accurate dog information, and the acquisition of reimbursement information is realized based on the determination of the information. By the method provided by the embodiment, rapid and accurate data processing can be realized, informatization management is realized, the efficiency of data processing and the relevance among data are improved, and a more complete management basis is provided for subsequent data management.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
The methods, systems, and/or programs in the accompanying drawings will be described further in terms of exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein the exemplary numbers represent like mechanisms throughout the various views of the drawings.
Fig. 1 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Fig. 2 is a flow chart of a canine medical insurance data processing method in accordance with some embodiments of the present application.
Fig. 3 is a first sub-flowchart of a processing method according to some embodiments of the application.
Fig. 4 is a schematic block diagram of an apparatus according to an embodiment of the present application.
Detailed Description
In order to better understand the above technical solutions, the following detailed description of the technical solutions of the present application is made by using the accompanying drawings and specific embodiments, and it should be understood that the specific features of the embodiments and the embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limiting the technical solutions of the present application, and the technical features of the embodiments and the embodiments of the present application may be combined with each other without conflict.
In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant teachings. It will be apparent, however, to one skilled in the art that the application can be practiced without these details. In other instances, well known methods, procedures, systems, components, and/or circuits have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.
The present application uses a flowchart to illustrate the execution of a system according to an embodiment of the present application. It should be clearly understood that the execution of the flowcharts may be performed out of order. Rather, these implementations may be performed in reverse order or concurrently. Additionally, at least one other execution may be added to the flowchart. One or more of the executions may be deleted from the flowchart.
Before describing embodiments of the present application in further detail, the terms and terminology involved in the embodiments of the present application will be described, and the terms and terminology involved in the embodiments of the present application will be used in the following explanation.
(1) In response to a condition or state that is used to represent the condition or state upon which the performed operation depends, the performed operation or operations may be in real-time or with a set delay when the condition or state upon which it depends is satisfied; without being specifically described, there is no limitation in the execution sequence of the plurality of operations performed.
(2) Based on the conditions or states that are used to represent the operations that are being performed, one or more of the operations that are being performed may be in real-time or with a set delay when the conditions or states that are being relied upon are satisfied; without being specifically described, there is no limitation in the execution sequence of the plurality of operations performed.
(3) Convolutional neural networks, a mathematical or computational model that mimics the structure and function of biological neural networks (the central nervous system of animals, particularly the brain), are used to estimate or approximate functions.
The technical scheme provided by the embodiment of the application has the main application scene that the medical insurance management of dogs is based on the type of insurance purchase of dogs and the type of diseases of dogs in the type of insurance purchase before, and the insurance range and whether the diseases of dogs belong to the insurance reimbursement range are determined according to the reimbursement requirements of the diseases. In the prior art, medical insurance management for dogs is determined based on the treatment condition and insurance type of the corresponding dogs after the identity confirmation based on the identity confirmation of the corresponding dogs by a hospital, and payment of insurance amount is performed for the dogs based on the determined treatment condition and insurance type. The identification of dogs is mainly based on the identification of the owners of dogs and the preliminary identification of dogs, but because the preliminary identification of dogs is identified by an identification ID card or registration information in the actual scene, the long phase gap between dogs is small, and the identification of dogs can be carried out by means of theft and borrowing, so that the identification of dogs has a larger risk, and the risk can be ignored in other use scenes, but the reimbursement of insurance is a bigger fund management risk. The identity of the dogs cannot be accurately identified in the prior art, and a management method for managing the health files and insurance types of the dogs is not available. Because the canine management is not accurate in the actual application scene, namely, the human can be identified through the configured identity identification information, and the traditional human-oriented medical insurance management system is accurate and clear in the medical insurance claim paying range, the canine medical insurance management cannot be duplicated according to the human medical insurance management, and a method capable of accurately identifying the canine identity and a medical insurance management method based on the canine identity are required to be provided.
Based on the above technical background, the embodiment of the present application provides a terminal device 100, which includes a memory 110, a processor 120, and a computer program stored in the memory and capable of running on the processor, where the processor executes a canine medical insurance data processing method, and performs accurate identification on a canine to be paid for medical insurance and management on medical insurance information after accurate identification. In this embodiment, the terminal device communicates with the user end, and transmits the acquired medical insurance information to the corresponding user end, so as to implement transmission of the medical insurance information on hardware. The method is based on network implementation aiming at the information sending mode, and an association relation between the user terminal and the terminal equipment is required to be established before the terminal equipment is applied, and the association between the terminal equipment and the user terminal can be realized through a registration mode. The terminal device can be aimed at a plurality of user terminals or one user terminal, and the user terminal communicates with the terminal device through passwords and other encryption modes. In this embodiment, when purchasing medical insurance and information about a canine, not only identity information of the canine but also information about a canine owner needs to be recorded, and a user terminal is configured for the canine owner, and the canine owner can extract insurance deposit and receive related information through the user terminal.
In this embodiment, the terminal may be a server, and includes a memory, a processor, and a communication unit for the physical structure of the server. The memory, the processor and the communication unit are electrically connected with each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory is used for storing specific information and programs, and the communication unit is used for sending the processed information to the corresponding user side.
In this embodiment, the storage module is divided into two storage areas, where one storage area is a program storage unit and the other storage area is a data storage unit. The program storage unit is equivalent to a firmware area, the read-write authority of the area is set to be in a read-only mode, and the data stored in the area can not be erased and changed. And the data in the data storage unit can be erased or read and written, and when the capacity of the data storage area is full, the newly written data can cover the earliest historical data.
The Memory may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Ele ultrasound ric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also Digital Signal Processors (DSPs)), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Referring to fig. 2, in this embodiment, for a canine medical insurance data processing method, the working logic is that image information of a canine is obtained, feature information in the image information is extracted, the feature information of the canine is compared with a preset feature database, and corresponding identity information is determined based on a comparison result. Determining corresponding historical treatment information and application information based on the acquired identity information, determining whether the insurance type and corresponding diseases belong to the insurance reimbursement range, and determining payment of an insurance amount or reimbursement of treatment fees based on the determined insurance reimbursement range and the application information.
Aiming at the working logic provided by the embodiment, the method for processing the canine medical insurance data comprises the following steps:
step S210, obtaining identity information of dogs to be reimbursed.
In this embodiment, this process is the first step of the processing method. Before acquiring the identity information of the dogs to be reimbursed, it can be known that the information of each dog to be reimbursed needs to be established, the comparison result is obtained based on the established information of the dogs and the dogs to be reimbursed, the accurate identification of the dogs is realized, the identification is mainly based on the biological identity aiming at the acquisition of the identity information, and the acquisition difficulty degree based on the biological identity is known, and the image information of the dogs mainly aiming at the biological identity in the embodiment is obtained, so the identity information of the dogs to be reimbursed is obtained in the embodiment, the image information of the dogs to be reimbursed is compared with the acquired image information of the dogs to be reimbursed, and the comparison result is obtained so as to determine the identity information of the dogs to be reimbursed.
The method specifically comprises the following steps:
and S211, obtaining canine species information of the dogs to be reimbursed.
S212, comparing corresponding stored specific canine image information in the acquired canine category information to obtain corresponding canine information, wherein the determined canine information is a to-be-reimbursed canine.
In this embodiment, the identification logic is mainly based on comparison with pre-stored image information, and the corresponding canine categories are divided during image information acquisition, that is, two corresponding databases, namely a category database and a canine database corresponding to the category database, are constructed after image information acquisition. Aiming at the word setting method, the problem to be solved is that the facial differences of dogs are smaller, but the differences of facial features of dogs of different types are larger, so that the identification of dogs to be reimbursed is aimed at reducing the difficulty of identification and the cost and data processing amount of feature traversal of dogs to be reimbursed, the types of dogs are firstly identified, the corresponding types of dogs are identified, the features of key target points of the faces of dogs are acquired based on the acquired types of dogs, and the accurate comparison result is obtained by comparing the features of the key target points with the features of a dog database, so that the final identity information is obtained.
In this embodiment, the extracting of the features of the dogs and the identification of the identities are mainly performed based on a first identity identification model and a second identity identification model, where the first identity identification model is used for identifying the types of dogs to be reimbursed, and the specific method includes: and extracting the first characteristics of the dogs to be reimbursed based on the first identity recognition model, and comparing the first characteristics with a preset first characteristic database to obtain the type information of the dogs to be reimbursed.
The second identity recognition model is used for recognizing the identity information of the dogs to be reimbursed, and the specific method comprises the following steps: and extracting a second characteristic of the to-be-reimbursed dog based on the second identity recognition model, and comparing the second characteristic with the second characteristic database to obtain the identity information of the to-be-reimbursed dog.
In this example, the identification of canine species includes the following methods:
acquiring facial image information of a dog to be reimbursed, acquiring a target feature map based on the facial image information, acquiring a plurality of target detection points in the target feature map, acquiring coordinate parameters of the plurality of target detection points, and acquiring relative distances of the plurality of target detection points based on the plurality of coordinate parameters.
Comparing the relative distances of a plurality of target detection points with the relative distances of a plurality of target detection points in a preset first characteristic database to obtain the kind information of the reimbursement dogs, wherein the method specifically comprises the following steps: comparing the similarity of the relative distance of any target detection point with the relative distance of the corresponding target detection point in the preset first characteristic database, and obtaining the similarity of the relative distances of a plurality of target detection points: fusing the similarity of the relative distances of a plurality of target detection points to obtain final similarity; and determining the category information of the corresponding reimbursement dogs based on the final similarity.
In this embodiment, the identification of the reimbursement dog is mainly based on the distance positions among the plurality of target detection points, and the identification of the reimbursement dog is based on the characteristic points of the face of the dog, which can be known, but not limited to, eyes, nose and ears. The training method for constructing the first identity recognition model based on the neural network model is mainly realized after a plurality of sample data sets are obtained, classified and marked. For example, the first identity recognition model may be trained based on, for example, a sample data set corresponding to a canine category, marking target detection points of a sufficient number of canine images in the sample data set corresponding to the canine category to obtain an identity recognition sub-model corresponding to the canine category, and fusing a plurality of identity recognition sub-models to obtain a final first identity recognition model, where the training of the neural network includes the scenario of the prior art application being the canine category.
The actual use scene of the process is that coordinate values between eyes and a nose are obtained, coordinate values between the eyes and the nose are obtained, coordinate values between the nose and the mouth are obtained, the coordinate values between the nose and the mouth are compared with corresponding coordinate values in a preset first characteristic database based on the coordinate values to obtain similarity, the similarity of the relative distances of a plurality of target detection points is obtained, the similarity of the relative distances of a plurality of target detection points is fused to obtain final similarity, and the corresponding reimbursement dog category information is determined based on the final similarity.
For step S212, specific identities of dogs to be reissued after the identification based on the type determination of the dogs to be reissued after the processing of step S211 are identified, for example, for a dog a, the type information obtained after the processing of step S211 is labrador, in step S212, a second feature database corresponding to labrador is called, the second feature is extracted based on the image information of the dogs to be reissued, the extracted second feature is secondarily compared with the second feature database corresponding to labrador, so that information of dogs corresponding to the second feature similarity in the labrador feature database, namely the second feature database, is obtained, and identity information of dogs to be reissued is determined.
The detailed process in this process includes:
obtaining a plurality of face images of the dogs to be reimbursed, processing the face images based on a binary method to obtain a plurality of preprocessed face image outlines, obtaining area values of the preprocessed face image outlines, comparing the area values to determine an optimal face image, extracting second features of the dogs to be reimbursed based on the optimal face image, and comparing the second features with a second feature database to obtain identity information of the dogs to be reimbursed.
In this embodiment, unlike step S211, step S211 only needs to identify the category, and the identification method is implemented based on the relative distance of coordinates, and the requirements for the definition and image quality of the facial image are low, but the requirements for the definition and image quality of the facial image are relatively high for the identification of the detail features in step S212, so that the preprocessing and selection of the image to be identified are required before the feature extraction and comparison. In this embodiment, gray processing is performed on the face image through binarization, so as to obtain the face image with the largest outline area, and the face image is the optimal face image to be processed.
In other possible implementations of the embodiment, in the case that the plurality of face images are all not ideal, that is, the contour area of the gray-scale pictures in the plurality of face images is smaller than the preset contour area threshold, enhancement processing needs to be performed on the face images, where the enhancement processing of the face images may be implemented by using the image compensation technology in the prior art, which is not described in detail in this embodiment.
And the method for extracting the characteristics of the acquired face image to be recognized comprises the following steps: obtaining a target feature map of the optimal face image, extracting key target detection points in the target feature map, extracting features to be identified of the key target detection points, comparing the features to be identified with the second feature database based on the features to be identified, comparing the similarity between the features to be identified and the second feature database, and obtaining identity information of dogs to be reimbursed based on the optimal similarity.
In this embodiment, the key target detection points are detection points capable of characterizing specific characteristics of the canine, such as eyes, nose, and the like, in the canine facial image, wherein the structure for extracting the extracted identification model includes: the convolutional neural network comprises a feature extraction layer, a first convolutional layer, a first splicing layer, a first upsampling layer, a second convolutional layer, a second splicing layer, a second upsampling layer, a third convolutional layer and a third splicing layer.
And the acquisition of the target feature map comprises the following steps:
the first characteristic diagram is input into the first convolution layer to be convolved to obtain a fourth characteristic diagram, the fourth characteristic diagram is input into the fourth convolution layer to be convolved to obtain a first target characteristic diagram, the fourth characteristic diagram is input into the first upsampling layer to be upsampled to obtain a fifth characteristic diagram, the second characteristic diagram and the fifth characteristic diagram are input into the first splicing layer to be spliced to obtain a first splicing characteristic diagram, the first splicing characteristic diagram is input into the second convolution layer to be convolved to obtain a sixth characteristic diagram, the sixth characteristic diagram is input into the fifth convolution layer to be convolved to obtain a second target characteristic diagram, the sixth characteristic diagram is input into the second upsampling layer to be upsampled to obtain a seventh characteristic diagram, the third characteristic diagram and the seventh characteristic diagram are input into the second splicing layer to be spliced to obtain a second splicing characteristic diagram, the second splicing characteristic diagram is input into the third convolution layer to be convolved to obtain an eighth characteristic diagram, the eighth characteristic diagram is input into the fifth convolution layer to be convolved to obtain the target characteristic diagram, and the target characteristic diagram is input into the third target characteristic diagram to be convolved to obtain the target characteristic diagram.
In this embodiment, each recognition model is configured with a convolutional neural network, and each convolutional neural network includes a first convolutional layer, a first splicing layer, a first upsampling layer, a second convolutional layer, a second splicing layer, a second upsampling layer, a third convolutional layer, and a third splicing layer, so that the number of the convolutional layers, the splicing layers, and the upsampling layers of the two corresponding recognition models is six.
The step S210 can obtain the identity information of the canine, and obtain the insurance information of the canine based on the identity information of the canine, which specifically comprises the following steps:
and step 220, acquiring historical treatment information and application historical information based on the identity information of the to-be-reimbursed dogs.
In this embodiment, the historical treatment information is configured in a preset treatment information database and ordered according to time, and the application history information is configured in a preset application information database and ordered according to time.
The specific process is as follows: and acquiring a treatment time interval in the historical treatment information and an application time interval in the application historical information, determining the treatment time interval in the application time interval, and determining the historical treatment information in the interval based on the treatment time interval.
In this embodiment, information related to insurance corresponding to dogs is mainly obtained, and the information is mainly history treatment information and application history information.
And S230, acquiring corresponding reimbursement classifications based on the application history information, and comparing the history treatment information with reimbursement classifications to obtain reimbursement results.
The method specifically comprises the following steps: based on a preset reimbursement tag, acquiring unredeemed treatment information in the historical treatment information, and comparing the unredeemed treatment information with reimbursement categories to obtain reimbursement results, wherein the reimbursement results comprise the following steps of:
and extracting treatment keywords in the unreported treatment information and reimbursement keywords in reimbursement categories, comparing the similarity between the treatment keywords and the reimbursement keywords, and determining the similarity between the unreported medical information and the reimbursement categories based on a similarity threshold value to obtain reimbursement results.
In this embodiment, the confirmation of the insurance information of the canine can be realized through step S220 and step S230, so as to determine whether the canine disease and the corresponding disease are in the coverage and specific insurance information, and the above information is determined through the time node. The configuration of the method can realize automatic acquisition of insurance information.
Referring to fig. 4, the present embodiment further provides a canine medical insurance management device 400, including: the identity information obtaining module 410 is configured to obtain identity information of a dog to be reimbursed. The historical information obtaining module 420 is configured to obtain historical treatment information and application historical information of the to-be-reimbursed dog. And the reimbursement result acquisition module 430 is configured to acquire a corresponding reimbursement classification based on the application history information, and compare the history treatment information with the reimbursement classification to obtain a reimbursement result.
In this embodiment, the historical treatment information is configured in a preset treatment information database and ordered according to time, and the application history information is configured in a preset application information database and ordered according to time.
In the technical scheme provided by the embodiment of the application, the identity of the dogs is accurately identified by arranging the identity identification model, the acquisition of insurance information and historical treatment information is realized based on the configuration-based management method for obtaining accurate dog information, and the acquisition of reimbursement information is realized based on the determination of the information. By the method provided by the embodiment, quick and accurate information management can be realized, management cost is reduced, and management safety is improved.
It is to be understood that the terminology which is not explained by terms of nouns in the foregoing description is not intended to be limiting, as those skilled in the art can make any arbitrary deduction from the foregoing disclosure.
The person skilled in the art can undoubtedly determine technical features/terms of some preset, reference, predetermined, set and preference labels, such as threshold values, threshold value intervals, threshold value ranges, etc., from the above disclosure. For some technical feature terms which are not explained, a person skilled in the art can reasonably and unambiguously derive based on the logical relation of the context, so that the technical scheme can be clearly and completely implemented. The prefixes of technical feature terms, such as "first", "second", "example", "target", etc., which are not explained, can be unambiguously deduced and determined from the context. Suffixes of technical feature terms, such as "set", "list", etc., which are not explained, can also be deduced and determined unambiguously from the context.
The foregoing disclosure of embodiments of the present application will be apparent to and complete in light of the foregoing disclosure to those skilled in the art. It should be appreciated that the development and analysis of technical terms not explained based on the above disclosure by those skilled in the art is based on the description of the present application, and thus the above is not an inventive judgment of the overall scheme.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements and adaptations of the application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within the present disclosure, and therefore, such modifications, improvements, and adaptations are intended to be within the spirit and scope of the exemplary embodiments of the present disclosure.
Meanwhile, the present application uses specific terms to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, certain features, structures, or characteristics of at least one embodiment of the present application may be combined as suitable.
In addition, those skilled in the art will appreciate that the various aspects of the application are illustrated and described in the context of a number of patentable categories or conditions, including any novel and useful processes, machines, products, or materials, or any novel and useful improvements thereof. Accordingly, aspects of the application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "unit," component, "or" system. Furthermore, aspects of the application may be embodied as a computer product in at least one computer-readable medium, the product comprising computer-readable program code.
The computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer readable signal medium may be propagated through any suitable medium including radio, electrical, fiber optic, RF, or the like, or any combination of the foregoing.
Computer program code required for carrying out aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python and the like, or similar conventional programming languages such as the "C" programming language, visual Basic, fortran 2003,Perl,COBOL 2002,PHP,ABAP, dynamic programming languages such as Python, ruby and Groovy or other programming languages. The programming code may execute entirely on the user's computer, or as a stand-alone software package, or partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as software as a service (SaaS).
Furthermore, the order in which the processing elements and sequences are described, the use of numerical letters, or other designations are used is not intended to limit the order in which the processes and methods of the application are performed unless specifically recited in the claims. While in the foregoing disclosure there has been discussed, by way of various examples, some embodiments of the application which are presently considered to be useful, it is to be understood that this detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments of the application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
It should also be appreciated that in the foregoing description of at least one embodiment of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of at least one embodiment of the application. This method of disclosure, however, is not intended to imply that more features than are required by the subject application. Indeed, less than all of the features of a single embodiment disclosed above.
Claims (6)
1. The canine medical insurance data processing method is characterized by comprising the following steps:
acquiring identity information of dogs to be reimbursed;
acquiring historical treatment information and application history information based on the identity information of the dogs to be reimbursed, wherein the historical treatment information is configured in a preset treatment information database and ordered according to time, and the application history information is configured in the preset application information database and ordered according to time;
acquiring corresponding reimbursement classifications based on the application history information, and comparing the history treatment information with reimbursement classifications to obtain reimbursement results;
the method for acquiring the identity information of the dog to be reimbursed comprises the following steps:
extracting the characteristics of the dogs to be reimbursed based on a preset identity recognition model;
comparing the characteristics with a preset characteristic database to obtain a comparison result;
determining the identity information of the to-be-reimbursed dogs based on the comparison result;
the identification model comprises a first identification model and a first characteristic database, wherein the first identification model is used for identifying the type of the dog to be reimbursed, and the specific method comprises the following steps: extracting first characteristics of the dogs to be reimbursed based on the first identity recognition model, and comparing the first characteristics with a preset first characteristic database to obtain category information of the dogs to be reimbursed;
The identification model also comprises a second identification model and a second characteristic database, wherein the second characteristic database is configured in the first characteristic database, and the second identification model is used for identifying the identification information of the dog to be reimbursed, and the specific method comprises the following steps: extracting a second characteristic of the to-be-reimbursed dog based on the second identity recognition model, and comparing the second characteristic with the second characteristic database to obtain identity information of the to-be-reimbursed dog;
extracting first characteristics of the to-be-reimbursed dogs based on the first identity recognition model, and comparing the first characteristics with a preset first characteristic database to obtain category information of the to-be-reimbursed dogs, wherein the method comprises the following steps:
acquiring facial image information of a dog to be reimbursed;
acquiring a target feature map based on the facial image information;
acquiring a plurality of target detection points in the target feature map;
acquiring coordinate parameters of a plurality of target detection points, and acquiring the relative distances of the plurality of target detection points based on the plurality of coordinate parameters;
comparing the relative distances of a plurality of target detection points with the relative distances of a plurality of target detection points in a preset first characteristic database to obtain the kind information of the reimbursement dogs, wherein the method specifically comprises the following steps:
Comparing the similarity of the relative distance of any target detection point with the relative distance of the corresponding target detection point in a preset first characteristic database, and obtaining the similarity of the relative distances of a plurality of target detection points;
fusing the similarity of the relative distances of a plurality of target detection points to obtain final similarity;
determining the category information of the corresponding reimbursement dogs based on the final similarity;
extracting second characteristics of the to-be-reimbursed dogs based on the second identification model, and comparing the second characteristics with the second characteristic database to obtain identity information of the to-be-reimbursed dogs, wherein the method comprises the following steps:
acquiring a plurality of facial images of dogs to be reimbursed;
processing the face images based on a binary method to obtain a plurality of preprocessed face image outlines, obtaining area values of the preprocessed face image outlines, and comparing the area values to determine an optimal face image;
extracting a second feature of the to-be-reimbursed dog based on the optimal facial image, and comparing the second feature with the second feature database to obtain identity information of the to-be-reimbursed dog;
extracting a second feature of the to-be-reimbursed dog based on the optimal facial image, and comparing the second feature with the second feature database to obtain identity information of the to-be-reimbursed dog, wherein the method comprises the following steps:
Acquiring a target feature map of the optimal face image;
extracting key target detection points in the target feature map;
extracting the feature to be identified of the key target detection point, comparing the similarity between the feature to be identified and the second feature database based on the comparison between the feature to be identified and the second feature database, and obtaining the identity information of the dog to be reimbursed based on the optimal similarity;
the first identity recognition model and the second identity recognition model comprise a convolutional neural network, wherein the convolutional neural network comprises a feature extraction layer, a first convolutional layer, a first splicing layer, a first upsampling layer, a second convolutional layer, a second splicing layer, a second upsampling layer, a third convolutional layer and a third splicing layer;
the acquisition of the target feature map comprises the following steps:
inputting a first feature image output by a feature extraction layer into the first convolution layer for convolution to obtain a fourth feature image, and inputting the fourth feature image into the fourth convolution layer for convolution to obtain a first target feature image;
inputting the fourth feature map into the first upsampling layer to perform upsampling to obtain a fifth feature map, inputting the second feature map and the fifth feature map which are output by the feature extraction layer into a first splicing layer to perform splicing to obtain a first spliced feature map, inputting the first spliced feature map into the second convolution layer to perform convolution to obtain a sixth feature map, and inputting the sixth feature map into a fifth convolution layer to perform convolution to obtain a second target feature map;
Inputting the sixth feature map into the second upsampling layer to perform upsampling to obtain a seventh feature map, inputting the third feature map output by the feature extraction layer and the seventh feature map into the second splicing layer to perform splicing to obtain a second spliced feature map, inputting the second spliced feature map into the third convolution layer to perform convolution to obtain an eighth feature map, and inputting the eighth feature map into the sixth convolution layer to perform convolution to obtain a third target feature map;
and inputting the first target feature map, the second target feature map and the third target feature map to the third splicing layer for splicing processing to obtain the target feature map.
2. The method for processing canine medical insurance data according to claim 1, wherein the history treatment information and the application history information are acquired based on the identity information of the to-be-reimbursed canine, comprising the following steps:
acquiring a treatment time interval in the historical treatment information and an application time interval in the application historical information;
a treatment time interval within the application time interval is determined, and historical treatment information within an interval is determined based on the treatment time interval.
3. The canine medical insurance data processing method according to claim 2, wherein the corresponding reimbursement classification is obtained based on the application history information, the history treatment information and reimbursement classification are compared, and reimbursement results are obtained, comprising the following steps:
Acquiring unreliability treatment information in the historical treatment information based on a preset reimbursement tag;
comparing the unreferenced therapeutic information with reimbursement categories to obtain reimbursement results, wherein the reimbursement results comprise the following steps of:
extracting treatment keywords in the unreferenced treatment information and reimbursement keywords in reimbursement categories;
and comparing the similarity of the treatment keywords and the reimbursement keywords, and determining the similarity of the unreported medical information and reimbursement categories based on a similarity threshold value to obtain reimbursement results.
4. A canine medical insurance management device, comprising:
the identity information acquisition module is used for acquiring the identity information of the dog to be reimbursed;
the historical information acquisition module is used for acquiring the historical treatment information and the application historical information of the dogs to be reimbursed;
the reimbursement result acquisition module is used for acquiring corresponding reimbursement classifications based on the application history information, and comparing the history treatment information with the reimbursement classifications to obtain reimbursement results;
the historical treatment information is configured in a preset treatment information database to be ordered according to time, and the application history information is configured in the preset application information database to be ordered according to time;
Acquiring corresponding reimbursement classifications based on the application history information, and comparing the history treatment information with reimbursement classifications to obtain reimbursement results;
the method for acquiring the identity information of the dog to be reimbursed comprises the following steps:
extracting the characteristics of the dogs to be reimbursed based on a preset identity recognition model;
comparing the characteristics with a preset characteristic database to obtain a comparison result;
determining the identity information of the to-be-reimbursed dogs based on the comparison result;
the identification model comprises a first identification model and a first characteristic database, wherein the first identification model is used for identifying the type of the dog to be reimbursed, and the specific method comprises the following steps: extracting first characteristics of the dogs to be reimbursed based on the first identity recognition model, and comparing the first characteristics with a preset first characteristic database to obtain category information of the dogs to be reimbursed;
the identification model also comprises a second identification model and a second characteristic database, wherein the second characteristic database is configured in the first characteristic database, and the second identification model is used for identifying the identification information of the dog to be reimbursed, and the specific method comprises the following steps: extracting a second characteristic of the to-be-reimbursed dog based on the second identity recognition model, and comparing the second characteristic with the second characteristic database to obtain identity information of the to-be-reimbursed dog;
Extracting first characteristics of the to-be-reimbursed dogs based on the first identity recognition model, and comparing the first characteristics with a preset first characteristic database to obtain category information of the to-be-reimbursed dogs, wherein the method comprises the following steps:
acquiring facial image information of a dog to be reimbursed;
acquiring a target feature map based on the facial image information;
acquiring a plurality of target detection points in the target feature map;
acquiring coordinate parameters of a plurality of target detection points, and acquiring the relative distances of the plurality of target detection points based on the plurality of coordinate parameters;
comparing the relative distances of a plurality of target detection points with the relative distances of a plurality of target detection points in a preset first characteristic database to obtain the kind information of the reimbursement dogs, wherein the method specifically comprises the following steps:
comparing the similarity of the relative distance of any target detection point with the relative distance of the corresponding target detection point in a preset first characteristic database, and obtaining the similarity of the relative distances of a plurality of target detection points;
fusing the similarity of the relative distances of a plurality of target detection points to obtain final similarity;
determining the category information of the corresponding reimbursement dogs based on the final similarity;
extracting second characteristics of the to-be-reimbursed dogs based on the second identification model, and comparing the second characteristics with the second characteristic database to obtain identity information of the to-be-reimbursed dogs, wherein the method comprises the following steps:
Acquiring a plurality of facial images of dogs to be reimbursed;
processing the face images based on a binary method to obtain a plurality of preprocessed face image outlines, obtaining area values of the preprocessed face image outlines, and comparing the area values to determine an optimal face image;
extracting a second feature of the to-be-reimbursed dog based on the optimal facial image, and comparing the second feature with the second feature database to obtain identity information of the to-be-reimbursed dog;
extracting a second feature of the to-be-reimbursed dog based on the optimal facial image, and comparing the second feature with the second feature database to obtain identity information of the to-be-reimbursed dog, wherein the method comprises the following steps:
acquiring a target feature map of the optimal face image;
extracting key target detection points in the target feature map;
extracting the feature to be identified of the key target detection point, comparing the similarity between the feature to be identified and the second feature database based on the comparison between the feature to be identified and the second feature database, and obtaining the identity information of the dog to be reimbursed based on the optimal similarity;
the first identity recognition model and the second identity recognition model comprise a convolutional neural network, wherein the convolutional neural network comprises a feature extraction layer, a first convolutional layer, a first splicing layer, a first upsampling layer, a second convolutional layer, a second splicing layer, a second upsampling layer, a third convolutional layer and a third splicing layer;
The acquisition of the target feature map comprises the following steps:
inputting a first feature image output by a feature extraction layer into the first convolution layer for convolution to obtain a fourth feature image, and inputting the fourth feature image into the fourth convolution layer for convolution to obtain a first target feature image;
inputting the fourth feature map into the first upsampling layer to perform upsampling to obtain a fifth feature map, inputting the second feature map and the fifth feature map which are output by the feature extraction layer into a first splicing layer to perform splicing to obtain a first spliced feature map, inputting the first spliced feature map into the second convolution layer to perform convolution to obtain a sixth feature map, and inputting the sixth feature map into a fifth convolution layer to perform convolution to obtain a second target feature map;
inputting the sixth feature map into the second upsampling layer to perform upsampling to obtain a seventh feature map, inputting the third feature map output by the feature extraction layer and the seventh feature map into the second splicing layer to perform splicing to obtain a second spliced feature map, inputting the second spliced feature map into the third convolution layer to perform convolution to obtain an eighth feature map, and inputting the eighth feature map into the sixth convolution layer to perform convolution to obtain a third target feature map;
And inputting the first target feature map, the second target feature map and the third target feature map to the third splicing layer for splicing processing to obtain the target feature map.
5. 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 method according to any of claims 1 to 3 when executing the computer program.
6. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, implements the method according to any one of claims 1 to 3.
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