Livestock insurance claim settlement method, electronic equipment and storage medium

Abstract

The invention provides a livestock insurance claim settlement method, an electronic device and a storage medium, and the method comprises the steps: carrying out the image collection of a livestock breeding site and livestock through a camera module, obtaining image data, determining the identity information of the livestock when a livestock insurance claim settlement event is determined according to the image data, and sending the identity information to a server; and according to the livestock insurance claim settlement event and the identity information, generating a livestock insurance claim settlement application form for the livestock, sending the livestock insurance claim settlement application form to a first user terminal, and identifying the livestock insurance claim settlement event through an image. The problem that an insurance claim settlement application must be initiated manually in an existing scheme is solved, and the claim settlement process speed can be increased.

Description

Technical Field

[0001] The present application relates to the technical field of livestock identification, and in particular to a livestock insurance claim settlement method, electronic equipment and storage medium. Background Art

[0002] With the development of agricultural modernization, agricultural insurance plays an increasingly important role in protecting farmers' interests and promoting the stable development of agriculture. In animal husbandry, cattle are important breeding animals with high value. Once an unexpected loss occurs, agricultural insurance claims are particularly critical.

[0003] Traditional agricultural insurance mainly relies on manual marking and ear tags to identify the insured cattle. However, these methods have some problems, such as ear tags are easily damaged or lost, manual markings are easily blurred, and the identification process is time-consuming and labor-intensive, and inefficient. At the same time, when an insurance event occurs, traditional identification methods are difficult to quickly and accurately determine the identity of the insured cattle, and the manual initiation of insurance claims leads to a slow claims process, affecting farmers' production recovery and the service quality of insurance institutions. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a livestock insurance claims method, an electronic device and a storage medium to solve the above-mentioned technical problems.

[0005] In one aspect, a livestock insurance claim settlement method is provided, the method comprising:

[0006] Capturing images of a livestock breeding ground and the livestock using a camera module to obtain image data;

[0007] When it is determined based on the image data that a livestock insurance claim event has occurred, determining the identity information of the livestock;

[0008] generating a livestock insurance claim application form for the livestock according to the livestock insurance claim event and the identity information;

[0009] The livestock insurance claim application form is sent to a first user terminal.

[0010] In one embodiment, the method of acquiring image data by capturing images of a livestock breeding ground and the livestock through a camera module includes:

[0011] When it is determined that the light intensity of the livestock breeding ground is lower than a preset light intensity threshold, the fill light function of the camera module is turned on to collect images of the breeding ground and the livestock.

[0012] In one embodiment, when determining that a livestock insurance claim event occurs according to the image data, determining the identity information of the livestock includes:

[0013] The camera module sends the image data to the intelligent recognition module;

[0014] The intelligent recognition module recognizes the identity information of the livestock according to the image data, and sends the identity information and the image data to the emergency response module;

[0015] The emergency response module determines whether a livestock insurance claim event occurs based on the identity information and the image data.

[0016] In one embodiment, the intelligent recognition module recognizes the identity information of the livestock according to the image data, including:

[0017] Preprocessing the image data;

[0018] Performing feature extraction on the preprocessed image data to obtain features of the livestock;

[0019] Inputting the feature into a preset feature comparison library; the feature comparison library pre-stores a correspondence between a plurality of preset features and preset identity information;

[0020] The preset identity information corresponding to the preset feature having the highest similarity to the feature of the livestock is used as the identity information of the livestock.

[0021] In one of the embodiments, the image data includes at least one of facial image data of the livestock, iris image data of the livestock, and limb image data of the livestock.

[0022] In one embodiment, sending the identity information and the image data to an emergency response module includes:

[0023] encrypting the identity information and the image data;

[0024] The encrypted identity information and the image data are sent to an emergency response module.

[0025] In one embodiment, sending the identity information and the image data to an emergency response module includes:

[0026] Performing compression processing on the image data;

[0027] The compressed image data is sent to the emergency response module.

[0028] In one embodiment, the method comprises:

[0029] When the livestock insurance claim application form is sent to the first user terminal, the livestock insurance claim application form is sent to the second user terminal.

[0030] On the other hand, an electronic device is provided, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement any of the above methods.

[0031] On the other hand, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, any of the above methods is implemented.

[0032] The livestock insurance claims method, electronic device and storage medium provided in the present application acquire image data by capturing images of livestock breeding grounds and livestock through a camera module, and when it is determined based on the image data that a livestock insurance claims event has occurred, the identity information of the livestock is determined, and a livestock insurance claims application form is generated for the livestock based on the livestock insurance claims event and the identity information, and the livestock insurance claims application form is sent to a first user terminal. The livestock insurance claims event is identified through images, and since the livestock insurance claims application form can be automatically generated, the problem of having to manually initiate an insurance claims application in the existing solution is solved, and the speed of the claims process can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A flowchart of a livestock insurance claims settlement method provided in Example 1 of the present application;

[0034] Figure 2 A schematic diagram of a process for determining a livestock insurance claim event provided in Example 1 of the present application;

[0035] Figure 3 A schematic diagram of the location of the camera device provided in the first embodiment of the present application;

[0036] Figure 4 A schematic diagram of the structure of an electronic device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] Embodiment 1:

[0039] This application embodiment provides a livestock insurance claim settlement method, see Figure 1 As shown, the livestock insurance claim settlement method provided in the embodiment of the present application includes the following steps:

[0040] S11: Capturing images of the livestock breeding ground and the livestock through a camera module to obtain image data.

[0041] S12: When it is determined based on the image data that a livestock insurance claim event has occurred, the identity information of the livestock is determined.

[0042] S13: Generate a livestock insurance claim application form for the livestock according to the livestock insurance claim event and the identity information.

[0043] S14: Sending the livestock insurance claim application form to the first user terminal.

[0044] The following is a detailed introduction to the contents of the above steps.

[0045] The camera module in the embodiment of the present application can be a high-definition smart camera with functions such as autofocus, optical image stabilization, and good imaging in low-light environments. The camera can be installed at key locations on the breeding ground, such as the entrance to the livestock resting place, the livestock activity area, etc., to ensure that the livestock activity range can be fully covered.

[0046] The camera can automatically adjust parameters according to the ambient light to ensure that clear images of livestock and breeding sites can be captured under different lighting conditions. At the same time, it has a dynamic capture function that can quickly capture the instantaneous movements of livestock to avoid image blur.

[0047] In step S11, when it is determined that the light intensity of the livestock breeding ground is lower than a preset light intensity threshold, the fill light function of the camera module is turned on to capture images of the breeding ground and the livestock.

[0048] The preset light intensity threshold in the embodiment of the present application can be flexibly set by the developer, for example, it can be set to 10 lux. After the fill light function is turned on, the camera module can use infrared fill light to ensure that a clear image can be captured even in a low light intensity environment.

[0049] The resolution of the camera module in the embodiment of the present application may meet the following conditions:

[0050] Horizontal resolution (pixels) = image width (mm) × horizontal pixel density (pixels / mm);

[0051] Vertical resolution (pixels) = image height (mm) × vertical pixel density (pixels / mm).

[0052] For example, if the area to be covered in the breeding site is 5 meters wide and 4 meters high, the horizontal pixel density of the selected camera module is 0.5 pixels / mm and the vertical pixel density is 0.4 pixels / mm. Then the horizontal resolution = 5000mm×0.5 pixels / mm=2500 pixels, and the vertical resolution = 4000mm×0.4 pixels / mm=1600 pixels. You can choose a high-definition camera module with a resolution close to or higher than 2500×1600.

[0053] It should be noted that the image data in the embodiment of the present application includes image data of breeding grounds and image data of livestock, wherein the image data of livestock includes but is not limited to at least one of facial image data of livestock, iris image data of the livestock, and limb image data of the livestock.

[0054] See also Figure 2 As shown, step S12 in the embodiment of the present application may include the following sub-steps:

[0055] S121: The camera module sends the image data to the intelligent recognition module.

[0056] S122: The intelligent recognition module recognizes the identity information of the livestock based on the image data, and sends the identity information and the image data to the emergency response module.

[0057] S123: The emergency response module determines whether a livestock insurance claim event occurs based on the identity information and image data.

[0058] In sub-step S122, the intelligent recognition module can pre-process the image data; perform feature extraction on the pre-processed image data to obtain the characteristics of the livestock; input the characteristics into a preset feature comparison library; the feature comparison library pre-stores the correspondence between multiple preset features and preset identity information; and use the preset identity information corresponding to the preset feature with the highest similarity to the characteristics of the livestock as the identity information of the livestock.

[0059] The livestock in the embodiment of the present application can be any animal that can be raised on a large scale, for example, it can be a cow. In this case, the camera equipment can be set in the cowshed of the breeding ground and the cattle activity area of ​​the breeding ground. For details, please refer to Figure 3 shown.

[0060] The following uses cattle as an example to introduce the process of identifying livestock identity information.

[0061] In the embodiment of the present application, a cow face recognition model can be constructed based on a deep learning algorithm and trained with a large amount of cow face image data. The training data covers cow face images of different breeds, different ages, different lighting and angles to improve the generalization ability of the model.

[0062] The model uses a convolutional neural network (CNN) architecture with multiple convolutional layers and pooling layers, which can automatically extract the features of a cow face. By continuously optimizing the network structure and parameters, the accuracy and speed of recognition can be improved.

[0063] In the recognition process, the collected images are first preprocessed, including image enhancement, denoising, cropping and other operations to improve image quality. The preprocessed images are then input into the recognition model for feature extraction and comparison to ultimately determine the identity information of the cattle.

[0064] In order to further improve the recognition accuracy, other biometric recognition technologies, such as cattle iris recognition and body shape feature recognition, can be combined for multimodal fusion recognition.

[0065] The formula for calculating the accuracy of the deep learning model is:

[0066] Accuracy = number of correctly identified samples / total number of samples × 100%

[0067] For example, after a large amount of data testing, the model identified 980 of 1,000 cow face images correctly, so the accuracy rate = 980 / 1,000 × 100% = 98%.

[0068] Model training data volume:

[0069] In order to ensure the accuracy and generalization ability of the model, a large amount of training data is usually required. For example, at least 100,000 cow face images of different breeds, ages, lighting and angles are collected for model training.

[0070] It should be noted that in the embodiment of the present application, a high-speed and stable wireless network or wired network can be used for data transmission. The wireless network can select high-speed communication technologies such as 5G technology or Wi-Fi 6 to ensure the speed and stability of data transmission.

[0071] In sub-step S122, the identity information and the image data may be encrypted and sent to the emergency response module to prevent data from being stolen or tampered with. At the same time, an access control mechanism may be set so that only authorized users can access system data.

[0072] In order to improve the efficiency of data transmission, data compression technology can be used to compress the image data before transmission. Decompression processing is performed at the receiving end to restore the original image data. Specifically, in sub-step S122, the image data can also be compressed; the compressed image data is sent to the emergency response module.

[0073] Data transfer rate calculation formula:

[0074] Transmission rate (bps) = data volume (bits) / transmission time (s)

[0075] For example, if a color cow face image with a resolution of 2500×1600 is transmitted (assuming each pixel occupies 24 bits), the transmission time is 1 second. Then the data volume = 2500×1600×24 bits = 96000000 bits. Transmission rate = 96000000 bits / 1s = 96Mbps. You can choose a network device with a transmission rate higher than 96Mbps to ensure that the image data can be transmitted quickly.

[0076] Data compression ratio:

[0077] Assume that the original cow face image data size is 10MB, and the size after compression is 2MB. Then the compression ratio = 10MB / 2MB = 5. Select a suitable data compression algorithm to ensure a high compression ratio without losing too much image quality, thereby improving data transmission efficiency.

[0078] In an embodiment of the present application, the association between the identity information of the livestock and the purchased insurance can be pre-stored, and each insurance is associated with a corresponding livestock insurance claim event.

[0079] The emergency response module determines whether a livestock insurance claim event occurs for the livestock based on the identity information and the image data, and generates a livestock insurance claim application form for the livestock based on the identity information and the livestock insurance claim event.

[0080] The livestock insurance claim settlement events in the embodiments of the present application include, but are not limited to, at least one of livestock loss, livestock illness, and livestock death.

[0081] In the specific implementation process, the identity information of each livestock on the breeding ground can be pre-stored, and the identity of each livestock on the breeding ground can be identified based on the image data in step S11. When the identity information of a certain livestock is not obtained in the collected image data, it means that the livestock does not exist on the breeding ground, and it is determined that the livestock is lost.

[0082] In a specific implementation process, the behavior of livestock can be analyzed based on the image data of livestock. When it is determined that the behavior of livestock is abnormal, the livestock is determined to be sick. When it is determined that the livestock is motionless for a preset continuous period of time, the livestock is determined to be dead.

[0083] In some embodiments, when the livestock insurance claim application form is sent to the first user terminal, the livestock insurance claim application form may also be sent to the second user terminal.

[0084] The first user terminal in the embodiment of the present application may be a terminal on the insurance institution side, and the second user terminal may be a terminal on the farmer side. The emergency response module may also be linked with other emergency rescue systems, such as animal disease prevention and control systems, veterinary service systems, etc., to provide farmers with all-round support and assistance.

[0085] The intelligent identification module and the emergency response module in the embodiment of the present application can be integrated into one electronic device or can be independent of each other.

[0086] In some embodiments, the intelligent recognition module can also send the identified livestock identity information and the image data to the integrated management platform. Insurance institutions and farmers can log in to the management platform through computers, mobile phones and other devices to view livestock identification results, insurance status, activity tracks and other information.

[0087] The integrated management platform has data analysis capabilities, which can analyze the collected livestock image data and insurance data to provide risk assessment and decision support for insurance institutions. For example, by analyzing the activity trajectories and behavioral changes of livestock, possible risk events can be predicted.

[0088] The integrated management platform can also perform system settings and management, including camera parameter settings, recognition model updates, user rights management, etc. At the same time, it provides a system log function to facilitate users to view system operation status and troubleshoot.

[0089] Data analysis indicators:

[0090] For example, the activity trajectory of cattle can be analyzed to determine whether the cattle have exceeded their activity range. The threshold for the activity range of cattle exceeding the normal range in one day is set at 500 meters. If the activity trajectory of cattle exceeds this range, the system automatically issues an early warning.

[0091] The livestock insurance claims settlement method provided by the embodiment of the present application realizes automated livestock identification and data processing, greatly reducing labor costs. Accurate identification can avoid economic losses caused by erroneous claims and reduce the operating costs of insurance institutions. At the same time, rapid response can also reduce subsequent losses caused by delayed claims, such as worsening of livestock conditions. The large amount of livestock image data and insurance data collected by the system can be analyzed to provide insurance institutions with more accurate risk assessment basis. For example, by analyzing the health status and activity patterns of livestock, potential risk factors can be predicted, and more reasonable insurance premiums and claims policies can be formulated. Based on the data analysis results, the system can provide farmers with some suggestions on livestock breeding management, disease prevention, etc., to help them improve breeding efficiency and reduce risks.

[0092] It should be understood that, although the various steps in the above-mentioned flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the above-mentioned flow chart may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0093] Embodiment 2:

[0094] Based on the same inventive concept, the present application embodiment provides an electronic device, see Figure 4 As shown, it includes a processor 401 and a memory 402. The memory 402 stores a computer program, the processor 401 and the memory 402 communicate via a communication bus, and the processor 401 executes the computer program to implement the steps of the above-mentioned method, which will not be repeated here.

[0095] It should be noted that the electronic device in the embodiment of the present application can be a terminal or a server. Among them, the terminal can be a smart phone, a tablet computer, a laptop, a personal computer (PC), a smart home, a wearable electronic device, a VR / AR module, a car computer, etc. The server can be an intercommunication server or a background server between multiple heterogeneous systems, or an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, and basic cloud computing services such as big data and artificial intelligence platforms, etc.

[0096] The processor 401 may be an integrated circuit chip with signal processing capabilities. The processor 401 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. It may implement or execute various methods, steps and logic block diagrams disclosed in the embodiments of the present application. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0097] The memory 402 may include, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), etc.

[0098] This embodiment also provides a computer-readable storage medium, such as a floppy disk, a CD, a hard disk, a flash memory, a USB flash disk, an SD card, an MMC card, etc., in which one or more programs for implementing the above steps are stored. These one or more programs can be executed by one or more processors to implement the steps of the method in the above embodiment 1, which will not be repeated here.

[0099] It should be noted that the diagram provided in the present embodiment only illustrates the basic concept of the present invention in a schematic manner, so the diagram only shows the components related to the present invention rather than drawing according to the number, shape and size of the components during actual implementation. The type, quantity and ratio of each component during actual implementation can be a random change, and the component layout type may also be more complicated. The structure, ratio, size, etc. illustrated in the drawings of the present specification are only used to match the content disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions that the present invention can implement, so they have no technical substantive significance. Any modification of the structure, change of the proportional relationship or adjustment of the size should still fall within the scope of the technical content disclosed by the present invention without affecting the effect that the present invention can produce and the purpose that can be achieved. At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in this specification are only for the convenience of narration, and are not used to limit the scope of the present invention. The change or adjustment of its relative relationship should also be regarded as the scope of the present invention without substantially changing the technical content.

[0100] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0101] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A livestock insurance claim settlement method, characterized in that: include: Capturing images of a livestock breeding ground and the livestock using a camera module to obtain image data; When it is determined based on the image data that a livestock insurance claim event has occurred, determining the identity information of the livestock; generating a livestock insurance claim application form for the livestock according to the livestock insurance claim event and the identity information; The livestock insurance claim application form is sent to a first user terminal.

2. The livestock insurance claim settlement method according to claim 1, characterized in that: The method of acquiring image data by capturing images of the livestock breeding ground and the livestock through a camera module includes: When it is determined that the light intensity of the livestock breeding ground is lower than a preset light intensity threshold, the fill light function of the camera module is turned on to collect images of the breeding ground and the livestock.

3. The livestock insurance claim settlement method according to claim 1, characterized in that: When it is determined according to the image data that a livestock insurance claim event has occurred, determining the identity information of the livestock includes: The camera module sends the image data to the intelligent recognition module; The intelligent recognition module recognizes the identity information of the livestock according to the image data, and sends the identity information and the image data to the emergency response module; The emergency response module determines whether a livestock insurance claim event occurs based on the identity information and the image data.

4. The livestock insurance claim settlement method according to claim 3, characterized in that: The intelligent recognition module recognizes the identity information of the livestock according to the image data, including: Preprocessing the image data; Performing feature extraction on the preprocessed image data to obtain features of the livestock; Inputting the feature into a preset feature comparison library; the feature comparison library pre-stores a correspondence between a plurality of preset features and preset identity information; The preset identity information corresponding to the preset feature having the highest similarity to the feature of the livestock is used as the identity information of the livestock.

5. The livestock insurance claim settlement method according to claim 4, characterized in that: The image data includes at least one of facial image data of the livestock, iris image data of the livestock, and limb image data of the livestock.

6. The livestock insurance claim settlement method according to claim 3, characterized in that: The sending the identity information and the image data to an emergency response module includes: encrypting the identity information and the image data; The encrypted identity information and the image data are sent to an emergency response module.

7. The livestock insurance claim settlement method according to claim 3, characterized in that: The sending the identity information and the image data to an emergency response module includes: Performing compression processing on the image data; The compressed image data is sent to the emergency response module.

8. The livestock insurance claim settlement method according to claim 1, characterized in that: The method comprises: When the livestock insurance claim application form is sent to the first user terminal, the livestock insurance claim application form is sent to the second user terminal.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and the processor executes the computer program to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by at least one processor, the method according to any one of claims 1 to 8 is implemented.

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