Credit transaction risk identification method and device, storage medium and electronic equipment

Abstract

The invention discloses a credit transaction risk identification method and device, a storage medium and electronic equipment. The method comprises the following steps: obtaining individual data of each living mortgage livestock transmitted by Internet of Things equipment and group event data of the living mortgage livestock transmitted by a target platform; performing data fusion processing on the individual data and the group event data to obtain a fused data set; and adopting a preset model to perform risk identification on the fused data set to obtain an identification result, the identification result being used for representing whether the credit transaction corresponding to the living mortgage livestock has a risk. Through the method and the device, the problem that the risk identification efficiency of credit transaction is relatively low due to the fact that the living credit collateral of an animal husbandry practitioner depends on manual verification in related technologies is solved.

Description

Technical Field

[0001] This application relates to the field of financial technology, and more specifically, to a risk identification method and apparatus, storage medium and electronic device for credit transactions. Background Technology

[0002] When providing credit services to livestock farmers, banking institutions typically accept live livestock as collateral. After loan disbursement, they rely on manual verification of the livestock as collateral for post-loan risk management. However, due to the often remote and vast geographical areas of pastoral regions, manual counting is difficult, resulting in low efficiency in risk identification for credit transactions. Furthermore, the process of manually counting livestock and assessing their health is cumbersome, error-prone, and cannot ensure the accuracy and timeliness of the data.

[0003] The reliance on manual verification of livestock collateral in related technologies has resulted in low efficiency in risk identification for credit transactions, and no effective solution has yet been proposed. Summary of the Invention

[0004] The main objective of this application is to provide a risk identification method, device, storage medium, and electronic device for credit transactions, in order to solve the problem that relying on manual verification of live animal collateral by livestock farmers in related technologies results in low efficiency in risk identification for credit transactions.

[0005] To achieve the above objectives, according to one aspect of this application, a risk identification method for credit transactions is provided. The method includes: acquiring individual data of each live collateralized livestock transmitted by an Internet of Things (IoT) device and group event data of live collateralized livestock transmitted by a target platform, wherein the individual data includes at least one of the following: identification data, real-time location coordinate data, and real-time physiological data; and the group event data includes at least one of the following: transshipment record data, quarantine data, entry data, and exit data; performing data fusion processing on the individual data and group event data to obtain a fused dataset; and using a preset model to identify risks in the fused dataset to obtain an identification result, wherein the identification result is used to characterize whether there is risk in the credit transaction corresponding to the live collateralized livestock.

[0006] Furthermore, a pre-defined model is used to identify risks in the fused dataset. The identification results include: sorting each data record in the fused dataset by time series and obtaining the latest status data of each live collateralized animal from the sorted data records; using the pre-defined model to calculate multi-dimensional risk factors for the latest status data of each live collateralized animal and obtaining calculation results, wherein the calculation results include at least health risk factor values ​​and location safety risk factor values; determining the risk information of each live collateralized animal based on the calculation results and the pre-defined assessment threshold, and determining the identification result based on the risk information of each live collateralized animal.

[0007] Furthermore, data fusion processing is performed on individual data and group event data to obtain a fused dataset, which includes: constructing an individual data dictionary using identity data as keys and real-time location coordinate data and real-time physiological data as values; determining the location data and physiological data corresponding to the time window information in the individual data dictionary based on the time window information contained in the group event data, and establishing a temporal correlation between the time window information and the corresponding location data and physiological data; and merging the data in the individual data dictionary with the group event data according to the temporal correlation to generate a fused dataset.

[0008] Furthermore, before acquiring individual data of each live collateralized animal transmitted by the IoT device and group event data of the live collateralized animals transmitted by the target platform, the method further includes: locating the position of each live collateralized animal in real time through the positioning module of the IoT device to obtain real-time location coordinate data; and monitoring the physiological indicators of each live collateralized animal in real time through the physiological monitoring module of the IoT device to obtain real-time physiological data.

[0009] Furthermore, after acquiring individual data of each live collateralized livestock transmitted by the IoT device, the method further includes: comparing the real-time location coordinate data with the preset electronic fence geographical boundary to obtain a comparison result, wherein the preset electronic fence geographical boundary is used to characterize the activity range of the live collateralized livestock; if the comparison result indicates that the real-time location coordinate data of the live collateralized livestock is outside the preset electronic fence geographical boundary, then the identification information and current time information of the current live collateralized livestock are acquired, and an early warning message is generated based on the identification information, current time information, and real-time location coordinate data of the current live collateralized livestock, and the early warning message is sent to the target object.

[0010] Furthermore, after using a preset model to identify risks in the fused dataset and obtaining the identification results, the method also includes: if the identification results indicate that there is a risk in the credit transaction corresponding to the live collateralized livestock, then a work order is generated based on the identification results and the work order is sent to the target object.

[0011] Furthermore, the IoT device includes at least a smart collar, and the method further includes: upon detecting a disassembly signal of the smart collar, acquiring first real-time location coordinate data and first real-time physiological data of the live collateral animal corresponding to the smart collar; if there are abnormalities in the first real-time location coordinate data and the first real-time physiological data, sending a prompt message to the target object.

[0012] To achieve the above objectives, according to another aspect of this application, a risk identification device for credit transactions is provided. The device includes: an acquisition unit for acquiring individual data of each live collateralized livestock transmitted by an Internet of Things (IoT) device and group event data of live collateralized livestock transmitted by a target platform, wherein the individual data includes at least one of the following: identification data, real-time location coordinate data, and real-time physiological data; and the group event data includes at least one of the following: transshipment record data, quarantine data, entry data, and exit data; a first processing unit for performing data fusion processing on the individual data and group event data to obtain a fused dataset; and a second processing unit for performing risk identification on the fused dataset using a preset model to obtain an identification result, wherein the identification result is used to characterize whether there is a risk in the credit transaction corresponding to the live collateralized livestock.

[0013] Furthermore, the second processing unit includes: a first processing subunit, used to sort each data record in the fused dataset by time series and obtain the latest status data of each live collateralized livestock from the sorted data records; a second processing subunit, used to perform multi-dimensional risk factor calculations on the latest status data of each live collateralized livestock using a preset model to obtain calculation results, wherein the calculation results include at least health risk factor values ​​and location safety risk factor values; and a third processing subunit, used to determine the risk information of each live collateralized livestock based on the calculation results and a preset evaluation threshold, and to determine the identification result based on the risk information of each live collateralized livestock.

[0014] Furthermore, the first processing unit includes: a fourth processing subunit, used to construct an individual data dictionary by using identity data as keys and real-time location coordinate data and real-time physiological data as values; a fifth processing subunit, used to determine the location data and physiological data corresponding to the time window information in the individual data dictionary based on the time window information contained in the group event data, and to establish a temporal correlation between the time window information and the corresponding location data and physiological data; and a sixth processing subunit, used to merge the data in the individual data dictionary with the group event data according to the temporal correlation to generate a fused dataset.

[0015] Furthermore, the device also includes: a first monitoring unit, used to locate the position of each live collateralized animal in real time through the positioning module of the IoT device before acquiring individual data of each live collateralized animal transmitted by the IoT device and group event data of the live collateralized animals transmitted by the target platform, and obtain real-time location coordinate data; and a second monitoring unit, used to monitor the physiological indicators of each live collateralized animal in real time through the physiological monitoring module of the IoT device, and obtain real-time physiological data.

[0016] Furthermore, the device also includes: a comparison unit, used to compare the real-time location coordinate data with a preset electronic fence geographical boundary after acquiring individual data of each live mortgaged animal transmitted by the Internet of Things device, and obtain a comparison result, wherein the preset electronic fence geographical boundary is used to characterize the activity range of the live mortgaged animal; and a first sending unit, used to acquire the identification information and current time information of the current live mortgaged animal if the comparison result indicates that the real-time location coordinate data of the live mortgaged animal is outside the preset electronic fence geographical boundary, generate warning information based on the identification information, current time information and real-time location coordinate data of the current live mortgaged animal, and send the warning information to the target object.

[0017] Furthermore, the device also includes a second sending unit, which, after performing risk identification on the fusion dataset using a preset model and obtaining the identification result, generates a work order based on the identification result if the identification result indicates that there is a risk in the credit transaction corresponding to the live collateralized livestock, and sends the work order to the target object.

[0018] Furthermore, the device also includes: a detection unit, used to acquire the first real-time position coordinate data and the first real-time physiological data of the live collateral animal corresponding to the smart collar when a disassembly signal is detected; and a third sending unit, used to send a prompt message to the target object if there is an abnormality in the first real-time position coordinate data and the first real-time physiological data.

[0019] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the risk identification method for credit transactions described above when running.

[0020] According to another aspect of the present invention, a computer-readable storage medium is also provided, wherein the storage medium stores a program, wherein the program controls the device where the storage medium is located to execute the risk identification method for any of the above-mentioned credit transactions when the program is running.

[0021] In this embodiment, the following steps are employed: Individual data of each live collateralized animal transmitted by an IoT device and group event data of the live collateralized animal transmitted by a target platform are acquired. The individual data includes at least one of the following: identification data, real-time location coordinate data, and real-time physiological data. The group event data includes at least one of the following: migration record data, quarantine data, entry data, and exit data. The individual data and group event data are fused to obtain a fused dataset. A preset model is used to identify risks in the fused dataset to obtain identification results. These identification results are used to characterize whether there is risk in the credit transaction corresponding to the live collateralized animal. This solves the technical problem in related technologies where relying on manual verification of live collateral by livestock farmers results in low efficiency in risk identification of credit transactions.

[0022] In this solution, the status information of live collateralized livestock can be monitored in real time through IoT devices and a livestock management platform (i.e., the target platform), and the breeding process and events of live collateralized livestock can be monitored. This ensures that credit transaction managers can understand the dynamics of collateralized livestock in a timely and accurate manner, improves the efficiency and accuracy of risk identification, and reduces labor costs. Attached Figure Description

[0023] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0024] Figure 1 A hardware block diagram of a computer terminal for implementing a risk identification method in credit transactions is shown.

[0025] Figure 2 This is a flowchart of a risk identification method for credit transactions provided according to an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of a risk identification device for credit transactions provided according to an embodiment of this application;

[0027] Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] It should be noted that the information collected in this application (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of this data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding access points are provided for users to choose to authorize or refuse. For example, interfaces are set up between this system and relevant users or organizations, providing users with corresponding access points to choose to agree to or refuse automated decision-making results; if the user chooses to refuse, the process proceeds to the expert decision-making stage.

[0031] Example 1

[0032] According to an embodiment of this application, a method embodiment for risk identification in credit transactions is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal (or mobile device) for implementing a risk identification method in credit transactions is shown. Figure 1As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0034] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0035] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the risk identification method for credit transactions in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned risk identification method for credit transactions. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0037] The display may be a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0038] Under the aforementioned operating environment, this application provides the following: Figure 2 The risk identification method for credit transactions is shown. Figure 2 This is a flowchart of a risk identification method for credit transactions according to Embodiment 1 of this application. The risk identification method for credit transactions includes:

[0039] Step S201: Obtain individual data of each live collateralized livestock transmitted by the IoT device and group event data of the live collateralized livestock transmitted by the target platform. The individual data includes at least one of the following: identification data, real-time location coordinate data, and real-time physiological data. The group event data includes at least one of the following: transfer record data, quarantine data, entry data, and exit data.

[0040] Optionally, a post-loan intelligent monitoring platform for livestock mortgage loan risk management is used as the implementing entity. This platform receives real-time individual livestock data (such as unique identification, real-time location coordinates, body temperature, and activity levels) from IoT devices (such as smart collars and smart ear tags) via standardized application programming interfaces (APIs), as well as group event data (such as environmental data, relocation records, quarantine events, treatment events, mortality events, culling events, entry events, and exit events) from the livestock management platform (i.e., the target platform). Optionally, the livestock management platform is used to report key event data to the post-loan intelligent monitoring platform and provide relevant environmental monitoring data (such as temperature and humidity) for the platform's reference.

[0041] Step S202: Perform data fusion processing on individual data and group event data to obtain a fused dataset.

[0042] Optionally, individual data and group event data can be combined in the post-loan intelligent supervision platform.

[0043] Optionally, in the risk identification method for credit transactions provided in this application embodiment, the data fusion processing of individual data and group event data to obtain a fused dataset includes: constructing an individual data dictionary by using identity data as keys and real-time location coordinate data and real-time physiological data as values; determining the location data and physiological data corresponding to the time window information in the individual data dictionary based on the time window information contained in the group event data, and establishing a time correlation between the time window information and the corresponding location data and physiological data; and merging the data in the individual data dictionary with the group event data according to the time correlation to generate a fused dataset.

[0044] In an optional embodiment, an individual data dictionary is first constructed, using identity data as keys and real-time location coordinates and real-time physiological data as values. Then, based on the time window information contained in the group event data, the location data and physiological data corresponding to the time window information in the individual data dictionary are determined, and a temporal association is established between the time window information and the corresponding location data and physiological data. Then, based on the temporal association, the data in the individual data dictionary is merged with the group event data to generate a fused dataset.

[0045] For example, through data standardization and cleaning, individual data and group event data can be converted into a unified data format, and invalid or redundant data points can be removed. Then, time series synchronization can be performed. Taking a specific time window recorded in the group event data as the center, the location and physiological data within the corresponding time period in the individual data dictionary can be queried to establish a time correlation. That is, the real-time location coordinate data and real-time physiological data in the individual data are aligned with the transfer record data, quarantine data, entry data, and exit data in the group event data in terms of time series, ensuring the consistency of events and data. Then, dynamic event tags such as "transfer" and "under quarantine" can be added to the real-time location coordinate data and real-time physiological data in the individual data, forming a fused dataset that includes location, physiological indicator changes, and event records.

[0046] By generating a fusion dataset, a data foundation is provided for analyzing the comprehensive risks of mortgaged livestock, thereby improving the efficiency of risk management.

[0047] Step S203: Use a preset model to identify risks in the fused dataset and obtain identification results. The identification results are used to characterize whether there are risks in credit transactions corresponding to live livestock as collateral.

[0048] Optionally, the post-loan intelligent supervision platform can be integrated with a risk assessment engine to dynamically calculate the risk data of live collateralized livestock using a risk assessment model, mainly including the following dimensions:

[0049] Health risk data: the frequency and magnitude of deviations of real-time physiological data (such as body temperature and exercise volume) from the normal range;

[0050] Location / security risk data: number and duration of boundary crossing incidents; frequency of abnormal behavior; disaster reports for the area;

[0051] Concentration / Management Risk Data: Concentration of mortgages per household, and management level score of livestock farms.

[0052] Optionally, in the risk identification method for credit transactions provided in this application embodiment, the risk identification of the fused dataset using a preset model to obtain the identification result includes: sorting each data record in the fused dataset by time series, and obtaining the latest status data of each live collateralized animal from the sorted data records; calculating multi-dimensional risk factors for the latest status data of each live collateralized animal using a preset model to obtain calculation results, wherein the calculation results include at least health risk factor values ​​and location safety risk factor values; determining the risk information of each live collateralized animal based on the calculation results and a preset assessment threshold, and determining the identification result based on the risk information of each live collateralized animal.

[0053] In an optional embodiment, each data record in the fused dataset is first sorted by time series, and the latest status data of each live collateralized animal is obtained from the sorted data records. Then, a widely used risk assessment model is used to calculate multi-dimensional risk factors for the latest status data of each live collateralized animal, and the calculation results are obtained. Based on the calculation results and preset assessment thresholds, the risk score (i.e., risk information) of each live collateralized animal can be determined, and the identification result is determined based on the risk information of each live collateralized animal. For example, when the health risk factor value is greater than a first threshold, or the location security risk factor value is greater than a second threshold, the current risk is scored according to preset scoring rules to obtain a risk score. If the risk score is higher than the risk threshold, it can be determined that the credit transaction corresponding to the live collateralized animal has a risk.

[0054] After data fusion processing, the data is sorted using time series analysis methods to ensure that the analysis is based on the latest status data. By dynamically calculating risk factors, risky credit transactions can be quickly identified, allowing for timely measures to be taken.

[0055] In summary, by using IoT devices and a livestock management platform (i.e., the target platform), it is possible to monitor the status information of live collateralized livestock in real time, and to monitor the breeding process and events of live collateralized livestock. This ensures that credit transaction managers can understand the dynamics of collateralized livestock in a timely and accurate manner, improves the efficiency and accuracy of risk identification, and reduces labor costs.

[0056] Optionally, in the risk identification method for credit transactions provided in this application embodiment, before obtaining individual data of each live collateralized animal transmitted by the Internet of Things device and group event data of the live collateralized animals transmitted by the target platform, the method further includes: locating the position of each live collateralized animal in real time through the positioning module of the Internet of Things device to obtain real-time location coordinate data; and monitoring the physiological indicators of each live collateralized animal in real time through the physiological monitoring module of the Internet of Things device to obtain real-time physiological data.

[0057] In an optional embodiment, the positioning module of the IoT device can locate the position of each live collateralized animal in real time and obtain real-time location coordinate data. The physiological monitoring module monitors the physiological indicators of each live collateralized animal in real time and obtains real-time physiological data. For example, a smart collar is used to provide a unique identification for each live collateralized animal. The positioning module of the smart collar locates the position of each live collateralized animal in real time, and the physiological monitoring module monitors the physiological indicators of each live collateralized animal in real time and reports the data to the post-loan intelligent supervision platform.

[0058] Real-time data collection via IoT devices provides a data foundation for subsequent risk identification, ensuring the timeliness and continuity of the data.

[0059] Optionally, in the risk identification method for credit transactions provided in this application embodiment, after obtaining the individual data of each live collateralized livestock transmitted by the Internet of Things device, the method further includes: comparing the real-time location coordinate data with the preset electronic fence geographical boundary to obtain a comparison result, wherein the preset electronic fence geographical boundary is used to characterize the activity range of the live collateralized livestock; if the comparison result indicates that the real-time location coordinate data of the live collateralized livestock is outside the preset electronic fence geographical boundary, then the identification information and current time information of the current live collateralized livestock are obtained, and a warning message is generated based on the identification information, current time information and real-time location coordinate data of the current live collateralized livestock, and the warning message is sent to the target object.

[0060] In an optional embodiment, the post-loan intelligent monitoring platform provides electronic fence and boundary crossing alarm functions. The geographical boundaries of the electronic fence (such as the pasture area) are preset. The post-loan intelligent monitoring platform can calculate the relationship between the livestock location and the fence in real time, that is, compare the real-time location coordinate data with the preset electronic fence geographical boundaries. Once the mortgaged livestock is detected to have crossed the boundary, that is, the real-time location coordinate data of the live mortgaged livestock is outside the preset electronic fence geographical boundaries, an alarm can be automatically triggered, and the identification of the crossed livestock, the current time, and the location can be recorded. Warning information is sent to the target object (such as credit management personnel or livestock practitioners), for example, by notifying credit management personnel through a pop-up window on the platform interface, or by notifying livestock practitioners through SMS.

[0061] By setting up electronic fences and using location data transmitted by IoT devices for real-time comparison, automatic alarms were implemented for boundary crossings.

[0062] Optionally, in the risk identification method for credit transactions provided in this application embodiment, after using a preset model to identify risks in the fused dataset and obtaining the identification results, the method further includes: if the identification results indicate that there is a risk in the credit transaction corresponding to the live mortgaged livestock, then a work order is generated based on the identification results and the work order is sent to the target object.

[0063] In an optional embodiment, the post-loan intelligent supervision platform provides a work order management function. If the identification result indicates that there is a risk in the credit transaction corresponding to the live animal mortgage, an inspection or disposal work order will be automatically generated based on the identification result and sent to the credit management personnel.

[0064] By automatically generating and issuing work orders, an automated handling process is achieved after risk identification, thereby improving the efficiency of risk handling.

[0065] Optionally, in the risk identification method for credit transactions provided in this application embodiment, the Internet of Things device includes at least a smart collar, and the method further includes: when a disassembly signal of the smart collar is detected, acquiring the first real-time location coordinate data and the first real-time physiological data of the live collateral corresponding to the smart collar; if there is an anomaly in the first real-time location coordinate data and the first real-time physiological data, sending a prompt message to the target object.

[0066] In an optional embodiment, the smart collar has an anti-disassembly alarm function. If a disassembly signal is detected, it is reported to the post-loan intelligent supervision platform. The post-loan intelligent supervision platform obtains the real-time location coordinates and real-time physiological data of the live collateral animal corresponding to the smart collar. If there is any abnormality in the data, it sends a prompt message to the credit management personnel.

[0067] The anti-disassembly alarm function enables the post-loan intelligent monitoring platform to respond quickly to abnormal situations involving mortgaged livestock, thereby improving the security of credit transactions.

[0068] In an optional embodiment, the post-loan intelligent supervision platform provides collateral status visualization and inventory functions. Through the geographic information system map interface, it can visualize the precise location distribution and basic status of all collateral livestock in real time, and support credit managers to initiate collateral inventory with one click or the system to automatically perform it at regular intervals. Based on location clustering and unique identification, it can quickly generate accurate collateral inventory reports.

[0069] In an optional embodiment, based on preset rules (such as prolonged stillness indicating disease, and a sudden increase in activity indicating stress or relocation), the post-loan intelligent monitoring platform can automatically analyze livestock behavior data, identify potential risk events, and trigger alarms of the corresponding level.

[0070] In an optional embodiment, the post-loan intelligent supervision platform provides file management functions to establish a unique and complete electronic file for each mortgaged livestock, which runs through the entire post-loan process and includes basic information (such as breed, age, etc.), mortgage status (in custody / released / dead / culled), all historical physiological data, location trajectory, health events, and related alarm / disposal records.

[0071] The risk identification method for credit transactions provided in this application, through IoT devices and a livestock management platform (i.e., the target platform), can monitor the status information of live mortgaged livestock in real time, and monitor the breeding process and events of live mortgaged livestock, ensuring that credit transaction managers can understand the dynamics of mortgaged livestock in a timely and accurate manner, thereby improving the efficiency and accuracy of risk identification and reducing labor costs.

[0072] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0073] Example 2

[0074] This application also provides a risk identification device for credit transactions. It should be noted that the risk identification device for credit transactions in this application can be used to execute the risk identification method for credit transactions provided in this application. The risk identification device for credit transactions provided in this application is described below.

[0075] According to embodiments of this application, a risk identification device for credit transactions, used to implement the above-described risk identification method for credit transactions, is also provided, such as... Figure 3 As shown, the device includes: an acquisition unit 301, a first processing unit 302, and a second processing unit 303.

[0076] The acquisition unit 301 is used to acquire individual data of each live collateralized livestock transmitted by the Internet of Things device and group event data of the live collateralized livestock transmitted by the target platform. The individual data includes at least one of the following: identification data, real-time location coordinate data, and real-time physiological data. The group event data includes at least one of the following: transfer record data, quarantine data, entry data, and exit data.

[0077] The first processing unit 302 is used to perform data fusion processing on individual data and group event data to obtain a fused dataset.

[0078] The second processing unit 303 is used to perform risk identification on the fused dataset using a preset model to obtain identification results, wherein the identification results are used to characterize whether there is a risk in the credit transaction corresponding to the live mortgaged livestock.

[0079] The risk identification device for credit transactions provided in this application embodiment acquires individual data of each live mortgaged livestock transmitted by an IoT device and group event data of live mortgaged livestock transmitted by a target platform through an acquisition unit 301. The individual data includes at least one of the following: identification data, real-time location coordinate data, and real-time physiological data. The group event data includes at least one of the following: transfer record data, quarantine data, entry data, and exit data. A first processing unit 302 performs data fusion processing on the individual data and group event data to obtain a fused dataset. A second processing unit 303 uses a preset model to perform risk identification on the fused dataset to obtain an identification result. The identification result is used to characterize whether there is a risk in the credit transaction corresponding to the live mortgaged livestock. Through the IoT device and the breeding management platform (i.e., the target platform), the status information of the live mortgaged livestock can be monitored in real time, and the breeding process and events of the live mortgaged livestock can be monitored, ensuring that credit transaction managers can understand the dynamics of the mortgaged livestock in a timely and accurate manner, improving the efficiency and accuracy of risk identification, and reducing labor costs.

[0080] Optionally, in the risk identification device for credit transactions provided in this application embodiment, the second processing unit 303 includes: a first processing subunit, used to sort each data record in the fused dataset by time series and obtain the latest status data of each live collateralized animal from the sorted data records; a second processing subunit, used to perform multi-dimensional risk factor calculation on the latest status data of each live collateralized animal using a preset model to obtain calculation results, wherein the calculation results include at least health risk factor values ​​and location safety risk factor values; and a third processing subunit, used to determine the risk information of each live collateralized animal based on the calculation results and a preset evaluation threshold, and to determine the identification result based on the risk information of each live collateralized animal.

[0081] Optionally, in the risk identification device for credit transactions provided in this application embodiment, the first processing unit 302 includes: a fourth processing subunit, used to construct an individual data dictionary by using identity identification data as a key and real-time location coordinate data and real-time physiological data as values; a fifth processing subunit, used to determine the location data and physiological data corresponding to the time window information in the individual data dictionary based on the time window information contained in the group event data, and to establish a time correlation between the time window information and the corresponding location data and physiological data; and a sixth processing subunit, used to merge the data in the individual data dictionary with the group event data according to the time correlation to generate a fused dataset.

[0082] Optionally, in the risk identification device for credit transactions provided in this application embodiment, the device further includes: a first monitoring unit, used to locate the position of each live collateralized animal in real time through the positioning module of the Internet of Things device before acquiring individual data of each live collateralized animal transmitted by the Internet of Things device and group event data of the live collateralized animals transmitted by the target platform, and obtain real-time location coordinate data; and a second monitoring unit, used to monitor the physiological indicators of each live collateralized animal in real time through the physiological monitoring module of the Internet of Things device, and obtain real-time physiological data.

[0083] Optionally, in the risk identification device for credit transactions provided in this application embodiment, the device further includes: a comparison unit, configured to compare the real-time location coordinate data with a preset electronic fence geographical boundary after acquiring individual data of each live collateralized livestock transmitted by the Internet of Things device, and obtain a comparison result, wherein the preset electronic fence geographical boundary is used to characterize the activity range of the live collateralized livestock; and a first sending unit, configured to, if the comparison result indicates that the real-time location coordinate data of the live collateralized livestock is outside the preset electronic fence geographical boundary, acquire the identification information and current time information of the current live collateralized livestock, generate warning information based on the identification information, current time information and real-time location coordinate data of the current live collateralized livestock, and send the warning information to the target object.

[0084] Optionally, in the risk identification device for credit transactions provided in this application embodiment, the device further includes: a second sending unit, used to generate a work order based on the identification result if the identification result indicates that there is a risk in the credit transaction corresponding to the live mortgaged livestock after the risk identification of the fused dataset is performed using a preset model and the identification result is obtained, and then send the work order to the target object.

[0085] Optionally, in the risk identification device for credit transactions provided in this application embodiment, the device further includes: a detection unit, used to acquire first real-time location coordinate data and first real-time physiological data of the live collateral corresponding to the smart collar when a disassembly signal of the smart collar is detected; and a third sending unit, used to send a prompt message to the target object if there is an abnormality in the first real-time location coordinate data and the first real-time physiological data.

[0086] It should be noted that the acquisition unit 301, the first processing unit 302, and the second processing unit 303 mentioned above correspond to steps S201 to S203 in Embodiment 1. The three units and the corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above units can also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0087] Example 3

[0088] Embodiments of this application may provide an electronic device. Figure 4 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 4 As shown, the electronic device may include: one or more ( Figure 4 Only one of the following is shown: processor 402, memory 404, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0089] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0090] The processor can access information and applications stored in the memory via a transmission device to perform the following steps: acquiring individual data of each live collateralized livestock transmitted by the IoT device and group event data of the live collateralized livestock transmitted by the target platform, wherein the individual data includes at least one of the following: identification data, real-time location coordinate data, and real-time physiological data, and the group event data includes at least one of the following: transshipment record data, quarantine data, entry data, and exit data; performing data fusion processing on the individual data and group event data to obtain a fused dataset; and using a preset model to perform risk identification on the fused dataset to obtain identification results, wherein the identification results are used to characterize whether there is risk in the credit transaction corresponding to the live collateralized livestock.

[0091] The processor can access the information and application programs stored in the memory via the transmission device to perform the following steps: sort each data record in the fused dataset by time series, and obtain the latest status data of each live collateralized animal from the sorted data records; use a preset model to calculate multi-dimensional risk factors for the latest status data of each live collateralized animal, and obtain the calculation results, wherein the calculation results include at least health risk factor values ​​and location safety risk factor values; determine the risk information of each live collateralized animal based on the calculation results and preset assessment thresholds, and determine the identification result based on the risk information of each live collateralized animal.

[0092] The processor can access information and applications stored in the memory via a transmission device to perform the following steps: construct an individual data dictionary using identity data as the key and real-time location coordinate data and real-time physiological data as the values; determine the location data and physiological data corresponding to the time window information in the individual data dictionary based on the time window information contained in the group event data, and establish a temporal correlation between the time window information and the corresponding location data and physiological data; and merge the data in the individual data dictionary with the group event data according to the temporal correlation to generate a fused dataset.

[0093] The processor can access the information and applications stored in the memory via the transmission device to perform the following steps: before acquiring individual data of each live collateral animal transmitted by the IoT device and group event data of the live collateral animals transmitted by the target platform, the location of each live collateral animal is located in real time through the positioning module of the IoT device to obtain real-time location coordinate data; the physiological indicators of each live collateral animal are monitored in real time through the physiological monitoring module of the IoT device to obtain real-time physiological data.

[0094] The processor can access information and applications stored in the memory via the transmission device to execute the following steps: After acquiring individual data of each live collateralized animal transmitted by the IoT device, the processor compares the real-time location coordinates with the preset electronic fence geographical boundary to obtain a comparison result. The preset electronic fence geographical boundary is used to characterize the activity range of the live collateralized animal. If the comparison result indicates that the real-time location coordinates of the live collateralized animal are outside the preset electronic fence geographical boundary, the processor acquires the identification information and current time information of the current live collateralized animal, generates an early warning message based on the identification information, current time information, and real-time location coordinates of the current live collateralized animal, and sends the early warning message to the target object.

[0095] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: After using a preset model to identify risks in the fused dataset and obtaining the identification results, if the identification results indicate that there is a risk in the credit transaction corresponding to the live mortgaged livestock, a work order is generated based on the identification results and the work order is sent to the target object.

[0096] The processor can access the information and application stored in the memory via the transmission device to execute the following steps: upon detecting a signal indicating the removal of the smart collar, acquire the first real-time location coordinates and first real-time physiological data of the live collateral animal corresponding to the smart collar; if there are any abnormalities in the first real-time location coordinates and first real-time physiological data, send a prompt message to the target.

[0097] Those skilled in the art will understand that Figure 4 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 4 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 4 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 4 The different configurations shown.

[0098] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0099] Example 4

[0100] Embodiments of this application also provide a computer-readable storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the risk identification method for credit transactions provided in Embodiment 1.

[0101] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0102] This application also provides a computer program product, which, when executed on a data processing device, is suitable for performing risk identification method steps in credit transactions.

[0103] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0104] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0105] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0106] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0108] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0109] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method of risk identification for credit transactions, characterized by, The method comprises the following steps: acquiring individual data of each live mortgaged livestock transmitted by an Internet of Things device and group event data of the live mortgaged livestock transmitted by a target platform, wherein the individual data comprises at least one of the following: identity data, real-time location coordinate data, real-time physiological data, and the group event data comprises at least one of the following: transfer record data, quarantine data, entry data, and exit data; performing data fusion processing on the individual data and the group event data to obtain a fusion data set; performing risk identification on the fusion data set by using a preset model to obtain an identification result, wherein the identification result is used to represent whether there is a risk in a credit transaction corresponding to the live mortgaged livestock.

2. The method of claim 1, wherein, The method comprises the following steps: sorting each data record in the fusion data set in a time sequence, and acquiring the latest state data of each live mortgaged livestock from the sorted data record; performing multi-dimensional risk factor calculation on the latest state data of each live mortgaged livestock by using the preset model to obtain a calculation result, wherein the calculation result at least comprises a health risk factor value and a location safety risk factor value; determining risk information of each live mortgaged livestock according to the calculation result and a preset evaluation threshold, and determining the identification result according to the risk information of each live mortgaged livestock.

3. The method of claim 1, wherein, The method comprises the following steps: constructing an individual data dictionary by taking the identity data as a key and taking the real-time location coordinate data and the real-time physiological data as values; determining location data and physiological data corresponding to time window information in the individual data dictionary based on the time window information contained in the group event data, and establishing a time association between the time window information and the corresponding location data and physiological data; merging data in the individual data dictionary with the group event data according to the time association to generate the fusion data set.

4. The method of claim 1, wherein, Before acquiring the individual data of each live mortgaged livestock transmitted by the Internet of Things device and the group event data of the live mortgaged livestock transmitted by the target platform, the method further comprises the following steps: locating the position of each live mortgaged livestock in real time by a positioning module of the Internet of Things device to obtain the real-time location coordinate data; monitoring physiological indexes of each live mortgaged livestock in real time by a physiological monitoring module of the Internet of Things device to obtain the real-time physiological data.

5. The method of claim 1, wherein, After acquiring the individual data of each live mortgaged livestock transmitted by the Internet of Things device, the method further comprises the following steps: comparing the real-time location coordinate data with a preset electronic fence geographical boundary to obtain a comparison result, wherein the preset electronic fence geographical boundary is used to represent an activity range of the live mortgaged livestock; If the comparison result represents that the real-time position coordinate data of the living collateral livestock is outside the preset electronic fence geographical boundary, identification information of a current living collateral livestock and current time information are acquired, early warning information is generated according to the identification information of the current living collateral livestock, the current time information and the real-time position coordinate data of the current living collateral livestock, and the early warning information is sent to a target object.

6. The method of claim 1, wherein, After the preset model is used to perform risk identification on the fusion data set to obtain an identification result, the method further includes: If the identification result represents that the credit transaction corresponding to the living collateral livestock has a risk, a work order is generated according to the identification result, and the work order is sent to a target object.

7. The method of claim 1, wherein, The Internet of Things device at least includes a smart collar, and the method further includes: In a case where the disassembly signal of the smart collar is detected, first real-time position coordinate data and first real-time physiological data of a living collateral livestock corresponding to the smart collar are acquired; If the first real-time position coordinate data and the first real-time physiological data are abnormal, prompt information is sent to a target object.

8. A risk identification apparatus for credit transactions, characterized by The method includes: An acquisition unit is configured to acquire individual data of each living collateral livestock transmitted by an Internet of Things device and group event data of living collateral livestock transmitted by a target platform, wherein the individual data includes at least one of the following: identity identification data, real-time position coordinate data and real-time physiological data, and the group event data includes at least one of the following: transfer record data, quarantine data, entry data and exit data. A first processing unit is configured to perform data fusion processing on the individual data and the group event data to obtain a fusion data set. A second processing unit is configured to perform risk identification on the fusion data set by using a preset model to obtain an identification result, wherein the identification result is used to represent whether a credit transaction corresponding to the living collateral livestock has a risk.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein the executable program controls a device where the computer-readable storage medium is located to perform the risk identification method of the credit transaction according to any one of claims 1 to 7 when the executable program is running.

10. An electronic device, comprising: The method includes: A memory stores an executable program; A processor is configured to run the program, wherein the program performs the risk identification method of the credit transaction according to any one of claims 1 to 7 when the program is running.

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