Insurance risk assessment method and device based on Internet of Things, equipment and medium

Abstract

The invention belongs to the technical field of the Internet of Things, is suitable for the financial field, and discloses an insurance risk assessment method, device and equipment based on the Internet of Things and a medium, and the method comprises the steps: obtaining multi-source heterogeneous data of a plurality of Internet of Things equipment, and analyzing the multi-source heterogeneous data through a standardized interface; constructing an alarm rule engine based on the analyzed multi-source heterogeneous data, and configuring alarm processing rules corresponding to different risk levels; responding to the alarm event of each piece of Internet of Things equipment in real time, and judging a risk level corresponding to the alarm event of each piece of Internet of Things equipment; obtaining a client insurance service bound with the alarm event of each piece of Internet of Things equipment, and executing alarm processing on the alarm event of each piece of Internet of Things equipment according to a configured alarm processing rule; and constructing a monthly risk assessment matrix of the customer insurance business based on the alarm processing result, and calculating a monthly risk score of the customer insurance business. According to the invention, the risk assessment efficiency of the power utilization safety insurance service is effectively improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of Internet of Things, and is suitable for the financial field, in particular to an insurance risk assessment method, device and equipment based on Internet of Things and a medium. BACKGROUND

[0002] In the electricity safety insurance business, Internet of Things devices are widely used to monitor the electrical parameters of the key areas of customers to improve the efficiency of risk management. When the monitored electrical parameters exceed the safety threshold, the system will automatically trigger an alarm processing, and assess the risk level of property insurance according to the alarm processing result.

[0003] However, in the actual insurance business processing, the insurance company relies too much on the personal experience of the business staff when processing the alarm of the Internet of Things device, and lacks a unified alarm processing standard. In addition, the existing system fails to effectively capture the dynamic changes of customer risks. For example, in the financial field, there may be a short-term risk surge phenomenon during the summer power usage peak period, and the insurance company may need additional financial reserves to deal with potential claims requests. More seriously, the insurance company still follows the annual cycle in adjusting the premium of the electricity safety liability insurance customer, and often fails to increase the premium in time to cover potential claims losses during the high-risk period, and also misses the opportunity to reduce the premium to enhance customer loyalty during the low-risk period, thereby affecting customer satisfaction.

[0004] Therefore, how to improve the risk assessment efficiency of the electricity safety insurance business is a technical problem to be solved. SUMMARY

[0005] The application provides an insurance risk assessment method, device and equipment based on Internet of Things to solve the technical problem of how to improve the risk assessment efficiency of the electricity safety insurance business.

[0006] In a first aspect, the application provides an insurance risk assessment method based on Internet of Things, comprising:

[0007] Obtaining multi-source heterogeneous data of a plurality of Internet of Things devices, and analyzing the multi-source heterogeneous data through a standardized interface;

[0008] Constructing an alarm rule engine based on the analyzed multi-source heterogeneous data, and configuring alarm processing rules corresponding to different risk levels in the alarm rule engine;

[0009] Real-time response to alarm events of each Internet of Things device, and judging the risk level corresponding to the alarm events of each Internet of Things device;

[0010] The processing module is configured to acquire a customer insurance business bound to the alarm event of each Internet of Things device, and perform alarm processing on the alarm event of each Internet of Things device according to the alarm processing rule corresponding to the different risk levels configured.

[0011] The computing module constructs a monthly risk assessment matrix of the customer insurance business based on the alarm processing result, and calculates a monthly risk score of the customer insurance business based on the constructed monthly risk assessment matrix.

[0012] In a second aspect, the present application provides an insurance risk assessment device based on Internet of Things, comprising:

[0013] The acquisition module is configured to acquire multi-source heterogeneous data of a plurality of Internet of Things devices, and parse the multi-source heterogeneous data through a standardized interface.

[0014] The configuration module is configured to construct an alarm rule engine based on the parsed multi-source heterogeneous data, and configure alarm processing rules corresponding to different risk levels in the alarm rule engine.

[0015] The judgment module is configured to respond to alarm events of each Internet of Things device in real time, and judge risk levels corresponding to the alarm events of each Internet of Things device.

[0016] The processing module is configured to acquire a customer insurance business bound to the alarm event of each Internet of Things device, and perform alarm processing on the alarm event of each Internet of Things device according to the alarm processing rule corresponding to the different risk levels configured.

[0017] The computing module constructs a monthly risk assessment matrix of the customer insurance business based on the alarm processing result, and calculates a monthly risk score of the customer insurance business based on the constructed monthly risk assessment matrix.

[0018] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned insurance risk assessment method based on Internet of Things when executing the computer program.

[0019] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the above-mentioned insurance risk assessment method based on Internet of Things.

[0020] The insurance risk assessment method, device, equipment and medium based on the Internet of Things can obtain multi-source heterogeneous data of a plurality of Internet of Things devices through a client, and parse the multi-source heterogeneous data through a standardized interface; an alarm rule engine is constructed based on the parsed multi-source heterogeneous data, and alarm processing rules corresponding to different risk levels are configured in the alarm rule engine; alarm events of each Internet of Things device are responded to in real time, and the risk level corresponding to the alarm event of each Internet of Things device is judged; the alarm event of each Internet of Things device is bound to a customer insurance business, and alarm processing is performed on the alarm event of each Internet of Things device according to the configured alarm processing rules corresponding to different risk levels; a monthly risk assessment matrix of the customer insurance business is constructed based on the alarm processing result, and a monthly risk score of the customer insurance business is calculated based on the constructed monthly risk assessment matrix. In the present application, the alarm processing rules corresponding to different risk levels are configured, which realizes the automation of the alarm response process of the Internet of Things device, reduces the need for human intervention, and improves the efficiency of processing alarm events. In addition, the monthly risk score of the customer insurance business is calculated based on the constructed monthly risk assessment matrix, which enables the insurance company to adjust the insurance strategy in real time to flexibly respond to changes in customer risk conditions, thereby improving the risk assessment efficiency of the electricity safety insurance business. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments of the present application. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0022] Figure 1 is an application environment schematic diagram of the insurance risk assessment method based on the Internet of Things in an embodiment of the present application;

[0023] Figure 2 is a flowchart of the insurance risk assessment method based on the Internet of Things in an embodiment of the present application;

[0024] Figure 3 is Figure 2 a specific implementation flowchart of step S20 in

[0025] Figure 4 is Figure 2 a specific implementation flowchart of step S50 in

[0026] Figure 5 is a structure schematic diagram of the insurance risk assessment device based on the Internet of Things in an embodiment of the present application;

[0027] Figure 6This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention;

[0028] Figure 7 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] The insurance risk assessment method based on the Internet of Things provided in this invention can be applied to, for example... Figure 1 In the application environment, Figure 1 This is a schematic diagram of an application environment for an IoT-based insurance risk assessment method according to an embodiment of the present invention. The client communicates with the server via a network. The server can obtain multi-source heterogeneous data from multiple IoT devices through the client and parse this data using a standardized interface. Based on the parsed multi-source heterogeneous data, an alarm rule engine is constructed, and alarm processing rules corresponding to different risk levels are configured within the alarm rule engine. The server responds to alarm events from each IoT device in real time and determines the risk level corresponding to each IoT device's alarm event. It obtains the customer insurance business linked to the alarm events of each IoT device and performs alarm processing on the alarm events of each IoT device according to the configured alarm processing rules corresponding to different risk levels. Based on the alarm processing results, a monthly risk assessment matrix for the customer insurance business is constructed, and the monthly risk score for the customer insurance business is calculated based on the constructed monthly risk assessment matrix. In this invention, by configuring alarm processing rules for different risk levels, the alarm response process of IoT devices is automated, which not only reduces the need for human intervention but also improves the efficiency of alarm event processing. Furthermore, by calculating the monthly risk score of a customer's insurance business based on the constructed monthly risk assessment matrix, insurance companies can adjust their insurance strategies in real time to flexibly respond to changes in customer risk status, thereby improving the efficiency of risk assessment for electricity safety insurance business. The invention will now be described in detail through specific embodiments.

[0031] Please see Figure 2 As shown, Figure 2 This is a flowchart illustrating an IoT-based insurance risk assessment method according to an embodiment of the present invention. The IoT-based insurance risk assessment method specifically includes the following steps:

[0032] S10: Acquire multi-source heterogeneous data from multiple IoT devices and parse the multi-source heterogeneous data through a standardized interface. In this embodiment of the invention, multi-source heterogeneous data can be accessed from IoT devices from multiple manufacturers, and unified parsing of data from IoT devices from different manufacturers can be achieved through a standardized data interface. Specifically, it includes the following step S11:

[0033] S11: Convert the multi-source heterogeneous data from various IoT devices into JSON format, and parse the current and temperature values ​​of each IoT device from the JSON data. Specifically, in this embodiment of the invention, converting data from different IoT devices into a unified JSON format helps standardize the data processing flow. For example, in a financial claims scenario, a customer has purchased electrical safety liability insurance, and the company's production workshop has installed multiple IoT devices, such as temperature sensors and current monitors, to monitor the operating status of production equipment in real time. During normal production, these devices continuously collect the current and temperature values ​​of the equipment, and the collected current and temperature values ​​are converted into JSON format.

[0034] S20: An alarm rule engine is constructed based on the parsed multi-source heterogeneous data, and alarm processing rules corresponding to different risk levels are configured in the alarm rule engine. Specifically, in this embodiment of the invention, by constructing an alarm rule engine, potential risk events can be monitored and responded to automatically. Configuring alarm processing rules for different risk levels allows for corresponding response measures to risks of different severity, improving response efficiency. Figure 3 The above, Figure 3 yes Figure 2 A schematic flowchart of a specific implementation of step S20 includes the following steps S21-S23:

[0035] S21: Preset safe current and safe temperature thresholds for each IoT device, and monitor the current and temperature values ​​of each IoT device in real time. Specifically, in this embodiment of the invention, by setting safe current and safe temperature thresholds, potential hazards can be immediately identified when the current or temperature exceeds the safe range, thereby taking measures to prevent accidents. For example, in a financial claims scenario, customers use IoT sensors to monitor the current and temperature of all devices in real time to prevent overheating and circuit failures. If the current of a device in the server room suddenly exceeds the preset safe threshold, the IoT device will immediately issue an alarm, and technicians can respond quickly, check and resolve potential problems, and avoid greater losses and insurance payouts.

[0036] S22: Calculate the current and temperature deviations between the monitored current and temperature values ​​of each IoT device and the preset safe current and temperature thresholds. Specifically, in this embodiment of the invention, by calculating the deviation values, the risk level of insurance business can be quantified, providing a basis for taking corresponding risk control measures. For example, in a financial claims scenario, factories require a large amount of electricity during production and therefore need to monitor current and temperature values ​​in real time. By calculating the deviation between the actual current of the equipment and the preset thresholds, current anomalies can be detected in a timely manner, and even small changes will not be ignored. Once an anomaly is detected, further inspection can be initiated or the power supply can be automatically cut off to prevent equipment overheating and fire, thereby reducing insurance payouts and improving production safety.

[0037] S23: Determine the risk level of each IoT device based on the calculated current deviation and temperature deviation values, and configure alarm handling rules corresponding to different risk levels. In this embodiment of the invention, the risk level determination can implement a hierarchical management strategy, taking more urgent measures for high-risk devices and performing routine monitoring for low-risk devices. This hierarchical response mechanism helps to rationally allocate resources and effectively control risks. Specifically, it includes the following steps S231-S234:

[0038] S231: When the calculated current deviation or temperature deviation exceeds a first preset risk threshold, the IoT device is determined to be at the first risk level, and a high-urgency work order is generated for the IoT device at the first risk level. Specifically, in this embodiment of the invention, the first risk level is a major risk, such as a fire hazard or serious equipment failure. If a major risk is detected, a high-urgency work order is immediately generated and pushed to the salesperson, and a warning contact is sent to the customer via SMS. For example, in a financial claims scenario, if the current of a critical piece of equipment suddenly exceeds the first preset risk threshold, the IoT device will immediately generate a high-urgency work order, and the technical team will be quickly dispatched to handle the problem to avoid equipment damage or more serious safety accidents.

[0039] S232: When the calculated current deviation value or temperature deviation value is greater than the second preset risk threshold and less than or equal to the first preset risk threshold, the IoT device is determined to be in the second risk level, and a medium-urgency work order is generated for the IoT device in the second risk level. Specifically, in this embodiment of the invention, the second risk level involves a relatively high risk. Although it will not immediately lead to serious consequences, it requires attention and resolution. Immediately generating a medium-urgency work order and pushing it to the salesperson can ensure that the problem is resolved within a reasonable time.

[0040] S233: When the calculated current deviation value or temperature deviation value is greater than the third preset risk threshold and less than or equal to the second preset risk threshold, the IoT device is determined to be at the third risk level, and a monthly risk assessment report is generated for the IoT device at the third risk level. Specifically, in this embodiment of the invention, the third risk level is a general risk level, and a monthly risk assessment report needs to be generated.

[0041] S234: When the calculated current deviation value or temperature deviation value is less than or equal to the third preset risk threshold, the IoT device is determined to be at the fourth risk level, and alarm data is generated for IoT devices at the fourth risk level. Specifically, in this embodiment of the invention, the fourth risk level is a low risk level, indicating that the device is currently operating normally, but continuous monitoring is still required. The generated alarm data can serve as a record of the device's normal operation for future analysis and auditing. For example, in financial claims scenarios, for most normally operating devices, IoT devices continuously monitor current and temperature values, generating alarm data to confirm the device's safety status. This data can help insurance companies assess overall operational risk and adjust insurance strategies and rates.

[0042] S30: Real-time response to alarm events from various IoT devices, and determination of the risk level corresponding to each IoT device's alarm event. In this embodiment of the invention, when IoT devices alarm, the real-time response mechanism ensures immediate handling of risk events, reducing the possibility of risk spread. By assessing the risk level, high-risk events can be prioritized, and processing resources can be allocated rationally. For example, in a financial claims scenario, when an abnormal temperature rise is detected to a level that may cause a fire, a high-priority alarm will be immediately activated, the relevant power supply will be cut off to reduce potential losses, and the insurance company will be notified to prepare for possible claims.

[0043] S40: Obtain the customer insurance business linked to the alarm events of each IoT device, and perform alarm processing on the alarm events of each IoT device according to the alarm processing rules corresponding to different risk levels configured in the configuration. In this embodiment of the invention, alarm events can be associated with specific customer insurance businesses, and data can be automatically generated or pushed to relevant personnel according to the alarm processing rules corresponding to different risk levels configured in the configuration, which can achieve more accurate risk management. For example, in a financial claims scenario, after an abnormal current event occurs, the IoT device is automatically associated with the customer's insurance business, and the claims process is automatically initiated according to the insurance terms in the insurance business, and all relevant data is recorded to support claims analysis.

[0044] S50: Construct a monthly risk assessment matrix for the customer's insurance business based on the alarm processing results, and calculate the monthly risk score for the customer's insurance business based on the constructed monthly risk assessment matrix. In this embodiment of the invention, by regularly assessing and updating the risk score, the insurance company can continuously monitor risk changes and adjust its insurance strategy in a timely manner. This dynamic risk management method improves the timeliness and accuracy of risk control. Figure 4 The above, Figure 4 yes Figure 2 A schematic flowchart of a specific implementation of step S50 includes the following steps S51-S52:

[0045] S51: Extract the cumulative frequency of alarm events, work order completion rate, and risk level from the alarm processing results, and assign weights to the extracted cumulative frequency of alarm events, work order completion rate, and risk level. Specifically, in this embodiment of the invention, by extracting key performance indicators such as the cumulative frequency of alarm events, work order completion rate, and risk level, the alarm processing results can be analyzed in depth. For example, for some high-risk equipment, the risk level may be more important than the alarm frequency, and therefore can be given a higher weight.

[0046] S52: Based on the cumulative frequency of alarm events, work order processing completion rate, and risk level after weighting, a monthly risk assessment matrix for the customer's insurance business is constructed, and the monthly risk score for the customer's insurance business is calculated based on the constructed monthly risk assessment matrix. In this embodiment of the invention, a risk assessment matrix is ​​constructed by assigning weights to the indicators. The monthly risk score is a quantitative risk indicator that can be used to track the changing trend of risk management effectiveness and to assess the basis for insurance demand and premium adjustments. Specifically, it includes:

[0047] The cumulative frequency of alarm events, work order processing completion rate, and risk level after weighting are linearly weighted to obtain the monthly risk score of the customer's insurance business.

[0048] The formula for calculating the monthly risk score of the customer's insurance business is as described in formula (1):

[0049] R Score =α·F+β·C+γ·S (1)

[0050] Among them, R Score This represents the monthly risk score of a customer's insurance business. F represents the cumulative frequency of alarm events, C represents the work order processing completion rate, S represents the risk level, α represents the weighting coefficient of the cumulative frequency of alarm events, β represents the weighting coefficient of the work order processing completion rate, and γ represents the weighting coefficient of the risk level.

[0051] Specifically, in this embodiment of the invention, a monthly risk assessment matrix is ​​constructed, which comprehensively derives the customer's monthly risk score from three dimensions: the cumulative frequency of alarm events from the customer's IoT devices, the work order processing completion rate, and the risk level. For example, the weighting coefficient α for the cumulative frequency of alarm events can be preset to 25%, the weighting coefficient β for the work order processing completion rate to 40%, and the weighting coefficient γ for the risk level to 35%. The cumulative frequency of alarm events, the work order processing completion rate, and the risk level after weighting are linearly weighted to obtain the monthly risk score for the customer's insurance business. Then, the obtained monthly risk score for the customer's insurance business is stored in the database.

[0052] As can be seen, the above solution automates the alarm response process for IoT devices by configuring alarm handling rules for different risk levels. This not only reduces the need for human intervention but also improves the efficiency of handling alarm events. Furthermore, by calculating the monthly risk score for customer insurance business based on the constructed monthly risk assessment matrix, insurance companies can adjust their insurance strategies in real time to flexibly respond to changes in customer risk status, thereby improving the risk assessment efficiency of electricity safety insurance business.

[0053] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0054] In one embodiment, an Internet of Things (IoT)-based insurance risk assessment device is provided, which corresponds one-to-one with the IoT-based insurance risk assessment method described in the above embodiments. For example... Figure 5 As shown, Figure 5 This is a schematic diagram of an IoT-based insurance risk assessment device according to an embodiment of the present invention. The IoT-based insurance risk assessment device includes an acquisition module 51, a configuration module 52, a judgment module 53, a processing module 54, and a calculation module 55. Detailed descriptions of each functional module are as follows:

[0055] The acquisition module 51 is used to acquire multi-source heterogeneous data from multiple IoT devices and parse the multi-source heterogeneous data through a standardized interface.

[0056] Configuration module 52 builds an alarm rule engine based on the parsed multi-source heterogeneous data, and configures alarm processing rules corresponding to different risk levels in the alarm rule engine;

[0057] The judgment module 53 is used to respond to alarm events of each IoT device in real time and judge the risk level corresponding to the alarm events of each IoT device.

[0058] The processing module 54 is used to obtain the customer insurance business bound to the alarm events of each IoT device, and to perform alarm processing on the alarm events of each IoT device according to the alarm processing rules corresponding to different risk levels configured.

[0059] Calculation module 55 constructs a monthly risk assessment matrix for customer insurance business based on alarm processing results, and calculates the monthly risk score for customer insurance business based on the constructed monthly risk assessment matrix.

[0060] In one embodiment, the acquisition module 51 is specifically used for:

[0061] Convert the multi-source heterogeneous data from various IoT devices into JSON format, and parse the current and temperature values ​​of each IoT device in the JSON data.

[0062] In one embodiment, the configuration module 52 is specifically used for:

[0063] Preset safe current and safe temperature thresholds for each IoT device, and monitor the current and temperature values ​​of each IoT device in real time;

[0064] Calculate the current and temperature deviations between the monitored current and temperature values ​​of each IoT device and the preset safe current and safe temperature thresholds;

[0065] The risk level of each IoT device is determined based on the calculated current deviation and temperature deviation values, and alarm handling rules corresponding to different risk levels are configured.

[0066] In one embodiment, the configuration module 52 is further configured to:

[0067] When the calculated current deviation value or temperature deviation value is greater than the first preset risk threshold, the IoT device is determined to be in the first risk level, and a high-urgency work order is generated for the IoT device in the first risk level.

[0068] When the calculated current deviation value or temperature deviation value is greater than the second preset risk threshold and less than or equal to the first preset risk threshold, the IoT device is determined to be in the second risk level, and a medium-urgent work order is generated for the IoT device in the second risk level.

[0069] When the calculated current deviation value or temperature deviation value is greater than the third preset risk threshold and less than or equal to the second preset risk threshold, the IoT device is determined to be in the third risk level, and a monthly risk assessment report is generated for the IoT device in the third risk level.

[0070] When the calculated current deviation value or temperature deviation value is less than or equal to the third preset risk threshold, the IoT device is determined to be in the fourth risk level, and alarm data is generated for the IoT device in the fourth risk level.

[0071] In one embodiment, the calculation module 55 is specifically used for:

[0072] Extract the cumulative frequency of alarm events, work order processing completion rate, and risk level from the alarm processing results, and assign weights to the extracted cumulative frequency of alarm events, work order processing completion rate, and risk level;

[0073] Based on the cumulative frequency of alarm events, work order processing completion rate, and risk level after weighting, a monthly risk assessment matrix for customer insurance business is constructed, and the monthly risk score for customer insurance business is calculated based on the constructed monthly risk assessment matrix.

[0074] In one embodiment, the calculation module 55 is further configured to:

[0075] The cumulative frequency of alarm events, work order processing completion rate, and risk level after weighting are linearly weighted to obtain the monthly risk score of the customer's insurance business.

[0076] The formula for calculating the monthly risk score of the customer's insurance business is as follows:

[0077] R Score =α·F + β·C + γ·S

[0078] Among them, R Score This represents the monthly risk score of a customer's insurance business. F represents the cumulative frequency of alarm events, C represents the work order processing completion rate, S represents the risk level, α represents the weighting coefficient of the cumulative frequency of alarm events, β represents the weighting coefficient of the work order processing completion rate, and γ represents the weighting coefficient of the risk level.

[0079] In one embodiment, the IoT-based insurance risk assessment device is further used for:

[0080] Based on the calculated monthly risk score of the customer's insurance business, the insurance factors of the customer's insurance business are dynamically adjusted.

[0081] This invention provides an IoT-based insurance risk assessment device. By configuring alarm handling rules for different risk levels, it automates the alarm response process of IoT devices, reducing the need for human intervention and improving the efficiency of alarm event handling. Furthermore, based on the constructed monthly risk assessment matrix, it calculates the monthly risk score for customer insurance business, enabling insurance companies to adjust insurance strategies in real time to flexibly respond to changes in customer risk status and improving the risk assessment efficiency of electricity safety insurance business.

[0082] Specific limitations regarding IoT-based insurance risk assessment devices can be found in the limitations of IoT-based insurance risk assessment methods described above, and will not be repeated here. Each module in the aforementioned IoT-based insurance risk assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0083] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, Figure 6 This is a schematic diagram of a computer device according to an embodiment of the present invention. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a server-side method for insurance risk assessment based on the Internet of Things.

[0084] In one embodiment, a computer device is provided, which may be a client, and its internal structure diagram may be as follows: Figure 7 As shown, Figure 7 This is another structural schematic diagram of a computer device according to an embodiment of the present invention. The computer device includes a processor, memory, network interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the client-side functions or steps of an Internet of Things-based insurance risk assessment method.

[0085] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0086] Acquire multi-source heterogeneous data from multiple IoT devices and parse the multi-source heterogeneous data through a standardized interface;

[0087] An alarm rule engine is built based on the parsed multi-source heterogeneous data, and alarm processing rules corresponding to different risk levels are configured in the alarm rule engine;

[0088] Real-time response to alarm events from various IoT devices, and assessment of the risk level corresponding to each IoT device's alarm event;

[0089] Obtain the customer insurance business associated with the alarm events of each IoT device, and perform alarm processing on the alarm events of each IoT device according to the alarm processing rules corresponding to different risk levels configured.

[0090] A monthly risk assessment matrix for customer insurance business is constructed based on the alarm processing results, and a monthly risk score for customer insurance business is calculated based on the constructed monthly risk assessment matrix.

[0091] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0092] Acquire multi-source heterogeneous data from multiple IoT devices and parse the multi-source heterogeneous data through a standardized interface;

[0093] An alarm rule engine is built based on the parsed multi-source heterogeneous data, and alarm processing rules corresponding to different risk levels are configured in the alarm rule engine;

[0094] Real-time response to alarm events from various IoT devices, and assessment of the risk level corresponding to each IoT device's alarm event;

[0095] Obtain the customer insurance business associated with the alarm events of each IoT device, and perform alarm processing on the alarm events of each IoT device according to the alarm processing rules corresponding to different risk levels configured.

[0096] A monthly risk assessment matrix for customer insurance business is constructed based on the alarm processing results, and a monthly risk score for customer insurance business is calculated based on the constructed monthly risk assessment matrix.

[0097] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and client side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0098] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0099] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0100] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An insurance risk assessment method based on Internet of Things, characterized in that, The method comprises the following steps: acquiring multi-source heterogeneous data of a plurality of Internet of Things devices and parsing the multi-source heterogeneous data through a standardized interface; constructing an alarm rule engine based on the parsed multi-source heterogeneous data and configuring alarm processing rules corresponding to different risk levels in the alarm rule engine; responding to alarm events of each Internet of Things device in real time and judging the risk level corresponding to the alarm events of each Internet of Things device; acquiring customer insurance businesses bound to the alarm events of each Internet of Things device and executing alarm processing on the alarm events of each Internet of Things device according to the configured alarm processing rules corresponding to different risk levels; constructing a monthly risk assessment matrix of the customer insurance business based on the alarm processing result and calculating a monthly risk score of the customer insurance business based on the constructed monthly risk assessment matrix. 2.The Internet of Things based insurance risk assessment method according to claim 1, characterized in that, The method comprises the following steps: converting the multi-source heterogeneous data of each Internet of Things device into JSON format and parsing the current value and temperature value of each Internet of Things device in the JSON format data. 3.The IoT-based insurance risk assessment method of claim 2, wherein, The method comprises the following steps: presetting the safe current threshold and safe temperature threshold of each Internet of Things device and monitoring the current value and temperature value of each Internet of Things device in real time; calculating the current deviation value and temperature deviation value between the monitored current value and temperature value of each Internet of Things device and the preset safe current threshold and safe temperature threshold; determining the risk level of each Internet of Things device according to the calculated current deviation value and temperature deviation value and configuring alarm processing rules corresponding to different risk levels. 4.The IoT-based insurance risk assessment method of claim 3, wherein, The method comprises the following steps: when the calculated current deviation value or temperature deviation value is greater than a first preset risk threshold, it is determined that the Internet of Things device is in a first risk level, and a high-urgency work order is generated for the Internet of Things device in the first risk level; when the calculated current deviation value or temperature deviation value is greater than a second preset risk threshold and less than or equal to the first preset risk threshold, it is determined that the Internet of Things device is in a second risk level, and a medium-urgency work order is generated for the Internet of Things device in the second risk level; when the calculated current deviation value or temperature deviation value is greater than a third preset risk threshold and less than or equal to the second preset risk threshold, it is determined that the Internet of Things device is in a third risk level, and a monthly risk assessment report is generated for the Internet of Things device in the third risk level; when the calculated current deviation value or temperature deviation value is less than or equal to the third preset risk threshold, it is determined that the Internet of Things device is in a fourth risk level, and alarm data is generated for the Internet of Things device in the fourth risk level. 5.The Internet of Things based insurance risk assessment method according to claim 1, characterized in that, The method comprises the following steps: extract the alarm event cumulative frequency, the work order processing completion rate and the risk level in the alarm processing result, and assign weights to the extracted alarm event cumulative frequency, the work order processing completion rate and the risk level; based on the alarm event cumulative frequency, the work order processing completion rate and the risk level after the weights are assigned, a monthly risk assessment matrix of the customer insurance business is constructed, and a monthly risk score of the customer insurance business is calculated based on the constructed monthly risk assessment matrix. 6.The IoT-based insurance risk assessment method of claim 5, wherein, The monthly risk assessment matrix of the customer insurance business is constructed based on the alarm event cumulative frequency, the work order processing completion rate and the risk level after the weights are assigned, and the monthly risk score of the customer insurance business is calculated based on the constructed monthly risk assessment matrix, including: linearly weighting the alarm event cumulative frequency, the work order processing completion rate and the risk level after the weights are assigned to obtain the monthly risk score of the customer insurance business; The calculation formula of the monthly risk score of the customer insurance business is: R Score = a · F + b · C + g · S wherein R Score represents the monthly risk score of the customer insurance business, F represents the cumulative frequency of alarm events, C represents the work order processing completion rate, S represents the risk level, a represents the weight coefficient of the cumulative frequency of alarm events, β represents the weight coefficient of the work order processing completion rate, and γ represents the weight coefficient of the risk level. 7.The IoT-based insurance risk assessment method of claim 1, wherein, After the monthly risk assessment matrix of the customer insurance business is constructed based on the alarm processing result, and the monthly risk score of the customer insurance business is calculated based on the constructed monthly risk assessment matrix, the method further includes: based on the calculated monthly risk score of the customer insurance business, dynamically adjusting the insurance factors of the customer insurance business.

8. An insurance risk assessment device based on Internet of Things, characterized in that, including: an acquisition module configured to acquire multi-source heterogeneous data of a plurality of Internet of Things devices and parse the multi-source heterogeneous data through a standardized interface; a configuration module configured to construct an alarm rule engine based on the parsed multi-source heterogeneous data and configure alarm processing rules corresponding to different risk levels in the alarm rule engine; a judgment module configured to respond to alarm events of each Internet of Things device in real time and determine the risk level corresponding to the alarm events of each Internet of Things device; a processing module configured to acquire customer insurance businesses bound to the alarm events of each Internet of Things device and execute alarm processing on the alarm events of each Internet of Things device according to the configured alarm processing rules corresponding to different risk levels; a calculation module configured to construct a monthly risk assessment matrix of the customer insurance business based on alarm processing results and calculate a monthly risk score of the customer insurance business based on the constructed monthly risk assessment matrix.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the steps of the insurance risk assessment method based on the Internet of Things according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to realize the steps of the insurance risk assessment method based on the Internet of Things according to any one of claims 1 to 7.

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