IT operation and maintenance insurance underwriting and claim checking evaluation system

Abstract

The invention relates to the technical field of calculation models, in particular to an IT operation and maintenance insurance underwriting and claim assessment system, which comprises a risk assessment model, which is a risk assessment model based on a fuzzy comprehensive evaluation method and is used for determining an index assessment score and a total risk assessment score of each assessment dimension index; the optimization feedback module is used for adjusting the risk assessment model according to historical operation and maintenance data of the customer; the insurance module is used for calculating the rate and the insurance expense of the insurance product according to the index assessment score and the total risk assessment score; the equipment loss assessment module is used for forming a loss assessment result by determining accident reasons, loss degrees and maintenance and replacement requirements related to lost equipment in the accident; and the maintenance accounting module determines the maintenance cost according to the service time, the service unit price and the spare part cost of the IT operation and maintenance service to be accounting. According to the invention, multi-dimensional factors such as personnel and technical cost, operation risk and the like are comprehensively considered, and a scientific and fair underwriting risk determination mechanism, an underwriting pricing mechanism and a claim factor pricing mechanism are provided for enterprises.

Description

Technical Field

[0001] The present invention relates to the field of computing model technology, and in particular to an IT operation and maintenance insurance underwriting and claims assessment system. Background Art

[0002] IT operation and maintenance services refer to the comprehensive services provided to the computer room infrastructure, physical resources, virtual resources, platform resources, applications and data of the information system, as well as to meet the needs of users in the process of using the information system, based on the service requirements put forward by the demander, using information technology means and methods.

[0003] Currently, for enterprise IT operation and maintenance insurance, it is impossible to accurately quantify the risk of facility failure and the quality risk of manual operation and maintenance services. There are problems such as opaque pricing, complex cost assessment, and inaccurate risk management.

[0004] Therefore, how to conduct a simple and effective risk assessment of IT operations and maintenance for insurance underwriting and claims assessment is a technical problem that needs to be solved. Summary of the invention

[0005] To this end, the present invention provides an IT operation and maintenance insurance underwriting and claims assessment system, which realizes the quantitative assessment of the enterprise IT operation and maintenance risks through a model based on the fuzzy comprehensive evaluation method, and comprehensively considers multi-dimensional factors such as personnel and technical costs, equipment life cycle management, and operational risks to provide enterprises with a scientific and fair risk determination mechanism, underwriting pricing mechanism, and claims factor pricing mechanism, thereby improving the transparency and quality of IT operation and maintenance insurance, and providing precise support for the enterprise's IT operation and maintenance cost control and risk management, helping enterprises to achieve intelligent upgrades and efficient operations in the field of maintenance services.

[0006] To achieve the above objectives, the present invention proposes an IT operation and maintenance insurance underwriting and claims assessment system, comprising:

[0007] The risk assessment model is a model based on the fuzzy comprehensive evaluation method, which is used to determine the indicator assessment score of each evaluation dimension indicator and the total risk assessment score according to the membership matrix reflecting the score of the quotation factor to the evaluation dimension indicator, the quotation weight matrix of the quotation factor, and the indicator weight matrix of the evaluation dimension indicator, wherein the quotation factor is subordinate to the evaluation dimension indicator, and the evaluation dimension indicator includes the project equipment structure characteristics, IT operation and maintenance system architecture stability, IT computer room environment reliability and IT operation and maintenance management assurance;

[0008] An optimization feedback module, used to adjust the membership matrix, the quotation weight matrix and the indicator weight matrix according to the customer's historical operation and maintenance data;

[0009] An insurance module, used to calculate the rate of the insurance product according to the indicator evaluation score and the total risk evaluation score, and calculate the insurance fee according to the rate;

[0010] Equipment damage assessment module, which is used to form damage assessment results by determining the cause of the accident, the extent of the loss, and the repair and replacement requirements of the equipment lost in the accident;

[0011] The maintenance accounting module is used to determine the maintenance cost based on the service hours, service unit price and spare parts cost of the IT operation and maintenance service to be accounted for;

[0012] The dynamic adjustment module is used to adjust the equipment damage assessment module and the maintenance accounting module through service requirements and multi-dimensional dynamic optimization strategies to determine the final maintenance plan and its amount.

[0013] Furthermore, the quotation factors are subordinate to the evaluation dimension indicators, and the quotation factors include: the concentration, homogeneity and scale of the equipment structure characteristics of the project, the IT equipment system architecture, equipment failure rate and equipment configuration subordinate to the stability of the IT operation and maintenance system architecture, the design specification achievement, environmental temperature and humidity guarantee and power supply stability subordinate to the reliability of the IT computer room environment, and the operation and maintenance region and operation and maintenance system subordinate to the IT operation and maintenance management guarantee;

[0014] The historical operation and maintenance data includes equipment information, data center system information, data center detection data and customer operation and maintenance information.

[0015] Furthermore, the membership matrix generation module is used to score the device fault type according to the processing time and priority of the fault in the device log file to generate the membership matrix.

[0016] Further, the customer operation and maintenance information includes the number of IT infrastructure operation and maintenance, and the optimization feedback module also includes an IT infrastructure operation and maintenance identification unit and an indicator weight adjustment unit;

[0017] The IT infrastructure operation and maintenance identification unit is used to identify the number of operation and maintenance services in the device log file through a text recognition model;

[0018] The IT infrastructure indicator weight adjustment unit is used to calculate and adjust the indicator weight matrix of the stability of the IT operation and maintenance system architecture and the indicator weight matrix of the IT operation and maintenance management assurance according to the number of operation and maintenance services.

[0019] Furthermore, the customer operation and maintenance information also includes the number of software operation and maintenance times, and the optimization feedback module also includes a software operation and maintenance identification unit and a software indicator weight adjustment unit;

[0020] The software operation and maintenance identification unit is used to identify the number of software operation and maintenance services in the application service log data through a machine learning model;

[0021] The software indicator weight adjustment unit is used to calculate and adjust the indicator weight matrix of the stability of the IT operation and maintenance system architecture and the indicator weight matrix of the IT operation and maintenance management assurance according to the number of software operation and maintenance services.

[0022] Furthermore, a weight matrix generation module is used to generate the quotation weight matrix and the indicator weight matrix according to the ratio of the IT infrastructure operation and maintenance times and the software operation and maintenance times.

[0023] Furthermore, the equipment damage assessment module is used to identify the cause of the accident, the degree of loss and the repair and replacement requirements in the equipment log file through the text recognition model;

[0024] The service accounting module is used to identify the service type of the IT operation and maintenance service to be accounted for in the device log file through the text recognition model, and determine the service hours and service unit price according to the service type.

[0025] Furthermore, the risk assessment model is used to perform consistency check on the quotation weight matrix and the indicator weight matrix. If the consistency check of the quotation weight matrix fails and / or the consistency check of the indicator weight matrix fails, the optimization feedback module is used to regenerate the quotation weight matrix, the indicator weight matrix and the factor values ​​of the membership matrix according to the customer's historical operation and maintenance data.

[0026] Furthermore, the risk assessment model, whose target layer is the total risk assessment score, the criterion layer is the assessment dimension indicators, and the solution layer is the quotation factors, is used to determine the indicator evaluation score through the membership matrix and the quotation weight matrix, and to determine the total risk assessment score through the indicator evaluation score and the indicator weight matrix.

[0027] Furthermore, the insurance module is used to calculate the total risk score based on the indicator evaluation score of the project equipment structure characteristics, the indicator evaluation score of the IT operation and maintenance system architecture stability, the indicator evaluation score of the IT computer room environment reliability, and the indicator evaluation score of IT operation and maintenance management assurance, and calculates the rate by weighted calculation based on the insurance amount of the insurance product and the total risk score.

[0028] In the above solution, the source of the amount in the underwriting and claims settlement process is made clear, ensuring the fairness and efficiency of the underwriting and claims settlement process.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. Through the model based on the fuzzy comprehensive evaluation method, a quantitative assessment of the enterprise's IT operation and maintenance risks is achieved, and comprehensive consideration is given to multi-dimensional factors such as personnel and technical costs, equipment life cycle management, and operational risks to provide enterprises with a scientific and fair risk determination mechanism, underwriting pricing mechanism, and claim factor pricing mechanism, thereby improving the transparency and quality of IT operation and maintenance insurance, and providing precise support for the enterprise's IT operation and maintenance cost control and risk management, helping enterprises to achieve intelligent upgrades and efficient operations in the field of maintenance services.

[0031] 2. It can automatically generate and update the data for inputting risk assessment models based on the customer's device log files and application service log data, update the underwriting risks, and ensure accurate risk assessment and cost control capabilities in different insurance products.

[0032] 3. The source of the amount in the underwriting and claims settlement process is made clear, ensuring the fairness and efficiency of the underwriting and claims settlement process. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a structural schematic diagram of an IT operation and maintenance insurance underwriting and claims assessment system according to an embodiment of the present invention;

[0034] Figure 2 It is a schematic diagram of the underwriting process of the IT operation and maintenance insurance underwriting and claims assessment system according to an embodiment of the present invention;

[0035] Figure 3 A schematic diagram of a claims settlement process of an IT operation and maintenance insurance underwriting and claims settlement assessment system according to an embodiment of the present invention;

[0036] Figure 4 It is a schematic diagram of the operation and maintenance event management process of the IT operation and maintenance insurance underwriting and claims assessment system according to an embodiment of the present invention;

[0037] Figure 5 This is a schematic diagram of the structure of an operation and maintenance management system of an IT operation and maintenance insurance underwriting and claims assessment system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0039] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0040] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0041] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0042] like Figures 1 to 5 As shown, the present invention provides an IT operation and maintenance insurance underwriting and claims assessment system, which realizes the quantitative assessment of enterprise IT operation and maintenance risks through a model based on the fuzzy comprehensive evaluation method, and comprehensively considers multi-dimensional factors such as personnel and technical costs, equipment life cycle management, and operational risks to provide enterprises with a scientific and fair risk determination mechanism, underwriting pricing mechanism, and claims factor pricing mechanism, thereby improving the transparency and quality of IT operation and maintenance insurance, and providing precise support for the enterprise's IT operation and maintenance cost control and risk management, helping enterprises to achieve intelligent upgrades and efficient operations in the field of maintenance services.

[0043] in, Figure 1 It is a structural schematic diagram of an IT operation and maintenance insurance underwriting and claims assessment system according to an embodiment of the present invention; Figure 2 It is a schematic diagram of the underwriting process of the IT operation and maintenance insurance underwriting and claims assessment system according to an embodiment of the present invention; Figure 3 A schematic diagram of a claims settlement process of an IT operation and maintenance insurance underwriting and claims settlement assessment system according to an embodiment of the present invention; Figure 4 It is a schematic diagram of the operation and maintenance event management process of the IT operation and maintenance insurance underwriting and claims assessment system according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an operation and maintenance management system of an IT operation and maintenance insurance underwriting and claims assessment system according to an embodiment of the present invention.

[0044] like Figures 1 to 5As shown, this embodiment proposes an IT operation and maintenance insurance underwriting and claims assessment system, including: a risk assessment model, which is a model based on a fuzzy comprehensive evaluation method, and is used to determine the indicator assessment score of each evaluation dimension indicator and the total risk assessment score according to a membership matrix reflecting the score of the quotation factor to the evaluation dimension indicator, a quotation weight matrix of the quotation factor, and an indicator weight matrix of the evaluation dimension indicator, wherein the quotation factor is subordinate to the evaluation dimension indicator, and the evaluation dimension indicator includes project equipment structure characteristics, IT operation and maintenance system architecture stability, IT computer room environment reliability, and IT operation and maintenance management assurance;

[0045] An optimization feedback module, used to adjust the membership matrix, the quotation weight matrix and the indicator weight matrix according to the customer's historical operation and maintenance data;

[0046] An insurance module, used to calculate the rate of the insurance product according to the indicator evaluation score and the total risk evaluation score, and calculate the insurance fee according to the rate;

[0047] Equipment damage assessment module, which is used to form damage assessment results by determining the cause of the accident, the extent of the loss, and the repair and replacement requirements of the equipment lost in the accident;

[0048] The maintenance accounting module is used to determine the maintenance cost based on the service hours, service unit price and spare parts cost of the IT operation and maintenance service to be accounted for;

[0049] The dynamic adjustment module is used to adjust the equipment damage assessment module and the maintenance accounting module through service requirements and multi-dimensional dynamic optimization strategies to determine the final maintenance plan and its amount.

[0050] It should be noted that the risk assessment model, optimization feedback module and insurance module described in this embodiment are applied to the underwriting stage, and the equipment damage assessment module, maintenance accounting module and dynamic adjustment module are applied to the claims settlement stage. The underwriting stage and the claims settlement stage are two different stages in the insurance business. Specifically, the underwriting stage is applied when the customer takes out insurance, that is, before the insurance contract is signed.

[0051] The insurance module is used to determine the corresponding estimated maintenance cost according to the equipment failure rate, and then comprehensively adjust the estimated maintenance cost according to the indicator evaluation score and the total risk assessment score to determine the rate of the insurance product, and then determine the value of the customer's equipment according to basic information such as the customer's equipment manufacturer, model, type, and purpose, and calculate the insurance premium of the insurance product based on the customer's equipment value and the rate.

[0052] The final output of the underwriting stage is: insurance advice and insurance premiums, and the insurance advice includes whether to accept the insurance.

[0053] The claim settlement phase is applied after an insurance accident occurs, that is, when the customer applies for a claim. The equipment damage assessment module is used to assess the equipment losses involved in the accident. The assessment content includes the cause of the accident, the extent of the loss, and the need for repair and replacement, and then forms the damage assessment result. The maintenance accounting module is used to calculate the labor costs and spare parts costs required to repair or replace equipment. The spare parts costs are determined by materials and related costs to ensure the comprehensiveness and accuracy of the service cost assessment. The above modules in the claim settlement phase work together to comprehensively determine the claim amount by assessing equipment losses and maintenance costs.

[0054] It should be noted that the input data of the service requirements and multi-dimensional dynamic optimization strategies of the dynamic adjustment module in the claims stage include: the equipment damage assessment results of the equipment damage assessment module, the service accounting data of the maintenance accounting module (the service accounting data includes service hours, service unit price and spare parts cost) and other relevant factors, such as service mode, service location, service timeliness, etc. The strategy dynamically adjusts the above input data based on the historical claims data of similar equipment, conducts service risk assessment based on factors such as the importance of the equipment and the difficulty of the service content, and revises the amount of the final maintenance plan based on the contract agreement and the insurance terms and conditions, and outputs it as a compensation amount recommendation. The strategy has self-learning and continuous optimization functions: the strategy has an embedded learning mechanism, and continuously optimizes and adjusts the basic data and accounting strategies through IT operation and maintenance service data and IT operation and maintenance insurance compensation data. The introduction of the service requirements and multi-dimensional dynamic optimization strategies has improved the flexibility and accuracy of the calculation of the compensation amount, which not only reduces the operating risk of the insurance company, but also improves the customer's claims experience, providing strong dynamic optimization support for the claims process.

[0055] The final output of the claims verification stage is: whether it falls within the insurance liability and the insurance claim amount, and whether it falls within the insurance liability includes the decision on whether to pay.

[0056] It should be noted that the insured equipment is the equipment / IT operation and maintenance equipment in the effective insurance product. The maintenance cost is the product of the IT service hours and the service unit price.

[0057] Specifically, the insured equipment information and IT operation and maintenance service information from the supplier's website or other public websites and the operation and maintenance service provider's website are automatically captured through crawler programs, third-party data collection tools or API interface calls, and data cleaning and processing are performed to extract valuable market price information and service hour information, remove duplicate data, process missing values, convert data formats, generate the market price and service hours, and thus achieve visualization and fairness of the source of the claim amount.

[0058] Furthermore, the quotation factors are subordinate to the evaluation dimension indicators, and the quotation factors include: the concentration, homogeneity and scale of the equipment structure characteristics of the project, the IT equipment system architecture, equipment failure rate and equipment configuration subordinate to the stability of the IT operation and maintenance system architecture, the design specification achievement, environmental temperature and humidity guarantee and power supply stability subordinate to the reliability of the IT computer room environment, and the operation and maintenance region and operation and maintenance system subordinate to the IT operation and maintenance management guarantee;

[0059] The historical operation and maintenance data includes equipment information, data center system information, data center detection data and customer operation and maintenance information.

[0060] Specifically, the evaluation set V={V1, V2, V3} of the fuzzy comprehensive evaluation method is defined, and its risk levels are low, medium, and high, respectively, and their scoring values ​​are 20, 60, and 100, respectively.

[0061] The data center detection data includes the ambient temperature and humidity of the IT room and the voltage value of the power supply. The corresponding number of times that the ambient temperature and humidity do not reach the appropriate value of the room for many times in a day is used as the ambient temperature and humidity guarantee status, and the value of the membership matrix of the ambient temperature and humidity guarantee status is generated. For example, the number of times that the ambient temperature and humidity do not reach the appropriate value of the room for less than 1 minute at the low risk level V1 is 4 times, the number of times that the ambient temperature and humidity do not reach the appropriate value of the room for more than 1 minute at the medium risk level V2 is 3 times minus 3 times, and the number of times that the ambient temperature and humidity do not reach the appropriate value of the room for more than 1 minute at the high risk level V3 is 1 time. The number of times the risk level is more than 5 minutes minus 3 is 3, and the membership matrix of the ambient temperature and humidity guarantee situation generated after normalizing 4, 3 and 3 is |0.40, 0.34, 0.33|, where 3 is subtracted to ensure that when the number of times the risk levels are low, medium and high is 0, 2, 3 respectively, the elements corresponding to the high risk will not be greater than when the number of times is 5, 1, 2 respectively, so that the membership matrix will not make the medium and high risks abnormally large due to the total number of times reaching the risk levels V1, V2 and V3 in a day being too small.

[0062] Similarly, the number of times that voltage instability reaches the time gradient in a day is used as the power supply stability to generate the value of the membership matrix of power supply stability. For example, the time gradient corresponding to the low risk level V1 is less than 30 seconds, the time gradient corresponding to the medium risk level V2 is more than 30 seconds and less than 2 minutes, and the time gradient corresponding to the high risk level V3 is more than 2 minutes. The number of times of the medium risk level V2 and the high risk level V3 is reduced by 10 times to ensure that the membership matrix will not make the medium and high risks abnormally large due to the total number of times reaching the risk levels V1, V2 and V3 in a day is too small. The voltage instability refers to the voltage value not being within the range of +10% to -15% of the system nominal voltage, and the system nominal voltage is 6kV, 10kV, 220V, 380V, etc.

[0063] It should be noted that the gradient values ​​and other values ​​corresponding to the above-mentioned risk levels are only illustrative examples. During the actual implementation, technical personnel in this field can make adaptive adjustments based on actual conditions, such as the voltage stability requirements, temperature and humidity requirements of the data center, and the sampling frequency of the sensor.

[0064] Specifically, for the definition and explanation of each quotation factor described in this embodiment, please refer to the following table:

[0065]

[0066] Specifically, for the definitions and explanations of the various evaluation dimension indicators described in this embodiment, please refer to the following table:

[0067]

[0068]

[0069] It is understandable that, based on the historical operation and maintenance data, it is easy to obtain the scores of each quotation factor for the evaluation dimension indicators under the evaluation set V = {V1, V2, V3}, for example, the score of the high risk level V3 is higher when the concentration is at a higher corresponding concentration gradient. For example, the membership matrix of the device configuration can be generated based on the number of devices at the corresponding device level.

[0070] Furthermore, the membership matrix generation module is used to score the device fault type according to the processing time and priority of the fault in the device log file to generate the membership matrix.

[0071] Specifically, the device log files and application service log data are stored in the customer's management software / platform, such as Splunk, Graylog, Jiandao Cloud, etc. The management software / platform has a data interface and can retrieve the device log files and application service log data.

[0072] Specifically, the data in the device log files and application service log data are collected through the Filebeat collector, a non-relational database of the collected data is generated through Elasticsearch, an indicator weight matrix, affiliation matrix and a maintenance amount database are generated through the non-relational database, the maintenance amount database stores the data used to determine the maintenance cost, and the non-relational database is displayed and queried and analyzed through the Kibana platform, all of which are implemented based on the ELKB architecture. Elasticsearch cluster, and configure the cluster name, node name, master node, data node and port number and other information on each node. Cluster nodes access and share data with each other through unicast or multicast. When the nodes follow the same rules, the information obtained is equal. The data retrieval efficiency and storage efficiency are improved by deploying the Elasticsearch cluster mode.

[0073] Specifically, the processing time can reflect the impact of the operation and maintenance event, and the priority can reflect the importance of the operation and maintenance event, which can be retrieved from the customer's management software / platform. The membership matrix is ​​generated according to the ratio of the time corresponding to the priority level. For example, the high priority processing time minus 1 hour (1 hour can be adaptively adjusted according to actual conditions) is 3 hours, the medium priority processing time is 1 hour, and the low priority processing time is 1 hour. Then the normalized membership matrix of the equipment failure rate is |0.7, 0.2, 0.1|.

[0074] Specifically, Figure 4 The period between the start time and the end time in is the processing duration.

[0075] Some of the membership matrices are shown in the following table:

[0076]

[0077] It should be noted that the above table is only used for example illustration. In actual applications, the evaluation set can also be V = {V1, V2, V3, V4, V5}, and the risk levels are low, general, medium, high, and very high, that is, the risk level is preferably 5, which makes the score more accurate.

[0078] Further, the customer operation and maintenance information includes the number of IT infrastructure operation and maintenance, and the optimization feedback module also includes an IT infrastructure operation and maintenance identification unit and an indicator weight adjustment unit;

[0079] The IT infrastructure operation and maintenance identification unit is used to identify the number of operation and maintenance services in the device log file through a text recognition model;

[0080] The IT infrastructure indicator weight adjustment unit is used to calculate and adjust the indicator weight matrix of the project equipment structure characteristics, the indicator weight matrix of the IT operation and maintenance system architecture stability, and the indicator weight matrix of the IT operation and maintenance management assurance according to the number of operation and maintenance services.

[0081] The text recognition model is a classification function written by the KMeans algorithm in the sklearn library, which can identify keywords related to facility operation and maintenance in the text.

[0082] Furthermore, the customer operation and maintenance information also includes the number of software operation and maintenance times, and the optimization feedback module also includes a software operation and maintenance identification unit and a software indicator weight adjustment unit; the software operation and maintenance identification unit is used to identify the number of software operation and maintenance services in the application service log data through a machine learning model; the software indicator weight adjustment unit is used to calculate and adjust the indicator weight matrix of the project equipment structure characteristics, the indicator weight matrix of the IT operation and maintenance system architecture stability, and the indicator weight matrix of the IT operation and maintenance management assurance according to the number of software operation and maintenance services.

[0083] Specifically, the event type, timestamp, and error code in the log data can be extracted as features for training a machine learning algorithm based on a support vector machine (SVM). The machine learning algorithm can then analyze the imbalance and sparsity of the transaction volume data of the application service log data, find the correlation between the transaction volume and the transaction volume upper limit of the software platform, and ultimately evaluate the transaction volume upper limit that the current system can withstand, and use the number of times the transaction volume upper limit is exceeded as the number of software operation and maintenance services.

[0084] Furthermore, a weight matrix generation module is used to generate the quotation weight matrix and the indicator weight matrix according to the ratio of the classified IT infrastructure operation and maintenance times and the software operation and maintenance times.

[0085] Specifically, the service plan of the insurance product has a corresponding initial quotation weight matrix and an initial indicator weight matrix. For example, if the insurance product is biased towards facilities and equipment, the values ​​of the structural characteristics of the project equipment and the stability of the IT operation and maintenance system architecture in the initial indicator weight matrix are both at most 0.4, and the values ​​of the other two elements are 0.1. The number of facility operation and maintenance times and the number of software operation and maintenance times are classified into various evaluation dimension indicators and various quotation factors to obtain the ratios of various evaluation dimension indicators and various quotation factor ratios. If the difference between the ratios of various evaluation dimension indicators and the ratios of various quotation factors and the elements in the initial quotation weight matrix and the initial indicator weight matrix, that is, the sum of the absolute values ​​of the differences of the corresponding elements, exceeds the difference threshold, the initial quotation weight matrix and the initial indicator weight matrix are adjusted to the ratios to generate the quotation weight matrix and the indicator weight matrix.

[0086] If the number of facility operation and maintenance in the current month exceeds the number of facility settings, the number of facility operation and maintenance minus the number of facility settings multiplied by the proportional coefficient 0.3, rounded to two decimal places, and added to the weights of the structural characteristics of the project equipment and the stability of the IT operation and maintenance system architecture in the indicator weight matrix, and finally the indicator weight matrix is ​​renormalized.

[0087] Similarly, if the number of software operation and maintenance services in the current month exceeds the number of software settings, the number of facility operation and maintenance services minus the number of facility settings multiplied by the proportional coefficient 0.3, rounded to two decimal places, and added to the weights of the IT operation and maintenance system architecture stability and IT operation and maintenance management assurance in the indicator weight matrix, and finally the indicator weight matrix is ​​renormalized.

[0088] Furthermore, the risk assessment model is used to perform consistency check on the quotation weight matrix and the indicator weight matrix. If the consistency check of the quotation weight matrix fails and / or the consistency check of the indicator weight matrix fails, the optimization feedback module is used to regenerate the quotation weight matrix, the indicator weight matrix and the factor values ​​of the membership matrix according to the historical operation and maintenance data of the industry / customer.

[0089] Specifically, the consistency test is to calculate the maximum eigenvalue root, consistency index CI and test coefficient CR of the quotation weight matrix and the indicator weight matrix. If the test coefficient CR is less than 0.1, the quotation weight matrix and the indicator weight matrix pass the consistency test, otherwise they fail.

[0090] Specifically, after determining that the quotation weight matrix and the indicator weight matrix have passed the consistency test, the risk assessment model cross-multiplies the membership matrix and the quotation weight matrix to obtain an evaluation set of indicators of each evaluation dimension, such as an evaluation set |0.25, 0.51, 0.23|, and cross-multiplies the evaluation set of evaluation dimension indicators by the transpose of the score matrix of the evaluation set |20, 60, 100| T Get an evaluation dimension indicator risk score of 49. Combine all evaluation sets to generate an evaluation dimension indicator evaluation matrix, multiply the indicator weights by the evaluation dimension indicator evaluation matrix to get the evaluation result vector, for example, |0.295, 0.311, 0.174|, cross-multiply the evaluation result vector by the transpose of the score matrix of the evaluation set |20, 60, 100| T The total risk score is 36.

[0091] In the above solution, data for inputting risk assessment models is automatically generated and updated based on the customer’s device log files and application service log data, achieving real-time updates of underwriting risks and ensuring accurate risk assessment and cost control capabilities in different insurance products.

[0092] The risk assessment model, whose target layer is the total risk assessment score, the criterion layer is the assessment dimension indicators, and the solution layer is the quotation factors, is used to determine the indicator assessment score through the membership matrix and the quotation weight matrix, and to determine the total risk assessment score through the indicator assessment score and the indicator weight matrix.

[0093] Furthermore, the equipment damage assessment module is used to identify the cause of the accident, the degree of loss and the repair and replacement requirements in the equipment log file through the text recognition model;

[0094] The service accounting module is used to identify the service type of the IT operation and maintenance service to be accounted for in the device log file through the text recognition model, and determine the service hours and service unit price according to the service type.

[0095] The insured equipment information includes the number of insured equipment corresponding to the degree of loss and the cause of the accident of the insured equipment. The cause of the accident of the insured equipment corresponds to the equipment damage coefficient. For example, the equipment damage coefficient corresponding to the burnout of the insured equipment mainboard is 0.9. The degree of equipment loss is obtained by multiplying the number of insured equipment by the equipment damage coefficient and the equipment damage cost.

[0096] The operation and maintenance service type includes the operation and maintenance type and the operation and maintenance operation mode. The operation and maintenance type corresponds to the service unit price. For example, the service unit price for operating system operation and maintenance is 25 yuan / hour, the service unit price for database operation and maintenance is 20 yuan / hour, the service unit price for application software operation and maintenance is 28 yuan / hour, and the service unit price for IT facility operation and maintenance is 20 yuan / hour to 40 yuan / hour. The operation and maintenance operation mode corresponds to the operation and maintenance type and corresponds to the service working hours. For example, the maintenance of the operating system is 3 hours.

[0097] Furthermore, the insurance module is used to calculate the total risk score based on the indicator evaluation score of the project equipment structure characteristics, the indicator evaluation score of the IT operation and maintenance system architecture stability, the indicator evaluation score of the IT computer room environment reliability, and the indicator evaluation score of IT operation and maintenance management assurance, and calculates the rate by weighted calculation based on the insurance amount of the insurance product and the total risk score.

[0098] The insurance cost includes equipment premium and service premium; the insurance rate generation module is used to weightedly calculate the initial equipment premium according to the equipment insurance amount in the service plan of the insurance product, the index evaluation score of the structural characteristics of the project equipment, the index evaluation score of the stability of the IT operation and maintenance system architecture, and the index evaluation score of the reliability of the IT computer room environment; the insurance rate generation module is used to calculate the initial service premium according to the service insurance amount in the service plan of the insurance product and the index evaluation score of the IT operation and maintenance management assurance; the insurance rate generation module is used to adjust the initial equipment premium according to the total risk assessment score to determine the equipment premium, and adjust the initial service premium according to the total risk assessment score to determine the service premium.

[0099] Specifically, the equipment insured amount, the index evaluation score of the structural characteristics of the project equipment, the index evaluation score of the IT operation and maintenance system architecture stability, and the index evaluation score of the IT computer room environment reliability are correspondingly multiplied to obtain the initial equipment premium. The insurance premium rate generation module is also used to multiply the service insured amount in the service plan of the insurance product and the index evaluation score of the IT operation and maintenance management assurance to obtain the initial service premium, and determine the equipment premium and the service premium according to the value of the total risk assessment score exceeding the threshold of 80 multiplied by 0.01 multiplied by the initial equipment premium and the initial service premium.

[0100] In the above solution, the source of the amount in the underwriting and claims settlement process is made clear, ensuring the fairness and efficiency of the underwriting and claims settlement process.

[0101] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0102] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An IT operation and maintenance insurance underwriting and claims assessment system, characterized in that: include: The risk assessment model is a model based on the fuzzy comprehensive evaluation method, which is used to determine the indicator assessment score of each evaluation dimension indicator and the total risk assessment score according to the membership matrix reflecting the score of the quotation factor to the evaluation dimension indicator, the quotation weight matrix of the quotation factor, and the indicator weight matrix of the evaluation dimension indicator, wherein the quotation factor is subordinate to the evaluation dimension indicator, and the evaluation dimension indicator includes the project equipment structure characteristics, IT operation and maintenance system architecture stability, IT computer room environment reliability and IT operation and maintenance management assurance; An optimization feedback module, used to adjust the membership matrix, the quotation weight matrix and the indicator weight matrix according to the customer's historical operation and maintenance data; An insurance module, used to calculate the rate of the insurance product according to the indicator evaluation score and the total risk evaluation score, and calculate the insurance fee according to the rate; Equipment damage assessment module, which is used to form damage assessment results by determining the cause of the accident, the extent of the loss, and the repair and replacement requirements of the equipment lost in the accident; The maintenance accounting module is used to determine the maintenance cost based on the service hours, service unit price and spare parts cost of the IT operation and maintenance service to be accounted for; The dynamic adjustment module is used to adjust the equipment damage assessment module and the maintenance accounting module through service requirements and multi-dimensional dynamic optimization strategies to determine the final maintenance plan and its amount.

2. The IT operation and maintenance insurance underwriting and claims assessment system according to claim 1 is characterized in that: The quotation factors are subordinate to the evaluation dimension indicators, and the quotation factors include: the concentration, homogeneity and scale of the equipment structure characteristics of the project, the IT equipment system architecture, equipment failure rate and equipment configuration subordinate to the stability of the IT operation and maintenance system architecture, the design specification achievement, environmental temperature and humidity guarantee and power supply stability subordinate to the reliability of the IT computer room environment, and the operation and maintenance region and operation and maintenance system subordinate to the IT operation and maintenance management guarantee; The historical operation and maintenance data includes equipment information, data center system information, data center detection data and customer operation and maintenance information.

3. The IT operation and maintenance insurance underwriting and claims assessment system according to claim 2 is characterized in that: Also includes: The membership matrix generation module is used to score the equipment fault type according to the processing time and priority of the fault in the equipment log file to generate the membership matrix.

4. The IT operation and maintenance insurance underwriting and claims assessment system according to claim 3 is characterized in that: The customer operation and maintenance information includes the number of IT infrastructure operation and maintenance times, and the optimization feedback module also includes an IT infrastructure operation and maintenance identification unit and an indicator weight adjustment unit; The IT infrastructure operation and maintenance identification unit is used to identify the number of operation and maintenance services in the device log file through a text recognition model; The IT infrastructure indicator weight adjustment unit is used to calculate and adjust the indicator weight matrix of the project equipment structure characteristics, the indicator weight matrix of the IT operation and maintenance system architecture stability, and the indicator weight matrix of the IT operation and maintenance management assurance according to the number of operation and maintenance services.

5. The IT operation and maintenance insurance underwriting and claims assessment system according to claim 4, characterized in that: The customer operation and maintenance information also includes the number of software operation and maintenance times, and the optimization feedback module also includes a software operation and maintenance identification unit and a software indicator weight adjustment unit; The software operation and maintenance identification unit is used to identify the number of software operation and maintenance services in the application service log data through a machine learning model; The software indicator weight adjustment unit is used to calculate and adjust the indicator weight matrix of the project equipment structure characteristics, the indicator weight matrix of the IT operation and maintenance system architecture stability, and the indicator weight matrix of the IT operation and maintenance management assurance according to the number of software operation and maintenance services.

6. The IT operation and maintenance insurance underwriting and claims assessment system according to claim 5, characterized in that: Also includes: The weight matrix generation module is used to generate the quotation weight matrix and the indicator weight matrix according to the classified ratio of the IT infrastructure operation and maintenance times and the software operation and maintenance times.

7. The IT operation and maintenance insurance underwriting and claims assessment system according to claim 5, characterized in that: The equipment damage assessment module is used to identify the cause of the accident, the degree of loss and the repair and replacement requirements in the equipment log file through the text recognition model; The service accounting module is used to identify the service type of the IT operation and maintenance service to be accounted for in the device log file through the text recognition model, and determine the service hours and service unit price according to the service type.

8. The IT operation and maintenance insurance underwriting and claims assessment system according to any one of claims 1 to 7, characterized in that: The risk assessment model is used to perform consistency check on the quotation weight matrix and the indicator weight matrix. If the consistency check of the quotation weight matrix fails and / or the consistency check of the indicator weight matrix fails, the optimization feedback module is used to regenerate the quotation weight matrix, the indicator weight matrix and the factor values ​​of the membership matrix according to the customer's historical operation and maintenance data.

9. The IT operation and maintenance insurance underwriting and claims assessment system according to any one of claims 1 to 7, characterized in that: The risk assessment model, whose target layer is the total risk assessment score, the criterion layer is the assessment dimension indicators, and the solution layer is the quotation factors, is used to determine the indicator assessment score through the membership matrix and the quotation weight matrix, and to determine the total risk assessment score through the indicator assessment score and the indicator weight matrix.

10. The IT operation and maintenance insurance underwriting and claims assessment system according to any one of claims 1 to 7, characterized in that: The insurance module is used to calculate the total risk score based on the indicator evaluation score of the project equipment structure characteristics, the indicator evaluation score of the IT operation and maintenance system architecture stability, the indicator evaluation score of the IT computer room environment reliability, and the indicator evaluation score of the IT operation and maintenance management assurance, and calculate the rate by weighted calculation based on the insurance amount of the insurance product and the total risk score.

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