An insurance data transmission method based on big data adaptive fusion

Abstract

The present invention relates to the technical field of data processing, and discloses an insurance data transmission method based on big data adaptive fusion, including: structuring the original insurance data based on the data attributes of the original insurance data to obtain structured insurance data, locking the insurance task chain where the original insurance data is located, wherein the insurance task chain is composed of multiple task nodes, and the task nodes are divided into a task starting point, a task intermediate point, and a task ending point, generating structured task data of the structured insurance data based on the insurance task chain, triggering the insurance task chain where the original insurance data is located until task intermediate data and task ending data corresponding to the task intermediate point and the task ending point are respectively generated, optimizing the task intermediate data and the task ending data to obtain optimized intermediate data and optimized ending data, and filling the optimized intermediate data and the optimized ending data into the structured task data to obtain complete task data. The present invention can solve the problem of waste of storage resources caused by storing insurance data.

Description

Technical Field

[0001] The present invention relates to an insurance data transmission method based on big data adaptive fusion, belonging to the technical field of data processing. Background Art

[0002] With the development of big data technology, the insurance industry has accumulated a vast amount of data, including customer information, transaction records, claim data, etc. The adaptive fusion technology based on big data can integrate these scattered data resources, provide a more comprehensive data perspective, and thus achieve accurate and personalized customer services.

[0003] Although big data technology has brought great potential to the insurance industry, in practical applications, the insurance data transmission based on big data adaptive fusion still faces the problem of waste of storage resources. Because insurance data has diversity and complexity, currently, it mainly relies on distributed storage technology to store insurance data from different sources and types. Although distributed storage technology can solve the problem of storing a vast amount of insurance data, it does not optimize insurance data, resulting in redundant storage of a large number of duplicate insurance data and waste of storage resources.

[0004] That is, there is still a lack of a method that can effectively avoid waste of storage resources when storing insurance data. Summary of the Invention

[0005] The present invention provides an insurance data transmission method, device, and computer-readable storage medium based on big data adaptive fusion, and its main purpose is to solve the problem of waste of storage resources when storing insurance data.

[0006] To achieve the above object, an insurance data transmission method based on big data adaptive fusion provided by the present invention includes:

[0007] Receiving a transmission instruction of original insurance data, and connecting to an insurance data management system by using the transmission instruction, wherein the insurance data management system includes an insurance task confirmation unit, an insurance data fusion unit, and an insurance data storage unit;

[0008] Receiving original insurance data according to the transmission instruction, analyzing the data attributes of the original insurance data by using the insurance task confirmation unit, and structuring the original insurance data based on the data attributes to obtain structured insurance data;

[0009] Locking the insurance task chain where the original insurance data is located, wherein the insurance task chain is composed of multiple task nodes, and the task nodes are divided into a task start point, task intermediate points, and a task end point, and the number of task intermediate points is [0, n], where n is a positive integer greater than 0;

[0010] Generate structured task data for structured insurance data based on an insurance task chain. Among them, the structured task data includes structured insurance data, and the structured insurance data is marked as the task starting point;

[0011] Trigger the insurance task chain where the original insurance data is located using the insurance data management system until task intermediate data and task end data corresponding to the task intermediate point and the task end point are generated respectively;

[0012] Optimize the task intermediate data and the task end data based on the insurance data fusion unit to obtain optimized intermediate data and optimized end data;

[0013] Fill the optimized intermediate data and the optimized end data into the structured task data to obtain complete task data, and store the complete task data in the insurance data storage unit to complete the insurance data transmission for big data adaptability fusion.

[0014] Optionally, receiving the original insurance data according to the transmission instruction, and analyzing the data attributes of the original insurance data using the insurance task confirmation unit, including:

[0015] Parse the transmission instruction to obtain the IP address of the originator of the original insurance data, send the IP address of the originator to the insurance data management system, and then use the insurance data management system to connect to the originator;

[0016] When the insurance data management system successfully connects to the originator, notify the originator to transmit the original insurance data to the insurance data management system;

[0017] When the insurance data management system has received the original insurance data, start the insurance task confirmation unit to analyze the data attributes of the original insurance data. Among them, the data attributes include the insured person data type, the insured policy data type, the risk assessment data type, and the claim record data type.

[0018] Optionally, structuring the original insurance data based on the data attributes to obtain structured insurance data, including:

[0019] Split the original insurance data according to the data type to obtain split policy data, and the split policy data consists of one or all of the insured person data, the insured policy data, the risk assessment data, and the claim record data;

[0020] Generate structured empty data, where the row dimension of the structured empty data is the same as the number of data types of the split policy data; fill the split policy data into the structured empty data in sequence to obtain structured insurance data, and the form of the structured insurance data is:

[0021]

[0022] Among them, Mi denotes the structured insurance data corresponding to the original insurance data initiated by the i-th initiator, m j denotes the j-th split policy data included in the structured insurance data, and j ≤ 4, m1 denotes the insured person data, m2 denotes the insured policy data, m3 denotes the risk assessment data, and m4 denotes the claim record data.

[0023] Optionally, the structured task data for generating structured insurance data based on the insurance task chain includes:

[0024] Based on the insurance task chain where the original insurance data is located, construct an empty task chain matrix, where the form of the empty task chain matrix is:

[0025]

[0026] where denotes the empty task chain matrix of the original insurance data, S denotes the identification set of the insurance task chain, which consists of a task start point, task intermediate points, and a task end point, E denotes the data dependency relationship between task nodes, and M denotes the structured data vector for storing structured insurance data;

[0027] Store the structured insurance data into the structured data vector of the empty task chain matrix to generate structured task data G.

[0028] Optionally, the forms of the identification set S, data dependency relationship E, and structured data vector M of the insurance task chain of the structured task data G are:

[0029] S = {s1, s2, …, s i , …, s h}

[0030] E = {e 12 , e 13 , …, e ij , …, e hh-1}

[0031] M = [M i

[0032] where s i denotes a task node. When i = 1, s1 denotes the task start point. When i = h, s h denotes the task end point. When i ≠ 1 and i ≠ h, s i denotes a task intermediate point. e ij denotes the data dependency relationship between the i-th task node and the j-th task node, and M i denotes the structured insurance data corresponding to the original insurance data initiated by the i-th initiator, and M i ​It has a corresponding relationship with s1. When the structural insurance data is stored in the structural data vector, the structural insurance data M is marked with the task start point s1 i .

[0033] Optionally, the e ij The data dependency relationships between the represented nodes are divided into three types, namely: the data dependency relationships between the task start point and the task intermediate point, the task start point and the task end point, and the task intermediate point and the task end point. And when e ij = 1, it means that the i-th task node and the j-th task node have a data dependency relationship. When e ij = 0, it means that the i-th task node and the j-th task node do not have a data dependency relationship.

[0034] Optionally, triggering the insurance task chain where the original insurance data is located by using the insurance data management system until the task intermediate data and the task end data corresponding to the task intermediate point and the task end point are respectively generated, including:

[0035] Using the insurance data management system to generate a notification instruction that the original insurance data can run normally;

[0036] According to the insurance task chain where the original insurance data is located, each task node is run sequentially, and node task data corresponding to the task node is generated;

[0037] Using the insurance task confirmation unit to analyze the data attributes of the node task data corresponding to each task node;

[0038] Based on the data attributes of the node task data, the node task data is structured to obtain the structured node data, where the structured node data includes the task intermediate data and the task end data, and the task intermediate data corresponds to the task intermediate point, and the task end data corresponds to the task end point.

[0039] Optionally, optimizing the task intermediate data and the task end data based on the insurance data fusion unit to obtain the optimized intermediate data and the optimized end data, including:

[0040] Optimizing the structured node data of the i-th task node and the j-th task node in sequence according to the following logic, where the i-th task node and the j-th task node include the task intermediate point and the task end point:

[0041] Using the insurance data fusion unit to obtain the data dependency relationship e between the i-th task node and the j-th task node ij ;

[0042] If the data dependency relationship e ij = 0, give up optimizing the structured node data of the i-th task node and the j-th task node;

[0043] If the data dependency relationship e ij = 1, a data dependency storage space is opened in the insurance data storage unit, and the structure node data of the i-th task node is traversed to obtain duplicate data that duplicates the structure node data of the j-th task node, resulting in duplicate node data, where the structure node data includes task intermediate data and task end data;

[0044] The duplicate node data is stored in the data dependency storage space to generate a data dependency IP;

[0045] The duplicate node data of the structure node data of the i-th task node and the j-th task node is replaced with the data dependency IP until the replacement is completed to obtain optimized intermediate data and optimized end data.

[0046] Optionally, filling the optimized intermediate data and the optimized end data into the structured task data to obtain complete task data includes:

[0047] The optimized intermediate data and the optimized end data are filled into the structure data vector M in the structured task data. After filling, the expression form of the structure data vector M is:

[0048] M = [M i , M j , …, M l

[0049] where M j represents the optimized intermediate data generated by the j-th initiator corresponding to the second task node, M l represents the optimized end data generated by the l-th initiator corresponding to the task end, and the number of M i , M j , …, M l is the same as the number of task nodes included in the identifier set S of the insurance task chain, both being h;

[0050] When the optimized intermediate data and the optimized end data are filled into the structure data vector M in the structured task data, complete task data is obtained.

[0051] To achieve the above object, the present invention also provides an insurance data transmission system based on big data adaptive fusion, including:

[0052] ​An insurance data structure module, which is used to receive a transmission instruction of original insurance data, connect to an insurance data management system by using the transmission instruction, wherein the insurance data management system includes an insurance task confirmation unit, an insurance data fusion unit and an insurance data storage unit, receive the original insurance data according to the transmission instruction, analyze the data attributes of the original insurance data by using the insurance task confirmation unit, and structure the original insurance data based on the data attributes to obtain structured insurance data;

[0053] An insurance task chain confirmation module, which is used to lock out the insurance task chain where the original insurance data is located. The insurance task chain is composed of multiple task nodes, and the task nodes are divided into a task start point, task intermediate points and a task end point, and the number of task intermediate points is [0,n], where n is a positive integer greater than 0:

[0054] A data optimization module, which is used to generate structured task data of the structured insurance data based on the insurance task chain. The structured task data includes the structured insurance data, and the structured insurance data is marked as the task start point. Trigger the insurance task chain where the original insurance data is located by using the insurance data management system until task intermediate data and task end data corresponding to the task intermediate points and the task end point are generated respectively, and optimize the task intermediate data and the task end data based on the insurance data fusion unit to obtain optimized intermediate data and optimized end data;

[0055] A structured task data storage module, which is used to fill the optimized intermediate data and the optimized end data into the structured task data to obtain complete task data, and store the complete task data in the insurance data storage unit to complete the insurance data transmission of big data adaptive fusion.

[0056] To solve the above problems, the present invention also provides an electronic device, which includes:

[0057] At least one processor; and,

[0058] A memory communicatively connected to the at least one processor; wherein,

[0059] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the above-mentioned insurance data transmission method based on big data adaptive fusion.

[0060] To solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned insurance data transmission method based on big data adaptive fusion.

[0061] Compared with the problems described in the background art, the present invention first receives a transmission instruction of original insurance data, and uses the transmission instruction to connect to an insurance data management system. The insurance data management system includes an insurance task confirmation unit, an insurance data fusion unit, and an insurance data storage unit. According to the transmission instruction, the original insurance data is received, and the data attributes of the original insurance data are analyzed by the insurance task confirmation unit, and the original insurance data is structured based on the data attributes to obtain structured insurance data. The present invention analyzes the data attributes of the original insurance data by the insurance task confirmation unit and structures such data, thereby removing invalid or redundant information and only retaining the data of practical value to the insurance business, reducing unnecessary occupation of storage space; secondly, the insurance task chain where the original insurance data is located is locked. The insurance task chain consists of multiple task nodes, and the task nodes are divided into a task start point, a task intermediate point, and a task end point. Structured task data of the structured insurance data is generated based on the insurance task chain. The structured task data includes the structured insurance data, and the structured insurance data is marked as the task start point. By locking the insurance task chain where the original insurance data is located, the position of the data in the entire insurance process can be identified, thereby providing a prerequisite for generating structured task data subsequently and improving the generation speed of generating structured task data; then, the insurance task chain where the original insurance data is located is triggered by the insurance data management system until task intermediate data and task end data corresponding to the task intermediate point and the task end point are generated respectively. The task intermediate data and the task end data are optimized by the insurance data fusion unit to obtain optimized intermediate data and optimized end data. The optimized intermediate data and the optimized end data are filled into the structured task data to obtain complete task data, and the complete task data is stored in the insurance data storage unit to complete the insurance data transmission of big data adaptive fusion. Since all the data of the insurance task chain related to the original insurance data are structured, the regularity of the entire insurance data is ensured. In addition, for the structured task data generated based on the insurance task chain, the structured insurance data is marked as the task start point, which can also ensure that only the data directly related to a specific task will be stored and further processed, so the regularity of the data is further improved. Moreover, the task intermediate data and the task end data are optimized by the insurance data fusion unit, which can further refine and streamline the data, improve the data quality, reduce storage redundancy and low-value information, thereby facilitating subsequent data storage and avoiding waste of excessive storage resources. Therefore, the insurance data transmission method and system based on big data adaptive fusion proposed by the present invention mainly aim to solve the problem of waste of storage resources caused by storing insurance data. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic flowchart of an insurance data transmission method based on big data adaptive fusion provided by an embodiment of the present invention;

[0063] Figure 2 This is a functional module diagram of an insurance data transmission system based on big data adaptive fusion provided by an embodiment of the present invention;

[0064] Figure 3 This is a schematic structural diagram of an electronic device for implementing the insurance data transmission method based on big data adaptive fusion provided by an embodiment of the present invention.

[0065] The implementation, functional features, and advantages of the present invention will be further described in conjunction with embodiments and with reference to the accompanying drawings. Specific Embodiments

[0066] 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.

[0067] An embodiment of the present application provides an insurance data transmission method based on big data adaptive fusion. The execution subject of the insurance data transmission method based on big data adaptive fusion includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the insurance data transmission method based on big data adaptive fusion can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0068] Embodiment 1:

[0069] Refer to Figure 1 As shown, this is a flowchart of an insurance data transmission method based on big data adaptive fusion provided by an embodiment of the present invention. In this embodiment, the insurance data transmission method based on big data adaptive fusion includes:

[0070] S1. Receive a transmission instruction for original insurance data, and use the transmission instruction to connect to an insurance data management system, where the insurance data management system includes an insurance task confirmation unit, an insurance data fusion unit, and an insurance data storage unit.

[0071] It should be explained that the transmission instruction for original insurance data can be initiated by insured persons, insurance practitioners, etc. Exemplarily, Xiao Zhang wants to buy a personal insurance for himself, so he prepares original insurance data including personal information, the type of insurance purchased, etc., and initiates a transmission instruction for original insurance data.

[0072] Specifically, in the embodiments of the present invention, an insurance data management system is used to uniformly manage the original insurance data, and the insurance data management system includes an insurance task confirmation unit, an insurance data fusion unit, and an insurance data storage unit. It should be emphasized that the three units in the embodiments of the present invention have a strong logical relationship, and through the logical relationship of the three units, the fusion of some insurance data is realized, thereby avoiding the waste of storage resources caused by repeatedly storing redundant insurance data.

[0073] S2. Receive the original insurance data according to the transmission instruction, analyze the data attributes of the original insurance data by using the insurance task confirmation unit, and structure the original insurance data based on the data attributes to obtain structured insurance data.

[0074] Specifically, the steps of receiving the original insurance data according to the transmission instruction and analyzing the data attributes of the original insurance data by using the insurance task confirmation unit include:

[0075] Parse the transmission instruction to obtain the IP address of the originator of the original insurance data, send the IP address of the originator to the insurance data management system, and then use the insurance data management system to connect to the originator;

[0076] When the insurance data management system successfully connects to the originator, notify the originator to transmit the original insurance data to the insurance data management system;

[0077] After the insurance data management system receives the original insurance data, start the insurance task confirmation unit to analyze the data attributes of the original insurance data. Among them, the data attributes include the data types of insured persons, insured policies, risk assessment data, and claim settlement records.

[0078] It should be explained that a natural language analysis model is embedded in the insurance task confirmation unit. Among them, the natural language analysis model includes, but is not limited to, technologies such as BERT, word segmentation models, and large models. Through the natural language analysis model, the data types of each data in the original insurance data can be effectively distinguished. Exemplarily, the above-mentioned Xiao Zhang prepared original insurance data including personal information, purchased insurance types, etc., specifically including: personal information such as male, 25 years old, bachelor's degree, engaged in logistics management; a policy for purchasing personal accident insurance, and a complete insurance application form has been filled out. Therefore, through the above-mentioned insurance task confirmation unit, it can be analyzed that "personal information such as male, 25 years old, bachelor's degree, engaged in logistics management" belongs to the data type of insured persons, and having filled out a complete insurance application form belongs to the data type of insured policies. It can be seen that the data types of the original insurance data uploaded by Xiao Zhang are 2 types, namely the data type of insured persons and the data type of insured policies.

[0079] Furthermore, the steps of structuring the original insurance data based on the data attributes to obtain structured insurance data include:

[0080] Perform splitting on the original insurance data according to the data type to obtain split policy data, and the split policy data consists of one or all of the insured person data, insured policy data, risk assessment data, and claim record data;

[0081] Generate structure empty data, where the row dimension of the structure empty data is the same as the number of data types of the split policy data; sequentially fill the split policy data into the structure empty data to obtain structure insurance data, and the form of the structure insurance data is:

[0082]

[0083] where M i represents the structure insurance data corresponding to the original insurance data initiated by the i-th initiator, and m j represents the j-th split policy data included in the structure insurance data, and j ≤ 4, and m1 represents the insured person data, m2 represents the insured policy data, m3 represents the risk assessment data, and m4 represents the claim record data.

[0084] Exemplarily, since the original insurance data uploaded by the above-mentioned Zhang only has the insured person data type and the insured policy data type, the structure insurance data of Zhang only has m1 and m2.

[0085] S3. Lock the insurance task chain where the original insurance data is located, where the insurance task chain consists of multiple task nodes, and the task nodes are divided into a task start point, task intermediate points, and a task end point.

[0086] It can be understood that different original insurance data has different functions. Exemplarily, the above-mentioned Zhang hopes to purchase an accidental personal insurance, so he uploads the original insurance data related to the purchase of accidental personal insurance. Therefore, the insurance task chain involved in the management of this type of original insurance data may include: receiving the original insurance data and generating the corresponding structure insurance data → auditing the structure insurance data to obtain the audit process data → generating the audit result to obtain the audit result data.

[0087] Therefore, the starting point of the task is to receive the original insurance data, the ending point is to generate the review result and obtain the review result data, and the intermediate point is to review the original insurance data to obtain the review process data. Further, the review process data depends on the specific environment. For example, if the original insurance data of Zhang is used to construct the structured insurance data including m1 and m2, then the structured insurance data belongs to the relevant data at the starting point of the task. In addition, when Zhao reviews Zhang's structured insurance data, it is found that Zhang omitted some personal information, so Zhang is required to continue to upload the omitted personal information, and the continuously uploaded omitted personal information is the review process data. In addition, if the review is passed and Zhang is allowed to purchase accidental personal insurance, it is the review result data.

[0088] It should be emphasized that in the embodiments of the present invention, the number of intermediate points of the task is [0, n], where n is a positive integer greater than 0. The number of intermediate points of the task is related to the actual operation environment of the insurance business. The more complex the actual operation environment is, the more intermediate points of the corresponding task are.

[0089] S4. Generate the structured task data of the structured insurance data based on the insurance task chain, where the structured task data includes the structured insurance data, and the structured insurance data is marked as the starting point of the task.

[0090] Specifically, the generation of the structured task data of the structured insurance data based on the insurance task chain includes:

[0091] Based on the insurance task chain where the original insurance data is located, construct an empty task chain matrix, where the form of the empty task chain matrix is:

[0092]

[0093] Among them, represents the empty task chain matrix of the original insurance data, S represents the identification set of the insurance task chain, which consists of the starting point, intermediate points, and ending point of the task, E represents the data dependency relationship between task nodes, and M represents the structured data vector for storing the structured insurance data;

[0094] Store the structured insurance data into the structured data vector of the empty task chain matrix to generate the structured task data G.

[0095] Importantly, one of the important technological innovation points in the embodiments of the present invention is to generate the structured task data corresponding to the structured insurance data, and the structured task data can effectively represent the full process relationship of the structured insurance data from generation to end. Specifically, the forms of the identification set S, data dependency relationship E, and structured data vector M of the insurance task chain of the structured task data G are:

[0096] S = {s1, s2,..., s i ,..., sh}

[0097] E = {e 12 , e 13 , …, e ij , …, e hh-1}

[0098] M = [M i

[0099] Among them, s i represents a task node. When i = 1, s1 represents the task start point. When i = h, s h represents the task end point. When i ≠ 1 and i ≠ h, s i represents the task intermediate point. e ij represents the data dependency relationship between the i-th task node and the j-th task node. M i represents the structured insurance data corresponding to the original insurance data initiated by the i-th initiator. And M i has a corresponding relationship with s1. When the structured insurance data is stored in the structured data vector, the structured insurance data M is marked with the task start point s1 i .

[0100] It can be understood that the data dependency relationships between the nodes represented by e ij are divided into three types, namely: the data dependency relationships between the task start point and the task intermediate point, the task start point and the task end point, and the task intermediate point and the task end point. And when e ij = 1, it means that the i-th task node and the j-th task node have a data dependency relationship. When e ij = 0, it means that the i-th task node and the j-th task node do not have a data dependency relationship.

[0101] Exemplarily, Zhang's insurance task chain is: receiving the original insurance data and generating the corresponding structured insurance data → reviewing the structured insurance data to obtain the review process data → generating the review result to obtain the review result data. It can be seen that the identification set S of the insurance task chain only includes s1, s2, s3, and the data dependency relationships E are respectively e 12 , e 13 and e 23 . Importantly, when the i-th task node and the j-th task node have a data dependency relationship, it means that there is room for optimization in the insurance data generated by the i-th task node and the j-th task node, because the insurance data generated by the j-th task node may depend on the insurance data generated by the i-th task node, and thus there may be some overlap in the partial insurance data of these two task nodes.

[0102] ​S5. Use the insurance data management system to trigger the insurance task chain where the original insurance data is located until task intermediate data and task end data corresponding to the task intermediate point and the task end point are respectively generated.

[0103] It should be explained that when the structural task data G is generated, it indicates that the insurance data management system is ready to receive other insurance data related to the original insurance data at all times until the tasks of the entire insurance task chain where the original insurance data is located are completed. Therefore, in detail, the use of the insurance data management system to trigger the insurance task chain where the original insurance data is located until task intermediate data and task end data corresponding to the task intermediate point and the task end point are respectively generated includes:

[0104] Use the insurance data management system to generate a notification instruction for the normal operation of the original insurance data;

[0105] According to the insurance task chain where the original insurance data is located, sequentially run each task node and generate node task data corresponding to the task node;

[0106] Use the insurance task confirmation unit to analyze the data attributes of the node task data corresponding to each task node;

[0107] Based on the data attributes of the node task data, structure the node task data to obtain structured node data, where the structured node data includes task intermediate data and task end data, and the task intermediate data corresponds to the task intermediate point, and the task end data corresponds to the task end point.

[0108] Exemplarily, when the original insurance data is received and the corresponding structured insurance data is generated, the following two task nodes will continue to be completed: → Review the structured insurance data to obtain review process data → Generate a review result to obtain review result data. Further, when each task node is completed, data corresponding to the task node will be generated. If the task node is a task intermediate point, this data is called task intermediate data. If the task node is a task end point, this data is called task end data.

[0109] It should be emphasized that using the insurance task confirmation unit to analyze the data attributes of the node task data corresponding to each task node and further realizing the structuring of the node task data have the same implementation principle as step S2 above, and will not be elaborated in this embodiment of the present invention.

[0110] S6. Optimize the task intermediate data and task end data based on the insurance data fusion unit to obtain optimized intermediate data and optimized end data.

[0111] In detail, the optimization of the task intermediate data and task end data based on the insurance data fusion unit to obtain optimized intermediate data and optimized end data includes:

[0112] Optimize the structural node data of the i-th task node and the j-th task node in sequence according to the following logic. Among them, the i-th task node and the j-th task node include task intermediate points and task end points:

[0113] Use the insurance data fusion unit to obtain the data dependency relationship e between the i-th task node and the j-th task node ij ;

[0114] If the data dependency relationship e ij = 0, abandon the optimization of the structural node data of the i-th task node and the j-th task node;

[0115] If the data dependency relationship e ij = 1, allocate a data dependency storage space in the insurance data storage unit, and traverse the structural node data of the i-th task node to obtain the duplicate data that duplicates the structural node data of the j-th task node, resulting in duplicate node data. Among them, the structural node data includes task intermediate data and task end data;

[0116] Store the duplicate node data into the data dependency storage space to generate a data dependency IP;

[0117] Use the data dependency IP to replace the duplicate node data of the structural node data of the i-th task node and the j-th task node until the replacement is completed to obtain optimized intermediate data and optimized end data.

[0118] It should be explained that the prerequisite for the embodiment of the present invention to perform optimization using the insurance data fusion unit is to determine whether there is a data dependency relationship between two task nodes. Assuming there is no data dependency relationship, it means that the two task nodes are independent of each other in the insurance task chain. Therefore, even if there are duplicate node data, in order to ensure the subsequent traceability and security of the data, no optimization operations such as elimination are performed. In other words, if there is a data dependency relationship between two task nodes, the duplicate node data can be extracted, stored in the data dependency storage space, and the generated data dependency IP can be used to replace the structural node data corresponding to each task node, so as to achieve the purpose of optimizing data storage and prevent excessive waste of storage resources.

[0119] In addition, in the embodiment of the present invention, in addition to using the data dependency IP to replace the duplicate node data, the insurance data fusion unit also includes operations such as compression and text optimization of the structural node data (task intermediate data and task end data), so as to maximize the purpose of data optimization.

[0120] S7. Fill the optimized intermediate data and the optimized end data into the structured task data to obtain the complete task data, and store the complete task data in the insurance data storage unit to complete the insurance data transmission for big data adaptive fusion.

[0121] Specifically, the step of filling the optimized intermediate data and the optimized end data into the structured task data to obtain the complete task data includes:

[0122] Fill the optimized intermediate data and the optimized end data into the structured data vector M in the structured task data. After filling, the structured data vector M is in the form of:

[0123] M = [M i , M j , …, M l

[0124] where M j represents the optimized intermediate data generated by the j-th initiator corresponding to the second task node, M l represents the optimized end data generated by the l-th initiator corresponding to the task end, and the number of M i , M j , …, M l is the same as the number of task nodes included in the identifier set S of the insurance task chain, both being h;

[0125] When the filling of the optimized intermediate data and the optimized end data into the structured data vector M in the structured task data is completed, the complete task data is obtained.

[0126] Exemplarily, when Zhang continues to complete the following two task nodes: → Review the structured insurance data to obtain the review process data → Generate the review result to obtain the review result data, the corresponding optimized intermediate data and optimized end data will be generated. Therefore, after successfully filling the generated optimized intermediate data and optimized end data into the structured data vector M, the original structured task data becomes the above-mentioned complete task data. Finally, continue to allocate a storage space in the insurance data storage unit to store the complete task data, thereby completing the insurance data transmission for big data adaptive fusion.

[0127] ​Compared with the problems described in the background art, the present invention first receives a transmission instruction for original insurance data, and uses the transmission instruction to connect to an insurance data management system. The insurance data management system includes an insurance task confirmation unit, an insurance data fusion unit, and an insurance data storage unit. According to the transmission instruction, the original insurance data is received, and the data attributes of the original insurance data are analyzed by the insurance task confirmation unit, and the original insurance data is structured based on the data attributes to obtain structured insurance data. The present invention analyzes the data attributes of the original insurance data by the insurance task confirmation unit and structures such data, thereby removing invalid or redundant information and only retaining the data of practical value for the insurance business, reducing unnecessary occupation of storage space; secondly, the insurance task chain where the original insurance data is located is locked. The insurance task chain consists of multiple task nodes, and the task nodes are divided into a task start point, a task intermediate point, and a task end point. Based on the insurance task chain, structured task data of the structured insurance data is generated. The structured task data includes the structured insurance data, and the structured insurance data is marked as the task start point. By locking the insurance task chain where the original insurance data is located, the position of the data in the entire insurance process can be identified, thereby providing a prerequisite for generating structured task data subsequently and improving the generation speed of generating structured task data; then, the insurance data management system is used to trigger the insurance task chain where the original insurance data is located until task intermediate data and task end data corresponding to the task intermediate point and the task end point are generated respectively. Based on the insurance data fusion unit, the task intermediate data and the task end data are optimized to obtain optimized intermediate data and optimized end data. The optimized intermediate data and the optimized end data are filled into the structured task data to obtain complete task data, and the complete task data is stored in the insurance data storage unit to complete the insurance data transmission of big data adaptive fusion. Since all the data of the insurance task chain related to the original insurance data is structured, the regularity of the entire insurance data is ensured. In addition, for the structured task data generated based on the insurance task chain, the structured insurance data is marked as the task start point, which can also ensure that only the data directly related to a specific task will be stored and further processed, so the regularity of the data is further improved. Moreover, using the insurance data fusion unit to optimize the task intermediate data and the task end data can further refine and streamline the data, improve the data quality, reduce storage redundancy and low-value information, thereby facilitating subsequent data storage and avoiding excessive waste of storage resources. Therefore, the insurance data transmission method and system based on big data adaptive fusion proposed by the present invention mainly aims to solve the problem of waste of storage resources caused by storing insurance data.

[0128] Embodiment 2:

[0129] Such as Figure 2As shown, it is a functional module diagram of an insurance data transmission system based on big data adaptive fusion provided by an embodiment of the present invention.

[0130] The insurance data transmission system 100 based on big data adaptive fusion according to the present invention can be installed in an electronic device. According to the functions implemented, the insurance data transmission system 100 based on big data adaptive fusion can include an insurance data structure module 101, an insurance task chain confirmation module 102, a data optimization module 103, and a structured task data storage module 104. The modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0131] The insurance data structure module 101 is used to receive the transmission instruction of the original insurance data, connect to the insurance data management system by using the transmission instruction. Among them, the insurance data management system includes an insurance task confirmation unit, an insurance data fusion unit, and an insurance data storage unit. According to the transmission instruction, it receives the original insurance data, analyzes the data attributes of the original insurance data by using the insurance task confirmation unit, and structures the original insurance data based on the data attributes to obtain structured insurance data.

[0132] The insurance task chain confirmation module 102 is used to lock out the insurance task chain where the original insurance data is located. Among them, the insurance task chain is composed of multiple task nodes, and the task nodes are divided into a task start point, task intermediate points, and a task end point, and the number of task intermediate points is [0, n], where n is a positive integer greater than 0.

[0133] The data optimization module 103 is used to generate structured task data of the structured insurance data based on the insurance task chain. Among them, the structured task data includes the structured insurance data, and the structured insurance data is marked as the task start point. Trigger the insurance task chain where the original insurance data is located by using the insurance data management system until the task intermediate data and task end data corresponding to the task intermediate points and the task end point are respectively generated, and optimize the task intermediate data and task end data based on the insurance data fusion unit to obtain optimized intermediate data and optimized end data.

[0134] The structured task data storage module 104 is used to fill the optimized intermediate data and optimized end data into the structured task data to obtain complete task data, and store the complete task data in the insurance data storage unit to complete the insurance data transmission of big data adaptive fusion.

[0135] Specifically, each module in the insurance data transmission system 100 based on big data adaptive fusion in the embodiment of the present invention adopts the same as the above-mentioned Figure 1The technical means are the same as those of the insurance data transmission method based on big data adaptive fusion described in [reference], and can produce the same technical effects, which will not be elaborated here.

[0136] Embodiment 3:

[0137] As Figure 3 shown, it is a schematic structural diagram of an electronic device for implementing the insurance data transmission method based on big data adaptive fusion provided by an embodiment of the present invention.

[0138] The electronic device 1 may include a processor 10, a memory 11, a bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as an insurance data transmission program based on big data adaptive fusion.

[0139] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as the mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 can not only be used to store application software installed in the electronic device 1 and various types of data, such as the code of the insurance data transmission program based on big data adaptive fusion, etc., but also be used to temporarily store data that has been output or will be output.

[0140] In some embodiments, the processor 10 may be composed of integrated circuits. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and lines, and by running or executing programs or modules stored in the memory 11 (such as the insurance data transmission program based on big data adaptive fusion, etc.), and calling data stored in the memory 11, to perform various functions of the electronic device 1 and process data.

[0141] The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to implement connection communication between the memory 11, at least one processor 10, and the like.

[0142] Figure 3 Only an electronic device having components is shown, and those skilled in the art can understand that Figure 2 the shown structure does not constitute a limitation on the electronic device 1, and it may include fewer or more components than shown, or combine certain components, or have a different component arrangement.

[0143] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component. Preferably, the power source can be logically connected to the at least one processor 10 through a power management device, so as to implement functions such as charge management, discharge management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or inverter, and a power status indicator. The electronic device 1 may further include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0144] Furthermore, the electronic device 1 may further include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0145] Optionally, the electronic device 1 may further include a user interface. The user interface may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visual user interface.

[0146] It should be understood that the above embodiments are only for illustration purposes and are not limited by this structure in the scope of the patent application.

[0147] The insurance data transmission program based on big data adaptive fusion stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve the following:

[0148] Receive the transmission instruction of the original insurance data, and use the transmission instruction to connect to the insurance data management system. Among them, the insurance data management system includes an insurance task confirmation unit, an insurance data fusion unit, and an insurance data storage unit;

[0149] Receive the original insurance data according to the transmission instruction, analyze the data attributes of the original insurance data by using the insurance task confirmation unit, and structure the original insurance data based on the data attributes to obtain structured insurance data;

[0150] Lock the insurance task chain where the original insurance data is located. Among them, the insurance task chain is composed of multiple task nodes, and the task nodes are divided into a task start point, task intermediate points, and a task end point, and the number of task intermediate points is [0,n], where n is a positive integer greater than 0;

[0151] Generate structured task data of the structured insurance data based on the insurance task chain. Among them, the structured task data includes the structured insurance data, and the structured insurance data is marked as the task start point;

[0152] Use the insurance data management system to trigger the insurance task chain where the original insurance data is located until task intermediate data and task end data corresponding to the task intermediate points and the task end point are generated respectively;

[0153] Optimize the task intermediate data and the task end data based on the insurance data fusion unit to obtain optimized intermediate data and optimized end data;

[0154] Fill the optimized intermediate data and the optimized end data into the structured task data to obtain complete task data, and store the complete task data in the insurance data storage unit to complete the insurance data transmission of big data adaptive fusion.

[0155] Specifically, for the specific implementation method of the above instructions by the processor 10, reference can be made to Figures 1 to 2 The description of the relevant steps in the corresponding embodiment, which will not be elaborated here.

[0156] Furthermore, if the modules / units integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory).

[0157] The present invention also provides a computer-readable storage medium storing a computer program, which when executed by a processor of an electronic device, can implement:

[0158] Receiving a transmission instruction of original insurance data, and using the transmission instruction to connect to an insurance data management system, where the insurance data management system includes an insurance task confirmation unit, an insurance data fusion unit, and an insurance data storage unit;

[0159] Receiving original insurance data according to the transmission instruction, analyzing the data attributes of the original insurance data by using the insurance task confirmation unit, and structuring the original insurance data based on the data attributes to obtain structured insurance data;

[0160] Locking the insurance task chain where the original insurance data is located, where the insurance task chain is composed of multiple task nodes, and the task nodes are divided into a task start point, task intermediate points, and a task end point, and the number of task intermediate points is [0, n], and n is a positive integer greater than 0;

[0161] Generating structured task data of the structured insurance data based on the insurance task chain, where the structured task data includes the structured insurance data, and the structured insurance data is marked as the task start point;

[0162] Triggering the insurance task chain where the original insurance data is located by using the insurance data management system until task intermediate data and task end data corresponding to the task intermediate points and the task end point are respectively generated;

[0163] Optimizing the task intermediate data and the task end data based on the insurance data fusion unit to obtain optimized intermediate data and optimized end data;

[0164] Filling the optimized intermediate data and the optimized end data into the structured task data to obtain complete task data, and storing the complete task data in the insurance data storage unit to complete the insurance data transmission of big data adaptability fusion.

[0165] The module described as a separation component may or may not be physically separated. The component shown as a module may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0166] In addition, in each embodiment of the present invention, each functional module may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit. The above integrated unit may be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0167] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An insurance data transmission method based on adaptive fusion of big data, characterized in that: The method comprises: Receiving a transmission instruction for original insurance data, and using the transmission instruction to connect to an insurance data management system, wherein the insurance data management system includes an insurance task confirmation unit, an insurance data fusion unit, and an insurance data storage unit; receiving original insurance data according to the transmission instruction, analyzing data attributes of the original insurance data using the insurance task confirmation unit, and structuring the original insurance data based on the data attributes to obtain structured insurance data; Locking out the insurance task chain where the original insurance data is located, wherein the insurance task chain is composed of a plurality of task nodes, and the task nodes are divided into a task starting point, a task middle point and a task end point, and the number of the task middle points is [0, n], where n is a positive integer greater than 0; Structural task data for generating structural insurance data based on the insurance task chain, wherein the structural task data includes structural insurance data, and the structural insurance data is marked as a task starting point; The structured task data for generating structured insurance data based on the insurance task chain includes: Based on the insurance task chain where the original insurance data is located, a task chain empty matrix is ​​constructed, where the task chain empty matrix is ​​expressed as follows: in, represents the empty matrix of the task chain of the original insurance data, S represents the identification set of the insurance task chain, which consists of the task starting point, task middle point and task end point, E represents the data dependency relationship between task nodes, and M represents the structural data vector used to store the structural insurance data; The structural insurance data is stored in the structural data vector of the task chain empty matrix to generate structural task data G; The insurance task chain where the original insurance data is located is triggered by using the insurance data management system until the task intermediate data and the task endpoint data corresponding to the task intermediate point and the task endpoint are generated respectively; Optimize task intermediate data and task endpoint data based on the insurance data fusion unit to obtain optimized intermediate data and optimized endpoint data; The optimizing task intermediate data and task endpoint data based on the insurance data fusion unit to obtain the optimized intermediate data and optimized endpoint data includes: The structural node data of the i-th task node and the j-th task node are optimized in sequence according to the following logic, where the i-th task node and the j-th task node include the task midpoint and the task end point: Use the insurance data fusion unit to obtain the data dependency relationship between the i-th task node and the j-th task node. ij ; If the data dependency relationship e ij =0, the optimization of the structure node data of the i-th task node and the j-th task node is abandoned; If the data dependency relationship e ij =1, a data dependent storage space is opened up in the insurance data storage unit, and the structure node data of the i-th task node and the repeated data with the structure node data of the j-th task node are traversed to obtain the repeated node data, wherein the structure node data includes the task intermediate data and the task endpoint data; Store the duplicate node data into the data-dependent storage space and generate the data-dependent IP; Use the data-dependent IP to replace the structure node data of the i-th task node and the repeated node data of the structure node data of the j-th task node until the replacement is completed to obtain the optimized intermediate data and the optimized endpoint data; Fill the optimized intermediate data and optimized endpoint data into the structural task data to obtain the complete task data, and store the complete task data in the insurance data storage unit to complete the insurance data transmission with adaptive fusion of big data.

2. The insurance data transmission method based on adaptive fusion of big data as claimed in claim 1 is characterized in that: The receiving of original insurance data according to the transmission instruction and analyzing data attributes of the original insurance data by using the insurance task confirmation unit include: Parse the transmission instruction to obtain the IP address of the initiator of the original insurance data, send the IP address of the initiator to the insurance data management system, and use the insurance data management system to connect to the initiator; When the insurance data management system successfully connects to the initiator, it notifies the initiator to transmit the original insurance data to the insurance data management system; After the insurance data management system receives the original insurance data, the insurance task confirmation unit is started to analyze the data attributes of the original insurance data, wherein the data attributes include the insured person data type, the insured policy data type, the risk assessment data type, and the claim record data type.

3. The insurance data transmission method based on adaptive fusion of big data as claimed in claim 2 is characterized in that: The method of structuring the original insurance data based on data attributes to obtain structured insurance data includes: Splitting the original insurance data according to data types to obtain split insurance policy data, wherein the split insurance policy data consists of one or all of insured person data, insured policy data, risk assessment data, and claim record data; Generate structured empty data, where the row dimension of the structured empty data is the same as the number of data types of the split policy data; Fill the split policy data into the structure empty data in sequence to obtain the structure insurance data, and the structure insurance data is in the form of: Among them, M i Indicates the structural insurance data corresponding to the original insurance data initiated by the i-th initiator, m j It represents the j-th split policy data included in the structural insurance data, and j≤4, m1 represents the insured person data, m2 represents the insured policy data, m3 represents the risk assessment data, and m4 represents the claim record data.

4. The insurance data transmission method based on adaptive fusion of big data as claimed in claim 3 is characterized in that: The identification set S of the insurance task chain of the structured task data G, the data dependency E and the structured data vector M are expressed as follows: S={s1,s2,…,s i ,…,s h } And={and 12 ,And 13 ,…,And ij ,…,And hh-1 } M=[M i ] Among them, s i represents the task node. When i=1, s1 represents the starting point of the task. When i=h, s h Indicates the end point of the task. When i≠1 and i≠h, s i represents the midpoint of the task, e ij represents the data dependency relationship between the i-th task node and the j-th task node, M i represents the structural insurance data corresponding to the original insurance data initiated by the i-th initiator, and M i It has a corresponding relationship with s1. When the structural insurance data is stored in the structural data vector, the structural insurance data M is marked with the task starting point s1. i .

5. The insurance data transmission method based on adaptive fusion of big data as claimed in claim 4 is characterized in that: The ij The data dependency relationships between the nodes represented by are divided into three types: the data dependency relationships between the task starting point and the task midpoint, the task starting point and the task end point, and the task midpoint and the task end point. ij = 1, it means that the i-th task node has a data dependency relationship with the j-th task node. ij When =0, it indicates that the i-th task node and the j-th task node have no data dependency relationship.

6. The insurance data transmission method based on adaptive fusion of big data as claimed in claim 5 is characterized in that: The method of using the insurance data management system to trigger the insurance task chain where the original insurance data is located, until the task intermediate data and task endpoint data corresponding to the task intermediate point and the task endpoint are generated respectively, includes: Using the insurance data management system to generate a notification instruction that the original insurance data can operate normally; According to the insurance task chain where the original insurance data is located, each task node is run in sequence, and node task data having a corresponding relationship with the task node is generated; Utilize the insurance task confirmation unit to analyze the data attributes of the node task data corresponding to each task node; Based on the data attributes of the node task data, the node task data is structured to obtain structured node data, wherein the structured node data includes task intermediate data and task endpoint data, and the task intermediate data corresponds to the task midpoint, and the task endpoint data corresponds to the task endpoint.

7. The insurance data transmission method based on adaptive fusion of big data as claimed in claim 6 is characterized in that: The optimization intermediate data and optimization endpoint data are filled into the structure task data to obtain complete task data, including: The optimization intermediate data and optimization endpoint data are filled into the structure data vector M in the structure task data. The structure data vector M after filling is expressed as: M=[M i ,M j ,…,M l ] Among them, M j represents the optimized intermediate data generated by the jth initiator corresponding to the second task node, M l represents the optimized endpoint data generated by the lth initiator corresponding to the task endpoint, and M i ,M j ,…,M l The number of is the same as the number of task nodes included in the identification set S of the insurance task chain, which is h; After the optimization intermediate data and optimization endpoint data are filled into the structure data vector M in the structure task data, the complete task data is obtained.

8. An insurance data transmission system based on adaptive fusion of big data, characterized in that: The system comprises: An insurance data structure module, used to receive a transmission instruction of original insurance data, and use the transmission instruction to connect to an insurance data management system, wherein the insurance data management system includes an insurance task confirmation unit, an insurance data fusion unit, and an insurance data storage unit, receives original insurance data according to the transmission instruction, uses the insurance task confirmation unit to analyze data attributes of the original insurance data, and structures the original insurance data based on the data attributes to obtain structured insurance data; The insurance task chain confirmation module is used to lock out the insurance task chain where the original insurance data is located, wherein the insurance task chain is composed of multiple task nodes, and the task nodes are divided into a task starting point, a task intermediate point and a task end point, and the number of task intermediate points is [0, n], where n is a positive integer greater than 0: A data optimization module is used to generate structural task data of structural insurance data based on an insurance task chain, wherein the structural task data includes structural insurance data, and the structural insurance data is marked as a task starting point, and the insurance task chain where the original insurance data is located is triggered by using an insurance data management system until task intermediate data and task endpoint data corresponding to a task intermediate point and a task endpoint are generated respectively, and the task intermediate data and task endpoint data are optimized based on an insurance data fusion unit to obtain optimized intermediate data and optimized endpoint data; The structured task data for generating structured insurance data based on the insurance task chain includes: Based on the insurance task chain where the original insurance data is located, a task chain empty matrix is ​​constructed, where the task chain empty matrix is ​​expressed as follows: in, represents the empty matrix of the task chain of the original insurance data, S represents the identification set of the insurance task chain, which consists of the task starting point, task middle point and task end point, E represents the data dependency relationship between task nodes, and M represents the structural data vector used to store the structural insurance data; The structural insurance data is stored in the structural data vector of the task chain empty matrix to generate structural task data G; The optimizing task intermediate data and task endpoint data based on the insurance data fusion unit to obtain the optimized intermediate data and optimized endpoint data includes: The structural node data of the i-th task node and the j-th task node are optimized in sequence according to the following logic, where the i-th task node and the j-th task node include the task midpoint and the task end point: Use the insurance data fusion unit to obtain the data dependency relationship between the i-th task node and the j-th task node. ij ; If the data dependency relationship e ij =0, the optimization of the structure node data of the i-th task node and the j-th task node is abandoned; If the data dependency relationship e ij =1, a data dependent storage space is opened up in the insurance data storage unit, and the structure node data of the i-th task node and the repeated data with the structure node data of the j-th task node are traversed to obtain the repeated node data, wherein the structure node data includes the task intermediate data and the task endpoint data; Store the duplicate node data into the data-dependent storage space and generate the data-dependent IP; Use the data-dependent IP to replace the structure node data of the i-th task node and the repeated node data of the structure node data of the j-th task node until the replacement is completed to obtain the optimized intermediate data and the optimized endpoint data; The structural task data storage module is used to fill the optimization intermediate data and optimization endpoint data into the structural task data to obtain the complete task data, and store the complete task data in the insurance data storage unit to complete the insurance data transmission with adaptive fusion of big data.

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