Medical insurance intelligent treatment method and system based on data fusion

Abstract

The invention provides a medical insurance intelligent treatment method and system based on data fusion, and belongs to the technical field of data processing, and the method specifically comprises the steps: obtaining the composition data of associated medical insurance users of different fusion processing demand types of a channel, when it is determined that the channel does not need to be detected and analyzed by adopting a preset strategy in combination with the associated data of the fusion deviation channels of different associated medical insurance users, response abnormal conditions of the channel during data fusion processing of different associated medical insurance users in a preset time period are obtained; the abnormal detection method is suitable for determining channels according to distribution discrete data of response abnormities between associated channels of associated medical insurance users, and reliability and accuracy of fusion processing of different channels of a medical insurance database are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly relates to a medical insurance intelligent governance method and system based on data fusion. Background Art

[0002] The medical insurance system involves a large number of people. Therefore, in order to achieve data governance of medical insurance data, it is often necessary to collect data from multiple channels such as medical insurance systems, medical institutions, health departments, civil affairs systems, and tax systems, covering insured information, medical consumption records, drug usage, disease diagnosis results, health records, etc., to form a comprehensive medical insurance data resource pool.

[0003] However, in the actual operation process, due to certain deviations in the information systems and data interfaces of different channels, it may lead to situations such as data format mismatch or data interface mismatch during data interaction, resulting in the difficulty of meeting the requirements for the data reliability of the medical insurance data resource pool. Therefore, how to monitor and analyze medical insurance data and timely and effectively discover abnormal situations and potential risks has become an urgent technical problem to be solved.

[0004] To solve the above technical problems, the present application provides a medical insurance intelligent governance method and system based on data fusion. Summary of the Invention

[0005] To achieve the object of the present invention, the present invention adopts the following technical solutions:

[0006] Specifically, in the first aspect, the present application provides a medical insurance intelligent governance method based on data fusion, which specifically includes:

[0007] S1 Using the fusion deviation situations of the associated data of different medical insurance users in different channels with other channels, determine the fusion deviation channels of the channels;

[0008] S2 Based on the associated data of the medical insurance users of the channels, determine the associated medical insurance users of the channels, and determine the type of fusion processing requirements of the associated medical insurance users based on the analysis results of the medical data of the associated medical insurance users;

[0009] S3 Obtain the composition data of the associated medical insurance users of different types of fusion processing requirements of the channels, and when it is determined that the channels do not need to be detected and analyzed using a preset strategy by combining the associated data of the fusion deviation channels of different associated medical insurance users, proceed to the next step;

[0010] S4 Obtain the response abnormal situations during the data fusion processing of the channels for different associated medical insurance users within a preset time period, and determine the abnormal detection method of the channels based on the distribution discrete data of the response abnormalities between the associated channels suitable for the associated medical insurance users.

[0011] The beneficial effects of the present invention are as follows:

[0012] Based on the analysis results of the medical data of associated medical insurance users, the types of fusion processing requirements for associated medical insurance users are determined, thereby realizing the determination of the disease types of associated medical insurance users from the parsing results of the medical data of associated medical insurance users. Furthermore, the differences in the follow-up visit requirements of associated medical insurance users caused by the differences in disease types are realized, and then the differences in the requirements for data fusion processing of medical insurance data from different channels are realized. This also lays a foundation for generating detection strategies for data ports of different channels according to the differences in the data fusion requirements of associated medical insurance users and ensuring the data reliability of the channels.

[0013] According to the response abnormal conditions during the data fusion processing of the channel for different associated medical insurance users within a preset time period and the distribution discrete data of response abnormalities among the associated channels of associated medical insurance users, the abnormal detection method of the channel is determined. This not only single-mindedly considers the response abnormal conditions during the data fusion processing of the channel, but also takes into account the response abnormal conditions of the associated channels of associated medical insurance users, realizing the determination of the abnormal detection method of the channel from the perspective of ensuring the reliability of the data fusion processing of associated medical insurance users, ensuring the data reliability and accuracy of the medical insurance database, and at the same time ensuring the reliability of data fusion processing among different channels.

[0014] A further technical solution lies in that the channels include pharmacies, medical insurance systems, medical institutions, health departments, civil affairs systems, and tax systems.

[0015] A further technical solution lies in that the fusion deviation situation is based on the number of fusion failures of different fusion failure types during the fusion processing between the channel and other channels.

[0016] A further technical solution lies in that the fusion failure types include response delay abnormality, data loss, and data format error.

[0017] A further technical solution lies in that the method for determining the fusion deviation channel of the channel is as follows:

[0018] Based on the fusion deviation situation between the channel and other channels during the fusion processing, determine the number of fusion failures of different medical insurance users;

[0019] According to the number of fusion failures of different medical insurance users, determine the fusion failure users among the medical insurance users;

[0020] Based on the number of the fusion failure users, determine whether the other channel is the fusion deviation channel of the channel.

[0021] A further technical solution is that the user with failed fusion is a medical insurance user whose number of failed fusion times is not within a preset failed - times range.

[0022] A further technical solution is that when the number of users with failed fusion between the other channel and the channel is greater than a preset failed - user quantity threshold, it is determined that the other channel is the fusion - deviation channel of the channel.

[0023] A further technical solution is that the method for determining the abnormal - detection method of the channel is as follows:

[0024] Based on the response - abnormal situation during the data - fusion processing of the channel with different associated medical - insurance users within a preset time period, determine the associated medical - insurance users with response abnormalities for the channel and use them as response - abnormal users;

[0025] According to the distributed discrete data of response abnormalities between the associated channels of the associated medical - insurance users, determine the medical - insurance users with response abnormalities for different associated channels and use them as the response - abnormal medical - insurance users of the associated channels. Utilize the response - abnormal medical - insurance users of different associated channels and the coincidence situation of the response - abnormal users to determine the response - abnormal coefficient of the associated channels;

[0026] Based on the response - abnormal coefficient of the associated channels and the number of response - abnormal users of the channel, determine the abnormal - detection method of the channel.

[0027] A further technical solution is that based on the response - abnormal coefficient of the associated channels and the number of response - abnormal users of the channel, determining the abnormal - detection method of the channel specifically includes:

[0028] Multiply the response - abnormal coefficient of the associated channels by the number of response - abnormal users of the channel to determine the response - abnormal factor of the channel;

[0029] When the response - abnormal factor is greater than a preset abnormal - factor threshold, adopt a preset detection strategy to perform abnormal - detection processing on the channel;

[0030] When the response - abnormal factor is not greater than the preset abnormal - factor threshold, adopt a second preset detection strategy to perform abnormal - detection processing on the channel.

[0031] A further technical solution is that the preset detection strategy is to perform real - time detection and processing on the network delay of the data port of the channel.

[0032] A further technical solution is that the second preset detection strategy is to perform detection and processing on the data port of the channel when there are response abnormalities.

[0033] In a second aspect, the present invention provides a computer system, comprising: a memory and a processor communicatively connected, and a computer program stored on the memory and capable of running on the processor, wherein when the processor runs the computer program, it executes the above-mentioned method for intelligent medical insurance governance based on data fusion.

[0034] Other features and advantages will be set forth in the following description. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the description and the drawings.

[0035] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, detailed descriptions are as follows. Description of the Drawings

[0036] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious.

[0037] Figure 1 is a flowchart of a method for intelligent medical insurance governance based on data fusion;

[0038] Figure 2 is a flowchart of a method for determining the fusion deviation channels of channels;

[0039] Figure 3 is a flowchart of a method for determining the types of fusion processing requirements related to medical insurance users;

[0040] Figure 4 is a flowchart of a method for determining the anomaly detection method of channels. Detailed Embodiments

[0041] In order to enable those skilled in the art of the present technology to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0042] Embodiment 1

[0043] As Figure 1 shown, the present application provides a method for intelligent medical insurance governance based on data fusion, specifically including:

[0044] S1 Utilize the fusion deviation situations of the associated data of different medical insurance users and other channels through different channels to determine the fusion deviation channels of the channels;

[0045] Further, the fusion deviation channel is another channel whose total number of fusion failure times with the channel for different medical insurance users is greater than the preset fusion failure times threshold.

[0046] S2 Based on the associated data of the medical insurance users of the channel, determine the associated medical insurance users of the channel, and determine the type of fusion processing requirement of the associated medical insurance users based on the analysis result of the medical data of the associated medical insurance users;

[0047] It should be noted that based on the analysis result of the medical data of the associated users, determine the type of diagnosed diseases of the associated users, and use the preset requirement type corresponding to the type of diagnosed diseases as the type of fusion processing requirement of the associated medical insurance users.

[0048] S3 Obtain the composition data of the associated medical insurance users with different types of fusion processing requirements of the channel, and when it is determined that the channel does not need to use the preset strategy for detection and analysis by combining the associated data of the fusion deviation channels of different associated medical insurance users, proceed to the next step;

[0049] It can be understood that based on the composition data of the associated medical insurance users with different types of fusion processing requirements of the channel, determine the associated medical insurance users of a type of processing requirement of the channel, and use them as the screened medical insurance users. According to the associated data of the fusion deviation channels of the screened medical insurance users, determine the number of screened medical insurance users in different fusion deviation channels. When the number of screened medical insurance users in the fusion deviation channel is greater than the preset screened user number threshold, it is determined that the channel needs to use the preset strategy for detection and analysis.

[0050] S4 Obtain the response anomaly situation during the data fusion processing of the channel for different associated medical insurance users within a preset time period, and determine the anomaly detection method of the channel according to the distribution discrete data of the response anomalies between the associated channels suitable for the associated medical insurance users.

[0051] It should be noted that based on the response anomaly situation during the data fusion processing of the channel for different associated medical insurance users within a preset time period, determine the associated medical insurance users with response anomalies in the channel as the response anomaly users of the channel. Use the proportion of the number of response anomaly medical insurance users that do not overlap between different associated channels and other associated channels to determine the distribution discrete coefficient of different associated channels. According to the average value of the distribution discrete coefficients of different associated channels and the average value of the proportion of the number of response anomaly users of the channel in the associated medical insurance users during the data fusion processing of the channel within the preset time period, determine the anomaly detection coefficient. When the anomaly detection coefficient is greater than the preset anomaly detection coefficient threshold, use the preset detection strategy for the anomaly detection processing of the channel. When the anomaly detection coefficient is not greater than the preset detection coefficient threshold, use the second preset detection strategy for the anomaly detection processing of the channel.

[0052] Further, the channels include pharmacies, the medical insurance system, medical institutions, health departments, the civil affairs system, and the tax system.

[0053] Specifically, the fusion deviation situation is based on the number of fusion failures of the channel and other channels in different types of fusion failures during the fusion process.

[0054] It can be understood that the types of fusion failures include response delay anomalies, data loss, and data format errors.

[0055] Specifically, as Figure 2 shown, the method for determining the fusion deviation channel of the channel is as follows:

[0056] Based on the fusion deviation situation of the channel and other channels during the fusion process, determine the number of fusion failures of different medical insurance users;

[0057] Based on the number of fusion failures of different medical insurance users, determine the fusion failure users among the medical insurance users;

[0058] Based on the number of the fusion failure users, determine whether the other channel is the fusion deviation channel of the channel.

[0059] Further, the fusion failure users are medical insurance users whose number of fusion failures is not within the preset failure number range.

[0060] It can be understood that when the number of fusion failure users of the other channel and the channel is greater than the preset failure user number threshold, it is determined that the other channel is the fusion deviation channel of the channel.

[0061] Further, when there is no fusion failure channel for the channel, directly proceed to step S4 to determine the anomaly detection method for the channel.

[0062] It should be further noted that when the number of fusion deviation channels of the channel does not meet the requirements, it is directly determined to use the preset strategy for detection and analysis.

[0063] Optionally, the method for determining the fusion deviation channel of the channel is as follows:

[0064] Based on the fusion deviation situation of the channel and other channels during the fusion process, determine the number of fusion failures of different medical insurance users;

[0065] Based on the proportion of the number of medical insurance users with fusion failure times of the channel on different dates, determine the fusion failure coefficient on different dates;

[0066] Based on the average value of the fusion failure coefficients on different dates, determine whether the other channel is a fusion deviation channel of the channel.

[0067] Furthermore, when the average value of the fusion failure coefficients on different dates is greater than the preset fusion failure coefficient threshold, it is determined that the other channel is a fusion deviation channel of the channel.

[0068] Optionally, the method for determining the fusion deviation channel of the channel is as follows:

[0069] Based on the fusion deviation situation between the channel and other channels during the fusion process, determine the number of fusion failures of different medical insurance users. When the total number of fusion failures between the channel and other channels does not meet the requirements, it is determined that the other channel is a fusion deviation channel of the channel;

[0070] When the total number of fusion failures between the channel and other channels meets the requirements:

[0071] Based on the number of fusion failures of different medical insurance users, determine the users with fusion failures among the medical insurance users. When the number of users with fusion failures does not meet the requirements, it is determined that the other channel is a fusion deviation channel of the channel;

[0072] When the number of users with fusion failures meets the requirements:

[0073] Regard the same associated medical insurance users of the channel and the other channel as the same users. When the proportion of the number of medical insurance users with fusion failure times among the same users does not meet the requirements, it is determined that the other channel is a fusion deviation channel of the channel;

[0074] When the proportion of the number of medical insurance users with fusion failure times among the same users meets the requirements:

[0075] Based on the proportion of the number of medical insurance users with fusion failure times of the channel on different dates, determine the fusion failure coefficients on different dates. When the average value of the fusion failure coefficients on different dates does not meet the requirements, it is determined that the other channel is a fusion deviation channel of the channel;

[0076] When the average value of the fusion failure coefficients on different dates meets the requirements:

[0077] Based on the number of the same users, determine the preset threshold of the fusion failure coefficient under the number of the same users, and obtain the proportion of the number of dates with fusion failure coefficients greater than the preset threshold. When the proportion of the number of dates with fusion failure coefficients greater than the preset threshold does not meet the requirements, it is determined that the other channel is a fusion deviation channel of the channel;

[0078] When the proportion of the number of dates when the fusion failure coefficient is greater than the preset threshold meets the requirements:

[0079] Obtain the number of the same users, and combine the fusion failure coefficients and the number of fusion failure users on different dates to determine the fusion deviation value, and determine whether the other channel is the fusion deviation channel of the channel based on the fusion deviation value.

[0080] Furthermore, when the fusion deviation value is greater than the preset fusion deviation threshold, it is determined that the other channel is the fusion deviation channel of the channel.

[0081] Specifically, as Figure 3 shown, the method for determining the fusion processing requirement type of the associated medical insurance users is:

[0082] Based on the analysis result of the medical data of the associated users, determine the type of diagnosed diseases of the associated users;

[0083] Based on the type of diagnosed diseases, determine the number of follow-up visits of the associated users within a preset time period in the future;

[0084] Determine the fusion processing requirement type of the associated medical insurance users according to the number of follow-up visits.

[0085] Furthermore, determining the fusion processing requirement type of the associated medical insurance users according to the number of follow-up visits specifically includes:

[0086] Determine the fusion processing requirement type of the associated medical insurance users with the preset requirement type corresponding to the number of follow-up visits.

[0087] It can be understood that the fusion processing requirement type includes a first type of processing requirement type, a second type of processing requirement type, and a third type of processing requirement type.

[0088] Specifically, the composition data of the associated medical insurance users includes the number of associated medical insurance users with different fusion processing requirement types.

[0089] It should be noted that the associated data of the fusion deviation channels of the associated medical insurance users includes the number of fusion deviation channels of different associated medical insurance users.

[0090] It can be understood that determining that the channel does not need to be detected and analyzed using the preset strategy specifically includes:

[0091] Based on the composition data of the associated medical insurance users with different fusion processing requirement types of the channel, determine the associated medical insurance users of the first type of processing requirement type of the channel and use them as the screened medical insurance users;

[0092] Determine the number of screened medical insurance users with a fusion deviation channel based on the associated data of the fusion deviation channels for screening medical insurance users;

[0093] Based on the number of screened medical insurance users with a fusion deviation channel, determine whether the channel needs to be detected and analyzed using a preset strategy.

[0094] Furthermore, when the number of screened medical insurance users with a fusion deviation channel is greater than the preset screening user number threshold, it is determined that the channel needs to be detected and analyzed using a preset strategy.

[0095] Optionally, determining that the channel does not need to be detected and analyzed using a preset strategy specifically includes:

[0096] Based on the composition data of the associated medical insurance users of different fusion processing requirement types of the channel, determine the associated medical insurance users of a certain type of processing requirement of the channel and use them as the screened medical insurance users;

[0097] Based on the associated data of the fusion deviation channels for the screened medical insurance users, determine the number of screened medical insurance users in different fusion deviation channels;

[0098] Based on the number of screened medical insurance users in different fusion deviation channels, determine whether the channel needs to be detected and analyzed using a preset strategy.

[0099] Specifically, when the number of fusion deviation channels with the number of screened medical insurance users within the preset screening user number range does not meet the requirements, it is determined that the channel needs to be detected and analyzed using a preset strategy.

[0100] It should be noted that the fusion processing requirement of the certain type of processing requirement is greater than that of the second type of processing requirement, and the second type of processing requirement is greater than that of the third type of processing requirement.

[0101] Optionally, determining that the channel does not need to be detected and analyzed using a preset strategy specifically includes:

[0102] S31 Based on the composition data of the associated medical insurance users of different fusion processing requirement types of the channel, determine the associated medical insurance users of different fusion processing requirement types in the channel. Based on the fusion processing requirement types of different associated medical insurance users, determine the preset processing requirement coefficients of different associated medical insurance users;

[0103] S32 Based on the associated data of the fusion deviation channels for the associated medical insurance users, determine the number of fusion deviation channels of different associated medical insurance users, and combine the preset processing requirement coefficients of different associated medical insurance users to determine the fusion deviation requirement values of different associated medical insurance users;

[0104] S33 determines a comprehensive fusion deviation demand value based on the fusion deviation demand values of different associated medical insurance users, and uses the comprehensive fusion deviation demand value to determine whether the channel needs to perform detection and analysis using a preset strategy.

[0105] Further, when the comprehensive fusion deviation demand value is greater than a preset deviation demand threshold, it is determined that the channel needs to perform detection and analysis using a preset strategy.

[0106] Optionally, the above step S31 includes the following content:

[0107] S311 determines the number of associated medical insurance users of different fusion processing demand types in the channel based on the composition data of the associated medical insurance users of different fusion processing demand types in the channel. When the number of associated medical insurance users in the channel does not meet the requirements, it is determined that the channel needs to perform detection and analysis using a preset strategy. When the number of associated medical insurance users in the channel meets the requirements, it proceeds to step S312;

[0108] S312 When the number of associated medical insurance users in the channel is less than a preset associated user number threshold, it is determined that the channel does not need to perform detection and analysis using a preset strategy. When the number of associated medical insurance users in the channel is not less than the preset associated user number threshold, it proceeds to step S313;

[0109] S313 determines the number of associated medical insurance users of a certain type of processing demand based on the number of associated medical insurance users of different fusion processing demand types. When the number of associated medical insurance users of a certain type of processing demand does not meet the requirements, it is determined that the channel needs to perform detection and analysis using a preset strategy. When the number of associated medical insurance users of a certain type of processing demand meets the requirements, it proceeds to step S314;

[0110] S314 determines the preset processing demand coefficients of different associated medical insurance users based on the fusion processing demand types of different associated medical insurance users. When the sum of the preset processing demand coefficients of different associated medical insurance users does not meet the requirements, it is determined that the channel needs to perform detection and analysis using a preset strategy. When the sum of the preset processing demand coefficients of different associated medical insurance users meets the requirements, it proceeds to step S32.

[0111] Optionally, the above step S32 includes the following content:

[0112] S321 determines the number of fusion deviation channels of different associated medical insurance users based on the associated data of the fusion deviation channels of the associated medical insurance users. When the sum of the number of fusion deviation channels of different associated medical insurance users does not meet the requirements, it is determined that the channel needs to perform detection and analysis using a preset strategy. When the sum of the number of fusion deviation channels of different associated medical insurance users meets the requirements, it proceeds to step S322;

[0113] S322 When there are associated medical insurance users with the number of fusion deviation channels greater than the preset deviation channel number threshold, proceed to step S323; when there are no associated medical insurance users with the number of fusion deviation channels greater than the preset deviation channel number threshold, proceed to step S324;

[0114] S323 When the number of associated medical insurance users with the number of fusion deviation channels greater than the preset deviation channel number threshold does not meet the requirements, it is determined that the channel needs to be detected and analyzed using a preset strategy; when the number of associated medical insurance users with the number of fusion deviation channels greater than the preset deviation channel number threshold meets the requirements, proceed to step S324;

[0115] S324 Determine the number of fusion deviation channels of different associated medical insurance users, and combine with the preset processing demand coefficients of different associated medical insurance users to determine the fusion deviation demand values of different associated medical insurance users. When the average value of the fusion deviation demand values of different associated medical insurance users does not meet the requirements, it is determined that the channel needs to be detected and analyzed using a preset strategy; when the average value of the fusion deviation demand values of different associated medical insurance users meets the requirements, proceed to step S33.

[0116] Specifically, as Figure 4 shown, the method for determining the abnormal detection method of the channel is:

[0117] Based on the response anomaly situation of the channel during data fusion processing of different associated medical insurance users within a preset time period, determine the associated medical insurance users with response anomalies of the channel, and use them as response anomaly users;

[0118] According to the distributed discrete data of response anomalies between the associated channels of the associated medical insurance users, determine the medical insurance users with response anomalies of different associated channels, and use them as the response anomaly medical insurance users of the associated channels. Use the response anomaly medical insurance users of different associated channels and the coincidence situation of the response anomaly users to determine the associated channel response anomaly coefficient;

[0119] Based on the associated channel response anomaly coefficient and the number of response anomaly users of the channel, determine the abnormal detection method of the channel.

[0120] Furthermore, the method for determining the associated channel response anomaly coefficient is:

[0121] Based on the coincidence situation of the response anomaly medical insurance users of different associated channels, determine the number of associated channels with response anomalies among different response anomaly medical insurance users;

[0122] According to the number of associated channels with response anomalies among different response anomaly medical insurance users, determine the preset coincidence coefficient of different response anomaly medical insurance users;

[0123] Based on the number of medical insurance users with abnormal responses from different associated channels, determine a preset abnormal response coefficient corresponding to the number of medical insurance users with abnormal responses. Based on the ratio of the average value of the preset abnormal response coefficient to the preset coincidence coefficient, determine the associated channel abnormal response coefficient.

[0124] It can be understood that the preset coincidence coefficient and the preset abnormal response coefficient are determined according to the preset corresponding relationships of the number of associated channels with abnormal responses among different medical insurance users with abnormal responses and the number of medical insurance users with abnormal responses from different associated channels respectively.

[0125] Furthermore, the abnormal response includes that the response delay is greater than a preset duration and no response.

[0126] It should be noted that based on the associated channel abnormal response coefficient and the number of users with abnormal responses on the channel, determine the abnormal detection method for the channel, which specifically includes:

[0127] Multiply the associated channel abnormal response coefficient by the number of users with abnormal responses on the channel to determine the response abnormal factor for the channel;

[0128] When the response abnormal factor is greater than a preset abnormal factor threshold, then adopt a preset detection strategy to perform abnormal detection processing on the channel;

[0129] When the response abnormal factor is not greater than the preset abnormal factor threshold, then adopt a second preset detection strategy to perform abnormal detection processing on the channel.

[0130] Furthermore, the preset detection strategy is to perform real-time detection and processing of the network delay of the data port of the channel.

[0131] It can be understood that the second preset detection strategy is to perform detection and processing of the data port of the channel when there is an abnormal response.

[0132] Embodiment 2

[0133] On the second aspect, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor. When the processor runs the computer program, it executes the above-mentioned medical insurance intelligent governance method based on data fusion.

[0134] Optionally, the method for determining the abnormal detection method for the channel is:

[0135] When determining that there are no associated medical insurance users with abnormal responses during the data fusion process of the channels within a preset time period, a second preset detection strategy is adopted for the abnormal detection process of the channels;

[0136] When there are associated medical insurance users with abnormal responses in the channel:

[0137] Take the associated medical insurance users with abnormal responses as response-abnormal users. When the number of the response-abnormal users does not meet the requirements, a preset detection strategy is adopted for the abnormal detection process of the channel;

[0138] When the number of the response-abnormal users meets the requirements:

[0139] Based on the distribution discrete data of the abnormal responses among the associated channels of the associated medical insurance users, determine the medical insurance users with abnormal responses in different associated channels, and take them as the response-abnormal medical insurance users of the associated channels. When the number of associated channels with response-abnormal medical insurance users that do not meet the requirements, a preset detection strategy is adopted for the abnormal detection process of the channel;

[0140] When there are no associated channels with the number of response-abnormal medical insurance users that do not meet the requirements:

[0141] When the sum of the number of response-abnormal medical insurance users of the associated channels and the number of response-abnormal medical insurance users of the channel does not meet the requirements, a preset detection strategy is adopted for the abnormal detection process of the channel;

[0142] When the sum of the number of response-abnormal medical insurance users of the associated channels and the number of response-abnormal medical insurance users of the channel meets the requirements:

[0143] Utilize the response-abnormal medical insurance users of different associated channels and the coincidence situation of the response-abnormal medical insurance users to determine the response-abnormal coefficient of the associated channels. When the response-abnormal coefficient of the associated channels does not meet the requirements, a preset detection strategy is adopted for the abnormal detection process of the channel;

[0144] When the response-abnormal coefficient of the associated channels meets the requirements:

[0145] Determine the response-abnormal factor of the channel by multiplying the response-abnormal coefficient of the associated channels by the number of response-abnormal users of the channel, and utilize the response-abnormal factor to determine the abnormal detection method of the channel.

[0146] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of devices, equipment, and non-volatile computer storage media, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments.

[0147] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0148] The above is only one or more embodiments of this specification and is not used to limit this specification. For those skilled in the art, one or more embodiments of this specification can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A medical insurance intelligent governance method based on data fusion, characterized in that Specifically, it includes: Determine the fusion deviation channels of the channels by using the fusion deviation situations of the associated data between different medical insurance users and other channels through different channels; Based on the associated data of the medical insurance users of the channels, determine the associated medical insurance users of the channels, and determine the types of fusion processing requirements of the associated medical insurance users based on the analysis results of the medical data of the associated medical insurance users; Obtain the composition data of the associated medical insurance users of different types of fusion processing requirements of the channels, and when it is determined that the channels do not need to use the preset strategy for detection and analysis by combining the associated data of the fusion deviation channels of different associated medical insurance users, proceed to the next step; Obtain the response anomaly situations during the data fusion processing of the channels for different associated medical insurance users within a preset time period, and determine the anomaly detection method of the channels based on the distribution discrete data of the response anomalies between the associated channels suitable for the associated medical insurance users; 2. The medical insurance intelligent governance method based on data fusion according to claim 1, characterized in that The channels include pharmacies, medical insurance systems, medical institutions, health departments, civil affairs systems, and tax systems.

3. The method for intelligent medical insurance governance based on data fusion according to claim 1, wherein The fusion deviation situation is based on the number of fusion failures of different fusion failure types during the fusion processing between the channels and other channels.

4. The method for intelligent medical insurance governance based on data fusion according to claim 3, wherein The fusion failure types include response delay anomalies, data loss, and data format errors.

5. The method for intelligent medical insurance governance based on data fusion according to claim 1, characterized in that The method for determining the fusion deviation channels of the channels is as follows: Determine the number of fusion failures of different medical insurance users based on the fusion deviation situation during the fusion processing between the channels and other channels; Determine the fusion failure users among the medical insurance users according to the number of fusion failures of different medical insurance users; Based on the number of the fusion failure users, determine whether the other channels are the fusion deviation channels of the channels.

6. The method for intelligent medical insurance governance based on data fusion according to claim 5, wherein, When the number of fusion failure users between the other channels and the channels is greater than the preset failure user number threshold, determine that the other channels are the fusion deviation channels of the channels.

7. The method for intelligent medical insurance governance based on data fusion according to claim 1, wherein When the number of the fusion deviation channels of the channels does not meet the requirements, directly determine to use the preset strategy for detection and analysis.

8. The method for intelligent medical insurance governance based on data fusion according to claim 1, wherein, The method for determining the anomaly detection method of the channels is as follows: Based on the response anomaly situations during the data fusion processing of the channels for different associated medical insurance users within a preset time period, determine the associated medical insurance users with response anomalies of the channels and use them as response anomaly users; According to the distribution discrete data of the response anomalies between the associated channels of the associated medical insurance users, determine the medical insurance users with response anomalies of different associated channels and use them as the response anomaly medical insurance users of the associated channels, and determine the response anomaly coefficient of the associated channels by using the response anomaly medical insurance users of different associated channels and the coincidence situation of the response anomaly medical insurance users; Based on the response anomaly coefficient of the associated channels and the number of the response anomaly users of the channels, determine the anomaly detection method of the channels.

9. The method for intelligent medical insurance governance based on data fusion according to claim 8, wherein Based on the response anomaly coefficient of the associated channels and the number of the response anomaly users of the channels, determine the anomaly detection method of the channels, specifically including: Determine the response anomaly factor of the channels by multiplying the response anomaly coefficient of the associated channels by the number of the response anomaly users of the channels; When the response anomaly factor is greater than a preset anomaly factor threshold, a preset detection strategy is adopted to perform anomaly detection processing on the channel; When the response anomaly factor is not greater than the preset anomaly factor threshold, a second preset detection strategy is adopted to perform anomaly detection processing on the channel.

10. A computer system, comprising: A memory and a processor in communication connection, and a computer program stored on the memory and capable of running on the processor, wherein when the processor runs the computer program, it executes a medical insurance intelligent governance method based on data fusion according to any one of claims 1-9.

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