Multi-modal sample sharing processing method and system

Abstract

The invention provides a multi-modal sample sharing processing method and system, and belongs to the technical field of data processing, and the method specifically comprises the steps: determining a data quality evaluation coefficient and a data quality deviation region of a region based on the analysis result of the data quality of sample data in different regions, taking other areas except the quality deviation area as residual areas, determining Internet of Things monitoring equipment corresponding to different sample data, and determining equipment setting similarity coefficients of the residual areas and the data quality deviation area according to the similarity condition of setting data of the Internet of Things monitoring equipment in the residual areas and the data quality deviation area; and on the basis of the data quality evaluation coefficient of the data quality deviation region and the equipment setting similarity coefficient of the data quality deviation region, whether the data quality control needs to be performed on the remaining region before sharing is determined, so that the reliability of the data quality is improved.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular relates to a multimodal sample sharing processing method and system. Background Art

[0002] The accuracy of large models in the power field is difficult to meet the requirements. The reason is that the multimodal samples that large models rely on lack accurate collection and sharing, which makes it difficult to support the improvement of model accuracy. At the same time, multimodal samples lack cross-modal collaborative labeling technology, which affects the model accuracy.

[0003] In order to solve the above technical problems, CN202411110491.5 "Method for enhancing multimodal feature fusion based on contrastive learning and structured information" performs feature alignment with text positive samples and image data in the contrastive learning module, and sends the aligned features to the feature fusion module for feature fusion, thereby improving the reliability of the training data. However, the following problems exist: Unlike other companies, multimodal data such as pictures, videos and texts often need to be aggregated and shared from companies in various counties and cities. Therefore, if the data quality of different types of data cannot be tested, the amount of invalid data obtained through aggregation and sharing may be large, which not only makes data transmission more difficult during the data sharing process, but also makes the identification and processing of invalid data more difficult.

[0004] In response to the above technical problems, the present invention provides a multimodal sample sharing processing method and system. Summary of the invention

[0005] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: In order to solve the above technical problems, the present invention provides the following technical solutions to achieve the purpose of the present invention: According to one aspect of the present invention, a multimodal sample sharing processing method is provided.

[0006] A multimodal sample sharing processing method specifically includes: S1 determines the change data of sample data of different modes based on the collection result of multimodal sample data, and when it is determined that advance data quality control is required in combination with the analysis result of data quality of the sample data, proceeds to the next step; S2 divides the sample data into different regions based on the data sources of different sample data, and determines the data quality assessment coefficient and data quality deviation region of the region based on the analysis results of the data quality of the sample data in different regions; S3: taking the other areas except the quality deviation area as the remaining area, determining the IoT monitoring devices corresponding to the different sample data, and determining the device setting similarity coefficient between the remaining area and the data quality deviation area through the similarity between the setting data of the IoT monitoring devices in the remaining area and the data quality deviation area; S4 determines whether the remaining area needs to be subjected to data quality control before sharing based on the data quality assessment coefficient of the data quality deviation area and the equipment setting similarity coefficient with the data quality deviation area.

[0007] The beneficial effects of the present invention are: The device setting similarity coefficient between the remaining area and the data quality deviation area is determined by similarity between the setting data of the IoT monitoring equipment in the remaining area and the data quality deviation area, thereby achieving accurate evaluation of the similarity between the setting data of the IoT monitoring equipment and the data quality deviation area, avoiding the technical problem of low accuracy of evaluation results caused by single consideration of the data quality of sample data, and laying a foundation for further evaluating the remaining areas that need data quality control before sharing based on similarity between the setting data of the IoT monitoring equipment and the data quality deviation area.

[0008] Based on the data quality assessment coefficient of the data quality deviation area and the equipment setting similarity coefficient with the data quality deviation area, it is determined whether the remaining areas need data quality control before sharing. Not only the similarity of the IoT monitoring equipment in the data quality deviation area and the remaining areas is taken into account, but also the data quality conditions of different data quality deviation areas are taken into account. This realizes the determination of the remaining areas that need quantitative quality control from two perspectives, and also lays the foundation for further improving the data quality of the sample data.

[0009] A further technical solution is that the multimodal sample data includes images, videos, text and operation data.

[0010] A further technical solution is that the change data of the sample data includes the amount of new data of the sample data on different dates.

[0011] A further technical solution is that the analysis result of the data quality includes the data volume and data volume proportion of sample data with different types of data quality defects.

[0012] A further technical solution is to determine the need for advance data quality control, including: Based on the change data of the sample data, determine the amount of new data of the sample data on different dates, determine the date when the amount of new data is greater than the preset amount of data through the amount of new data, and use it as the date of data amount change; According to the analysis result of the data quality of the sample data, determine the data volume ratio of the quality defect data of the sample data on different dates, and use the data volume ratio to determine the quality defect deviation date; The data quality control requirement coefficient is determined based on the quantity ratio of the data volume change dates and the quantity ratio of the quality defect deviation dates, and whether early data quality control is required is determined by the data quality control requirement coefficient.

[0013] A further technical solution is that when the data quality control requirement coefficient is greater than a preset requirement coefficient, it is determined that early data quality control is required.

[0014] A further technical solution is to determine whether the remaining area needs to be subjected to data quality control before sharing, specifically including: Determine data quality similarity coefficients of different data quality deviation areas based on the product of the data quality assessment coefficient of the data quality deviation area and the device setting similarity coefficient of the data quality deviation area; The data quality defect probability of the remaining area is determined by the average value of the data quality similarity coefficients of different data quality deviation areas, and based on the data quality defect probability, it is determined whether the remaining area needs to be subjected to data quality control before sharing.

[0015] A further technical solution is that, when the data quality defect probability is less than a preset defect probability, it is determined that the remaining area does not need to be subjected to data quality control before sharing.

[0016] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned multimodal sample sharing processing method when running the computer program.

[0017] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0020] Figure 1 is a flow chart of a multimodal sample sharing processing method; Figure 2 is a flow chart to identify the methods that require advance data quality control; Figure 3 is a flow chart of a method for determining a data quality assessment coefficient for a region; Figure 4 The present invention is a flowchart of a method for determining a device setting similarity coefficient between a remaining area and the data quality deviation area. DETAILED DESCRIPTION

[0021] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their detailed description will be omitted.

[0022] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to express an open-ended inclusive meaning and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc.

[0023] Example 1 To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, according to one aspect of the present invention, a multimodal sample sharing processing method is provided, which specifically includes: S1 determines the change data of sample data of different modes based on the collection result of multimodal sample data, and when it is determined that advance data quality control is required in combination with the analysis result of data quality of the sample data, proceeds to the next step; Furthermore, the multimodal sample data includes images, videos, text and operation data.

[0024] Specifically, the change data of the sample data includes the amount of new data of the sample data on different dates.

[0025] It can be explained that the analysis result of the data quality includes the data volume and data volume proportion of sample data with different types of data quality defects.

[0026] Further, such as Figure 2 As shown, it is determined that advance data quality control is required, including: Based on the change data of the sample data, determine the amount of new data of the sample data on different dates, determine the date when the amount of new data is greater than the preset amount of data through the amount of new data, and use it as the date of data amount change; According to the analysis result of the data quality of the sample data, determine the data volume ratio of the quality defect data of the sample data on different dates, and use the data volume ratio to determine the quality defect deviation date; The data quality control requirement coefficient is determined based on the quantity ratio of the data volume change dates and the quantity ratio of the quality defect deviation dates, and whether early data quality control is required is determined by the data quality control requirement coefficient.

[0027] It should be noted that when the data quality control requirement coefficient is greater than the preset requirement coefficient, it is determined that early data quality control is required.

[0028] It can be understood that the data quality control requirement coefficient is determined based on the average value of the proportion of the number of data volume change dates and the proportion of the number of quality defect deviation dates.

[0029] It should also be noted that the data quality control requirement coefficient is determined based on the maximum value of the proportion of the number of data volume change dates and the proportion of the number of quality defect deviation dates.

[0030] It is understandable that determining that advance data quality control is required includes steps S11-S13, specifically: S11 determines the amount of new data of the sample data on different dates based on the change data of the sample data, determines the date when the amount of new data is greater than the preset amount of data through the amount of new data, and uses it as the date of data amount change, and determines the data amount change coefficient of the sample data based on the proportion of the data amount change date and the amount of new data; S12: determining the data volume ratio of the quality defect data of the sample data on different dates according to the data quality analysis result of the sample data, and determining the quality defect deviation date by using the data volume ratio, and determining the data quality deviation coefficient of the sample data based on the quantity ratio of the quality defect deviation date and the data volume ratio of the quality defect data on different dates; S13 determines a data quality control requirement coefficient based on the data volume variation coefficient of the sample data and the weight of the data quality deviation coefficient, and determines whether early data quality control is required through the data quality control requirement coefficient.

[0031] Optionally, before entering step S11, it is also necessary to determine whether the total data amount of the sample data is less than the preset data amount. When the total data amount of the sample data is less than the preset data amount, it can be directly determined that no advance data quality control is required. When the total data amount of the sample data is not less than the preset data amount, proceed to step S11.

[0032] S2 divides the sample data into different regions based on the data sources of different sample data, and determines the data quality assessment coefficient and data quality deviation region of the region based on the analysis results of the data quality of the sample data in different regions; Furthermore, the sample data is divided into different areas, specifically including: The region corresponding to the sample data is determined according to the data source of the sample data, and the sample data is divided into different regions according to the region corresponding to the sample data.

[0033] Specifically, the region is divided into administrative regions or unit areas.

[0034] Specifically, Figure 3 As shown, the method for determining the data quality assessment coefficient of the region is: Determine the data volume ratio of quality defect data of sample data of different modes in the region based on the data quality analysis result of the sample data in the region, and determine the quality defect coefficient of the sample data of different modes according to the data volume ratio of the quality defect data; Determining weight coefficients of the sample data of different modalities in the region based on the data amounts of the sample data of different modalities in the region; The data quality assessment coefficient of the region is determined by the weight coefficients and quality defect coefficients of the sample data of different modes in the region.

[0035] Furthermore, the data quality assessment coefficient of the region is determined by the weight coefficient and the quality defect coefficient of the sample data of different modes in the region, specifically including: The data quality assessment coefficient of the region is determined by the sum of the products of the weight coefficients of the sample data of different modes and the quality defect coefficients.

[0036] It should also be noted that the value range of the data quality assessment coefficient of the area is between 0 and 1. When the data quality assessment coefficient of the area is less than a preset coefficient threshold, the area is determined to be a data quality deviation area.

[0037] Optionally, the method for determining the data quality assessment coefficient of the region includes steps S21-S23, specifically: S21: determining the data volume ratio of quality defect data of sample data of different modes in the region based on the data quality analysis result of the sample data in the region, and determining the quality defect coefficient of the sample data of different modes according to the data volume ratio of the quality defect data and the data volume of the quality defect data; S22 determines the quality defect modal sample data in the sample data by using the quality defect coefficients of the sample data of different modalities in the region; S23 determines the data quality assessment coefficient of the region by using the data amount of the quality defect modal sample data of different dates.

[0038] Optionally, the above step S21 includes steps S211-S213, which are specifically: S211 determines the data volume ratio of quality defective data of sample data of different modes in the region based on the data quality analysis result of the sample data in the region. When the data volume ratio of the sample data without quality defective data in the region is greater than the preset data volume ratio, it is determined that the region does not belong to the data quality deviation region. When the data volume ratio of the sample data with quality defective data in the region is greater than the preset data volume ratio, the process proceeds to step S212. S212: The sample data with a data volume ratio greater than the preset data volume ratio is regarded as the suspected defect sample data. When the modal number of the suspected defect sample data meets the requirement, the process proceeds to step S213. When the modal number of the suspected defect sample data does not meet the requirement, the process proceeds to step S214. S213: When there is no suspected defective sample data whose data volume of quality defective data does not meet the requirement, it is determined that the region does not belong to the data quality deviation region; when there is suspected defective sample data whose data volume of quality defective data does not meet the requirement, the process proceeds to step S214; S214 determines the quality defect coefficient of sample data of different modes according to the data volume proportion of quality defect data and the data volume of quality defect data. When there is no sample data whose quality defect coefficient does not meet the requirements, it is determined that the area does not belong to the data quality deviation area. When there is sample data whose quality defect coefficient does not meet the requirements, it goes to step S22.

[0039] Optionally, the above step S23 includes steps S231 and S232, which are specifically: S231 uses the data volume of quality defect modal sample data on different dates. When the data volume of quality defect modal sample data on different dates is within the preset defect data volume range, it is determined that the area does not belong to the data quality deviation area. When there is a date when the data volume of quality defect modal sample data is not within the preset defect data volume range, step S232 is entered; S232: The date whose data volume of the quality defect modal sample data is not within the preset defect data volume range is taken as the quality defect date. When the quantity ratio of the quality defect date is less than the preset date quantity ratio, the process proceeds to step S233. When the quantity ratio of the quality defect date is not less than the preset date quantity ratio, the process proceeds to step S234. S233: When the data amounts of the quality defect modal sample data of different quality defect dates are all within the preset defect data amount range, it is determined that the region does not belong to the data quality deviation region; when the data amounts of the quality defect modal sample data of different quality defect dates are not all within the preset defect data amount range, the process proceeds to step S234; S234 determines the data quality assessment coefficient of the region by using the data volume of the quality defect modal sample data of different dates.

[0040] S3: taking the other areas except the quality deviation area as the remaining area, determining the IoT monitoring devices corresponding to the different sample data, and determining the device setting similarity coefficient between the remaining area and the data quality deviation area through the similarity between the setting data of the IoT monitoring devices in the remaining area and the data quality deviation area; Specifically, Figure 4 As shown, the method for determining the device setting similarity coefficient between the remaining area and the data quality deviation area is: Based on the setting data of the IoT monitoring equipment in the remaining area, determine the equipment manufacturers of the IoT monitoring equipment in different types in the remaining area and the data quality deviation area; Based on the device manufacturer, determine the number of deviations between the remaining area and the data quality deviation area in different types of IoT monitoring devices, and determine the device similarity coefficient between the remaining area and the data quality deviation area in different types of IoT monitoring devices through the number of deviations; The device setting similarity coefficient between the remaining area and the data quality deviation area is determined by the average value of the device similarity coefficients of different types of Internet of Things monitoring devices.

[0041] Furthermore, the value range of the equipment setting similarity coefficient between the remaining area and the data quality deviation area is between 0 and 1, wherein the larger the equipment setting similarity coefficient is, the more similar the settings of the Internet of Things monitoring equipment between the remaining area and the data quality deviation area are.

[0042] S4 determines whether the remaining area needs to be subjected to data quality control before sharing based on the data quality assessment coefficient of the data quality deviation area and the equipment setting similarity coefficient with the data quality deviation area.

[0043] It should be noted that determining whether the remaining area needs to be subjected to data quality control before sharing specifically includes: Determine data quality similarity coefficients of different data quality deviation areas based on the product of the data quality assessment coefficient of the data quality deviation area and the device setting similarity coefficient of the data quality deviation area; The data quality defect probability of the remaining area is determined by the average value of the data quality similarity coefficients of different data quality deviation areas, and based on the data quality defect probability, it is determined whether the remaining area needs to be subjected to data quality control before sharing.

[0044] Further, when the data quality defect probability is less than a preset defect probability, it is determined that the remaining area does not need to be subjected to data quality control before sharing.

[0045] Optionally, determining whether the remaining area needs to be subjected to data quality control before sharing includes steps S41-S43, specifically: S41, based on the device setting similarity coefficients of the remaining area and the different data quality deviation areas, determine the average values ​​of the device setting similarity coefficients of the different data quality deviation areas, and determine the comprehensive similarity coefficient of the remaining area by the average values ​​of the device setting similarity coefficients of the different data quality deviation areas; S42: averaging the data quality assessment coefficients of different data quality deviation regions through the data quality assessment coefficients of the data quality deviation region, and determining the comprehensive quality assessment coefficient of the data quality deviation region based on the average values ​​of the data quality assessment coefficients of the different data quality deviation regions; S43 determines the data quality defect probability of the remaining area by using the comprehensive quality assessment coefficient of the data quality deviation area and the comprehensive similarity coefficient of the remaining area, and determines whether the remaining area needs to be subjected to data quality control before sharing based on the data quality defect probability.

[0046] Example 2 In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned multimodal sample sharing processing method when running the computer program.

[0047] Optionally, the above step S11 includes steps S111-S113, which are specifically: S111 determines the amount of new data of the sample data on different dates based on the change data of the sample data. When the average amount of new data of the sample data on different dates does not meet the requirement, it is determined that advance data quality control is required. When the average amount of new data of the sample data on different dates meets the requirement, the process proceeds to step S112. S112 determines the date when the amount of newly added data is greater than the preset amount of data based on the amount of newly added data, and uses it as the date of data amount change. When the amount ratio of the date of data amount change does not meet the requirement, it is determined that early data quality control is required. When the amount ratio of the date of data amount change meets the requirement, the process proceeds to step S113. S113 determines the data volume change coefficient of the sample data based on the quantity ratio of the data volume change date and the newly added data volume. When the data volume change coefficient of the sample data is within the preset change coefficient range, proceeds to step S12. When the data volume change coefficient of the sample data is not within the preset change coefficient range, it is directly determined that no advance data quality control is required.

[0048] Optionally, the above step S12 includes steps S121-S123, specifically: S121 determines the data volume ratio of the quality defect data of the sample data on different dates according to the data quality analysis result of the sample data, and determines the quality defect deviation date by using the data volume ratio. When the date volume ratio of the quality defect deviation date does not meet the requirement, it is determined that early data quality control is required. When the date volume ratio of the quality defect deviation date meets the requirement, it proceeds to step S122; S122 obtains the amount of new data of the sample data on the quality defect deviation date. When the sum of the amount of new data on the quality defect deviation date is greater than the preset amount, it is determined that advance data quality control is required. When the sum of the amount of new data on the quality defect deviation date is not greater than the preset amount, the process proceeds to step S123. S123 determines the data quality deviation coefficient of the sample data based on the proportion of the number of quality defect deviation dates and the proportion of the data volume of quality defect data on different dates. When the data quality deviation coefficient of the sample data does not meet the requirements, it is determined that early data quality control is required. When the data quality deviation coefficient of the sample data meets the requirements, it proceeds to step S13.

[0049] Optionally, the above step S41 includes steps S411-S413, which are specifically: S411: based on the device setting similarity coefficients between the remaining area and the different data quality deviation areas, when it is determined that the device setting similarity coefficients between the remaining area and the different data quality deviation areas are all less than a preset setting similarity coefficient threshold, it is determined that the remaining area does not need to be subjected to data quality control before sharing; when the device setting similarity coefficients between the remaining area and the data quality deviation area are not all less than the preset setting similarity coefficient threshold, the process proceeds to step S412; S412: The data quality deviation area whose device setting similarity coefficient is not less than the preset setting similarity coefficient threshold is taken as the similarity deviation area, and the data quality similarity coefficients of different similarity deviation areas are determined by multiplying the device setting similarity coefficients of different similarity deviation areas by the comprehensive quality assessment coefficients. When there is no similarity deviation area whose data quality similarity coefficient is greater than the preset quality similarity coefficient threshold, it is determined that the remaining area is not subjected to data quality control before sharing. When there is a similarity deviation area whose data quality similarity coefficient is greater than the preset quality similarity coefficient threshold, the process proceeds to step S413. S413 determines the comprehensive similarity coefficient of the remaining area by averaging the similarity coefficients of the devices in different data quality deviation areas. When the comprehensive similarity coefficient of the remaining area is less than the preset comprehensive similarity coefficient threshold, it is determined that the remaining area does not undergo data quality control before sharing. When the comprehensive similarity coefficient of the remaining area is not less than the preset comprehensive similarity coefficient threshold, proceed to step S42.

[0050] Optionally, the above step S42 includes steps S421-S422, which are specifically: S421: When it is determined through the data quality assessment coefficient of the data quality deviation area that the data quality assessment coefficients of different data quality deviation areas are all less than the preset quality assessment coefficient threshold, it is determined that the remaining areas are not subject to data quality control before sharing. When there is a data quality deviation area whose data quality assessment coefficient is not less than the preset quality assessment coefficient threshold, the process proceeds to step S422; S422 determines the comprehensive quality assessment coefficient of the data quality deviation area based on the average value of the data quality assessment coefficients of different data quality deviation areas. When the comprehensive quality assessment coefficient of the data quality deviation area is within the preset quality coefficient range, it is determined that the remaining area does not perform data quality control before sharing. When the comprehensive quality assessment coefficient of the data quality deviation area is not within the preset quality coefficient range, proceed to step S43.

[0051] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0052] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0053] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A multimodal sample sharing processing method, characterized in that: Specifically include: Based on the collection results of the multimodal sample data, the change data of the sample data of different modes is determined, and when it is determined that the data quality control needs to be performed in advance in combination with the analysis results of the data quality of the sample data, the next step is entered; Based on the data sources of different sample data, the sample data are divided into different regions, and based on the data quality analysis results of the sample data in different regions, the data quality assessment coefficients and data quality deviation regions of the regions are determined; The other areas except the quality deviation area are taken as the remaining areas, and the Internet of Things monitoring devices corresponding to different sample data are determined, and the device setting similarity coefficient between the remaining area and the data quality deviation area is determined by the similarity between the setting data of the Internet of Things monitoring devices in the remaining area and the data quality deviation area; Based on the data quality assessment coefficient of the data quality deviation area and the equipment setting similarity coefficient with the data quality deviation area, it is determined whether the remaining area needs to be subjected to data quality control before sharing.

2. The multimodal sample sharing processing method according to claim 1, characterized in that: The multimodal sample data includes images, videos, text and operation data.

3. The multimodal sample sharing processing method according to claim 1, characterized in that: The change data of the sample data includes the amount of new data of the sample data on different dates.

4. The multimodal sample sharing processing method according to claim 1, characterized in that: The data quality analysis results include the data volume and data volume ratio of sample data with different types of data quality defects.

5. The multimodal sample sharing processing method according to claim 1, characterized in that: Determine the need for advance data quality control, including: Based on the change data of the sample data, determine the amount of new data of the sample data on different dates, determine the date when the amount of new data is greater than the preset amount of data through the amount of new data, and use it as the date of data amount change; According to the analysis result of the data quality of the sample data, determine the data volume ratio of the quality defect data of the sample data on different dates, and use the data volume ratio to determine the quality defect deviation date; The data quality control requirement coefficient is determined based on the quantity ratio of the data volume change dates and the quantity ratio of the quality defect deviation dates, and whether early data quality control is required is determined by the data quality control requirement coefficient.

6. The multimodal sample sharing processing method according to claim 5, characterized in that: The data quality control requirement coefficient is determined based on the average value of the quantity proportion of the data volume change dates and the quantity proportion of the quality defect deviation dates.

7. The multimodal sample sharing processing method according to claim 1, characterized in that: The data quality control requirement coefficient is determined according to the maximum value of the quantity proportion of the data volume change date and the quantity proportion of the quality defect deviation date.

8. The multimodal sample sharing processing method according to claim 1, characterized in that: Determine whether the remaining areas require data quality control before sharing, including: Determine data quality similarity coefficients of different data quality deviation areas based on the product of the data quality assessment coefficient of the data quality deviation area and the device setting similarity coefficient of the data quality deviation area; The data quality defect probability of the remaining area is determined by the average value of the data quality similarity coefficients of different data quality deviation areas, and based on the data quality defect probability, it is determined whether the remaining area needs to be subjected to data quality control before sharing.

9. The multimodal sample sharing processing method according to claim 8, characterized in that: When the data quality defect probability is less than a preset defect probability, it is determined that the remaining area does not need to be subjected to data quality control before sharing.

10. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that the processor executes a multimodal sample sharing processing method as described in any one of claims 1-9 when running the computer program.

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