Blood data collection method and system based on block chain technology

Abstract

According to the blood data collection method and system based on the block chain technology provided by the invention, since the collection network performs collection through artificial intelligence and has the standard specified abnormal data, the collection of the blood pollution data is that the feature description fragments are collected according to the collection instruction and the specified abnormal data, and the collection efficiency is improved. Therefore, the collection process is more detailed and accurate, the feature description fragment is identified from the to-be-collected blood sample data, and finally, the collection result obtained by collecting the feature description fragment is fed back to the to-be-collected blood sample data, so that the collection of the blood pollution data in the obtained blood sample data collection result is accurate.

Description

Technical Field

[0001] The present application relates to the field of data collection technology, and specifically, to a blood data collection method and system based on blockchain technology. Background Art

[0002] Blood disorders (such as leukemia, anemia, and hemophilia) are pressing medical challenges worldwide. Advances in medical technology, particularly the application of genomics and precision medicine, have enabled significant progress in early screening, personalized treatment, and drug development for blood disorders. However, fragmentation, inadequate privacy protection, and difficulties in data sharing severely hinder the research and development efforts of research institutions, hospitals, and pharmaceutical companies. Therefore, a digital pathology database integrating global blood disease data is needed, utilizing advanced technologies to process, analyze, and innovate this data. This will facilitate early screening, drug development, and accelerated clinical trials for blood disorders. However, currently, collecting blood data remains a difficult technical challenge. Summary of the Invention

[0003] In order to improve the technical problems existing in related technologies, this application provides a blood data collection method and system based on blockchain technology.

[0004] In a first aspect, a blood data collection method based on blockchain technology is provided, the method comprising: obtaining blood sample data to be collected, wherein the blood sample data to be collected includes blood contamination data; identifying a first contamination result of the blood contamination data, and generating a first matching relationship between the first contamination result and a standard in a blood analysis network, wherein the first contamination result is a feature description segment in a feature description segment of the blood contamination data that is in an abnormal description of the blood contamination data; generating a collection result of the first contamination result according to a collection instruction, specified abnormal data of the standard in the blood analysis network, and the first matching relationship; determining a blood contamination data elimination network for the first contamination result, wherein the standard in the blood contamination data elimination network includes at least a collection result of the first contamination result and a second contamination result mined based on the first contamination result; optimizing the blood contamination data elimination network in the blood sample data to be collected based on the blood contamination data elimination network and the blood sample data to be collected to obtain a blood sample data collection result.

[0005] In the present application, the identifying of the first contamination result of the blood contamination data includes: identifying a characteristic description segment of the blood contamination data; obtaining a projection result of the characteristic description segment in the blood sample data to be collected; generating a projection result on the abnormal description of the blood contamination data based on the projection result of the blood contamination data and the characteristic description segment in the blood sample data to be collected; and using the characteristic description segment corresponding to the projection result on the abnormal description of the blood contamination data as the first contamination result.

[0006] In the present application, the projection results of the blood contamination data and the feature description fragments in the blood sample data to be collected are used to generate the projection results on the abnormal description of the blood contamination data, including: training multiple conditions in a specified direction; and determining two projection results on the abnormal description of the blood contamination data from the projection results of the multiple feature description fragments on each of the conditions.

[0007] In the present application, it also includes: obtaining a second matching relationship between the standard in the blood analysis network and the standard in the blood analysis network; generating a first matching relationship between the first contamination result and the standard in the blood analysis network, including: generating a third matching relationship between the first contamination result and the standard in the blood analysis network based on a directory of the first contamination result and a directory of the standard in the blood analysis network; and generating the first matching relationship based on the second matching relationship and the third matching relationship.

[0008] In the present application, obtaining a second matching relationship between standards in the blood analysis network includes: converting the types of the standards in the blood analysis network and the types of the standards in the blood analysis network into the same type system; generating differences between each standard in the blood analysis network and each standard in the blood analysis network; and generating the second matching relationship based on the differences between each standard in the blood analysis network and each standard in the blood analysis network.

[0009] In the present application, generating a third matching relationship between the first contamination result and the standard in the blood analysis network based on the directory of the first contamination result and the directory of standards in the blood analysis network includes: using the first contamination result and the standard of the blood analysis network with the same directory as each other as a corresponding standard pair to obtain the third matching relationship.

[0010] In the present application, generating a collection result of the first contamination result based on a collection instruction, designated abnormal data of a standard in the blood analysis network, and the first matching relationship includes: determining a collection instruction for blood contamination data based on the collection instruction; obtaining first abnormal data corresponding to the collection instruction from the designated abnormal data of a standard in the blood analysis network; and analyzing the first contamination result corresponding to at least one standard in the blood analysis network based on the first abnormal data to obtain a collection result of the first contamination result.

[0011] In the present application, before analyzing the first contamination result corresponding to at least one standard in the blood analysis network based on the first abnormal data and obtaining the collection result of the first contamination result, it also includes: generating a collection vector of the collection instruction based on the collection instruction; and collecting the first abnormal data based on the collection vector.

[0012] In the present application, the blood contamination data elimination network for determining the first contamination result includes: on the premise that there is a match between the benchmark of the feature description segment and the first contamination result, determining that the result of the specified abnormality in the first contamination result is the second contamination result corresponding to the first contamination result; according to the second regression analysis unit trained in advance, forming a second matching result between the collection result of the first contamination result and the second contamination result to obtain the blood contamination data elimination network.

[0013] In a second aspect, a blood data collection system based on blockchain technology is provided, comprising a processor and a memory that communicate with each other, wherein the processor is used to read a computer program from the memory and execute it to implement the above method.

[0014] The blockchain-based blood data collection method and system provided in the embodiments of the present application obtains the blood sample data to be collected and identifies the characteristic description segments of the blood contamination data in the blood sample data to be collected, determines the first matching relationship between the characteristic description segments and the standard in the blood analysis network of the blood contamination data, and then determines the collection result of the characteristic description segments based on the collection instructions, the standard specified abnormal data in the blood analysis network, and the first matching relationship. Finally, the blood sample data collection result is determined based on the collection result of the characteristic description segments and the blood sample data to be collected. Since the collection network is collected through artificial intelligence and has standard specified abnormal data, and the collection of blood contamination data is based on the collection instructions and the specified abnormal data, the collection process is more detailed and accurate. Furthermore, the characteristic description segments are identified from the blood sample data to be collected, and finally, the collection results obtained by collecting the characteristic description segments are fed back to the blood sample data to be collected. Therefore, the collection of blood contamination data in the blood sample data collection result is accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 A flowchart of a blood data collection method based on blockchain technology provided in an embodiment of the present application.

[0017] Figure 2 A block diagram of a blood data collection device based on blockchain technology provided in an embodiment of the present application.

[0018] Figure 3 This is an architectural diagram of a blood data collection system based on blockchain technology provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0020] See also Figure 1, shows a blood data collection method based on blockchain technology, which may include the technical solutions described in the following steps 100-400.

[0021] Step 100: Acquire data of a blood sample to be collected, wherein the data of the blood sample to be collected includes blood contamination data.

[0022] Step 200: Identify a first contamination result of the blood contamination data and generate a first matching relationship between the first contamination result and a standard in a blood analysis network, wherein the first contamination result is a feature description segment in a feature description segment of the blood contamination data that is in an abnormal description of the blood contamination data.

[0023] Step 300: Generate a collection result of the first contamination result based on the collection instruction, the standard specified abnormal data in the blood analysis network, and the first matching relationship; and determine a blood contamination data elimination network for the first contamination result, wherein the standards in the blood contamination data elimination network include at least the collection result of the first contamination result and a second contamination result mined based on the first contamination result.

[0024] Step 400 : Based on the blood contamination data elimination network and the blood sample data to be collected, the blood contamination data elimination network is optimized in the blood sample data to be collected to obtain a blood sample data collection result.

[0025] It can be understood that when executing the technical content described in steps 100-400 above, the blood sample data to be collected is obtained, and the characteristic description segments of the blood contamination data in the blood sample data to be collected are identified, and a first matching relationship between the characteristic description segments and the standard in the blood analysis network of the blood contamination data is determined. Then, based on the collection instructions, the standard specified abnormal data in the blood analysis network, and the first matching relationship, the collection results of the characteristic description segments are determined. Finally, based on the collection results of the characteristic description segments and the blood sample data to be collected, the blood sample data collection results are determined. Because the collection network is collected through artificial intelligence and has standard specified abnormal data, and the collection of blood contamination data is based on the characteristic description segments according to the collection instructions and the specified abnormal data, the collection process is more detailed and accurate. Furthermore, the characteristic description segments are identified from the blood sample data to be collected, and finally, the collection results obtained by collecting the characteristic description segments are fed back to the blood sample data to be collected. Therefore, the blood contamination data in the blood sample data collection results obtained are accurately collected.

[0026] For some possible implementation embodiments, the identifying the first contamination result of the blood contamination data includes: identifying a characteristic description segment of the blood contamination data; obtaining a projection result of the characteristic description segment in the blood sample data to be collected; generating a projection result on the abnormal description of the blood contamination data based on the projection result of the blood contamination data and the characteristic description segment in the blood sample data to be collected; and using the characteristic description segment corresponding to the projection result on the abnormal description of the blood contamination data as the first contamination result.

[0027] For some possible implementation embodiments, generating a projection result on the abnormal description of the blood contamination data based on the projection results of the blood contamination data and the feature description fragments in the blood sample data to be collected includes: training multiple conditions in a specified direction; and determining two projection results on the abnormal description of the blood contamination data from the projection results of the multiple feature description fragments on each of the conditions.

[0028] For some possible implementation embodiments, the method further includes: obtaining a second matching relationship between the standard in the blood analysis network and the standard in the blood analysis network; generating a first matching relationship between the first contamination result and the standard in the blood analysis network, including: generating a third matching relationship between the first contamination result and the standard in the blood analysis network based on a directory of the first contamination result and a directory of the standard in the blood analysis network; and generating the first matching relationship based on the second matching relationship and the third matching relationship.

[0029] In some possible implementations, obtaining a second matching relationship between standards in the blood analysis network includes: converting the types of the standards in the blood analysis network and the types of the standards in the blood analysis network into the same type system; generating a difference between each standard in the blood analysis network and each standard in the blood analysis network; and generating the second matching relationship based on the difference between each standard in the blood analysis network and each standard in the blood analysis network.

[0030] In some possible implementations, generating a third matching relationship between the first contamination result and the standard in the blood analysis network based on the catalog of the first contamination result and the catalog of standards in the blood analysis network includes: using the first contamination result and the standard in the blood analysis network having the same catalog as a corresponding standard pair to obtain the third matching relationship.

[0031] In some possible implementations, generating a collection result of the first contamination result based on a collection instruction, specified abnormal data of a standard in the blood analysis network, and the first matching relationship includes: determining a collection instruction for blood contamination data based on the collection instruction; obtaining first abnormal data corresponding to the collection instruction from the specified abnormal data of the standard in the blood analysis network; and analyzing, based on the first abnormal data, the first contamination result corresponding to at least one standard in the blood analysis network to obtain a collection result of the first contamination result.

[0032] For some possible implementation embodiments, before analyzing the first contamination result corresponding to at least one standard in the blood analysis network based on the first abnormal data and obtaining a collection result of the first contamination result, the method further includes: generating a collection vector of the collection instruction based on the collection instruction; and collecting the first abnormal data based on the collection vector.

[0033] For some possible implementation embodiments, the blood contamination data elimination network for determining the first contamination result includes: on the premise that there is a match between the baseline of the feature description segment and the first contamination result, determining that the result of the specified abnormality in the first contamination result is the second contamination result corresponding to the first contamination result; according to the second regression analysis unit trained in advance, forming a second matching result between the collection result of the first contamination result and the second contamination result to obtain the blood contamination data elimination network.

[0034] Under the above premise, please refer to Figure 2 , provides a blood data collection device 200 based on blockchain technology, which is applied to a blood data collection system based on blockchain technology, and the device includes: The data acquisition module 210 is used to acquire the blood sample data to be collected, wherein the blood sample data to be collected includes blood contamination data; a matching relationship determination module 220, configured to identify a first contamination result of the blood contamination data and generate a first matching relationship between the first contamination result and a standard in a blood analysis network, wherein the first contamination result is a feature description segment in a feature description segment of the blood contamination data that is in an abnormal description of the blood contamination data; The network determination module 230 is configured to generate a collection result of the first contamination result based on the collection instruction, the standard specified abnormal data in the blood analysis network, and the first matching relationship; and determine a blood contamination data elimination network for the first contamination result, wherein the standard in the blood contamination data elimination network includes at least the collection result of the first contamination result and a second contamination result mined based on the first contamination result; The result collection module 240 is configured to optimize the blood contamination data elimination network in the blood sample data to be collected based on the blood contamination data elimination network and the blood sample data to be collected, so as to obtain a blood sample data collection result.

[0035] Under the above premise, please refer to Figure 3 , shows a blood data collection system 300 based on blockchain technology, including a processor 310 and a memory 320 that communicate with each other, and the processor 310 is used to read and execute a computer program from the memory 320 to implement the above method.

[0036] Under the above premise, a computer-readable storage medium is also provided, on which a computer program stored implements the above method when running.

[0037] In summary, based on the above scheme, by obtaining the blood sample data to be collected and identifying the characteristic description segments of the blood contamination data in the blood sample data to be collected, a first matching relationship between the characteristic description segments and the standard in the blood analysis network of the blood contamination data is determined. Then, based on the collection instructions, the standard specified abnormal data in the blood analysis network, and the first matching relationship, the collection results of the characteristic description segments are determined. Finally, based on the collection results of the characteristic description segments and the blood sample data to be collected, the blood sample data collection results are determined. Because the collection network is collected through artificial intelligence and has standard specified abnormal data, and the collection of blood contamination data is based on the collection instructions and the specified abnormal data, the collection process is more detailed and accurate. Furthermore, the characteristic description segments are identified from the blood sample data to be collected, and finally, the collection results obtained by collecting the characteristic description segments are fed back to the blood sample data to be collected. Therefore, the blood contamination data in the blood sample data collection results obtained are accurately collected.

[0038] It should be understood that the system and its modules shown above can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-mentioned methods and systems can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application can not only be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software executed by various types of processors, or by a combination of the above-mentioned hardware circuits and software (for example, firmware).

[0039] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced may be any one or a combination of the above, or any other possible beneficial effects.

[0040] The basic concepts have been described above. It will be apparent to those skilled in the art that the detailed disclosure above is merely illustrative and does not limit the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and amendments to the present application. Such modifications, improvements, and amendments are suggested in the present application and remain within the spirit and scope of the exemplary embodiments of the present application.

[0041] At the same time, this application uses specific terms to describe the embodiments of this application. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a certain feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "one embodiment," "an embodiment," or "an alternative embodiment" mentioned twice or multiple times in different locations in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application may be appropriately combined.

[0042] In addition, it will be understood by those skilled in the art that the various aspects of the present application can be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or material combination, or any new and useful improvement thereof. Accordingly, each aspect of the present application can be performed entirely by hardware, can be performed entirely by software (including firmware, resident software, microcode, etc.), or can be performed by a combination of hardware and software. The above hardware or software can all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be manifested as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0043] A computer storage medium may include a propagated data signal embodying the computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, or any suitable combination thereof. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium that can be connected to an instruction execution system, apparatus, or device to communicate, propagate, or transfer the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of these.

[0044] The computer program code required for the operation of the various parts of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages. The program code can be executed entirely on the user's computer, or as a stand-alone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any network, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0045] In addition, unless expressly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0046] Similarly, it should be noted that, in order to simplify the presentation of this application and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this application sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not mean that the subject matter of this application requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single embodiment disclosed above.

[0047] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers allow adaptive changes. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which can be changed according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of the present application are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0048] Each patent, patent application, patent application disclosure, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this application is hereby incorporated by reference in its entirety. This includes application history documents that are inconsistent with or conflict with the content of this application, as well as documents (currently or subsequently attached to this application) that limit the broadest scope of the claims of this application. It should be noted that if the descriptions, definitions, and / or use of terms in the accompanying materials of this application are inconsistent or conflicting with the content of this application, the descriptions, definitions, and / or use of terms in this application shall prevail.

[0049] Finally, it should be understood that the embodiments described in this application are intended only to illustrate the principles of the embodiments of this application. Other variations may also fall within the scope of this application. Therefore, by way of example and not limitation, alternative exercises of the embodiments of this application may be considered consistent with the teachings of this application. Accordingly, the embodiments of this application are not limited to the embodiments explicitly described and illustrated in this application.

[0050] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A blood data collection method based on blockchain technology, characterized in that: The method comprises: Acquiring data of a blood sample to be collected, wherein the data of the blood sample to be collected includes blood contamination data; identifying a first contamination result of the blood contamination data, and generating a first matching relationship between the first contamination result and a standard in a blood analysis network, wherein the first contamination result is a feature description segment in a feature description segment of the blood contamination data that is in an abnormal description of the blood contamination data; Generate a collection result of the first contamination result based on the collection instruction, the standard specified abnormal data in the blood analysis network, and the first matching relationship; determine a blood contamination data elimination network for the first contamination result, wherein the standard in the blood contamination data elimination network includes at least the collection result of the first contamination result and a second contamination result mined based on the first contamination result; Based on the blood contamination data elimination network and the blood sample data to be collected, the blood contamination data elimination network is optimized in the blood sample data to be collected to obtain a blood sample data collection result.

2. The blood data collection method based on blockchain technology according to claim 1, characterized in that: The identifying the first contamination result of the blood contamination data includes: Identifying a feature description segment of the blood contamination data; obtaining a projection result of the feature description segment in the blood sample data to be collected; generating a projection result on the abnormal description of the blood contamination data based on the projection result of the blood contamination data and the feature description segment in the blood sample data to be collected; The feature description segment corresponding to the projection result on the abnormal description of the blood contamination data is used as the first contamination result.

3. The blood data collection method based on blockchain technology according to claim 2, characterized in that: The generating of the projection result on the abnormal description of the blood contamination data based on the projection result of the blood contamination data and the feature description segment in the blood sample data to be collected includes: Training multiple conditions in a given direction; Among the projection results of the plurality of feature description segments on each of the conditions, two projection results on abnormal descriptions of the blood contamination data are determined.

4. The blood data collection method based on blockchain technology according to claim 1, characterized in that: Also includes: obtaining a second matching relationship between the standard in the blood analysis network and the standard in the blood analysis network; Generating a first matching relationship between the first contamination result and a standard in the blood analysis network includes: generating a third matching relationship between the first contamination result and the standard in the blood analysis network based on the catalog of the first contamination result and the catalog of standards in the blood analysis network; The first matching relationship is generated based on the second matching relationship and the third matching relationship.

5. The blood data collection method based on blockchain technology according to claim 4, characterized in that: The obtaining of a second matching relationship between the standard in the blood analysis network and the standard in the blood analysis network includes: Converting the types of the standards in the blood analysis network and the types of the standards in the blood analysis network to the same type system; generating the difference between each standard in the blood analysis network and each standard in the blood analysis network; The second matching relationship is generated based on the difference between each standard in the blood analysis network and each standard in the blood analysis network.

6. The blood data collection method based on blockchain technology according to claim 4, characterized in that: Generating a third matching relationship between the first contamination result and the standard in the blood analysis network based on the directory of the first contamination result and the directory of standards in the blood analysis network includes: using the first contamination result and the standard in the blood analysis network having the same directory as each other as a corresponding standard pair to obtain the third matching relationship.

7. The blood data collection method based on blockchain technology according to claim 6, characterized in that: Generating a collection result of the first contamination result according to the collection instruction, the standard specified abnormal data in the blood analysis network, and the first matching relationship includes: Determining a collection instruction for blood contamination data based on the collection instruction; acquiring first abnormal data corresponding to the collection instruction from the standard specified abnormal data in the blood analysis network; Based on the first abnormal data, the first contamination result corresponding to at least one standard in the blood analysis network is analyzed to obtain a collection result of the first contamination result.

8. The blood data collection method based on blockchain technology according to claim 7, characterized in that: Before analyzing the first contamination result corresponding to at least one standard in the blood analysis network based on the first abnormal data to obtain a collection result of the first contamination result, the method further includes: generating a collection vector of the collection instruction based on the collection instruction; and collecting the first abnormal data based on the collection vector.

9. The blood data collection method based on blockchain technology according to claim 2, characterized in that: The blood contamination data elimination network for determining the first contamination result includes: On the premise that there is a match between the benchmark of the feature description segment and the first contamination result, determining a result corresponding to the specified anomaly in the first contamination result as a second contamination result corresponding to the first contamination result; According to the second regression analysis unit trained in advance, a second matching result is formed between the collection result of the first contamination result and the second contamination result to obtain the blood contamination data elimination network.

10. A blood data collection system based on blockchain technology, characterized in that: The invention comprises a processor and a memory communicating with each other, wherein the processor is used to read a computer program from the memory and execute the computer program to implement the method according to any one of claims 1 to 9.

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