Medical document electronic signature and data storage method based on multi-terminal collaboration

Abstract

The invention discloses a medical document electronic signature and data storage method based on multi-terminal collaboration, and the method comprises the steps: carrying out the dynamic weight distribution of related data of basic medical treatment, patients and medical personnel during data fusion, obtaining an initial weight vector, carrying out the reliability adjustment according to a data source, obtaining an adjusted weight vector, and fusing the data according to the adjusted weight vector. Data importance and source reliability are fully considered, so that fused data are closer to real medical conditions; in an electronic signature link, a private key based on multiple factors is used for signing fused data, so that the security and reliability of the private key are enhanced, data counterfeiting and tampering are prevented, and the security of a medical document is guaranteed; in the aspect of evidence storage, the fusion data and the electronic signature are uploaded to a distributed evidence storage platform, the platform adopts a distributed storage architecture, decentralization, high availability and tamper prevention are achieved, each node stores a complete data copy, a unique evidence storage identifier is generated for each medical document, management and tracing are facilitated, the data are ensured to be real and complete, and the risk of data tamper is effectively avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of medical document signature and evidence storage technology, and in particular to a medical document electronic signature and data evidence storage method based on multi-terminal collaboration. Background Art

[0002] Medical document management is of great significance in medical decision-making, quality assessment, dispute resolution and medical research. However, traditional medical document management methods have many problems: (1) Insufficient data fusion: In terms of data fusion, existing solutions may simply combine data collected by different devices without considering the importance and reliability of different data, resulting in the fused data not accurately reflecting the actual medical situation; (2) Single factor: Although some existing electronic signature technologies have adopted multi-factor authentication, the selection of factors is not comprehensive and reasonable. This single or incomplete factor combination method still poses certain risks to the security of electronic signatures; (3) Poor management of evidence identification: In terms of the generation and management of evidence identification, existing technologies may have problems such as non-unique identification and easy forgery, leading to the reuse or abuse of identification. Summary of the Invention

[0003] In view of this, the present invention proposes a medical document electronic signature and data notarization method based on multi-terminal collaboration, which can effectively solve the defects of insufficient data fusion, single factor and poor management of notarization identification in the existing technology.

[0004] The technical solution of the present invention is achieved as follows:

[0005] A method for electronic signature and data storage of medical documents based on multi-terminal collaboration, specifically including:

[0006] During the medical document generation process, data related to the medical document is collected through medical terminal devices, patient mobile terminal devices, and medical staff mobile terminal devices. Among them, medical terminal devices collect basic medical data, patient mobile terminal devices collect patient-related data, and medical staff mobile terminal devices collect medical staff-related data;

[0007] Dynamically assign weights to the collected basic medical data, patient-related data, and medical staff-related data to obtain an initial weight vector;

[0008] Adjust the initial weight vector according to the reliability of the data source to obtain an adjusted weight vector;

[0009] The basic medical data, patient-related data and medical staff-related data are fused according to the adjusted weight vector to obtain fused data;

[0010] Use a multi-factor-based private key to digitally sign the fused data and generate an electronic signature;

[0011] The fused data and electronic signature are uploaded to the distributed evidence storage platform, which generates a unique evidence identification for each medical document.

[0012] As a further optional solution to the multi-terminal collaborative medical document electronic signature and data notarization method, the initial weight vector is determined according to the type of medical document, specifically including:

[0013] Pre-build a medical document classification system and, for each medical document type T, extract characteristic information about its correlation with basic medical data, patient-related data, and medical staff-related data;

[0014] Based on the extracted feature information, a basic weight vector calculation model is constructed. The basic weight vector calculation model adopts a weighted scoring mechanism. For each medical document type T, the importance of basic medical data, patient-related data, and medical staff-related data in the medical document is scored to obtain a score value;

[0015] Determine the initial weight vector based on the score value calculated by the basic weight vector calculation model ,in 、 、 They are the initial weights of basic medical data, patient-related data and medical staff-related data under type T respectively.

[0016] As a further optional solution to the multi-terminal collaborative medical document electronic signature and data storage method, the initial weight vector is adjusted according to the reliability of the data source to obtain an adjusted weight vector. The adjustment formula is:

[0017] ;

[0018] in, Represents the multiplication of corresponding elements of vectors, , 、 、 They are the data source reliability of medical terminal devices, patient mobile terminal devices and medical staff mobile terminal devices.

[0019] As a further optional solution to the multi-terminal collaborative medical document electronic signature and data evidence storage method, the basic medical data, patient-related data, and medical staff-related data are fused according to the adjusted weight vector. The fusion formula is:

[0020] ;

[0021] in, 、 、 are the weight vectors corresponding to the adjusted basic medical data, patient-related data, and medical staff-related data, respectively. 、 、 They are basic medical data, patient-related data and medical staff-related data. To fuse data.

[0022] As a further optional solution to the multi-terminal collaborative medical document electronic signature and data evidence storage method, the method of using a multi-factor-based private key to digitally sign the fused data and generate an electronic signature specifically includes:

[0023] Obtain the static private key generated by the hardware security module, the dynamic factor generated based on the medical staff's current operation time and location, and the biometric factor preset by the medical staff;

[0024] Generate a multi-factor private key based on the static private key, dynamic factor and biometric factor;

[0025] The fused data is digitally signed based on the multi-factor private key to generate an electronic signature.

[0026] As a further optional solution to the multi-terminal collaborative medical document electronic signature and data evidence storage method, the distributed evidence storage platform generates a unique evidence identification for each medical document. The evidence identification calculation formula is:

[0027] ;

[0028] Among them, the evidence storage platform records the evidence identification ID on the distributed ledger and integrates the data The electronic signature S is stored in the distributed storage system, and the evidence identification ID and fusion data are established and the mapping relationship between the storage address of the electronic signature S.

[0029] As a further optional solution to the multi-terminal collaborative medical document electronic signature and data storage method, the method further includes:

[0030] After obtaining the fused data, before using the multi-factor based private key to digitally sign the fused data, the integrity, accuracy and consistency of each data in the fused data are checked using preset verification rules, and the verification index is calculated.

[0031] A medical document electronic signature and data evidence storage system based on multi-terminal collaboration, including:

[0032] The multi-terminal data collection module is used to collect data related to medical documents through medical terminal devices, patient mobile terminal devices, and medical staff mobile terminal devices during the medical document generation process. Among them, the medical terminal devices collect basic medical data, the patient mobile terminal devices collect patient-related data, and the medical staff mobile terminal devices collect medical staff-related data;

[0033] The weight vector allocation module is used to dynamically allocate weights to the collected basic medical data, patient-related data, and medical staff-related data to obtain the initial weight vector;

[0034] A weight vector adjustment module is used to adjust the initial weight vector according to the reliability of the data source to obtain an adjusted weight vector;

[0035] A data fusion module is used to fuse basic medical data, patient-related data, and medical staff-related data according to the adjusted weight vector to obtain fused data;

[0036] An electronic signature generation module, used to digitally sign the fused data using a multi-factor based private key to generate an electronic signature;

[0037] The distributed evidence storage module is used to upload the integrated data and electronic signature to the distributed evidence storage platform. The distributed evidence storage platform generates a unique evidence identification for each medical document.

[0038] A computing device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements any one of the steps of the above-mentioned method for electronic signature and data storage of medical documents based on multi-terminal collaboration.

[0039] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements the steps of any one of the above-mentioned methods for electronic signature and data storage of medical documents based on multi-terminal collaboration.

[0040] The beneficial effects of the present invention are as follows: in the process of data fusion, the technical solution first dynamically assigns weights to the collected basic medical data, patient-related data and medical staff-related data to obtain an initial weight vector, then adjusts the initial weight vector according to the reliability of the data source to obtain an adjusted weight vector, and finally fuses the data based on the adjusted weight vector. This dynamic weight assignment and adjustment mechanism based on source reliability fully consider the importance of different data in medical documents and the reliability of the source, and can give basic medical data a higher weight and give patient-related data a relatively low but reasonable weight, so that the fused data can more accurately reflect the real medical situation; using a private key based on multiple factors The private key is used to digitally sign the fused data. By combining multiple factors, the security and reliability of the private key are increased, which can effectively prevent the data from being forged and tampered with, and ensure the security of medical documents. The fused data and the electronic signature are uploaded to the distributed evidence storage platform. The distributed evidence storage platform generates a unique evidence identification for each medical document. The distributed evidence storage platform adopts a distributed storage architecture with the characteristics of decentralization, high availability and tamper-proofing. Each node saves a complete copy of the data. Once the data is stored, it is difficult to be tampered with. At the same time, the unique evidence identification can ensure that each medical document has a unique identity, which is convenient for management and traceability, and can ensure the authenticity and integrity of the data, avoiding the risk of data tampering. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 This is a flow chart of a method for electronic signature and data storage of medical documents based on multi-terminal collaboration according to the present invention;

[0043] Figure 2 This is a schematic diagram of the composition of a medical document electronic signature and data evidence storage system based on multi-terminal collaboration of the present invention;

[0044] Figure 3 A schematic diagram of the composition of a computing device according to the present invention. DETAILED DESCRIPTION

[0045] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0046] refer to Figures 1 to 3 , a medical document electronic signature and data evidence storage method based on multi-terminal collaboration, specifically including:

[0047] During the process of generating medical documents, medical terminal devices, patient mobile terminal devices, and medical staff mobile terminal devices are used to collect data related to medical documents. Among them, basic medical data such as medical examination data and diagnosis data collected by medical terminal devices are recorded as , patient-related data such as patient identity verification information, informed consent confirmation information, etc. collected by the patient's mobile terminal device is recorded as , the medical staff identity verification information, operation records and other medical staff related data collected by the medical staff mobile terminal device are recorded as .

[0048] The collected basic medical data , patient-related data Data related to medical staff Perform dynamic weight allocation to obtain the initial weight vector.

[0049] In some embodiments, the initial weight vector is determined based on the type of medical document, specifically including:

[0050] A medical document classification system is pre-established. For each medical document type T, characteristic information on the degree of correlation between the document and basic medical data, patient-related data, and medical staff-related data is extracted. For medical records, characteristic information includes, but is not limited to, the reliance of the diagnostic basis on basic medical data for detail and the importance of patient-related data such as the patient's medical history and family history in diagnosis. For examination reports, characteristic information covers the direct correlation between examination items and basic medical examination data, the degree to which patient-related data such as the patient's pre-examination preparation influences the examination results. For prescriptions, characteristic information includes the correlation between drug selection and basic medical diagnostic data, and the key role of patient-related data such as the patient's allergy history in prescribing.

[0051] Based on the extracted feature information, a basic weight vector calculation model is constructed. The model adopts a weighted scoring mechanism to score the importance of basic medical data, patient-related data, and medical staff-related data in each medical document type T. The basic medical data scoring function is set as , the patient-related data scoring function is , the scoring function for medical staff related data is , the scoring range is [0, 100]; the scoring function is pre-set according to the characteristic information of various types of medical documents, for example, for medical records, A higher score will be given to reflect the reliance of the diagnosis on basic medical data. The corresponding score will also be given according to factors such as past medical history; for the type of examination report, Scoring is based on the degree of correlation between the inspection items and the basic inspection data. Scoring is based on factors such as pre-examination preparation;

[0052] Determine the initial weight vector based on the score value calculated by the basic weight vector calculation model ,in 、 、 are the initial weights of basic medical data, patient-related data, and medical staff-related data under type T, respectively, and + + =1, the calculation method is:

[0053] ;

[0054] ;

[0055] .

[0056] Specifically, a classification system for medical document types is pre-established, and for each type, characteristic information related to the degree of correlation with different data is extracted. This ensures that the determination of the initial weight vector is not based on subjective assumptions, but is based on an in-depth analysis of the characteristics of each type of medical document. For example, for medical records, the degree of correlation between basic medical data (such as symptoms and test results), patient-related data (such as medical history and allergy history), and medical staff-related data (such as diagnosis and treatment plans) can be more accurately identified, providing a more targeted basis for subsequent weight calculations.

[0057] The basic weight vector calculation model uses a weighted scoring mechanism to score the importance of different types of data in medical documents. This approach avoids arbitrary weight settings and determines weights through quantified scoring values, making the initial weight vector more reflective of actual conditions. For example, if basic medical data plays a key role in diagnosis in a certain specialized medical document, the weight of basic medical data will be increased accordingly through the weighted scoring mechanism, ensuring that the weight distribution is consistent with the actual importance of the data.

[0058] A reasonable initial weight vector helps highlight important data and deemphasize secondary data during the data fusion process. When fusing multi-source data, it avoids the loss of key information or noise interference caused by data averaging. For example, for diagnostic reports, the examination results and symptom descriptions in basic medical data may be key to diagnosis. By assigning reasonable initial weights, these data can receive sufficient attention during the fusion process, increasing the support provided by the fused data for diagnosis and, in turn, improving the accuracy of medical decisions.

[0059] When the fused data is subsequently digitally signed using a multi-factor private key, the quality of the fused data depends on the rationality of the initial weight vector. An accurate initial weight vector ensures that the fused data accurately reflects the key information of the medical document, allowing the electronic signature to truly represent the integrity and authenticity of the medical document. For example, if the initial weight vector is unreasonable, resulting in the lack of important information in the fused data, then the electronic signature cannot effectively guarantee the reliability of the medical document. This technical solution provides a solid data foundation for the electronic signature by scientifically determining the initial weight vector.

[0060] The initial weight vector is adjusted according to the reliability R of the data source (obtained through historical data quality assessment, with a value range of [0, 1]) to obtain the adjusted weight vector.

[0061] In some embodiments, the initial weight vector is adjusted according to the reliability R of the data source to obtain an adjusted weight vector, and the adjustment formula is:

[0062] ;

[0063] in, Represents the multiplication of corresponding elements of vectors, , 、 、 They are the data source reliability of medical terminal devices, patient mobile terminal devices and medical staff mobile terminal devices.

[0064] Specifically, the reliability of different data sources (medical terminal devices, patient mobile terminal devices, and medical staff mobile terminal devices) varies. By adjusting the initial weight vector according to the reliability R of the data source, the credibility of each data in the actual situation can be more accurately reflected. For example, medical terminal devices are usually strictly calibrated and maintained, and their data reliability may be high; while patient mobile terminal devices may have relatively low data reliability due to factors such as changing usage environments and non-standard operations. Adjusting the formula The initial weights can be reasonably adjusted based on these differences to make the weight distribution more consistent with the actual data quality;

[0065] The reliability of the data source is derived from the historical data quality assessment, which means that the reliability assessment will be updated with the change of time and data. Therefore, the weight adjustment is not a one-time adjustment, but can be dynamically adjusted according to the latest data quality. For example, if a certain type of medical terminal equipment frequently fails over a period of time, resulting in a decline in data quality, its reliability assessment value will be adjusted. It will be reduced, and then by adjusting the formula, the weight of the data collected by the device during fusion will be reduced, ensuring that the weight adjustment can adapt to the actual situation in different periods;

[0066] The adjusted weight vector can optimize the proportion of basic medical data, patient-related data, and medical staff-related data in the fusion process. Reliable data sources are given higher weights, making these data occupy a more important position in the fusion results, thereby improving the quality of the fused data. For example, when fusing data, if the data source of the medical staff's mobile terminal device is highly reliable, the adjusted weight will make this part of the data have a greater impact on the final result in the fusion data, making the fused data more accurately reflect the key information of the medical process.

[0067] When using a multi-factor-based private key to digitally sign fused data, the quality of the fused data directly affects the accuracy and reliability of the electronic signature. By adjusting the weight vector based on the reliability of the data source, the fused data can more accurately represent the true content of the medical document, thereby ensuring that the electronic signature can effectively verify the integrity and authenticity of the medical document. For example: if the fused data contains a large amount of unreliable data, the electronic signature may not accurately reflect the actual status of the medical document, but the fused data after weight adjustment can avoid this situation.

[0068] The basic medical data, patient-related data and medical staff-related data are fused according to the adjusted weight vector to obtain fused data.

[0069] In some embodiments, the basic medical data, patient-related data, and medical staff-related data are fused according to the adjusted weight vector, and the fusion formula is:

[0070] ;

[0071] in, 、 、 are the weight vectors corresponding to the adjusted basic medical data, patient-related data, and medical staff-related data, namely , , , 、 、 They are basic medical data, patient-related data and medical staff-related data. To fuse data.

[0072] Specifically, in the formula 、 、 , the adjusted weight vector is compared with the data source reliability ( 、 、 ) and the initial weights ( 、 、 ) is associated, which means that in the integration of basic medical data , patient-related data Data related to medical staff When considering the importance of the data in the initial situation, we also consider the reliability of the data source. For example, if the data source reliability of a medical terminal device is high ( The value is large), and basic medical data is more important in the initial weight distribution ( Large), then the basic medical data will affect the fusion data during fusion. Produce greater impact, so that the fusion results can better reflect key and reliable information;

[0073] Since the reliability of data sources is derived through historical data quality assessment and will be updated over time and with changes in data, the fusion formula can dynamically adapt to data of different periods and qualities. When the reliability of a data source changes, the corresponding weight will also be automatically adjusted, thus ensuring that the fused data is always based on the latest and most reliable information, thereby improving the accuracy of data fusion.

[0074] For data sources with low reliability, the corresponding R value is small, and the impact of such data on the fusion results in the fusion formula will be reduced accordingly. For example, if the data source of the patient's mobile terminal device is of low reliability ( If the value is small), the proportion of patient-related data in the fused data will be reduced, reducing the interference of unreliable data on the fusion results and improving the reliability of the fused data;

[0075] By integrating basic medical data, patient-related data, and medical staff-related data and assigning reasonable weights, this formula can combine the advantages of multiple sources of data. Basic medical data is generally more objective and accurate, patient-related data can provide patients' subjective feelings and actual conditions, and medical staff-related data includes professional judgments and operation records. The fused data combines information from all aspects and can more comprehensively and accurately reflect the medical situation than a single data source, thereby enhancing the reliability of the fused data.

[0076] Fusion Data This data, encompassing diverse information, provides a more comprehensive basis for medical decision-making. When formulating treatment plans and making diagnoses, doctors can draw on the objective examination results provided by the basic medical data, the subjective symptoms and medical history reflected in the patient-related data, and the treatment process and judgments recorded by the medical staff in the fused data, thereby making more accurate and rational medical decisions.

[0077] The fused data is digitally signed using a multi-factor based private key to generate an electronic signature.

[0078] In some embodiments, the digitally signing the fused data using a multi-factor based private key to generate an electronic signature specifically includes:

[0079] Obtain a static private key generated by a hardware security module (HSM) , dynamic factors generated based on the medical staff's current operation time t (accurate to seconds) and operation location l (obtained through GPS positioning) , and biometric factors preset by medical staff (such as fingerprints, facial features, etc., extracted through biometric recognition technology);

[0080] Based on static private key , dynamic factors and biometric factors , generate a multi-factor private key , the calculation formula is:

[0081] ,in Represents the exclusive OR operation;

[0082] The fused data is digitally signed based on the multi-factor private key to generate an electronic signature. The generation formula is:

[0083] ,in Indicates the use of a multi-factor private key The signature algorithm.

[0084] Specifically, using a multi-factor based private key Digitally sign the fused data, and the multi-factor private key is a static private key generated by the hardware security module , dynamic factors generated based on the current operation time t and operation location l of the medical staff and biometric factors preset by medical staff This combination of multiple factors significantly increases the complexity and security of the private key. For example, the static private key provides basic security. The dynamic factor is related to the time and location of the operation, making the private key time-sensitive and location-dependent. The biometric factor utilizes the unique physiological or behavioral characteristics of medical personnel, further enhancing the uniqueness and unforgeability of the private key. Forging an electronic signature requires obtaining all three factors simultaneously, making it extremely difficult.

[0085] Since the private key is generated based on multiple factors of different types, even if a factor is partially leaked, the attacker cannot obtain the private key in its entirety. For example, if the static private key The hardware security module has some security vulnerabilities and has been partially cracked, but due to dynamic factors Biometric factors vary over time and place Unique, attackers still cannot generate valid multi-factor private keys To forge signatures, thus effectively preventing the risk of electronic signatures being forged due to private key leakage;

[0086] Based on multi-factor private key For fused data Digital signature generates electronic signature S. The signing process uses a specific signature algorithm This signature method tightly binds the electronic signature to the fused data. Any tampering with the fused data will cause signature verification failure. For example, if the fused data is maliciously modified during transmission or storage, when verifying the electronic signature, since the signature is generated based on the original fused data, the verification algorithm will detect the data inconsistency and reject the signature, thus ensuring data integrity.

[0087] Biometric factors in multi-factor private keys Closely related to the identity of medical staff, dynamic factors Related to the time and place of the operation, during the signature verification process, not only the integrity of the fused data can be verified, but also whether the signature is generated by a legitimate medical staff at a specific time and place can be verified. For example: in the process of medical disputes or audits, by verifying the electronic signature, it can be confirmed whether the medical document is signed by the designated medical staff at the specified time and place, thus ensuring the authenticity and legality of the medical behavior.

[0088] The fused data and electronic signature are uploaded to the distributed evidence storage platform, which generates a unique evidence identification for each medical document.

[0089] In some embodiments, the distributed evidence storage platform generates a unique evidence identification for each medical document. The formula for calculating the evidence identification is:

[0090] ;

[0091] Among them, the evidence storage platform records the evidence identification ID on the distributed ledger and integrates the data The electronic signature S is stored in the distributed storage system, and the evidence identification ID and fusion data are established and the mapping relationship between the storage address of the electronic signature S.

[0092] Specifically, in the calculation formula of the evidence identification, The fusion data, S is the electronic signature, and T is the medical document type. This information is processed through a hash function. The hash function is one-way and unique. Different input combinations will produce different hash values. Therefore, a unique identification can be generated for each medical document. This avoids duplication of identification, ensuring that each medical document has a unique identity in the evidence storage system, facilitating accurate distinction and management.

[0093] Distributed evidence storage platform will integrate data The electronic signature S is stored in a distributed storage system. Distributed storage has the characteristics of decentralization and redundant storage. Compared with traditional centralized storage, it can effectively prevent single point failures and data loss. Even if some storage nodes fail, other nodes still store complete data, ensuring data security and availability.

[0094] A mapping relationship is established between the evidence identification and the storage address of the fused data and electronic signature, and recorded on the distributed ledger. When the integrity of the medical document needs to be verified, the corresponding fused data and electronic signature can be quickly located through the evidence identification, and the hash value can be recalculated and compared with the original evidence identification. If the two are consistent, it means that the data has not been tampered with, ensuring the integrity of the data. This mechanism provides traceable and verifiable protection for the data evidence of medical documents.

[0095] The method further comprises:

[0096] After obtaining the fused data, before using the multi-factor based private key to digitally sign the fused data, the integrity, accuracy and consistency of each data in the fused data are checked using the preset verification rules, and the verification index is calculated. The verification index calculation formula is:

[0097] ;

[0098] Where I is the integrity index (obtained by checking whether the data field is complete, the value range is [0,1]), A is the accuracy index (obtained by comparing with the standard data, the value range is [0,1]), and C is the consistency index (obtained by checking whether the internal logic of the data is consistent, the value range is [0,1]). 、 、 are the weights of the completeness, accuracy and consistency indicators respectively, and + + =1; if Q is greater than the preset verification threshold , the verification passes.

[0099] A medical document electronic signature and data evidence storage system based on multi-terminal collaboration, including:

[0100] The multi-terminal data collection module is used to collect data related to medical documents through medical terminal devices, patient mobile terminal devices, and medical staff mobile terminal devices during the medical document generation process. Among them, the medical terminal devices collect basic medical data, the patient mobile terminal devices collect patient-related data, and the medical staff mobile terminal devices collect medical staff-related data;

[0101] The weight vector allocation module is used to dynamically allocate weights to the collected basic medical data, patient-related data, and medical staff-related data to obtain the initial weight vector;

[0102] A weight vector adjustment module is used to adjust the initial weight vector according to the reliability of the data source to obtain an adjusted weight vector;

[0103] A data fusion module is used to fuse basic medical data, patient-related data, and medical staff-related data according to the adjusted weight vector to obtain fused data;

[0104] An electronic signature generation module, used to digitally sign the fused data using a multi-factor based private key to generate an electronic signature;

[0105] The distributed evidence storage module is used to upload the integrated data and electronic signature to the distributed evidence storage platform. The distributed evidence storage platform generates a unique evidence identification for each medical document.

[0106] A computing device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements any one of the steps of the above-mentioned method for electronic signature and data storage of medical documents based on multi-terminal collaboration.

[0107] A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements the steps of any one of the above-mentioned methods for electronic signature and data storage of medical documents based on multi-terminal collaboration.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for electronic signature and data storage of medical documents based on multi-terminal collaboration, characterized in that: Specifically include: During the medical document generation process, data related to the medical document is collected through medical terminal devices, patient mobile terminal devices, and medical staff mobile terminal devices. Among them, medical terminal devices collect basic medical data, patient mobile terminal devices collect patient-related data, and medical staff mobile terminal devices collect medical staff-related data; Dynamically assign weights to the collected basic medical data, patient-related data, and medical staff-related data to obtain an initial weight vector; Adjust the initial weight vector according to the reliability of the data source to obtain an adjusted weight vector; The basic medical data, patient-related data and medical staff-related data are fused according to the adjusted weight vector to obtain fused data; Use a multi-factor-based private key to digitally sign the fused data and generate an electronic signature; The fused data and electronic signature are uploaded to the distributed evidence storage platform, which generates a unique evidence identification for each medical document.

2. The method for electronic signature and data storage of medical documents based on multi-terminal collaboration according to claim 1 is characterized in that: The initial weight vector is determined according to the type of medical document, specifically including: Pre-build a medical document classification system and, for each medical document type T, extract characteristic information about its correlation with basic medical data, patient-related data, and medical staff-related data; Based on the extracted feature information, a basic weight vector calculation model is constructed. The basic weight vector calculation model adopts a weighted scoring mechanism. For each medical document type T, the importance of basic medical data, patient-related data, and medical staff-related data in the medical document is scored to obtain a score value; Determine the initial weight vector based on the score value calculated by the basic weight vector calculation model ,in 、 、 They are the initial weights of basic medical data, patient-related data and medical staff-related data under type T respectively.

3. The method for electronic signature and data storage of medical documents based on multi-terminal collaboration according to claim 2 is characterized in that: The initial weight vector is adjusted according to the reliability of the data source to obtain the adjusted weight vector. The adjustment formula is: ； in, Represents the multiplication of corresponding elements of vectors, , 、 、 They are the data source reliability of medical terminal devices, patient mobile terminal devices and medical staff mobile terminal devices.

4. The method for electronic signature and data storage of medical documents based on multi-terminal collaboration according to claim 3 is characterized in that: The basic medical data, patient-related data and medical staff-related data are fused according to the adjusted weight vector, and the fusion formula is: ； in, 、 、 are the weight vectors corresponding to the adjusted basic medical data, patient-related data, and medical staff-related data, 、 、 They are basic medical data, patient-related data and medical staff-related data. To fuse data.

5. The method for electronic signature and data storage of medical documents based on multi-terminal collaboration according to claim 4 is characterized in that: The digital signature of the fused data is performed using a multi-factor based private key to generate an electronic signature, specifically including: Obtain the static private key generated by the hardware security module, the dynamic factor generated based on the medical staff's current operation time and location, and the biometric factor preset by the medical staff; Generate a multi-factor private key based on the static private key, dynamic factor and biometric factor; The fused data is digitally signed based on the multi-factor private key to generate an electronic signature.

6. The method for electronic signature and data storage of medical documents based on multi-terminal collaboration according to claim 5 is characterized in that: The distributed evidence storage platform generates a unique evidence identification for each medical document. The formula for calculating the evidence identification is: ; Among them, the evidence storage platform records the evidence identification ID on the distributed ledger and integrates the data The electronic signature S is stored in the distributed storage system, and the evidence identification ID and fusion data are established and the mapping relationship between the storage address of the electronic signature S.

7. The method for electronic signature and data storage of medical documents based on multi-terminal collaboration according to claim 6 is characterized in that: The method further comprises: After obtaining the fused data, before using the multi-factor based private key to digitally sign the fused data, the integrity, accuracy and consistency of each data in the fused data are checked using preset verification rules, and the verification index is calculated.

8. A medical document electronic signature and data evidence storage system based on multi-terminal collaboration, characterized by: include: The multi-terminal data collection module is used to collect data related to medical documents through medical terminal devices, patient mobile terminal devices, and medical staff mobile terminal devices during the medical document generation process. Among them, the medical terminal devices collect basic medical data, the patient mobile terminal devices collect patient-related data, and the medical staff mobile terminal devices collect medical staff-related data; The weight vector allocation module is used to dynamically allocate weights to the collected basic medical data, patient-related data, and medical staff-related data to obtain the initial weight vector; A weight vector adjustment module is used to adjust the initial weight vector according to the reliability of the data source to obtain an adjusted weight vector; A data fusion module is used to fuse basic medical data, patient-related data, and medical staff-related data according to the adjusted weight vector to obtain fused data; An electronic signature generation module, used to digitally sign the fused data using a multi-factor based private key to generate an electronic signature; The distributed evidence storage module is used to upload the integrated data and electronic signature to the distributed evidence storage platform. The distributed evidence storage platform generates a unique evidence identification for each medical document.

9. A computing device, characterized in that It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method for electronic signature and data storage of medical documents based on multi-terminal collaboration as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for electronic signature and data storage of medical documents based on multi-terminal collaboration as described in any one of claims 1 to 7.

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