Blockchain-based clinical diagnosis and treatment data encryption entry method

Abstract

The present application relates to the technical field of data processing, and more particularly to a clinical diagnosis and treatment data encryption input method based on a blockchain, which determines an interference cross coefficient by analyzing interference source data, determines a propagation path coefficient by analyzing a propagation point and a relative distance, calculates a static interference eigenvalue based on the interference cross coefficient and the propagation path coefficient, determines a dynamic interference eigenvalue by analyzing a diagnosis and treatment sampling sequence and an interference source sampling sequence, calculates a data interference representation value based on the static interference eigenvalue and the dynamic interference eigenvalue, and divides the interference tendency of diagnosis and treatment data; for weak interference tendency, a propagation distortion coefficient is determined by analyzing a propagation path, an environmental influence coefficient is determined by analyzing environmental data, a data input index is calculated in combination with the data interference representation value, the input state of diagnosis and treatment data is determined, and inputtable data is encrypted and input to the blockchain. The present application improves the accuracy of data chaining and the reliability of data by quality evaluation of clinical data before input.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a blockchain-based method for encrypted entry of clinical diagnosis and treatment data. Background Technology

[0002] With the continuous improvement of medical informatization, clinical diagnosis and treatment data has become a crucial foundational resource in the modern medical system. During the input, transmission, and storage processes, data is often stored in plaintext or with simple encryption, making sensitive information vulnerable to unauthorized access by internal personnel or external attackers. Blockchain technology, as a decentralized, immutable, and traceable distributed ledger technology, can precisely solve these problems. Blockchain ensures data consistency across multiple nodes through a consensus mechanism, prevents tampering of historical data through a hash chain structure, and enhances data access control and operational traceability by combining asymmetric encryption and digital signatures. In recent years, the medical industry has gradually explored the application of blockchain in areas such as electronic medical record sharing, drug traceability, and clinical trial data management, initially validating its potential in improving data security, enhancing interoperability, and establishing trusted auditing mechanisms.

[0003] Chinese Patent Publication No. CN118522396A discloses a method and system for entering clinical diagnosis and treatment data. The method includes: firstly, acquiring historical clinical diagnosis and treatment data and preprocessing the historical clinical diagnosis and treatment data; determining the logical consistency between the current patient's diagnosis and treatment data and the preprocessed historical clinical diagnosis and treatment data; if the logical consistency is greater than a first preset threshold, then entering the current patient's diagnosis and treatment data; otherwise, determining the logical rationality of the current patient's diagnosis and treatment data; if the logical rationality is greater than a second preset threshold, then entering the current patient's diagnosis and treatment data. This invention analyzes the current patient's diagnosis and treatment data with historical clinical diagnosis and treatment data, allowing only diagnosis and treatment data whose logical consistency and logical rationality meet certain requirements to be entered, thus preventing logically flawed diagnosis and treatment data from being entered into the hospital information system.

[0004] Chinese Patent Publication No. CN111667922A discloses a clinical diagnosis and treatment data entry system and method. The system includes: a data acquisition unit that receives raw clinical diagnosis and treatment data, filters the data using a standard data template, and obtains multiple diagnosis and treatment data items matching the template; a processing unit that, for any one of the multiple data items, receives two entry results from at least two entry clients based on the same data item and determines whether there is a difference between the two results; and an execution unit that, in response to the processing unit's determination that there is a difference between the two results, pushes the difference information to an expert system to request the expert system to re-enter the diagnosis and treatment data item corresponding to the differing entry result. Using this invention, the accuracy of diagnosis and treatment data entry can be improved.

[0005] However, the following problems still exist in the existing technology. Most existing technologies only focus on the storage stage, with a single quality verification mechanism before data entry and a lack of dynamic assessment of data quality. This can lead to corrupted data being directly uploaded to the blockchain, weakening the clinical credibility of on-chain data. Summary of the Invention

[0006] To address this issue, the present invention provides a blockchain-based method for encrypted entry of clinical diagnosis and treatment data. This method solves the problem that most existing technologies only focus on the storage stage, lack a single quality verification mechanism before data entry, and lack dynamic evaluation of data quality. As a result, interfered data may be directly uploaded to the blockchain, which weakens the clinical credibility of the on-chain data.

[0007] To achieve the above objectives, the present invention provides a blockchain-based method for encrypted entry of clinical diagnosis and treatment data, comprising: Acquire the diagnosis and treatment sampling sequence, interference source sampling sequence, interference source data and propagation data within a preset time window. The interference source data includes the number of interference sources and the working time of the interference sources. The propagation data includes the propagation location, relative distance and propagation path. The interference source data is analyzed to determine the interference cross-coefficient, the propagation point and relative distance are analyzed to determine the propagation path coefficient, and the static interference characteristic value is calculated based on the interference cross-coefficient and the propagation path coefficient. The dynamic interference characteristic value is determined by analyzing the diagnostic and treatment sampling sequence and the interference source sampling sequence. Based on the static interference characteristic value and the dynamic interference characteristic value, the data interference characterization value is calculated to classify the interference tendency of the diagnostic and treatment data. To address the weak interference tendency, environmental data is acquired, the propagation path is analyzed to determine the propagation distortion coefficient, the environmental data is analyzed to determine the environmental impact coefficient, and the data entry index is calculated in conjunction with the data interference characterization value to determine the data entry status of the diagnosis and treatment data and to determine the data that can be entered. The data that can be entered is encrypted and entered into the blockchain.

[0008] Furthermore, the process of determining the interference cross coefficient includes, The ratio of the number of interference sources to the area of ​​a preset spatial region is determined as the interference source overlap density; The ratio of the average working time of each interference source to the duration of the preset time window is determined as the interference source action time domain factor; The product of the interference source overlap density and the interference source action time-domain factor is determined as the interference crossover coefficient.

[0009] Furthermore, the process of determining the propagation path coefficients includes, Determine the spatial distance between the transmission point and the diagnostic data collection point; The ratio of the relative distance to the spatial distance is determined as the propagation path coefficient.

[0010] Furthermore, the process of calculating the static interference characteristic value includes, The ratio of the interference cross coefficient to the reference interference cross coefficient is determined as a first static factor; The ratio of the propagation path coefficient to the reference propagation path coefficient is determined as the second static factor; The summation of the first static factor and the second static factor is determined as the static disturbance characteristic value.

[0011] Furthermore, the process of determining the dynamic interference characteristic values ​​includes, Construct the diagnosis and treatment time-domain curve of the diagnosis and treatment sampling sequence relative to time within the preset time window; Construct the interference time-domain curve of the interference source sampling sequence relative to time within the preset time window; A correlation analysis is performed on the diagnosis and treatment time-domain curve and the interference time-domain curve, and the calculated correlation coefficient is used as the dynamic interference characteristic value.

[0012] Furthermore, the calculated data interference characterization value is used to classify the interference tendency of the diagnostic and treatment data, wherein... The ratio of the dynamic interference characteristic value to the reference dynamic interference characteristic value is determined as the first interference factor; The weighted sum of the first interference factor and the static interference feature value is determined to be the data interference characterization value; If the data interference characterization value is greater than the data interference characterization value threshold, then the interference tendency of the diagnostic and treatment data is classified as strong interference tendency. If the data interference characterization value is less than or equal to the data interference characterization value threshold, then the interference tendency of the diagnostic and treatment data is classified as weak interference tendency.

[0013] Furthermore, the process of determining the propagation distortion coefficient includes, Several propagation change points are determined based on the propagation path; The ratio of the number of propagation change points to the number of propagation points is determined as the propagation distortion coefficient.

[0014] Furthermore, the process of determining the environmental impact coefficient includes, Obtain the temperature and humidity data from the environmental data; The absolute value of the difference between the temperature and the reference temperature is determined as the first difference, and the ratio of the first difference to the reference temperature is determined as the temperature deviation influence factor. The absolute value of the difference between the humidity and the reference humidity is determined as the second difference value, and the ratio of the second difference value to the reference humidity value is determined as the humidity offset influence factor. The product of the temperature offset influence factor and the humidity offset influence factor is determined to be the environmental impact coefficient.

[0015] Furthermore, the process of calculating and inputting the data indicators includes, The ratio of the propagation distortion coefficient to the baseline propagation distortion coefficient is determined as the first input influence factor; The ratio of the environmental impact coefficient to the benchmark environmental impact coefficient is determined as the second input impact factor; The ratio of the data interference characterization value to the benchmark data interference characterization value is determined as the third input influence factor; The weighted sum of the first input impact factor, the second input impact factor, and the third input impact factor is determined as the data input index.

[0016] Further, the step of determining the input-ready status of the diagnostic and treatment data, and determining the input-ready data, wherein, If the data entry index is greater than the data entry index threshold, then the data entry status of the diagnosis and treatment data is determined to be unenterable. If the data entry index is less than or equal to the data entry index threshold, then the data entry status of the diagnosis and treatment data is determined to be directly enterable, and thus determined to be enterable data.

[0017] Compared with existing technologies, this invention analyzes interference source data to determine the interference cross-coefficient, analyzes propagation points and relative distances to determine the propagation path coefficient, calculates static interference characteristic values ​​based on the interference cross-coefficient and propagation path coefficient, analyzes diagnostic and treatment sampling sequences and interference source sampling sequences to determine dynamic interference characteristic values, calculates data interference characterization values ​​based on static and dynamic interference characteristic values, and classifies the interference tendency of diagnostic and treatment data. For weak interference tendencies, it analyzes propagation paths to determine propagation distortion coefficients, analyzes environmental data to determine environmental impact coefficients, and combines data interference characterization values ​​to calculate data entry indicators, determine the data's readability status, and encrypts and enters the readable data into the blockchain. This invention improves the accuracy and credibility of data on-chain by conducting quality assessments of clinical data before entry.

[0018] In particular, by analyzing static data to determine the impact of static parameters on diagnostic data, the distribution density and operating duration of interference sources determine the degree of cross-coupling of interference signals. The relative distance between the propagation point and the acquisition point, as well as the diffraction degree of the actual propagation path, jointly determine the energy attenuation characteristics of the interference signal. In practice, the acquisition and entry of clinical diagnostic data often only focuses on the characteristics of the data itself or the identification of a single-dimensional interference source, ignoring the spatiotemporal distribution characteristics of multiple interference sources in the static environment and the propagation path loss of interference signals. If the original methods are still used to judge diagnostic data in a multi-interference source environment, especially since the interference intensity of multiple interference sources is not simply superimposed but nonlinearly amplified due to overlap density and operating time domain overlap, the energy attenuation characteristics of the interference signal will be compromised. The actual level of interference is easily underestimated, causing data that should be identified as having strong interference tendencies to be misjudged as having low interference. This leads to contaminated data being entered into the blockchain, affecting the reliability of clinical data and the accuracy of subsequent treatment decisions. Furthermore, existing technologies only consider a single distance factor and have not even established a quantitative relationship between propagation path and interference intensity. This makes static interference assessments unable to reflect the true propagation loss, resulting in misjudgments of the degree of interference. Based on this, this invention considers, in addition to the previous direct analysis of dynamic data, to analyze key parameters such as the spatiotemporal distribution of interference sources and propagation path characteristics in static factors in advance. This provides data support for subsequently combining dynamic influencing factors to jointly determine the true level of interference in clinical data, improving the reliability, integrity, and security of clinical data on the blockchain.

[0019] In particular, by analyzing dynamic data, this invention examines the impact of interference on diagnostic and treatment data from a dynamic perspective. In reality, interference signals often exhibit time-varying characteristics, with dynamic fluctuations in intensity, frequency, and duration. Diagnostic and treatment data itself also changes continuously over time. Simply relying on static parameters cannot capture the real-time interaction between interference and diagnostic and treatment signals. Existing technologies, when dealing with dynamic interference, typically only perform single-dimensional filtering or denoising on the diagnostic and treatment data sequence, or analyze the interference source sampling sequence independently, without establishing a temporal correlation between the diagnostic and treatment data and the interference source data. This results in the inability to accurately identify the coupling strength of the interference signal on the time axis. When the interference source operates intermittently or the characteristics of the diagnostic and treatment data change over time, the lack of quantitative analysis of temporal correlation can lead to the misjudgment of instantaneous strong interference as continuous interference, resulting in abnormal judgments of the data. Based on this, this invention considers a comprehensive analysis of the dynamic influencing factors of diagnostic and treatment data to determine dynamic interference characteristic values, providing a data foundation for subsequent calculation of data interference characterization values, and improving the reliability, integrity, and security of on-chain diagnostic and treatment data.

[0020] In particular, comprehensive analysis of static and dynamic data is crucial for calculating data interference characteristics and classifying the interference tendencies of diagnostic and treatment data. This provides a data foundation for subsequent targeted analysis. In practice, interference assessment of clinical diagnostic and treatment data often employs single-dimensional analysis methods, relies solely on static parameters for rough judgment, or only filters or denoises dynamic data. For example, when the interference source operates intermittently or the characteristics of diagnostic and treatment data change over time, static assessment cannot reflect the time-varying characteristics of dynamic interference, easily misjudging strong instantaneous interference as continuous interference, or completely ignoring intermittent interference. Furthermore, when multiple interference sources exist... When interference sources overlap in time and space or suffer losses due to complex propagation paths, dynamic analysis struggles to distinguish the true source and intensity of the interference, and cannot quantify the fundamental contribution of static factors to the interference. This results in a lack of environmental benchmarks for calculating dynamic interference characteristic values. This fragmented and isolated analysis approach has limitations, leading to inaccurate analysis of abnormal data and causing anomalies in the on-chain data. Based on this, this invention considers the comprehensive analysis of static and dynamic data, adjusting the coarse judgment based on a single dimension to the precise quantification of multi-dimensional factors. This improves the comprehensiveness, accuracy, and reliability of interference assessment in clinical diagnosis and treatment data, and enhances the quality of data screening.

[0021] In particular, this invention further analyzes the impact of environmental factors on diagnostic and treatment data with a weak tendency to interfere. By calculating data entry indicators, the degree of multi-source coupling influence is quantified to determine the final data that can be entered. Existing technologies, when processing diagnostic and treatment data with a slight tendency to interfere, usually consider weak interference to be equivalent to no risk, and thus store the data with slight anomalies on the blockchain. Furthermore, for diagnostic and treatment data with a weak tendency to interfere, if there are multiple propagation change points on the propagation path of interference signals during the collection process, and there are also abnormal environmental data collection conditions, even if these weak coupling factors are not enough to classify the data as having a strong tendency to interfere, if they are directly entered without quantitative evaluation, they may have a potential impact on the long-term reliability and traceability accuracy of the diagnostic and treatment data under the cumulative effect of multiple factors. Based on this, this invention considers calculating targeted data entry indicators for diagnostic and treatment data with a weak tendency to interfere, avoiding the direct storage of diagnostic and treatment data with a weak tendency to interfere, and further improving the reliability, integrity and security of the diagnostic and treatment data on the blockchain. Attached Figure Description

[0022] Figure 1 This is a schematic diagram illustrating the steps of a blockchain-based encrypted data entry method for clinical diagnosis and treatment, as described in an embodiment of the invention. Figure 2 A logical block diagram illustrating the interference tendencies of diagnostic and treatment data in an embodiment of the invention; Figure 3 The following is a logical block diagram for determining the data that can be entered, as described in the embodiments of the invention. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] Please see Figure 1 The diagram illustrates the steps of a blockchain-based encrypted entry method for clinical diagnosis and treatment data, as described in an embodiment of the invention. The blockchain-based encrypted entry method for clinical diagnosis and treatment data of the present invention includes: Step S1: Obtain the diagnosis and treatment sampling sequence, interference source sampling sequence, interference source data and propagation data within a preset time window. The interference source data includes the number of interference sources and the working time of the interference sources. The propagation data includes the propagation location, relative distance and propagation path.

[0026] Specifically, there is no limit to the exact duration of the preset time window; in practice, the duration is the same as the clinical testing time window.

[0027] Specifically, there are no restrictions on the method of obtaining the diagnostic sampling sequence. For example, in practice, the patient's physiological signals can be collected in real time at a preset sampling frequency using clinical diagnostic equipment such as electrocardiogram monitors, electroencephalogram (EEG) machines, and blood pressure monitors to form continuous time series data. This sequence data is then stored in association with the collection timestamp as a diagnostic sampling sequence within a preset time window. The preset sampling frequency is determined according to the actual situation and can be 250Hz, 500Hz, or 1000Hz.

[0028] Specifically, the method for obtaining the interference source sampling sequence is not limited. For example, in implementation, electromagnetic interference sensors deployed around the diagnostic and treatment equipment synchronously collect interference signals in the environment at the same sampling frequency as the diagnostic and treatment sampling sequence. The interference signals can be mobile communication signals, wireless network signals, radiation signals from the medical equipment itself, and power line interference signals. Preferably, to ensure that the collected interference source sampling sequence accurately reflects the most significant and unavoidable interference factors affecting the diagnostic and treatment data, the interference source sampling sequence is preferably a medical equipment radiation signal sampling sequence. It is understandable that the radiation signals from medical devices originate from nearby medical devices that operate simultaneously with the diagnosis and treatment process. These devices are usually deployed next to the patient's bedside, in very close spatial distance to the data collection point. Their radiation energy is relatively strong and their frequency band overlaps with the physiological signal collection frequency band of the diagnostic and treatment devices. The interference intensity on the diagnostic and treatment sampling sequence is much higher than that of far-field interference sources, which is the main factor leading to the decline in the quality of clinical diagnostic and treatment data.

[0029] Specifically, there are no restrictions on the method of obtaining interference source data. For example, in implementation, a list of medical devices located in the same spatial area as the data collection point within a preset time window can be determined based on the department layout diagram of the treatment site. Based on the device usage schedule or device operation log, the actual working time of each device in the list within the preset time window can be obtained. Of course, those skilled in the art can also adopt other methods, as long as the required data can be obtained, which will not be elaborated here.

[0030] Specifically, there are no restrictions on the method of acquiring propagation data. For example, in implementation, the spatial coordinates of each interference source are obtained through an indoor positioning system as the propagation point. The straight-line distance between the propagation point of each interference source and the diagnostic data collection point is calculated using the spatial distance calculation formula and used as the relative distance. The actual propagation path of the interference signal is simulated based on the location of the interference source, the location of the collection point, and the three-dimensional map of the environment using ray tracing algorithm or channel measurement method. The coordinates of the propagation change points on the path are recorded to form propagation path data.

[0031] Step S2: Analyze the interference source data to determine the interference cross coefficient, analyze the propagation point and relative distance to determine the propagation path coefficient, and calculate the static interference characteristic value based on the interference cross coefficient and the propagation path coefficient.

[0032] Specifically, the process of determining the interference crossover coefficient includes, The ratio of the number of interference sources to the area of ​​a preset spatial region is determined as the interference source overlap density; The ratio of the average working time of each interference source to the duration of the preset time window is determined as the interference source action time domain factor; The product of the interference source overlap density and the interference source action time-domain factor is determined as the interference crossover coefficient.

[0033] Specifically, the number of interference sources refers to the total number of interference sources that are located within a preset time window, in a preset spatial area, and in operation.

[0034] Specifically, there is no limitation on the specific method for determining the area of ​​the preset space. In practice, it can be the area of ​​an independent room divided according to the department layout. Of course, those skilled in the art can also determine it according to the actual situation, as long as it is reasonable. This will not be elaborated further.

[0035] It is understandable that when multiple interference sources exist, taking the average value is to comprehensively reflect the overall temporal activity of multiple interference sources, rather than considering a single interference source alone.

[0036] Specifically, for determining the interference cross coefficient, the interference source overlap density reflects the degree of overlap in the spatial dimension, the interference source action time-domain factor reflects the degree of overlap in the time dimension, and the product is used to quantify the comprehensive cross interference intensity of the interference sources in both the spatial and temporal dimensions.

[0037] Specifically, the process of determining the propagation path coefficient includes, Determine the spatial distance between the transmission point and the diagnostic data collection point; The ratio of the relative distance to the spatial distance is determined as the propagation path coefficient.

[0038] Specifically, the spatial distance is the Euclidean straight-line distance between the propagation point and the diagnostic data collection point, where the diagnostic data collection point is determined based on the actual collection location.

[0039] Specifically, the process of calculating static interference characteristic values ​​includes, The ratio of the interference cross coefficient to the reference interference cross coefficient is determined as a first static factor; The ratio of the propagation path coefficient to the reference propagation path coefficient is determined as the second static factor; The summation of the first static factor and the second static factor is determined as the static disturbance characteristic value.

[0040] Specifically, the baseline interference cross coefficient is calculated in advance. Several historical interference cross coefficients corresponding to historical data that can be entered into the medical records are obtained in advance, and the mean of each historical interference cross coefficient is determined as the baseline interference cross coefficient.

[0041] Specifically, the baseline transmission path coefficient is calculated in advance by obtaining the historical transmission path coefficients corresponding to several historical records of medical data, and determining the average of each historical transmission path coefficient as the baseline transmission path coefficient.

[0042] Specifically, by analyzing static data, the impact of static parameters on diagnostic data is determined. The distribution density and operating duration of interference sources determine the degree of cross-coupling of interference signals, while the relative distance between the propagation point and the acquisition point, as well as the diffraction degree of the actual propagation path, jointly determine the energy attenuation characteristics of the interference signal. In practice, the acquisition and entry of clinical diagnostic data often only focus on the characteristics of the data itself or the identification of a single-dimensional interference source, ignoring the spatiotemporal distribution characteristics of multiple interference sources in the static environment and the propagation path loss of interference signals. If the original methods are still used to judge diagnostic data in a multi-interference source environment, especially since the interference intensity of multiple interference sources is not simply superimposed but nonlinearly amplified due to overlap density and operating time domain overlap, the problem becomes even more serious. The actual level of interference is easily underestimated, causing data that should be identified as having strong interference tendencies to be misjudged as having low interference. This leads to contaminated data being entered into the blockchain, affecting the reliability of clinical data and the accuracy of subsequent treatment decisions. Furthermore, existing technologies only consider a single distance factor and have not even established a quantitative relationship between propagation path and interference intensity. This makes static interference assessments unable to reflect the true propagation loss, resulting in misjudgments of the degree of interference. Based on this, this invention considers, in addition to the previous direct analysis of dynamic data, to analyze key parameters such as the spatiotemporal distribution of interference sources and propagation path characteristics in static factors in advance. This provides data support for subsequently combining dynamic influencing factors to jointly determine the true level of interference in clinical data, improving the reliability, integrity, and security of clinical data on the blockchain.

[0043] Step S3: Analyze the diagnostic sampling sequence and the interference source sampling sequence to determine the dynamic interference characteristic value, calculate the data interference characterization value based on the static interference characteristic value and the dynamic interference characteristic value, and classify the interference tendency of the diagnostic data. Specifically, the process of determining the dynamic interference characteristic values ​​includes, Construct the diagnosis and treatment time-domain curve of the diagnosis and treatment sampling sequence relative to time within the preset time window; Construct the interference time-domain curve of the interference source sampling sequence relative to time within the preset time window; A correlation analysis is performed on the diagnosis and treatment time-domain curve and the interference time-domain curve, and the calculated correlation coefficient is used as the dynamic interference characteristic value.

[0044] Specifically, by analyzing dynamic data, this invention examines the impact of interference on diagnostic and treatment data from a dynamic perspective. In reality, interference signals often exhibit time-varying characteristics, with dynamic fluctuations in intensity, frequency, and duration. Diagnostic and treatment data themselves also change continuously over time. Simply relying on static parameters cannot capture the real-time interaction between interference and diagnostic and treatment signals. Existing technologies, when dealing with dynamic interference, typically only perform single-dimensional filtering or denoising on the diagnostic and treatment data sequence, or analyze the interference source sampling sequence independently, without establishing a temporal correlation between the diagnostic and treatment data and the interference source data. This leads to an inability to accurately identify the coupling strength of the interference signal on the time axis. When the interference source operates intermittently or the characteristics of the diagnostic and treatment data change over time, a lack of quantitative analysis of temporal correlation can lead to misjudging instantaneous strong interference as continuous interference, resulting in abnormal data assessments. Therefore, this invention considers a comprehensive analysis of the dynamic influencing factors of diagnostic and treatment data to determine dynamic interference characteristic values, providing a data foundation for subsequent calculations of data interference characterization values ​​and improving the reliability, integrity, and security of on-chain diagnostic and treatment data.

[0045] Please see Figure 2 The diagram shown is a logical block diagram of the method for classifying the interference tendency of diagnostic and treatment data according to an embodiment of the invention. Specifically, it calculates the data interference characterization value and classifies the interference tendency of the diagnostic and treatment data, wherein... The ratio of the dynamic interference characteristic value to the reference dynamic interference characteristic value is determined as the first interference factor; The weighted sum of the first interference factor and the static interference feature value is determined to be the data interference characterization value; If the data interference characterization value is greater than the data interference characterization value threshold, then the interference tendency of the diagnostic and treatment data is classified as strong interference tendency. If the data interference characterization value is less than or equal to the data interference characterization value threshold, then the interference tendency of the diagnostic and treatment data is classified as weak interference tendency.

[0046] Specifically, the baseline dynamic interference characteristic value is calculated in advance, and the historical dynamic interference characteristic values ​​corresponding to several historical records of medical data are obtained in advance. The average value of each historical dynamic interference characteristic value is determined as the baseline dynamic interference characteristic value.

[0047] Specifically, the sum of the weight coefficients of the first interference factor and the static interference feature value is 1. When configuring the weights, considering that both static and dynamic factors will have a direct impact on data quality, the weight coefficients of the first interference factor and the static interference feature value are both set to 0.5.

[0048] Specifically, the data interference characterization threshold represents a boundary at which data needs to be directly removed. It is calculated in advance by obtaining the historical data interference characterization values ​​corresponding to several historical data that can be entered into the diagnosis and treatment data. The product of the mean of each historical data interference characterization value and the interference precision is determined as the data interference characterization threshold. The interference precision is determined in the interval [0,1]. In practice, in order to improve the reliability of data entry and ensure that only higher quality data can enter the subsequent stages, the interference precision is determined to be 0.9.

[0049] Specifically, a comprehensive analysis of static and dynamic data is used to calculate data interference characterization values ​​and classify the interference tendencies of diagnostic and treatment data. This provides a data foundation for subsequent targeted analysis. In practice, interference assessment of clinical diagnostic and treatment data often employs single-dimensional analysis methods, or relies solely on static parameters for rough judgment, or only filters or denoises dynamic data. For example, when the interference source operates intermittently or the characteristics of diagnostic and treatment data change over time, static assessment cannot reflect the time-varying characteristics of dynamic interference, easily misjudging instantaneous strong interference as continuous interference, or completely ignoring intermittent interference. Furthermore, when multiple... When interference sources overlap in time and space or suffer losses due to complex propagation paths, dynamic analysis struggles to distinguish the true source and intensity of the interference, and cannot quantify the fundamental contribution of static factors to the interference. This results in a lack of environmental benchmarks for calculating dynamic interference characteristic values. This fragmented and isolated analysis approach has limitations, leading to inaccurate analysis of abnormal data and causing anomalies in the on-chain data. Based on this, this invention considers the comprehensive analysis of static and dynamic data, adjusting the coarse judgment based on a single dimension to the precise quantification of multi-dimensional factors. This improves the comprehensiveness, accuracy, and reliability of interference assessment in clinical diagnosis and treatment data, and enhances the quality of data screening.

[0050] Please continue reading. Figure 1 Step S4: For weak interference tendency, acquire environmental data, analyze the propagation path to determine the propagation distortion coefficient, analyze the environmental data to determine the environmental impact coefficient, calculate the data entry index in combination with the data interference characterization value, determine the data entry status of the diagnosis and treatment data, and determine the data that can be entered.

[0051] Specifically, the process of determining the propagation distortion coefficient includes, Several propagation change points are determined based on the propagation path; The ratio of the number of propagation change points to the number of propagation points is determined as the propagation distortion coefficient.

[0052] Specifically, propagation change points refer to characteristic points where the direction, intensity, or waveform of the interference signal changes along the propagation path. These include, but are not limited to, reflection points, refraction points, diffraction points, and scattering points. Propagation change points are obtained by scanning and statistically analyzing the propagation path. Furthermore, the propagation path data is analyzed segment by segment to identify the locations where reflection, refraction, diffraction, or scattering occurs during signal propagation. The total number of these change points is the number of propagation change points. For example, the propagation of the interference signal can be simulated using a ray tracing algorithm to record all points on the path that interact with the interface of obstacles or media.

[0053] Specifically, the process of determining the environmental impact factor includes, Obtain the temperature and humidity data from the environmental data; The absolute value of the difference between the temperature and the reference temperature is determined as the first difference, and the ratio of the first difference to the reference temperature is determined as the temperature deviation influence factor. The absolute value of the difference between the humidity and the reference humidity is determined as the second difference value, and the ratio of the second difference value to the reference humidity value is determined as the humidity offset influence factor. The product of the temperature offset influence factor and the humidity offset influence factor is determined to be the environmental impact coefficient.

[0054] Specifically, the baseline temperature is calculated in advance by acquiring historical temperatures corresponding to several historical records of medical data and determining the average of these historical temperatures as the baseline temperature.

[0055] Specifically, the baseline humidity is calculated in advance by acquiring historical humidity corresponding to several historical medical data entries and determining the average of each historical humidity as the baseline humidity.

[0056] Specifically, the process of calculating data entry metrics includes, The ratio of the propagation distortion coefficient to the baseline propagation distortion coefficient is determined as the first input influence factor; The ratio of the environmental impact coefficient to the benchmark environmental impact coefficient is determined as the second input impact factor; The ratio of the data interference characterization value to the benchmark data interference characterization value is determined as the third input influence factor; The weighted sum of the first input impact factor, the second input impact factor, and the third input impact factor is determined as the data input index.

[0057] Specifically, the baseline propagation distortion coefficient is calculated in advance. Several historical propagation distortion coefficients corresponding to historical data that can be entered into the medical records are obtained in advance, and the average of each historical propagation distortion coefficient is determined as the baseline propagation distortion coefficient.

[0058] Specifically, the baseline environmental impact coefficient is calculated in advance. Several historical environmental impact coefficients corresponding to historical data that can be entered into the medical records are obtained in advance, and the average of each historical environmental impact coefficient is determined as the baseline environmental impact coefficient.

[0059] Specifically, the reference data interference characterization values ​​are the reference static interference characteristic values ​​and the data interference characterization values ​​corresponding to the reference dynamic interference characteristic values.

[0060] Specifically, the baseline static interference feature value is calculated in advance, and several historical static interference feature values ​​corresponding to historical data that can be entered into the medical records are obtained in advance. The mean value of each historical static interference feature value is determined as the baseline static interference feature value.

[0061] Specifically, the sum of the weight coefficients of the first, second, and third input impact factors is 1. When configuring the weights, considering that the third input impact factor is a direct influencing factor, the weight coefficients of the first and second input impact factors are both determined to be 0.3, and the weight coefficient of the third input impact factor is 0.4.

[0062] Specifically, this invention further analyzes the impact of environmental factors on diagnostic and treatment data with a weak tendency to interfere. By calculating data entry indicators, the degree of multi-source coupling influence is quantified to determine the final data that can be entered. Existing technologies, when processing diagnostic and treatment data with a slight tendency to interfere, usually consider weak interference to be equivalent to no risk, and thus store the data with slight anomalies on the blockchain. Furthermore, for diagnostic and treatment data with a weak tendency to interfere, if there are multiple propagation change points on the propagation path of interference signals during the collection process, and there are also abnormal environmental data collection conditions, even if these weak coupling factors are not enough to classify the data as having a strong tendency to interfere, if they are directly entered without quantitative evaluation, they may have a potential impact on the long-term reliability and traceability accuracy of the diagnostic and treatment data under the cumulative effect of multiple sources. Based on this, this invention considers calculating targeted data entry indicators for diagnostic and treatment data with a weak tendency to interfere, avoiding the direct storage of diagnostic and treatment data with a weak tendency to interfere, and further improving the reliability, integrity, and security of the diagnostic and treatment data on the blockchain.

[0063] Please see Figure 3 The diagram shown illustrates a logic block diagram for determining the input-readiness status of the diagnostic and treatment data, as per an embodiment of the invention. Specifically, determining the input-readiness status of the diagnostic and treatment data involves identifying the input-ready data, wherein... If the data entry index is greater than the data entry index threshold, then the data entry status of the diagnosis and treatment data is determined to be unenterable. If the data entry index is less than or equal to the data entry index threshold, then the data entry status of the diagnosis and treatment data is determined to be directly enterable, and thus determined to be enterable data.

[0064] Specifically, the data entry indicator threshold represents the boundary of data that can be directly entered. It is calculated in advance by obtaining historical data entry indicators corresponding to several historical data that can be entered in advance. The product of the average value of each historical data entry indicator and the entry accuracy is determined as the data entry indicator threshold. The entry accuracy is obtained in the interval [0, 1]. In practice, in order to improve the reliability of data entry, the entry accuracy is determined to be 0.9.

[0065] Please continue reading. Figure 1 Step S5: Encrypt the data to be recorded and record it into the blockchain.

[0066] Specifically, there are no restrictions on the encryption method. In implementation, one or more combinations of symmetric encryption algorithms, asymmetric encryption algorithms, or hash algorithms can be used to encrypt the data that can be entered. Any reasonable method will suffice, and will not be elaborated further.

[0067] By using the above methods, the data that can be entered is encrypted and entered into the blockchain. The distributed storage and immutability of the blockchain are used to ensure the integrity and authenticity of the medical data, and encryption technology is used to ensure the privacy and security of the data.

[0068] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0069] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A blockchain-based method for encrypted entry of clinical diagnosis and treatment data, characterized in that, include: Acquire the diagnosis and treatment sampling sequence, interference source sampling sequence, interference source data and propagation data within a preset time window. The interference source data includes the number of interference sources and the working time of the interference sources. The propagation data includes the propagation location, relative distance and propagation path. The interference source data is analyzed to determine the interference cross-coefficient, the propagation point and relative distance are analyzed to determine the propagation path coefficient, and the static interference characteristic value is calculated based on the interference cross-coefficient and the propagation path coefficient. The dynamic interference characteristic value is determined by analyzing the diagnostic and treatment sampling sequence and the interference source sampling sequence. Based on the static interference characteristic value and the dynamic interference characteristic value, the data interference characterization value is calculated to classify the interference tendency of the diagnostic and treatment data. To address the weak interference tendency, environmental data is acquired, the propagation path is analyzed to determine the propagation distortion coefficient, the environmental data is analyzed to determine the environmental impact coefficient, and the data entry index is calculated in conjunction with the data interference characterization value to determine the data entry status of the diagnosis and treatment data and to determine the data that can be entered. The data that can be entered is encrypted and entered into the blockchain.

2. The method for encrypted entry of clinical diagnosis and treatment data based on blockchain according to claim 1, characterized in that, The process of determining the interference cross coefficient includes, The ratio of the number of interference sources to the area of ​​a preset spatial region is determined as the interference source overlap density; The ratio of the average working time of each interference source to the duration of the preset time window is determined as the interference source action time domain factor; The product of the interference source overlap density and the interference source action time-domain factor is determined as the interference crossover coefficient.

3. The method for encrypted entry of clinical diagnosis and treatment data based on blockchain according to claim 1, characterized in that, The process of determining the propagation path coefficient includes, Determine the spatial distance between the transmission point and the diagnostic data collection point; The ratio of the relative distance to the spatial distance is determined as the propagation path coefficient.

4. The method for encrypted entry of clinical diagnosis and treatment data based on blockchain according to claim 1, characterized in that, The process of calculating the static interference characteristic value includes: The ratio of the interference cross coefficient to the reference interference cross coefficient is determined as a first static factor; The ratio of the propagation path coefficient to the reference propagation path coefficient is determined as the second static factor; The summation of the first static factor and the second static factor is determined as the static disturbance characteristic value.

5. The method for encrypted entry of clinical diagnosis and treatment data based on blockchain according to claim 1, characterized in that, The process of determining the dynamic interference characteristic value includes, Construct the diagnosis and treatment time-domain curve of the diagnosis and treatment sampling sequence relative to time within the preset time window; Construct the interference time-domain curve of the interference source sampling sequence relative to time within the preset time window; A correlation analysis is performed on the diagnosis and treatment time-domain curve and the interference time-domain curve, and the calculated correlation coefficient is used as the dynamic interference characteristic value.

6. The method for encrypted entry of clinical diagnosis and treatment data based on blockchain according to claim 1, characterized in that, The calculated data interference characterization value is used to classify the interference tendency of the diagnostic and treatment data, wherein... The ratio of the dynamic interference characteristic value to the reference dynamic interference characteristic value is determined as the first interference factor; The weighted sum of the first interference factor and the static interference feature value is determined to be the data interference characterization value; If the data interference characterization value is greater than the data interference characterization value threshold, then the interference tendency of the diagnostic and treatment data is classified as strong interference tendency. If the data interference characterization value is less than or equal to the data interference characterization value threshold, then the interference tendency of the diagnostic and treatment data is classified as weak interference tendency.

7. The method for encrypted entry of clinical diagnosis and treatment data based on blockchain according to claim 1, characterized in that, The process of determining the propagation distortion coefficient includes, Several propagation change points are determined based on the propagation path; The ratio of the number of propagation change points to the number of propagation points is determined as the propagation distortion coefficient.

8. The method for encrypted entry of clinical diagnosis and treatment data based on blockchain according to claim 1, characterized in that, The process of determining the environmental impact coefficient includes: Obtain the temperature and humidity data from the environmental data; The absolute value of the difference between the temperature and the reference temperature is determined as the first difference, and the ratio of the first difference to the reference temperature is determined as the temperature deviation influence factor. The absolute value of the difference between the humidity and the reference humidity is determined as the second difference value, and the ratio of the second difference value to the reference humidity value is determined as the humidity offset influence factor. The product of the temperature offset influence factor and the humidity offset influence factor is determined to be the environmental impact coefficient.

9. The method for encrypted entry of clinical diagnosis and treatment data based on blockchain according to claim 1, characterized in that, The process of calculating and entering data indicators includes, The ratio of the propagation distortion coefficient to the baseline propagation distortion coefficient is determined as the first input influence factor; The ratio of the environmental impact coefficient to the benchmark environmental impact coefficient is determined as the second input impact factor; The ratio of the data interference characterization value to the benchmark data interference characterization value is determined as the third input influence factor; The weighted sum of the first input impact factor, the second input impact factor, and the third input impact factor is determined as the data input index.

10. The method for encrypted entry of clinical diagnosis and treatment data based on blockchain according to claim 1, characterized in that, The process of determining the input-ready status of the diagnostic and treatment data, and determining the input-ready data, wherein... If the data entry index is greater than the data entry index threshold, then the data entry status of the diagnosis and treatment data is determined to be unenterable. If the data entry index is less than or equal to the data entry index threshold, then the data entry status of the diagnosis and treatment data is determined to be directly enterable, and thus determined to be enterable data.

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