A medical data traceability management method and system based on the Internet of Things

Abstract

The present invention relates to the technical field of medical data tracing, and specifically discloses a medical data traceability management method and system based on the Internet of Things. The method comprises analyzing the medical data transmission flow of a medical data upload path, wherein the analysis results include load signals and non-load signals. Based on the load signals, the duration and number of occurrences of the load signals are processed and analyzed, and the corresponding historical upload paths are marked. Based on the fluctuation paths, the number of fluctuation paths in the historical paths and the load performance values of the fluctuation paths are analyzed to generate fluctuation signals. Based on the fluctuation signals, medical data that needs to be traced is analyzed to obtain a tracing sequence table. The present invention classifies the medical data that needs to be traced according to the urgency of the tracing and prioritizes the tracing of urgent tracing data, thereby effectively reducing the system resource usage of tracing requests.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data tracing, and in particular to a medical data traceability management method and system based on the Internet of Things. Background Art

[0002] In the modern healthcare environment, data sources are no longer limited to traditional hospital information systems and paper medical records. The widespread use of IoT devices, including wearable health monitors, home medical monitoring instruments, and various smart medical devices in hospitals, generates massive amounts of diverse data. This data includes physiological parameters, device operating status, environmental information, and other types, making data integration and management extremely complex. In medical disputes, malpractice investigations, and clinical research, the ability to clearly trace the generation, modification, and use of medical data can help determine responsibility, identify problems, and improve healthcare services.

[0003] However, the generation of massive and diverse medical data often causes server load. Under server load, medical data tracing may cause a certain degree of confusion.

[0004] To this end, we propose a medical data traceability management method and system based on the Internet of Things. Summary of the Invention

[0005] The purpose of the present invention is to provide a medical data traceability management method and system based on the Internet of Things to solve the technical problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a method for traceable management of medical data based on the Internet of Things, comprising:

[0008] Step 1: Analyze the medical data transmission traffic of the medical data upload path, and the analysis results include load signals and non-load signals;

[0009] Step 2: Based on the load signal, the duration and occurrence frequency of the load signal are processed and analyzed to obtain a load performance value. If the load performance value FB is greater than or equal to the load performance threshold, the corresponding historical upload path is marked as a fluctuating path.

[0010] Step 3: Based on the fluctuation path, the number of fluctuation paths in the historical path and the load performance value of the fluctuation path are analyzed to obtain the fluctuation performance value BB. If the fluctuation performance value BB is greater than the fluctuation performance threshold, a fluctuation signal is generated;

[0011] Step 4: Based on the fluctuation signal, analyze the medical data that needs to be traced to obtain the traceability performance value ZS, sort based on the traceability performance value ZS, obtain a traceability sequence table, and trace the emergency traceability data during the load period based on the traceability sequence table.

[0012] As a further solution of the present invention: the load performance value FB is obtained as follows:

[0013] Obtain the time period for generating the load signal and the number of times the load signal appears, and analyze to obtain the load period ratio FS and the load frequency ratio FC;

[0014] The load period ratio FS and the load frequency ratio FC are weighted to obtain the load performance value FB.

[0015] As a further solution of the present invention, the load period ratio FS is obtained as follows:

[0016] In the historical monitoring period, the time period in which the load signal is generated is marked as a load period, and the duration corresponding to the load period is compared with the duration corresponding to the historical monitoring period to obtain a load period ratio FS.

[0017] As a further solution of the present invention: the load frequency ratio FC is obtained as follows:

[0018] During the historical monitoring period, the total number of load signals and non-load signals is counted and marked as the total signal number. The number of load signals is counted and compared with the total signal number to obtain the load number ratio FC.

[0019] As a further solution of the present invention: the method of obtaining the fluctuation performance value BB is:

[0020] Obtain the number of fluctuation paths in the historical path and the load performance value of the fluctuation path, process and analyze to obtain the fluctuation number ratio BS and fluctuation degree ratio BC;

[0021] The volatility number ratio BS and the volatility degree ratio BC are weighted to obtain the volatility performance value BB.

[0022] As a further solution of the present invention: the fluctuation quantity ratio BS and the fluctuation degree ratio BC are obtained as follows:

[0023] The number of fluctuation paths in the historical paths is counted and the ratio is calculated with the total number of historical paths to obtain the fluctuation number ratio BS.

[0024] Obtain the load performance value of the fluctuation path, perform subtraction processing on it and the load performance threshold to obtain the fluctuation deviation value, sum and average all the fluctuation deviation values to obtain the fluctuation deviation mean, and perform ratio processing on the fluctuation deviation mean and the data anomaly threshold to obtain the fluctuation degree ratio BC.

[0025] As a further solution of the present invention: the retrospective performance value ZS is obtained as follows:

[0026] Obtain the medical data that needs to be traced, analyze it to obtain emergency medical data, obtain the cosine similarity value and the bytes of the emergency medical data corresponding to the emergency medical data, and analyze it to obtain the cosine similarity value deviation ratio YP and the byte ratio ZJ;

[0027] The cosine similarity deviation ratio YP and the byte ratio ZJ are processed by the formula: The retrospective performance value ZS is calculated, where b1 and b2 are preset proportional coefficients, and both b1 and b2 are greater than 0.

[0028] As a further solution of the present invention: the cosine similarity value deviation ratio YP and the byte ratio ZJ are obtained as follows:

[0029] Obtain the cosine similarity value corresponding to the emergency medical data, perform absolute value processing on the difference between the cosine similarity value and the cosine similarity threshold to obtain the cosine similarity value deviation, perform ratio processing on the cosine similarity value deviation and the cosine similarity threshold to obtain the cosine similarity value deviation ratio YP;

[0030] All emergency medical data bytes are obtained, summed and averaged to obtain a byte mean, and each emergency medical data byte is compared with the byte mean to obtain a byte ratio ZJ.

[0031] As a further solution of the present invention: the cosine similarity value is obtained as follows:

[0032] Use text analysis tools in natural language processing (NLP) technology to extract keywords from medical data that need to be traced and mark them as traceability keywords;

[0033] Integrate all preset emergency keywords into a preset keyword group, and combine the traceability keyword with the preset keyword in the preset keyword group to obtain a recombined keyword group;

[0034] The cosine similarity calculation formula is: , where A is the tracing keyword vector in the reorganized keyword group, B is the preset keyword vector of the reorganized keyword group, A・B is the dot product of the two vectors, ||A|| and ||B|| are the moduli of vector A and vector B respectively, and the cosine similarity value between the tracing keyword in the reorganized keyword group and the preset keyword is obtained.

[0035] In a second aspect, the present invention provides a medical data traceability management system based on the Internet of Things, comprising:

[0036] Data acquisition module: analyzes the medical data transmission traffic of the medical data upload path, and the analysis results include load signals and non-load signals;

[0037] Fluctuation path marking module: Based on the load signal, the module processes and analyzes the load signal duration and occurrence frequency to obtain a load performance value. If the load performance value FB is greater than or equal to the load performance threshold, the corresponding historical upload path is marked as a fluctuation path.

[0038] Fluctuation signal generation module: Based on the fluctuation path, the module analyzes the number of fluctuation paths in the historical path and the load performance value of the fluctuation path to obtain the fluctuation performance value BB. If the fluctuation performance value BB is greater than the fluctuation performance threshold, a fluctuation signal is generated.

[0039] Tracing sequence table determination module: Based on the fluctuation signal, the medical data that needs to be traced is analyzed to obtain the tracing performance value ZS, and the tracing performance value ZS is sorted to obtain the tracing sequence table. Based on the tracing sequence table, the emergency tracing data is traced during the load period.

[0040] Beneficial effects of the present invention:

[0041] (1) The present invention is based on analyzing the medical data transmission flow of the medical data upload path. The analysis results include load signals and non-load signals. Based on the load signal, the duration and number of occurrences of the load signal are processed and analyzed to obtain a load performance value. If the load performance value FB is greater than or equal to the load performance threshold, the corresponding historical upload path is marked as a fluctuation path. Based on the fluctuation path, the number of fluctuation paths in the historical path and the load performance value of the fluctuation path are analyzed to obtain a fluctuation performance value BB. If the fluctuation performance value BB is greater than the fluctuation performance threshold, a fluctuation signal is generated. By analyzing the historical data of the medical data upload path, the present invention is conducive to judging the overall volatility of historical medical data uploads;

[0042] (2) The present invention analyzes the medical data that needs to be traced based on the fluctuation signal, obtains the tracing performance value ZS, sorts the data based on the tracing performance value ZS, obtains the tracing sequence table, and traces the urgent tracing data during the load period based on the tracing sequence table. The present invention classifies the medical data that needs to be traced according to the urgency of the tracing and gives priority to tracing the urgent tracing data, which can effectively reduce the system resource usage of the tracing request. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be further described below with reference to the accompanying drawings.

[0044] Figure 1 This is a flowchart of a medical data traceability management method based on the Internet of Things according to an embodiment of the present invention;

[0045] Figure 2 This is a system block diagram of a medical data traceability management system based on the Internet of Things according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0047] Example 1:

[0048] See also Figure 1 As shown, the medical data traceability management method based on the Internet of Things according to an embodiment of the present invention includes the following steps:

[0049] Step 1: Obtain historical data of the medical data upload path, where the historical data includes data transmission traffic, analyze the historical data, and the analysis results include load signals and non-load signals;

[0050] During the historical monitoring period, obtain the data transmission traffic of the medical data upload path and compare it with the medical data transmission traffic threshold:

[0051] If the data transmission flow rate is greater than the data transmission flow rate threshold, a load signal is generated;

[0052] If the data transmission flow rate is less than or equal to the data transmission flow rate threshold, a non-load signal is generated;

[0053] It should be noted that the historical monitoring period includes but is not limited to one month, two months, and three months;

[0054] Step 2: Based on the load signal, the duration and occurrence frequency of the load signal are processed and analyzed to obtain a load performance value. If the load performance value FB is greater than or equal to the load performance threshold, the corresponding historical upload path is marked as a fluctuating path.

[0055] In the historical monitoring period, the time period in which the load signal is generated is marked as the load period, and the duration corresponding to the load period is compared with the duration corresponding to the historical monitoring period to obtain the load period ratio FS;

[0056] During the historical monitoring period, the total number of load signals and non-load signals is counted and marked as the total signal number. The number of load signals is counted and compared with the total signal number to obtain the load number ratio FC.

[0057] The load period ratio FS and load frequency ratio FC are processed by the formula: The load performance value FB is calculated, where s1 and s2 are preset proportional coefficients, and both s1 and s2 are greater than 0;

[0058] It should be noted that the load performance value FB is obtained by processing the load period ratio FS and the load number ratio FC, which reflects the fluctuation of each historical path;

[0059] Compare the load performance value FB with the load performance threshold:

[0060] If the load performance value FB is greater than or equal to the load performance threshold, the corresponding historical upload path is marked as a fluctuating path;

[0061] If the load performance value FB is less than the load performance threshold, the corresponding historical upload path is marked as a stable path;

[0062] Step 3: Based on the fluctuation path, the number of fluctuation paths in the historical path and the load performance value of the fluctuation path are analyzed to obtain the fluctuation performance value BB. If the fluctuation performance value BB is greater than the fluctuation performance threshold, a fluctuation signal is generated;

[0063] Count the number of fluctuation paths in the historical path, and compare it with the total number of historical paths to obtain the fluctuation number ratio BS;

[0064] Obtain the load performance value of the fluctuation path, subtract it from the load performance threshold to obtain the fluctuation deviation value, sum and average all the fluctuation deviation values to obtain the fluctuation deviation mean, and compare the fluctuation deviation mean with the data anomaly threshold to obtain the fluctuation degree ratio BC;

[0065] The fluctuation quantity ratio BS and the fluctuation degree ratio BC are processed by the formula: The volatility performance value BB is calculated, where a1 and a2 are both preset proportional coefficients, and both a1 and a2 are greater than 0;

[0066] It should be noted that the fluctuation performance value BB is obtained by processing the fluctuation number ratio BS and the fluctuation degree ratio BC. The fluctuation number ratio BS reflects the number of fluctuation paths in the historical path. The greater the number of fluctuation paths, the more unstable the historical upload path is. The fluctuation degree ratio BC reflects the degree of fluctuation of the fluctuation path in the historical path. The greater the fluctuation degree, the more unstable the historical upload path is.

[0067] Compare the volatility performance value BB with the volatility performance threshold:

[0068] If the volatility performance value BB is greater than the volatility performance threshold, a volatility signal is generated;

[0069] If the volatility performance value BB is less than or equal to the volatility performance threshold, a stable signal is generated;

[0070] The technical solution of the embodiment of the present invention is mainly as follows: analyzing the medical data transmission traffic of the medical data upload path, the analysis results include load signals and non-load signals, based on the load signal, processing and analyzing the duration and number of occurrences of the load signal to obtain a load performance value, if the load performance value FB is greater than or equal to the load performance threshold, then the corresponding historical upload path is marked as a fluctuation path, based on the fluctuation path, the number of fluctuation paths in the historical path and the load performance value of the fluctuation path are analyzed to obtain a fluctuation performance value BB, if the fluctuation performance value BB is greater than the fluctuation performance threshold, then a fluctuation signal is generated. The present invention analyzes the historical data of the medical data upload path, which is conducive to judging the overall volatility of historical medical data uploads.

[0071] Example 2:

[0072] Based on Example 1, please refer to Figure 1 As shown, the medical data traceability management method based on the Internet of Things according to the embodiment of the present invention further includes the following steps:

[0073] Step 4: Based on the fluctuation signal, analyze the medical data that needs to be traced to obtain the traceability performance value ZS, sort the data based on the traceability performance value ZS to obtain a traceability sequence table, and trace the emergency traceability data during the load period based on the traceability sequence table;

[0074] Use text analysis tools in natural language processing (NLP) technology to extract keywords from medical data that need to be traced and mark them as traceability keywords;

[0075] Integrate all preset emergency keywords into a preset keyword group, and combine the traceability keyword with the preset keyword in the preset keyword group to obtain a recombined keyword group;

[0076] It should be noted that the preset emergency keywords are set by those skilled in the art based on historical experience;

[0077] Get the cosine similarity value of each recombined keyword group and compare the cosine similarity value of each recombined keyword group with the cosine similarity threshold:

[0078] If the cosine similarity value is less than the cosine similarity threshold, the recombined keyword group will be ungrouped;

[0079] If the cosine similarity value is greater than or equal to the cosine similarity threshold, it means that the traceability keyword in the reorganized keyword group is similar to the preset keyword, and the medical data corresponding to the traceability keyword in the reorganized keyword group is marked as urgent traceability data;

[0080] It should be noted that if no preset keywords or traceable keywords remain after the reorganization is completed, the reorganization operation is stopped;

[0081] If the number of remaining traceability keywords is not 1 after the reorganization is completed, the reorganization operation will continue;

[0082] The cosine similarity value is obtained as follows:

[0083] The cosine similarity calculation formula is: , where A is the tracing keyword vector in the reorganized keyword group, B is the preset keyword vector of the reorganized keyword group, A・B is the dot product of the two vectors, ||A|| and ||B|| are the moduli of vector A and vector B respectively, and the cosine similarity value between the tracing keyword in the reorganized keyword group and the preset keyword is obtained;

[0084] For example, for "hypertension" and "blood pressure rises", all possible characters are used as a vocabulary, and the number of times the characters appear in each string is counted to form a vector. For "hypertension", the characters "high", "blood", "pressure", and "rise" in the vocabulary, and the character occurrence vector A is [1,1,1,0]; for "blood pressure rises", the character occurrence vector B is [1,1,1,1];

[0085] It should be noted that, among all the recombined keyword groups, if there is a cosine similarity value corresponding to the recombined keyword group, it means that there is a preset keyword similar to the tracing keyword in the preset keyword group, and thus the medical data corresponding to the tracing keyword can be marked as urgent medical data. If there is no cosine similarity value corresponding to the recombined keyword group, it means that there is no keyword similar to the tracing keyword in the preset keywords, and thus the medical data corresponding to the tracing keyword can be marked as non-urgent medical data. Based on the non-urgent medical data, the non-urgent medical data can be traced when the non-load signal is generated.

[0086] Obtain the cosine similarity value corresponding to the emergency medical data, perform absolute value processing on the difference between the cosine similarity value and the cosine similarity threshold to obtain the cosine similarity value deviation, perform ratio processing on the cosine similarity value deviation and the cosine similarity threshold to obtain the cosine similarity value deviation ratio YP;

[0087] Obtain all emergency medical data bytes, sum and average them to obtain a byte mean, and compare each emergency medical data byte with the byte mean to obtain a byte ratio ZJ;

[0088] The cosine similarity deviation ratio YP and the byte ratio ZJ are processed by the formula: Calculate the retrospective performance value ZS, where b1 and b2 are preset proportional coefficients and both b1 and b2 are greater than 0;

[0089] Sort the traceability performance values ZS corresponding to the emergency medical data from large to small to obtain a traceability sequence table. Based on the traceability sequence table, trace the emergency traceability data during the load period to reduce the system resources occupied by traceability requests.

[0090] The technical solution of the embodiment of the present invention mainly includes: analyzing the medical data that needs to be traced based on the fluctuation signal to obtain the tracing performance value ZS, sorting the data based on the tracing performance value ZS to obtain a tracing sequence table, and tracing the urgent tracing data during the load period based on the tracing sequence table. By classifying the medical data that needs to be traced according to the urgency of tracing, the present invention prioritizes tracing of urgent tracing data, which can effectively reduce the system resource usage of tracing requests.

[0091] Example 3:

[0092] Based on Example 1 and Example 2, please refer to Figure 1 、 Figure 2 As shown, an embodiment of the present invention provides an Internet of Things-based medical data traceability management system, including:

[0093] Data acquisition module: Analyzes the data transmission traffic of the medical data upload path. The analysis results include load signals and non-load signals.

[0094] Fluctuation path marking module: Based on the load signal, the module processes and analyzes the load signal duration and occurrence frequency to obtain a load performance value. If the load performance value FB is greater than or equal to the load performance threshold, the corresponding historical upload path is marked as a fluctuation path.

[0095] Fluctuation signal generation module: Based on the fluctuation path, the module analyzes the number of fluctuation paths in the historical path and the load performance value of the fluctuation path to obtain the fluctuation performance value BB. If the fluctuation performance value BB is greater than the fluctuation performance threshold, a fluctuation signal is generated.

[0096] Tracing sequence table determination module: Based on the fluctuation signal, the medical data that needs to be traced is analyzed to obtain the tracing performance value ZS, and the tracing performance value ZS is sorted to obtain the tracing sequence table. Based on the tracing sequence table, the emergency tracing data is traced during the load period.

[0097] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the foregoing embodiments. The foregoing embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A medical data traceability management method based on the Internet of Things, characterized in that: include: Step 1: Analyze the medical data transmission traffic of the medical data upload path, and the analysis results include load signals and non-load signals; Step 2: Based on the load signal, the duration and occurrence frequency of the load signal are processed and analyzed to obtain a load performance value. If the load performance value FB is greater than or equal to the load performance threshold, the corresponding historical upload path is marked as a fluctuating path. Step 3: Based on the fluctuation path, the number of fluctuation paths in the historical path and the load performance value of the fluctuation path are analyzed to obtain the fluctuation performance value BB. If the fluctuation performance value BB is greater than the fluctuation performance threshold, a fluctuation signal is generated; Step 4: Based on the fluctuation signal, analyze the medical data that needs to be traced to obtain the traceability performance value ZS, sort the data based on the traceability performance value ZS to obtain a traceability sequence table, and trace the emergency traceability data during the load period based on the traceability sequence table; The retrospective performance value ZS is obtained as follows: Obtain the medical data that needs to be traced, analyze it to obtain emergency medical data, obtain the cosine similarity value and the bytes of the emergency medical data corresponding to the emergency medical data, and analyze it to obtain the cosine similarity value deviation ratio YP and the byte ratio ZJ; The cosine similarity deviation ratio YP and the byte ratio ZJ are processed by the formula: Calculate the retrospective performance value ZS, where b1 and b2 are preset proportional coefficients and both b1 and b2 are greater than 0; The cosine similarity value deviation ratio YP and the byte ratio ZJ are obtained as follows: Obtain the cosine similarity value corresponding to the emergency medical data, perform absolute value processing on the difference between the cosine similarity value and the cosine similarity threshold to obtain the cosine similarity value deviation, perform ratio processing on the cosine similarity value deviation and the cosine similarity threshold to obtain the cosine similarity value deviation ratio YP; Obtain all emergency medical data bytes, sum and average them to obtain a byte mean, and compare each emergency medical data byte with the byte mean to obtain a byte ratio ZJ; The cosine similarity value is obtained as follows: Use text analysis tools in natural language processing (NLP) technology to extract keywords from medical data that need to be traced and mark them as traceability keywords; Integrate all preset emergency keywords into a preset keyword group, and combine the traceability keyword with the preset keyword in the preset keyword group to obtain a recombined keyword group; The cosine similarity calculation formula is: , where A is the tracing keyword vector in the reorganized keyword group, B is the preset keyword vector of the reorganized keyword group, A・B is the dot product of the two vectors, ||A|| and ||B|| are the moduli of vector A and vector B respectively, and the cosine similarity value between the tracing keyword in the reorganized keyword group and the preset keyword is obtained.

2. The method for traceable management of medical data based on the Internet of Things according to claim 1, characterized in that: The load performance value FB is obtained as follows: Obtain the time period for generating the load signal and the number of times the load signal appears, and analyze to obtain the load period ratio FS and the load frequency ratio FC; The load period ratio FS and the load frequency ratio FC are weighted to obtain the load performance value FB.

3. The method for traceable management of medical data based on the Internet of Things according to claim 2, characterized in that: The load period ratio FS is obtained as follows: In the historical monitoring period, the time period in which the load signal is generated is marked as a load period, and the duration corresponding to the load period is compared with the duration corresponding to the historical monitoring period to obtain a load period ratio FS.

4. The method for traceable management of medical data based on the Internet of Things according to claim 2, characterized in that: The load factor ratio FC is obtained as follows: During the historical monitoring period, the total number of load signals and non-load signals is counted and marked as the total signal number. The number of load signals is counted and compared with the total signal number to obtain the load number ratio FC.

5. The method for traceable management of medical data based on the Internet of Things according to claim 1, characterized in that: The method for obtaining the fluctuation performance value BB is as follows: Obtain the number of fluctuation paths in the historical path and the load performance value of the fluctuation path, process and analyze to obtain the fluctuation number ratio BS and fluctuation degree ratio BC; The volatility number ratio BS and the volatility degree ratio BC are weighted to obtain the volatility performance value BB.

6. The method for traceable management of medical data based on the Internet of Things according to claim 5, characterized in that: The method for obtaining the fluctuation quantity ratio BS and the fluctuation degree ratio BC is as follows: Count the number of fluctuation paths in the historical path, and compare it with the total number of historical paths to obtain the fluctuation number ratio BS; Obtain the load performance value of the fluctuation path, perform subtraction processing on it and the load performance threshold to obtain the fluctuation deviation value, sum and average all the fluctuation deviation values to obtain the fluctuation deviation mean, and perform ratio processing on the fluctuation deviation mean and the data anomaly threshold to obtain the fluctuation degree ratio BC.

7. A medical data traceability management system based on the Internet of Things, characterized by: The system is used to execute the method according to any one of claims 1 to 6, and the system comprises: Data acquisition module: analyzes the medical data transmission traffic of the medical data upload path, and the analysis results include load signals and non-load signals; Fluctuation path marking module: Based on the load signal, the module processes and analyzes the load signal duration and occurrence frequency to obtain a load performance value. If the load performance value FB is greater than or equal to the load performance threshold, the corresponding historical upload path is marked as a fluctuation path. Fluctuation signal generation module: Based on the fluctuation path, the module analyzes the number of fluctuation paths in the historical path and the load performance value of the fluctuation path to obtain the fluctuation performance value BB. If the fluctuation performance value BB is greater than the fluctuation performance threshold, a fluctuation signal is generated. Tracing sequence table determination module: Based on the fluctuation signal, the medical data that needs to be traced is analyzed to obtain the tracing performance value ZS, and the tracing performance value ZS is sorted to obtain the tracing sequence table. Based on the tracing sequence table, the emergency tracing data is traced during the load period.

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