Railway worker cardiovascular and cerebrovascular disease risk assessment system and method

Abstract

The invention provides a cardiovascular and cerebrovascular disease risk assessment system and method for railway workers, and relates to the technical field of cardiovascular and cerebrovascular disease risk assessment, and the system comprises a data collection module which is used for collecting personal data of target workers in multiple aspects in a preset time period; the data screening module is connected with the data acquisition module and is used for preprocessing and screening the historical doctor seeing data, the working state data, the basic physiological data and the life data to obtain a corresponding data screening result; the risk analysis module is used for performing risk analysis according to the data screening result to obtain a corresponding risk analysis result; the evaluation module is used for evaluating the risk analysis result; according to the invention, the health condition of the worker can be comprehensively evaluated, and the evaluation deviation caused by a single data dimension is avoided.

Description

Technical Field

[0001] This invention relates to the field of cardiovascular and cerebrovascular disease risk assessment technology, and more specifically to a cardiovascular and cerebrovascular disease risk assessment system and method for railway workers. Background Technology

[0002] Currently, railway workers are a large group with a clear division of labor, playing an indispensable role in all aspects of the railway transportation system.

[0003] However, the daily work of railway staff is quite tedious and lasts for a long time, which can affect their physical health in the long run and may also affect subsequent railway operations. Moreover, even if the relevant health parameters of railway staff are normal, there are still certain risks. The existing technology lacks an information system for assessing the risk of cardiovascular and cerebrovascular diseases among railway staff, making it impossible to conduct a comprehensive risk assessment of the health status of railway staff.

[0004] Therefore, how to provide a risk assessment system for cardiovascular and cerebrovascular diseases of railway workers that can solve the above problems is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a system and method for assessing the risk of cardiovascular and cerebrovascular diseases among railway workers, which can comprehensively assess the health status of workers and avoid assessment bias caused by a single data dimension.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A risk assessment system for cardiovascular and cerebrovascular diseases among railway workers includes: The data acquisition module is used to collect personal data of the target staff in multiple aspects within a preset time period, including historical medical data, work status data, basic physiological data, and life data. A data filtering module, connected to the data acquisition module, is used to preprocess and filter the historical medical data, work status data, basic physiological data, and life data to obtain corresponding data filtering results. A risk analysis module, which is connected to the data filtering module, is used to perform risk analysis based on the data filtering results and obtain corresponding risk analysis results. An evaluation module, connected to the risk analysis module, is used to evaluate the risk analysis results.

[0007] Preferred options also include: A physiological analysis module, which is connected to the evaluation module, is used to generate a corresponding adjustment plan based on the evaluation results.

[0008] Preferably, the specific implementation process of the data filtering module includes: A preprocessing unit, connected to the data acquisition module, is used to perform preliminary preprocessing on the historical medical data, work status data, basic physiological data, and life data. A threshold comparison unit is connected to the preprocessing unit, and the threshold comparison unit stores multiple data thresholds internally. It is used to compare the multiple data thresholds with the historical medical data, work status data, basic physiological data and life data to determine whether there are any abnormalities in the historical medical data, work status data, basic physiological data and life data. The first processing unit, which is connected to the threshold comparison unit, is used to directly output the risk of cardiovascular and cerebrovascular diseases without trend when there are no abnormalities in the historical medical data, work status data, basic physiological data and life data. The second processing unit, connected to the threshold comparison unit and the risk analysis module, is used to treat any one or any two of the historical medical data, work status data, basic physiological data and life data as risk factor data when they do not meet the threshold requirements. The unit then extracts features from the risk factor data to obtain the corresponding abnormal feature sequence, and inputs it into the risk analysis module for risk analysis. The third processing unit, connected to the threshold comparison unit, is used to treat the abnormal data as risk factor data when more than two of the historical medical data, work status data, basic physiological data, and life data fail to meet the threshold requirements. After performing cluster analysis on the risk factor data, the data is input into the risk analysis module for risk analysis.

[0009] Preferably, the data filtering module further includes: An abnormality cause analysis unit, which is connected to the second processing unit, is used to analyze whether the cause of the abnormality is a long-term factor or a short-term factor when any one or any two of the historical medical data, work status data, basic physiological data, and life data do not meet the threshold requirements.

[0010] Preferably, the risk analysis module includes: A risk analysis unit, connected to the second processing unit and the third processing unit, is used to construct and train a risk analysis model when the output of the abnormal cause analysis unit is a long-term factor, and to perform risk analysis on the processing results output by the second processing unit and the third processing unit to obtain the corresponding risk analysis results. A health management unit, which is connected to the physiological analysis module of the first processing unit, the second processing unit, the third processing unit, the risk analysis unit, and the abnormal cause analysis unit, is used to record the processing results of the first processing unit, the second processing unit, the third processing unit, the abnormal cause analysis unit, and the risk analysis unit.

[0011] Preferably, the evaluation module includes: A health status analysis unit, which is connected to the health management unit, the risk analysis unit, the abnormal cause analysis unit, and the first processing unit, is used to perform health status analysis based on the risk analysis result, the record file output by the health management unit, the judgment result output by the first processing unit, or the result output by the abnormal cause analysis unit when it is a short-term factor, and obtain the corresponding analysis result. A triggering unit, connected to the health status analysis unit, is used to issue a prompt when the analysis results do not meet the work requirements.

[0012] Preferably, the physiological analysis module includes: A generation unit, which is connected to the triggering unit, the health management unit, the risk analysis unit, and the first processing unit, is used to analyze the analysis results, the risk analysis results, the record file, or the judgment results output by the first processing unit to obtain an adjustment plan. A communication unit, connected to the generation unit, is used to send the adjustment plan to the user.

[0013] This invention also provides a method for assessing the risk of cardiovascular and cerebrovascular diseases among railway workers, comprising the following steps: The data acquisition module collects personal data from target staff in multiple aspects within a preset time period. The personal data includes historical medical records, work status data, basic physiological data, and lifestyle data. The data filtering module preprocesses and filters the historical medical data, work status data, basic physiological data, and life data to obtain the corresponding data filtering results. The risk analysis module performs risk analysis based on the data filtering results to obtain the corresponding risk analysis results; The risk analysis results are evaluated by the assessment module to determine the cardiovascular and cerebrovascular disease risk of the target staff, and whether an alarm is required is determined based on the assessment results.

[0014] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a system and method for assessing the risk of cardiovascular and cerebrovascular diseases among railway workers, which has the following beneficial effects: 1. This invention collects personal data from various aspects of railway staff, preprocesses and filters the collected data, and can comprehensively assess the health status of staff, avoiding assessment bias caused by a single data dimension. At the same time, the data filtering process can provide an accurate data foundation for subsequent risk analysis and assessment, effectively improving the pertinence and accuracy of the assessment. 2. The risk analysis module set up in this invention performs in-depth risk analysis based on the screened data. It can identify various cardiovascular and cerebrovascular disease risks that employees may face, and the triggering unit will issue a prompt when the health status analysis results do not meet the work requirements. 3. The physiological analysis module of this invention generates a corresponding adjustment plan based on the evaluation results and sends the adjustment plan to the user through the communication unit, so that staff can obtain and implement it in a timely manner, which helps to improve their health status, prevent the occurrence of cardiovascular and cerebrovascular diseases, and improve the quality of life and work efficiency of staff.

[0015] 4. The health management unit set up in this invention records various data and results throughout the entire assessment process. These records can then be used to create health profiles for staff, facilitating long-term tracking of their health trends. Simultaneously, these historical records can also provide reference for railway departments in personnel management and work arrangements, thereby better protecting the health of railway staff and ensuring the safety of railway transportation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 The present invention provides a structural principle block diagram of a risk assessment system for cardiovascular and cerebrovascular diseases for railway workers. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a risk assessment system for cardiovascular and cerebrovascular diseases among railway workers, comprising: Data acquisition module 1 is used to collect personal data of target staff in multiple aspects within a preset time period. Personal data may include historical medical data, work status data, basic physiological data and life data. Work status data may include parameters such as working hours and job type. Basic physiological data may include parameters such as height, age, weight, gender, and family history of cardiovascular and cerebrovascular diseases. Life data may include parameters such as dietary habits and exercise data. Data filtering module 2 is connected to data acquisition module 1 and is used to preprocess and filter historical medical data, work status data, basic physiological data and life data to obtain corresponding data filtering results. Risk analysis module 3 is connected to data filtering module 2 and is used to perform risk analysis based on the data filtering results to obtain the corresponding risk analysis results. The assessment module 4 is connected to the risk analysis module 3 and is used to assess the risk analysis results.

[0020] In one specific embodiment, it also includes: Physiological analysis module 5 is connected to evaluation module 4 and is used to generate corresponding adjustment plans based on evaluation results.

[0021] In a specific embodiment, the implementation process of data filtering module 2 includes: The preprocessing unit 21 is connected to the data acquisition module 1 and is used to perform preliminary preprocessing on historical medical data, work status data, basic physiological data and life data. The specific implementation process of the preprocessing unit 21 may include processing steps such as outlier removal and normalization, which can improve the accuracy of subsequent risk assessment. Threshold comparison unit 22 is connected to preprocessing unit 21 and is used to compare multiple data thresholds with historical medical data, work status data, basic physiological data and life data to determine whether there are any abnormalities in historical medical data, work status data, basic physiological data and life data. The first processing unit 23 is connected to the threshold comparison unit 22. When there are no abnormalities in historical medical data, work status data, basic physiological data and life data, it directly outputs the risk of cardiovascular and cerebrovascular diseases without trend, and at the same time determines whether an adjustment plan needs to be generated through the assessment module 4. The second processing unit 24 is connected to the threshold comparison unit 22 and the risk analysis module 3. When any one or any two of the historical medical data, work status data, basic physiological data and life data do not meet the threshold requirements, the abnormal data at this time is taken as risk factor data, and the risk factor data is used to extract features to obtain the corresponding abnormal feature sequence, which is then input into the risk analysis module 3 for risk analysis. The third processing unit 25 is connected to the threshold comparison unit 22. When more than two of the historical medical data, work status data, basic physiological data, and life data do not meet the threshold requirements or are high-risk factors for cardiovascular and cerebrovascular diseases, the abnormal data at this time is taken as risk factor data. After performing cluster analysis on the risk factor data, it is input into the risk analysis module 3 for risk analysis. The cluster analysis can be implemented using the DBSCAN algorithm. By grouping similar data through clustering, it is more conducive to improving the efficiency of subsequent risk analysis.

[0022] Specifically, the first processing unit 23, the second processing unit 24, and the third processing unit 25 determine whether the data is abnormal by judging the data type contained in each category.

[0023] In one specific embodiment, the data filtering module 2 further includes: The abnormal cause analysis unit 26 is connected to the second processing unit 24. It is used to analyze whether the cause of the abnormality is a long-term factor or a short-term factor when any one or any two of the historical medical data, work status data, basic physiological data and life data do not meet the threshold requirements.

[0024] Specifically, the abnormal cause analysis unit 26 can be implemented using a random forest model to determine whether it is caused by short-term factors (such as staying up late on the same day, or a brief illness) or long-term factors (such as long-term unhealthy lifestyle habits), providing more accurate input for subsequent risk analysis.

[0025] Specifically, the threshold comparison unit 22 can use a dynamic threshold, which can adaptively adjust the threshold according to the actual data situation, making it more adaptable.

[0026] In one specific embodiment, risk analysis module 3 includes: Risk analysis unit 31 is connected to the second processing unit 24 and the third processing unit 25. It is used to build and train a risk analysis model when the output of the abnormal cause analysis unit 26 is a long-term factor, and to perform risk analysis on the processing results output by the second processing unit 24 and the third processing unit 25 to obtain the corresponding risk analysis results. The health management unit 32 is connected to the first processing unit 23, the second processing unit 24, the third processing unit 25, the risk analysis unit 31, and the abnormal cause analysis unit 26 physiological analysis module 5. It is used to record the processing results of the first processing unit 23, the second processing unit 24, the third processing unit 25, the abnormal cause analysis unit 26, and the risk analysis unit 31. The content of the record file obtained by the health management unit 32 includes the risk analysis result, abnormal data type, and data collection period, or the normal judgment result and data collection period.

[0027] Specifically, the risk analysis model used by risk analysis unit 31 can be a risk analysis model based on a combination of support vector machine and neural network models. The specific risk analysis process may include the following steps: 1. When any one or two of the historical medical data, work status data, basic physiological data, and life data do not meet the threshold requirements, feature extraction is performed on the abnormal data. Based on the feature extraction results, further extraction is performed to obtain the corresponding abnormal feature sequence. The abnormal feature sequence is then input into the risk analysis model for processing to obtain the corresponding risk analysis results. 2. When more than two of the historical medical data, work status data, basic physiological data, and life data fail to meet the threshold requirements, the abnormal data are clustered. Then, feature extraction is performed based on the clustering results to obtain the corresponding abnormal features. The abnormal features are then input into the risk analysis model for processing to obtain the corresponding risk analysis results.

[0028] In addition, embodiments of the present invention can also generate a corresponding employee health record database based on the record files obtained by the health management unit 32, which is convenient for managers to view.

[0029] The embodiments of the present invention can achieve dynamic data analysis and processing through the above process, avoiding evaluation bias caused by a single data dimension.

[0030] In one specific embodiment, the evaluation module 4 includes: The health status analysis unit 41 is connected to the health management unit 32, the risk analysis unit 31, and the first processing unit 23. It is used to perform health status analysis based on the risk analysis results, the record file output by the health management unit 32, or the judgment result output by the first processing unit 23, and obtain the corresponding analysis results. Trigger unit 42 is connected to health status analysis unit 41 and is used to provide a prompt when the analysis results do not meet the work requirements.

[0031] Specifically, the implementation process of evaluation module 4 may include the following steps: (1) When the record file output by the health management unit 32 is received, and when there are no abnormalities in the analysis record file, an indicator decay analysis model is constructed, and the relevant parameters of the target staff are substituted into the indicator decay analysis model to obtain the corresponding decay probability. When the decay probability is greater than or equal to the preset probability threshold, it indicates that the health status is likely to deteriorate. At this time, a control command is sent to the triggering unit 42 for prompting. When the decay probability is less than the preset probability threshold, the record file is updated and the result is sent to the relevant management personnel. The indicator decay analysis model can be any one of the decay probability calculation model or the neural network model. (2) When the record file output by the health management unit 32 is received, and when the analysis of the record file shows an anomaly, the overall health status assessment score is obtained based on the risk analysis results, abnormal data types, data collection period and corresponding weights recorded in the record file. When the assessment score is greater than or equal to the preset score threshold, a control command is sent to the trigger unit 42 for prompting. When the assessment score is less than the preset score threshold, the record file is updated and the result is sent to the relevant management personnel.

[0032] In one specific embodiment, the physiological analysis module 5 includes: The generation unit 51 is connected to the triggering unit 42, the health management unit 32, the risk analysis unit 31, and the first processing unit 23. It is used to analyze the analysis results, risk analysis results, and record files, or to analyze the judgment results output by the first processing unit 23, and to obtain the adjustment plan. Communication unit 52 is connected to generation unit 51 and is used to send the adjustment plan to the user.

[0033] This invention also provides an assessment method for the cardiovascular and cerebrovascular disease risk assessment system for railway workers using any of the above embodiments, comprising the following steps: The data acquisition module 1 collects personal data from the target staff in multiple aspects within a preset time period. The personal data includes historical medical records, work status data, basic physiological data, and lifestyle data. The data filtering module 2 preprocesses and filters historical medical data, work status data, basic physiological data and life data to obtain the corresponding data filtering results. Risk analysis module 3 performs risk analysis based on the data filtering results to obtain the corresponding risk analysis results. The risk analysis results are evaluated using assessment module 4 to determine the cardiovascular and cerebrovascular disease risk of the target staff and to determine whether an alarm is required based on the assessment results.

[0034] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0035] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A risk assessment system for cardiovascular and cerebrovascular diseases among railway workers, characterized in that, include: The data acquisition module (1) is used to collect personal data of the target staff in multiple aspects within a preset time period, wherein the personal data includes historical medical data, work status data, basic physiological data and life data; The data filtering module (2) is connected to the data acquisition module (1) and is used to preprocess and filter the historical medical data, work status data, basic physiological data and life data to obtain the corresponding data filtering results. Risk analysis module (3), which is connected to the data filtering module (2), is used to perform risk analysis based on the data filtering results and obtain the corresponding risk analysis results; The evaluation module (4) is connected to the risk analysis module (3) and is used to evaluate the risk analysis results.

2. The cardiovascular and cerebrovascular disease risk assessment system for railway workers according to claim 1, characterized in that, Also includes: Physiological analysis module (5), which is connected to the evaluation module (4), is used to generate corresponding adjustment plans based on the evaluation results.

3. The cardiovascular and cerebrovascular disease risk assessment system for railway workers according to claim 1, characterized in that, The specific implementation process of the data filtering module (2) includes: The preprocessing unit (21) is connected to the data acquisition module (1) and is used to perform preliminary preprocessing on the historical medical data, work status data, basic physiological data and life data. Threshold comparison unit (22) is connected to the preprocessing unit (21), and the threshold comparison unit (22) stores multiple data thresholds, which are used to compare the multiple data thresholds with the historical medical data, work status data, basic physiological data and life data to determine whether the historical medical data, work status data, basic physiological data and life data are abnormal. The first processing unit (23) is connected to the threshold comparison unit (22) and is used to directly output the risk of cardiovascular and cerebrovascular diseases without trend when there are no abnormalities in the historical medical data, work status data, basic physiological data and life data. The second processing unit (24) is connected to the threshold comparison unit (22) and the risk analysis module (3). When any one or any two of the historical medical data, work status data, basic physiological data and life data do not meet the threshold requirements, the abnormal data at this time is taken as risk factor data, and the risk factor data is subjected to feature extraction to obtain the corresponding abnormal feature sequence, which is then input into the risk analysis module (3) for risk analysis. The third processing unit (25) is connected to the threshold comparison unit (22). When more than two of the historical medical data, work status data, basic physiological data and life data do not meet the threshold requirements, the abnormal data at this time is taken as risk factor data, and the risk factor data is clustered and then input into the risk analysis module (3) for risk analysis.

4. The cardiovascular and cerebrovascular disease risk assessment system for railway workers according to claim 3, characterized in that, The data filtering module (2) also includes: An abnormal cause analysis unit (26) is connected to the second processing unit (24) and is used to analyze whether the cause of the abnormality is a long-term factor or a short-term factor when any one or any two of the historical medical data, work status data, basic physiological data and life data do not meet the threshold requirements.

5. A risk assessment system for cardiovascular and cerebrovascular diseases for railway workers according to claim 4, characterized in that, The risk analysis module (3) includes: Risk analysis unit (31), which is connected to the second processing unit (24) and the third processing unit (25), is used to construct and train a risk analysis model when the result output by the abnormal cause analysis unit (26) is a long-term factor, and to perform risk analysis on the processing results output by the second processing unit (24) and the third processing unit (25) to obtain the corresponding risk analysis results; The health management unit (32) is connected to the physiological analysis module (5) of the first processing unit (23), the second processing unit (24), the third processing unit (25), the risk analysis unit (31), and the abnormal cause analysis unit (26), and is used to record the processing results of the first processing unit (23), the second processing unit (24), the third processing unit (25), the abnormal cause analysis unit (26), and the risk analysis unit (31).

6. A risk assessment system for cardiovascular and cerebrovascular diseases for railway workers according to claim 5, characterized in that, The evaluation module (4) includes: A health status analysis unit (41) is connected to the health management unit (32), the risk analysis unit (31), the abnormal cause analysis unit (26), and the first processing unit (23). It is used to perform health status analysis based on the risk analysis results, the record file output by the health management unit (32), the judgment result output by the first processing unit (23), or the result output by the abnormal cause analysis unit (26) when it is a short-term factor, and obtain the corresponding analysis results. Trigger unit (42), which is connected to the health status analysis unit (41), is used to provide a prompt when the analysis result does not meet the work requirements.

7. A risk assessment system for cardiovascular and cerebrovascular diseases for railway workers according to claim 6, characterized in that, The physiological analysis module (5) includes: The generation unit (51) is connected to the triggering unit (42), the health management unit (32), the risk analysis unit (31) and the first processing unit (23), and is used to analyze the analysis results, risk analysis results, the record file, or the judgment results output by the first processing unit (23) to obtain the adjustment plan; The communication unit (52) is connected to the generation unit (51) and is used to send the adjustment scheme to the user.

8. An assessment method using the cardiovascular and cerebrovascular disease risk assessment system for railway workers as described in any one of claims 1-7, characterized in that, Includes the following steps: The data acquisition module (1) collects personal data of the target staff in multiple aspects within a preset time period, including historical medical data, work status data, basic physiological data and life data. The data filtering module (2) preprocesses and filters the historical medical data, work status data, basic physiological data and life data to obtain the corresponding data filtering results. The risk analysis module (3) performs risk analysis based on the data filtering results to obtain the corresponding risk analysis results; The risk analysis results are evaluated by the assessment module (4) to determine the cardiovascular and cerebrovascular disease risk of the target staff and to determine whether an alarm is required based on the assessment results.

Images